Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 290 appraisals
Sleep medicine
Concordance of wearable device sleep metrics with patient-reported sleep quality: A systematic review
BACKGROUND: Commercial wearable devices increasingly monitor sleep, but their concordance with patient-reported sleep quality remains poorly characterized. This systematic review evaluates concordance between wearable sleep metrics and validated subjective measures. METHODS: Following PRISMA guidelines, we searched PubMed/MEDLINE, Embase, and Cochrane Library through October 2025 for studies comparing consumer wrist-worn actigraphy-based devices with validated subjective sleep quality measures in adults (≥18 years). Two reviewers independently performed screening, extraction, and QUADAS-2 quality assessment. GRADE criteria evaluated evidence certainty. RESULTS: Five observational studies (2006 participants) were included. Wearable devices showed poor to moderate agreement with subjective assessments, explaining only 2.5-16.2% variance. Total sleep time moderately correlated with same-day diaries (r = 0.367), but devices failed to capture Pittsburgh Sleep Quality Index scores. Agreement varied substantially by population: good sleepers showed 82.4% concordance versus 39.4% in insomnia patients (p = 0.006). Clinical populations and older adults demonstrated poor agreement. Polysomnography concordance was also poor: sleep efficiency showed fair intraclass correlation coefficient values (0.478-0.570) with systematic overestimation (+1.75% to +7.9%), sleep onset latency correlated poorly (r = 0.033), and wake after sleep onset was underestimated (-7 to -30 min). Evidence certainty ranged from low to moderate. CONCLUSIONS: Commercial wearable sleep trackers demonstrate poor to moderate agreement with validated subjective sleep quality measures, with significant population-specific variation. Device data should complement, not replace, validated subjective assessments, as current technology inadequately captures patient-reported sleep quality and shows systematic bias in objective parameters.
3 Aug 2026
Read appraisal →Sleep medicine reviews
Machine and deep learning in REM sleep behavior disorder: a scoping review and analysis of reporting quality
Rapid eye movement (REM) sleep behavior disorder (RBD) is a parasomnia, and its isolated form is of particular interest, as it is an early phase alpha-synucleinopathy. Machine learning (ML) and deep learning (DL) models offer potential for automated detection, prediction of phenoconversion, and phenotyping. This scoping review identified 75 studies applying ML/DL in RBD and evaluated their methodological and reporting quality using the APPRAISE-AI tool. Most studies (73.3%) focused on RBD detection and mainly used polysomnographic data for this, while 16% addressed prediction of phenoconversion, with imaging data being the most employed modality. Sample sizes were generally small (most studies including only 20-100 individuals). According to APPRAISE-AI scores, 80% of studies had moderate overall methodological and reporting quality. Common deficiencies included lack of transparency in data and code sharing (23.3%), and poor reporting of hyperparameter tuning (17.1%), bias assessment (26.9%), and error analysis (0.66%). Data leakage was observed in 32% of studies. These issues hinder clinical translation and prevent incremental progress between research groups. Without transparent reporting and shared resources, replication and model comparisons become nearly impossible. Future work should adopt open science principles and rigorous validation to advance AI-based tools in sleep medicine.
3 Aug 2026
Read appraisal →Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
Efficacy and safety of fecal microbiota transplantation in Parkinson's disease: a systematic review and meta-analysis of randomized controlled trials with GRADE assessment
BACKGROUND: Parkinson's disease (PD) is a common neurodegenerative disease that is a growing public health challenge. Recent evidence showed that gut microbiota dysbiosis is involved in the pathogenesis of PD, prompting interest in fecal microbiota transplantation (FMT) as a potential disease-modifying intervention. This systematic review and meta-analysis aim to evaluate the efficacy and safety of FMT in patients with PD. METHODS: We conducted a systematic literature review using PubMed, Scopus, Web of Science, and the Cochrane Library till June 4, 2026, to identify RCTs comparing FMT with placebo or control interventions in PD. After removal of duplicates alongside title and abstract screening, followed by full-text screening, seven randomized controlled trials were included in this systematic review and meta-analysis. RESULTS: A total of seven randomized controlled trials (RCTs) with 318 subjects were analyzed. FMT was delivered via oral capsules, nasojejunal tubes, or colonoscopy, with follow-up periods ranging from 12 to 52 weeks. The meta-analysis indicated a notable short-term enhancement in daily motor activities, as assessed by MDSUPDRS Part II, after one month (mean difference - 2.19, 95% confidence interval - 4.32 to -0.06; P = 0.044). Nevertheless, no significant estimates were noted for MDS-UPDRS Part III, aggregate MDS-UPDRS scores, motor complications, non-motor symptoms, or overall life quality at any assessed time frame. Some individual studies reported gains in cognitive ability, anxious feelings, quality of life related to constipation, and certain PDQ-39 categories, although these improvements were not uniformly observed. Overall, FMT was well tolerated by participants, with adverse reactions mostly comprising mild, self-resolving gastrointestinal issues and no major safety risks. CONCLUSIONS: FMT is a safe treatment for individuals with PD and may offer modest short-term advantages in performing daily motor tasks. However, current evidence does not indicate lasting enhancements in overall motor capabilities, non-motor symptoms, or life quality. More extensive and sufficiently powered trials utilizing standardized FMT procedures and longer observation periods are necessary to elucidate the therapeutic benefits of FMT in Parkinson's disease.
1 Aug 2026
Read appraisal →Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
Handwritten signatures in neurological and psychiatric disorders: a systematic review with clinical and forensic implications.
BACKGROUND: Signature production represents a complex motor and cognitive behavior, highly automatized and repeated countless times throughout life. It ranks among the most durable graphic forms, often preserved even when writing processes are impaired. Studies examining signature characteristics in clinical cohorts are limited. The available evidence raises questions about its potential as a behavioral marker of neurological and psychiatric disorders. METHODS: We conducted a PRISMA-compliant systematic review up to 4th December 2025 to summarize evidence on how neurological and psychiatric conditions may affect signature features. Eligible articles on this topic were searched in Scopus, Web of Science, and PubMed databases. Sixteen papers were included, and their results were systematically described and qualitatively analyzed. RESULTS: The available literature suggests that signature production appears relatively independent of global cognitive decline, although it is sensitive to specific pathological conditions (e.g., micrographia in Parkinson's disease, increased tortuosity in cerebral small vessel disease, and alterations observed in mania, delirium, obsessive-compulsive disorder, and major depressive disorder). Alterations commonly affect kinematic parameters, as well as visuospatial and constructional features. However, no disorder-specific signature pattern emerges from current evidence, largely due to the methodological heterogeneity across studies. CONCLUSIONS: The handwritten signature holds promise as a behavioral marker of brain disorders, serving as a potentially useful, complementary - rather than a stand-alone - indicator of neurological and psychiatric conditions, which could be potentially useful in clinical settings. The implications for forensic applications remain largely theoretical and have not yet been standardized.
30 July 2026
Read appraisal →JMIR cancer
Piloting a Clinical Decision Support System for Unintended Weight Loss in Primary Care: Mixed Methods Study on Early Cancer Detection
BACKGROUND: Delayed cancer diagnosis leads to poorer outcomes. Unintended weight loss (UWL) is a nonspecific symptom associated with cancer and other serious conditions, which can make it complex to identify the underlying cause. Clinical decision support systems (CDSSs) can provide evidence-based recommendations to facilitate timely investigation and diagnosis. OBJECTIVE: This study aimed to pilot the implementation of a CDSS for improving early cancer detection in primary care patients with UWL. METHODS: Five practices reviewed patients identified by the UWL CDSS. Staff were interviewed on the acceptability and feasibility of the CDSS. Interviews were analyzed thematically using 2 relevant frameworks: the acceptability of health care interventions and the sociotechnical model for evaluation of digital interventions framework. Clinical audits assessed the correct identification of UWL, follow-up rates, and patient characteristics. RESULTS: Of 60 patients identified by the CDSS as potentially having UWL, 36 (60%) had true UWL. Among the misclassified cases, most patients (16/55, 30%) were intentionally trying to lose weight; this intention was documented as free text in the clinical notes for 98% (58/60) of patients and in a structured field for only 20% of patients. Of the 36 patients, 5 (14%) were actively recalled by their practices for further follow-up; the others (31/36, 86%) were not recalled, as most were deemed already under appropriate follow-up. By 6 months, 94% (34/36) of the cohort had received follow-up care regardless of whether they had been formally recalled. One-third (12/36, 33%) of patients had no additional symptoms, while 36% (13/36) had recorded additional abdominal symptoms. Mental health conditions accounted for 19% of diagnoses linked to UWL consultation. Practice staff were generally receptive to the UWL CDSS concept. It was particularly appreciated by practices with strong quality improvement processes and prior CDSS experience. A high proportion of misclassified patients, poor workflow integration, and conflicting patients' agendas were cited as implementation barriers. CONCLUSIONS: Our study revealed challenges and potential benefits of implementing a CDSS for identifying patients with UWL at risk of cancer in primary care. While integration issues and misclassification of patients were noted, high rates of follow-up care were observed regardless of the CDSS. These high follow-up rates prompt consideration of whether they reflect Australian primary care and whether a CDSS with these characteristics is the most appropriate approach to support early cancer detection. Future research should focus on improving CDSS integration with existing workflows, enhancing correct identification through access to clinical notes and the use of more sophisticated digital methods (eg, AI). These findings highlight the potential of CDSS in improving patient care and the complexities involved in their successful implementation.
30 July 2026
Read appraisal →JMIR mHealth and uHealth
Perceptions of Adults Aged 50 Years and Older Regarding the Use of Wearable mHealth Technologies to Promote Physical Activity: Systematic Review and Meta-Ethnography
BACKGROUND: Despite advances in wearable mobile health (mHealth) technologies and their associated apps designed to promote physical activity, and the importance of adapting them to users, little is known about older adults' perceptions of these technologies. OBJECTIVE: This review aimed to synthesize and analyze qualitative evidence exploring the perceptions of adults aged 50 years and older regarding areas to improve, barriers to, and facilitators of wearable mHealth technologies (activity trackers and companion apps) to promote physical activity. METHODS: A qualitative systematic review and meta-ethnography was conducted. Comprehensive searches were performed across 8 databases (MEDLINE, Scopus, Web of Science, CINAHL, The Cochrane Library Plus, PsycINFO, ProQuest, and ÍnDICEs-CSIC) for articles published in English or Spanish between January 2013 and January 2024. The synthesis followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) and ENTREQ (Enhancing Transparency in Reporting the Synthesis of Qualitative Research) guidelines. RESULTS: Ten articles met the inclusion criteria and were synthesized using meta-ethnography. Three main themes emerged: (1) barriers to promoting physical activity caused by wearable mHealth technologies: personal barriers (physical aspects, perceptions about technology, and personal preferences), technological barriers (functionality, content, design, alarms, availability, and accessibility), and environmental barriers (season of the year); (2) personal facilitators (consideration that these apps improve health, perceptions about technology, and personal preferences), technological facilitators (functionality, content, and design), relational facilitators (technological and social support), environmental facilitators (seasonal variations), and health care professionals (support and monitoring by health care services); and (3) personal areas (perceptions about technology and personal preferences), technological areas (functionality, content, and design), and relational areas (technological support). CONCLUSIONS: Although older adults acknowledge the potential of wearable mHealth technologies to promote physical activity, their effective engagement is hindered by distinct personal, technological, and environmental barriers. Bridging the digital divide requires designers to prioritize user-centered, age-friendly interfaces that are integrated with continuous support from health care professionals. To promote genuine health equity, future research must rigorously report intersectional demographics to ensure that mHealth interventions mitigate, rather than inadvertently exacerbate, existing disparities.
30 July 2026
Read appraisal →Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
Suicide in neurodegenerative diseases: a systematic review.
BACKGROUND AND OBJECTIVE: Suicide is a public health issue, which differs from suicidality, the continuum from suicidal ideation to the suicidal act, including suicide attempts and completed suicide. The main goal of the present study is to determine the relationship between Alzheimer's disease (AD), Parkinson's disease (PD), amyotrophic lateral sclerosis (ALS), and multiple sclerosis (MS) with suicidality. METHODS: This is a systematic review aiming to determine the relationship between AD, PD, ALS, and MS with suicidality following PRISMA 2020 guidelines by collecting cross-sectional, case-control, and cohort studies; case series; case reports; and retrospective and prospective studies from Google Scholar, PubMed, and Cochrane Library. The protocol of this systematic review was registered on PROSPERO; the registration number is CRD420261422354. RESULTS: From 2247 records identified from electronic databases, only 24 articles were included: three studies focusing on AD, nine on PD, and six studies focusing on ALS and MS, respectively. These studies exhibited moderate to low risk of bias. Despite the broad differences regarding the neurochemistry, pathophysiology, diagnosis, symptoms, and treatments of the selected diseases, patients are at a higher risk of suicidality. Depression and low social connectivity are the most prevalent risk factors. Suicidality is mainly detected during the first years post-diagnosis in PD, ALS, and MS patients, while the results in AD are confusing. CONCLUSIONS: Data about this topic is scarce and largely varying. Further research is required to elucidate this paradigmatic realm, fostering awareness, enhancing therapies, and providing explanations and interpretations of the mechanisms involved.
30 July 2026
Read appraisal →Proceedings of the National Academy of Sciences of the United States of America
Active linking through narratives facilitates associative inference
In daily life, we often draw inferences about novel associations from prior experiences. This ability, associative inference, is thought to be a key process by which memory guides future behavior. Prior studies have typically instructed participants to use narratives or mental imagery to aid memory of object associations. However, these studies have not explicitly tested the role of narratives, leaving an important question: how crucial are narratives in associative inference, and what role might they play? We used a modified associative inference paradigm to answer these questions. Our design factorially combined two factors during associative encoding: use of narratives, and explicitly linking the two objects. Participants separately learned AB and BC object pairs within ABC triads using four sets of instructions: i) generate narratives to link objects together, ii) generate narratives for objects separately, iii) compare perceptual properties of two objects, and iv) describe perceptual properties of objects individually. We tested participants' ability to infer AC associations as a function of encoding strategy. Across two experiments, using narratives to actively link objects produced a robust associative memory advantage that persisted across 1-day delay and remained substantial under more stringent retrieval conditions. This benefit was not attributable to a simple linking effect or to surface-level linguistic similarity between stories. A follow-up experiment revealed that this benefit did not generalize to perceptual memory for individual objects; instead, perceptual encoding facilitated perceptual object memory. These results reveal that deep, extended encoding emphasizing either narrativity or perceptual processing can benefit different aspects of retrieval.
28 July 2026
Read appraisal →Genome biology
IDEAL-Age: an interpretable deep learning framework for single-cell resolution profiling of immunological aging
Immunosenescence increases susceptibility to infection and reduces vaccine responsiveness, yet bulk transcriptomic clocks obscure the cellular heterogeneity underlying this process. Here, we present IDEAL-Age, an interpretable deep learning framework that operates directly on single-cell PBMC transcriptomes. Benchmarking against 35 methods across independent cohorts demonstrates superior predictive performance. The framework's interpretability uncovers linear and non-linear gene contribution trajectories that reveal phase-specific physiological transitions, and identifies youth-associated or aging-associated cellular roles. Application to systemic lupus erythematosus reveals accelerated immunological aging driven by interferon-associated monocyte shifts. IDEAL-Age establishes a high-resolution computational framework for deciphering systemic immune aging.
27 July 2026
Read appraisal →Lasers in medical science
Facial contour modulation and skin tightening using 40.68-MHz unipolar radiofrequency
To evaluate changes in facial contour and skin elasticity following full-face treatment with 40.68-MHz unipolar radiofrequency (RF), and to assess its safety and tolerability in adults with mild-to-moderate facial laxity. This prospective single-arm interventional clinical study evaluated adults with mild-to-moderate facial laxity who underwent three full-face 40.68-MHz unipolar RF treatment sessions at 4-week intervals. The primary endpoint was the change in VECTRA-derived three-dimensional facial volumetric parameters from baseline to Week 20. Secondary outcomes included Cutometer-derived skin elasticity parameters (R2 and R5), Global Aesthetic Improvement Scale (GAIS), FACE-Q satisfaction scores, visual analog scale (VAS) pain scores, and adverse event monitoring. Of the 30 enrolled participants, 26 completed follow-up and were included in the final analysis. By Week 20, VECTRA 3D analysis demonstrated modest volume reductions in the cheek (- 2.2 mL), jowl (- 1.7 mL), and submental region (- 2.9 mL). Skin elasticity parameters increased over time, with R2 increasing from 0.62 to 0.69 and R5 from 0.53 to 0.59. GAIS improvement was observed in 88.5% of participants, and the mean FACE-Q score increased to 72.4 ± 10.1. Treatment was well tolerated, with low mean pain scores (VAS 1.2 ± 0.9). Only transient erythema and mild edema were reported, with no serious adverse events observed. Three sessions of 40.68-MHz unipolar RF were associated with modest measurable improvements in facial contour and skin elasticity that were maintained during the 20-week observation period, with good tolerability and no serious adverse events. Because this was an uncontrolled single-arm study, these findings should be interpreted as preliminary and require confirmation in controlled studies with longer follow-up.
27 July 2026
Read appraisal →Journal of radiation research
Prioritizing radiotherapy infrastructure in depopulating societies: a 2050 blueprint from Akita, Japan.
In super-aged and depopulating societies, repeated travel requirements for radiotherapy (RT) can amplify geographic inequities in access to advanced techniques. The aim of this study was to determine optimal placement of intensity-modulated radiotherapy (IMRT)-capable facilities in Akita Prefecture-a leading-edge case of Japan's demographic transition-using p-median optimization under population projections through 2050. Using 1-km mesh population data for 2020 and official projections for 2030, 2040 and 2050, we calculated one-way automobile travel times from each mesh centroid to RT facilities with the Open Source Routing Machine based on OpenStreetMap road data. A 60-min travel-time threshold defined access coverage. IMRT-capable facilities were identified from official insurance-designation lists. Optimal placement was determined using a population-weighted p-median model, with the number of facilities prespecified as one per 200 000 population. In 2020, 10 external-beam RT facilities provided near-universal 60-min coverage (99.99%; mean 12.8 min), whereas the single IMRT-capable facility covered only 70.3% (uncovered 285 273; mean 42.5 min). Optimized placement with five IMRT-capable facilities increased coverage to 99.99% (uncovered 89) and reduced mean travel time to 14.2 min. Under population projections, optimized placement maintained >99% coverage through 2050 with three facilities, and three core locations were consistently selected across all time horizons. These temporally stable core locations can serve as priority targets for long-term IMRT infrastructure investment in aging and depopulating regions.
27 July 2026
Read appraisal →Scientific data
Anonymized but Useful Synthetic Tabular Health Data for AI based Fall Risk Assessment
Artificial Intelligence (AI) bears potential for improving health care, but this depends on the availability of open-access, realistic, and useful data. To facilitate AI model development in health care we release SynTabFall, a novel synthetic dataset for fall risk assessment. With a total of 745,380 samples and 44 attributes such as demographics, diseases, mobility and cognition related risk factors, this tabular dataset allows for training fall risk prediction models without access to the original patient data. Models trained on our synthetic dataset can reach predictive performance scores in fall risk assessment which are on par with models trained on real data. To support others in sharing data we also describe a process that was developed over multiple years in one of Germany's largest hospitals in close collaboration between data protection officers, health care staff, informaticians and AI engineers. The proposed data sharing approach combines established methods for anonymization and modern generative AI (genAI) methods for synthesizing tabular data and allows for sharing health care data responsibly without sacrificing its utility. We release the synthetic fall risk dataset along with the software developed for synthetic data generation and evaluation.
26 July 2026
Read appraisal →Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
The past and future of neuropsychological assessment: Back to the origins to face digital technological changes.
BACKGROUND: The procedures used for the neuropsychological assessment of patients with dementia have been undergoing significant transformations relating to technological developments and the diffusion of artificial intelligence tools. OBJECTIVES: The integration of a theoretical position and a practical approach is needed in clinical contexts so that newly available tools can enhance the fundamental principles underlying neuropsychological assessment. METHODS: Following a historical review of the various approaches to neuropsychological assessment, an analysis of the risks associated with the introduction of digital techniques, when not supervised by expert clinicians, is presented. The need to integrate new technologies in a comprehensive assessment involving clinical observation, ad-hoc tests, and other sources of information such as neuroimaging and neurophysiological data is discussed. RESULTS: The integration of new technologies with a clinical, personalised approach to the patient represents a challenge for neuropsychology that deserves ad-hoc research and discussion.
26 July 2026
Read appraisal →Scientific reports
Recognition of everyday activities using experiment data from wearable sensors: a deep learning-based framework
Tracking everyday activities is vital for detecting changes in older adults' health, allowing timely support to promote well-being. Wearable sensors and deep learning provide continuous monitoring, making them a supportive tool in detecting such changes. However, a more refined method is needed to recognise precise activities with a minimal set of sensors. This study aimed to develop a method to recognise everyday activities among older adults by utilising wearable sensors and a deep learning model. This is a small-scale home lab experiment to develop a method to recognise 14 everyday activities. We compared five models that recognised everyday activities with different sensor signal counts and accuracy. Our results showed that sensor placement is important. Based on the results, we proposed a two-sensor method (pelvis and right hand) to collect and correctly recognise everyday activities among older adults. This model, which utilises two sensors, classified 12 activities with an accuracy of 89.3%. Another model recognised all 14 activities with a lower accuracy of 88.2% using five sensors. We also explored a one-sensor approach, which showed low recognition performance and struggled to distinguish activity variability. The two-sensor-based system will allow for large-scale data collection on everyday activities of older adults.
26 July 2026
Read appraisal →Journal of medicinal chemistry
SMDNet: A Self-Training-Aware and Multi-Modal-Adaptive Deep Learning Network for Low-Data Aβ42 Probe Design and Optimization
Amyloid-β (Aβ) plaque accumulation is a crucial hallmark of Alzheimer's disease, and fluorescence imaging can support disease diagnosis and monitoring. However, Aβ42 probe development is often hindered by trial-and-error experiments due to subtle structure-property effects. Here, we developed SMDNet, a self-training-aware and multimodal-adaptive deep learning (DL) framework for low-data Aβ42 probe design and optimization. SMDNet combines iterative self-training with confidence-aware and distribution-aware sampling to improve data quality, while integrating molecular graphs, fingerprints and protein descriptors through cross-attention and protein-conditional adaptive layer normalization. Ablation studies, external validation and generalization analyses confirmed the strong predictive ability of SMDNet. Interpretability analyses further highlighted chemically meaningful substructures associated with model predictions. As a proof of concept, SMDNet guided the rational design of five ThT-derived probe candidates, with TA3 showing favorable binding affinity and high-contrast imaging performance. Additional validation on coumarin- and naphthalimide-based candidates further supported useful predictive discrimination across distinct fluorescent scaffold classes.
24 July 2026
Read appraisal →Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
Differential effects of hormone therapy formulations on Parkinson's disease risk: a systematic review and meta-analysis
BACKGROUND: The relationship between menopausal hormone therapy (HT) and Parkinson's disease (PD) risk remains controversial, with inconsistent findings potentially driven by differences in hormonal formulations. METHODS: We conducted a systematic review and meta-analysis following PRISMA 2020 guidelines. MEDLINE/PubMed and EMBASE were searched between January 8 and March 14, 2026. Observational studies evaluating the association between menopausal HT and PD risk were included. Effect estimates were pooled using random-effects models. Subgroup analyses were performed according to HT formulation (estrogen-only vs. combined estrogen-progestin therapy). A multilevel meta-analysis was conducted to account for within-study dependence. RESULTS: Fourteen studies including 753,749 participants (4,433 PD cases) were analyzed. Overall, HT was not significantly associated with PD risk (RR 1.08; 95% CI 0.94-1.23; I² = 49.4%). In subgroup analyses, combined therapy was associated with an increased PD risk (RR 1.40; 95% CI 1.07-1.82), whereas estrogen-only therapy showed no significant association (RR 1.01; 95% CI 0.81-1.27). Multilevel analysis yielded consistent results (combined: RR 1.33; 95% CI 1.00-1.77; estrogen-only: RR 1.03; 95% CI 0.83-1.27), with no statistically significant interaction between formulations (p = 0.12). CONCLUSIONS: Combined menopausal HT was associated with an increased risk of PD, while estrogen-only therapy showed no significant association. Although differences between formulations were not statistically significant, these findings suggest that hormone composition may influence neurological outcomes and warrant further investigation into individualized HT strategies.
23 July 2026
Read appraisal →Current pain and headache reports
Chronic Pain and the Risk of Dementia: A Systematic Review and Meta-Analysis
BACKGROUND: The relationship between chronic pain and dementia risk has been extensively studied, but findings remain inconclusive. We conducted an updated meta-analysis to synthesize evidence from recent large-scale cohort studies. METHODS: Two authors independently and systematically searched PubMed, Web of Science, Embase, Cochrane Library, and Chinese National Knowledge Infrastructure for cohort studies published through April 2025, with a minimum follow-up of one year. Hazard ratios (HRs) were considered equivalent to risk ratios (RRs) assuming low event rates. Random-effect models pooled risk ratios (RR) with 95% confidence intervals (CI). Subgroup analyses and meta-regression explored heterogeneity. RESULTS: Of 3,823 publications, 30 studies met the inclusion criteria. After excluding five studies with overlapping populations, 25 studies involving 2,091,835 participants were pooled in the primary analysis. Individuals with chronic pain had a 28% higher dementia risk (RR = 1.28, 95% CI = 1.18-1.38). Heterogeneity was high (I²=91%). Subgroup analyses showed migraine, headaches, arthritis-related pain, and widespread pain were associated with increased risk. The association was significant in Asia but not in America or Europe, and when using ICD (but not DSM) criteria. Chronic pain increased the risk of Alzheimer's disease and non-vascular dementia, but not vascular dementia. Study quality, region, measurement methods, and pain types influenced the relationship. Pain type was a source of heterogeneity. CONCLUSIONS: In this meta-analysis of cohort studies, chronic pain was associated with an increased risk of dementia. Due to the observational nature of the included studies and high heterogeneity, this association should not be interpreted as causal. Further research is needed to determine whether pain management can mitigate dementia risk.
23 July 2026
Read appraisal →European journal of clinical pharmacology
Clinical decision support systems for polypharmacy optimization in older patients: a narrative review
PURPOSE: Multimorbidity and polypharmacy are increasingly prevalent in the older population and are associated with a higher risk of potentially inappropriate medications (PIM), drug-drug interactions (DDI), and adverse drug reactions (ADR). Although medication review (MR) and deprescribing are effective strategies, their manual implementation can be complex, time-consuming, and prone to clinical variability. Clinical Decision Support Systems (CDSS) offer advanced digital solutions to optimize polypharmacy by analyzing multidimensional clinical data and generating personalized recommendations. METHODS: A narrative review was conducted to identify and compare the main CDSS developed for the polypharmacy management, MR and deprescribing in older adults and patients with multimorbidity. Systems were classified as manual, hybrid, or automated according to their data acquisition modalities. Operational characteristics, integration into clinical workflows, decision-support functions, generated outputs, and available validation evidence across different healthcare settings were assessed. Owing to the narrative nature of the review and the heterogeneity of the included evidence, no formal risk-of-bias assessment or certainty-of-evidence evaluation was performed. RESULTS: Manual CDSS require direct data entry by clinicians and are associated with a high operational burden. Hybrid systems combine automatic data acquisition with manual integration, balancing efficiency and clinical oversight. Automated systems, integrated into electronic health records (EHR), provide real-time decision support with minimal human intervention. Considerable heterogeneity was observed across identified platforms in terms of automation, implementation characteristics, and stage of validation, with evidence ranging from development and feasibility studies to observational analyses and randomized controlled trials (RCT). CONCLUSION: CDSS represent promising tools for safer and more effective management of polypharmacy in complex patients. Advanced integration into clinical workflows and systematic use of multidimensional data may enhance their impact. However, the heterogeneity of available systems and the variability in their level of clinical validation highlight the need for comparative studies, pragmatic trials, and real-world implementation evaluations. Such studies are necessary to clarify the impact of different CDSS models on prescribing appropriateness, medication-related risks, patient outcomes, and long-term sustainability within routine healthcare settings.
20 July 2026
Read appraisal →Biomedical physics & engineering express
Benchtop validation of a low-cost wearable triaxial accelerometer
Wearable inertial sensors have become a cornerstone technology for detecting fall-related events, such as tripping while walking, in older adults. However, low-cost devices must undergo rigorous validation to ensure fidelity and stability in measurement. This study aimed to evaluate the concurrent validity of a newly developed, low-cost triaxial accelerometer by comparing its linear acceleration measurements with those obtained from a widely used commercial reference sensor (RS). A methodological validation study was conducted to assess the accuracy and precision of the proposed accelerometer. Triaxial linear acceleration data were simultaneously collected from the new device (sensor under test, SUT) and a commercial RS (Physilog®5, GaitUp, Lausanne, Switzerland). Measurements were obtained across three independent axes from 49 SUT units, with three repeated trials performed for each axis. The RS exhibited a more negative mean bias on the mediolateral (x) axis and a slightly larger mean bias on the vertical (y) axis. The SUT showed a greater mean bias (offset) on the anteroposterior (z) axis. Root mean squared error values were highly similar between thexandyaxes (<3.2% difference), while thez-axis presented moderately larger errors (∼9%). Mean absolute error was similar in thexandyaxes, whereas thez-axis was slightly higher. Visual waveform comparisons demonstrated strong overlap in mean time-series profiles, and near-unity correlations indicated high correspondence in signal variation. Bland-Altman analysis confirmed minimal bias and narrow limits of agreement for thexandyaxes, with greater variability observed along thezaxis, but no evidence of substantial systematic deviation. Overall, the low-cost accelerometer (∼60 USD) showed strong agreement with the commercial reference device (>1 000 USD), supporting its validity for three-dimensional linear acceleration measurement under controlled laboratory conditions. This low-cost device represents a promising solution for scalable motion monitoring and fall-related event detection.
20 July 2026
Read appraisal →Nature communications
Enhancing sit-to-stand transitions and walking efficiency in older adults with a soft robotic suit
Age-related declines in muscle strength and neuromuscular control make sit-to-stand transitions and walking progressively more difficult, compromising mobility and independence. Although wearable assistive technologies have been proposed to alleviate these challenges, few have demonstrated clear benefits in facilitating sit-to-stand movements for older adults who retain a degree of independent mobility. Here, we introduce a soft hip exosuit designed to assist both sit-to-stand transitions and walking activities. In a feasibility study involving ten older adults, the exosuit increased 1-minute sit-to-stand repetitions by an average of 1.8 and reduced the metabolic cost of walking by 13.6% compared with the unassisted condition. These improvements were achieved while preserving natural kinematics, lower-limb stability, and maintaining a strong sense of agency. Our findings demonstrate that soft exosuits can enhance sit-to-stand and walking performance in older adults while preserving biomechanical naturalness and user autonomy, highlighting their potential for practical home-integrated mobility assistance.
20 July 2026
Read appraisal →Scientific reports
Walking stability prediction for pedestrians using gait energy images and hybrid deep and few-shot learning models
The prediction and recognition of unstable human walking patterns are of high importance for active video surveillance, smart environments, and assistive healthcare, particularly for fall detection in the elderly. This research investigates the utility of Gait Energy Images (GEIs) combined with deep learning, vision transformers, and few-shot learning architectures to enhance the classification of stable and unstable pedestrian walking patterns. We evaluate and compare twelve methodologies: six classical or feature-based machine learning models (Linear SVM, HOG + SVM, LBP + RBF-SVM, Random Forest, XGBoost, and an adapted GaitSet baseline), three deep learning models (MobileNet, Vision Transformer (ViT), and YOLOv8-cls), and three episodic few-shot learning techniques (Prototypical, Matching, and Relation Networks) under data-scarcity regimes. To facilitate this evaluation, we introduce the Unstable and Stable Walking Pedestrian (USWP) dataset, constructed by fusing and harmonizing sequences from seven public action recognition databases, containing 3250 unique GEIs with a subject-independent evaluation protocol to prevent identity-based domain leakage. Our experiments demonstrate that the YOLOv8-cls model achieves an overall accuracy of 96.92% (97.14% on Loss-of-Balance anomalies and 94.67% on Active Motion anomalies), significantly outperforming the conventional Linear SVM baseline (75.38%) and MobileNet (91.08%). Conversely, Relation Networks exhibit lower few-shot performance (71.08%) due to optimization complexities in learning similarity metrics from sparse data. Leave-One-Dataset-Out (LODO) cross-validation reveals an average generalization accuracy of 83.28%, indicating significant domain bias across source databases and underscoring that within-dataset evaluations overestimate real-world generalization. Computational complexity analysis shows that MobileNet provides an optimal trade-off for real-time edge deployment (4.2 ms latency), while preprocessing ablation studies demonstrate that integrating the Segment Anything Model (SAM) with MediaPipe-derived Regions of Interest (ROIs) yields a 12.50% absolute improvement in accuracy by eliminating background noise.
19 July 2026
Read appraisal →International journal of technology assessment in health care
Consideration of intersectoral costs and benefits in health economic evaluations of pneumococcal conjugate vaccines: a systematic review.
OBJECTIVES: This systematic review explores which intersectoral costs and benefits (ICBs) are considered in the economic evaluation of pneumococcal conjugate vaccines (PCVs) across different age groups and the related impacts on the results of economic evaluations. METHODS: Seven databases were searched from 2009 to 2024 for full economic evaluations (Ees) of PCVs from a societal perspective. ICBs were presented narratively and in tabular form. Studies were appraised on quality, and PRISMA guidelines were followed. RESULTS: In all, 57 studies were included. ICBs were captured in the following sectors: patient and family, paid labor (productivity), nonpaid productivity, other sectors (education, leisure, consumption), unspecified sectors for ecological effects (herd immunity, serotype replacement, cross-protection), and equity. In cases where the comparator was no vaccination, 10 studies reported that PCVs were dominant when ICBs were included. After excluding dominant analyses, the median QALY-based incremental cost-effectiveness ratio (ICER) reduction ranged from -3.25 percent (for patient/family costs and productivity loss) to -46.02 percent (in children with ecological effects). Among DALY-based studies, the inclusion of ecological effects reduced the median ICER by -22.97 percent. In head-to-head comparisons, 16 studies reported that PCVs were dominant when ICBs were included. CONCLUSION: Inclusion of ICBs, except for serotype replacement, showed favorable cost-effectiveness results. This study provides a framework for identifying the intersectoral costs and benefits for the health economic evaluation of PCVs in children and adults.
19 July 2026
Read appraisal →American journal of physical medicine & rehabilitation
Virtual Reality-assisted Physiotherapeutic Training for Patients With Knee Osteoarthritis: A Systematic Review and Meta-analysis
OBJECTIVE: To evaluate the effectiveness of virtual reality (VR)-assisted physiotherapy in improving pain, physical function, and balance in patients with knee osteoarthritis (KOA) compared with traditional rehabilitation treatments. DESIGN: Systematic review and meta-analysis of randomized controlled trials (RCTs). RESULTS: This analysis included 5 randomized controlled trials (RCTs). The pooled results showed that, compared with consistency rehabilitation, virtual reality (VR)-assisted physiotherapy produced significantly better outcomes in patients with knee osteoarthritis (KOA) for pain relief (SMD = 0.40, 95% CI: 0.14-0.65, P = 0.002), physical function (SMD = 0.37, 95% CI: 0.05-0.70, P = 0.003), and balance (SMD = 0.44, 95% CI: 0.16-0.72, P = 0.002). CONCLUSIONS: Virtual reality-assisted physiotherapy is an effective intervention for patients with knee osteoarthritis, showing superior outcomes in pain reduction, functional improvement, and balance enhancement compared to traditional rehabilitation approaches.
18 July 2026
Read appraisal →JMIR human factors
Engaging Older Adults With Neurocognitive Disorders in Digital Health Technologies: Scoping Review
BACKGROUND: Population aging is associated with a growing prevalence of neurocognitive disorders among adults aged 65 years and older. Digital health technologies offer promising opportunities to support cognitive health and well-being in this population. However, their effectiveness largely depends on users' level of engagement. Despite the recognized importance of engagement in digital health, limited evidence exists on how engagement is conceptualized, measured, and related to intervention outcomes among older adults living with neurocognitive disorders. OBJECTIVE: This scoping review aimed to describe how engagement with digital health technologies among older adults with neurocognitive disorders is conceptualized and measured, examine the relationship between engagement and the effectiveness of digital health interventions, and identify factors that facilitate or hinder engagement. METHODS: A scoping review was conducted following the Joanna Briggs Institute methodological guidance and reported in accordance with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) checklist. A comprehensive search strategy, developed in collaboration with an information specialist, was applied to MEDLINE, Embase, CINAHL, Web of Science, and Google Scholar, without date restrictions. Empirical studies involving adults aged 65 years and older living with neurocognitive disorders and using digital health technologies were included. Study selection and data extraction were performed independently by at least 2 reviewers, and the results were synthesized narratively. RESULTS: Of the 1665 records identified after duplicate removal, 2 studies met the inclusion criteria. One study examined computerized cognitive stimulation and cognitive engagement programs among community-dwelling older adults with mild neurocognitive disorders, whereas the other explored the use of a personalized digital reminiscence application in long-term care settings among individuals with major neurocognitive disorders. No study used a validated instrument to directly measure engagement. However, observable indicators and markers related to the behavioral, cognitive, and affective components of engagement were reported. Both studies also documented concurrent cognitive or psychosocial outcomes. Factors facilitating engagement included professional support, content personalization, and involvement of informal caregivers, whereas limiting factors included cognitive fluctuations, fatigue, technical complexity, and reliance on external support. CONCLUSIONS: This scoping review highlights a significant gap in the literature regarding the explicit conceptualization and standardized measurement of engagement with digital health technologies among older adults living with neurocognitive disorders. The findings underscore the need to develop and apply multidimensional, context-sensitive engagement measurement tools tailored to this population to better understand and optimize digital health interventions.
18 July 2026
Read appraisal →Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
Creative thinking in Parkinson's disease: A systematic review and meta-analysis
INTRODUCTION: Creativity in Parkinson's disease (PD) has aroused research interest due to its neurological underpinnings, which involve brain regions crucial for creative and divergent thinking (a core-process of creative thinking), and because of some patients who displayed an increased artistic drive following dopaminergic treatment. From a cognitive point of view, creative thinking underlies several cognitive abilities, such as executive functions and memory. Therefore, a better understanding of whether it is increased (or, at least, preserved) in PD patients can provide useful insights for sustaining cognitive functioning. The aim of the present study was to investigate whether PD patients regularly assuming dopaminergic medications present higher divergent thinking skills than healthy controls. METHODS: A meta-analysis was conducted according to the PRISMA guidelines to provide a statistical synthesis of the studies. Study quality was assessed using the QUADAS -2 tool. RESULTS: Ten studies were included in the meta-analysis, which indicated the absence of significant differences between PD patients and healthy controls in tasks assessing divergent thinking (Cohen's d = -0,095 (95% CI: -0,308, 0,118)). CONCLUSIONS: Such findings support the notion that divergent thinking can be spared by the disease, maybe constituting a possible resource for patients' cognitive functioning, as it involves those cognitive abilities that can compensate impairments in PD. Clinical implications and guidance to further studies and interventions to support PD patients' cognition were discussed.
17 July 2026
Read appraisal →Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
Association between circadian rhythm disturbances and cognitive decline in the elderly: a systematic review
BACKGROUND: Circadian rhythm dysregulation may contribute to sleep-wake disorders and cognitive impairment. In the elderly, circadian abnormalities have been observed in Mild Cognitive Impairment (MCI), suggesting a possible association between neurocognitive deficits and circadian dysfunctions. OBJECTIVE: To analyze the association between circadian rhythm disturbances and cognitive decline in the population aged ≥ 60 years. METHODS: This systematic review included studies evaluating the association between circadian rhythm disturbances and cognitive decline in elderly individuals aged 60 years or older, with self-reported or objectively measured sleep data and cognitive assessment via questionnaires. Studies tracking progression from MCI to Alzheimer's disease (AD) were retained, as they capture circadian changes across the MCI-to-dementia continuum. Studies that evaluated other sleep disorders and/or other conditions of cognitive impairment exclusively or that did not specify the mean age of the participants were excluded. RESULTS: Ten studies were included, totaling 5,731 participants (5,707 eligible for analysis). Circadian exposures were organized into three domains: (1) actigraphic rest-activity rhythm metrics (amplitude, robustness, interdaily stability, intradaily variability, and acrophase); (2) sleep quality and architecture parameters (efficiency, fragmentation, latency, WASO, and melatonin timing); and (3) circadian-disrupting lifestyle exposures (rotating night shift work). Across these domains, circadian dysregulation was consistently associated with a higher risk of development or progression of MCI and dementia. CONCLUSION: Circadian and sleep disturbances negatively impact cognitive health in the elderly, reinforcing the need for further research on this association and its public health implications.
17 July 2026
Read appraisal →Sleep
Consensus roadmap on periodic leg movements during sleep: outcomes of the international PLMS taskforce
Periodic leg movements during sleep (PLMS) are a frequently observed motor phenomenon in polysomnography, yet their biological meaning and clinical relevance are debated. Further, how these should be assessed and tabulated for optimal evaluation is uncertain. To address these gaps, an international, multidisciplinary taskforce convened a series of online meetings followed by an in person taskforce workshop held in Troina, Italy, on November 14-15, 2025, where agreement was reached through iterative discussion (qualitative; no Delphi or predefined voting thresholds). This taskforce roadmap summarizes the taskforce conclusions across five priority domains: (1) clinical significance and impact, (2) measurement and characterization, (3) treatment, (4) basic and translational research, and (5) PLMS across the life span and epidemiology. For each domain, we delineate where the field currently stands, identify critical gaps in knowledge, and propose concrete strategies, including key milestones, study designs, and infrastructure needs, to advance the field in a coordinated and standardized manner. A central unifying theme is that PLMS should no longer be viewed solely as a countable polysomnographic index, but rather as a spectrum of sleep-related motor activity with heterogeneous neurophysiological substrates and variable clinical impact. Achieving progress will require harmonized scoring standards, validated automated and wearable technologies, large longitudinal and outcome-oriented studies, and sustained international collaboration. This roadmap provides a framework to guide research, clinical innovation, and future guideline development, but it is not a clinical practice guideline or standards statement.
14 July 2026
Read appraisal →Journal of molecular neuroscience : MN
Scalp Acupuncture Combined with Mouse Nerve Growth Factor Acupoint Injection for Post-stroke Cognitive Impairment
Post-stroke cognitive impairment (PSCI) markedly impairs rehabilitation efficacy and quality of life. Safe and effective interventions are urgently needed. Eighty-four patients with PSCI were randomly allocated into three groups (n = 28 per group). Group A received conventional cognitive training. Group B received conventional training plus scalp acupuncture. Group C received the treatment of Group B combined with mouse nerve growth factor (mNGF) acupoint injection. All interventions lasted 4 weeks. All groups exhibited significant within-group improvements in MoCA, ADL, MBI, and FMA scores after intervention. Group C achieved the optimal cognitive efficacy: the post-treatment MoCA score was 26.7 ± 2.9 (95% CI, 25.53-27.87), significantly higher than that in Group A (21.5 ± 3.5 (95% CI, 20.22-22.78), P < 0.001) and Group B (23.8 ± 3.2)(95% CI, 22.48-25.12), P = 0.003). The ADL score in Group C was the lowest among the three groups (F = 28.963, P < 0.001), suggesting better independence. Moreover, motor function (FMA) and basic self-care ability (MBI) were also significantly improved in all groups after intervention. There were significant inter-group differences in post-treatment FMA and MBI scores (F-FMA = 29.745, P < 0.001; F-MBI = 35.872, P < 0.001). The scores increased gradually from Group A to Group C, with Group C achieving the best improvement. Conventional cognitive training improves cognitive function and daily living ability in patients with PSCI. Scalp acupuncture combined with conventional training is more effective than training alone. The triple therapy of conventional training, scalp acupuncture, and mNGF acupoint injection further enhances rehabilitation outcomes, with prominent clinical application value.
13 July 2026
Read appraisal →Science advances
Motor-free hip exosuit via high-output fibrous dielectric elastomer actuators
Exosuits can assist gait and reduce fatigue for both healthy and pathological populations, yet their bulky, rigid actuators (usually motors or pneumatic actuators) hinder natural, comfortable movement. Dielectric elastomer actuators (DEAs) provide a lightweight, compliant alternative, but are constrained by insufficient force, energy output, and integration challenges. Herein, we propose a motor-free hip exosuit driven by high-output fibrous DEAs, offering a previously unexplored paradigm for lower-limb assistance. We develop high-aspect-ratio fibrous DEAs that deliver high blocked stress (381.6 mN·mm-2), energy density (260 J/kg), and power density (1,664 W/kg), enabled by a dual-polar molecular design of the elastomer to overcome the intrinsic trade-offs between dielectric and mechanical properties. A Lego-like integration strategy is established to efficiently bundle fibers for force amplification. The resulting exosuit reduces the walking metabolic cost by 13.9% compared to no assistance, surpassing most hip exoskeletons. These findings advance DEAs toward practical wearable robotics for real-world human assistance.
12 July 2026
Read appraisal →PloS one
Intergenerational patterns of digital use: Evidence from a large cross-sectional study
This study aims to determine the level of digital use across six generations (Greatest, Silent, Baby Boomers, and Generations X, Y and Z) within a district in North East England (population ~200,000). A cross-sectional descriptive study was conducted via an online and paper-based survey from February to May 2022, mailed to households in the district (N = 98,260). Respondents estimated their weekly use of digital tools and the internet at home. One-way ANOVA and Tukey-Kramer HSD tests were used to analyse differences in digital resource uses across six generations. To account for underlying sociodemographic characteristics, a follow-up ANCOVA analysis was also performed. Content Analysis was used for qualitative responses from an open-ended survey question. A total of 9,181 completed surveys were analysed. The sample was skewed towards older, homeowner adults (mean age 63, 60% female). Findings revealed that respondents spend less time online than other British cohorts. Baby Boomers and Generation X self-reported statistically significant differences in the level of digital use compared to all other generations. Younger generations (Y and Z) self-reported, on average, a larger amount of time spent on both digital tools and the internet. Members of Greatest and Silent Generations had the lowest hours spent on digital tools and the internet. The results suggest that public health initiatives should prioritise strategies bridging the digital divide between generations. Targeted training programs pairing younger, tech-savvy individuals with older adults could enhance digital literacy. Additionally, integrating user-friendly digital health platforms that cater to varying levels of technological proficiency will encourage wider adoption. These strategies not only foster intergenerational collaboration but also drive successful digital health and care transformations, ensuring equitable access to technological advancements for all age groups.
11 July 2026
Read appraisal →Journal of medical Internet research
The Association Between eHealth Literacy and Health Behaviors During and Since the COVID-19 Pandemic: Systematic Review and Meta-Analysis
BACKGROUND: Since COVID-19, health information seeking, service navigation, and routine care have become increasingly digitally mediated. It remains unclear whether the association between eHealth literacy and health behaviors is consistent across behavioral domains, populations, and analytic frameworks. OBJECTIVE: This systematic review and meta-analysis synthesized COVID-19 and post-COVID-19 evidence on the association between eHealth literacy and health behaviors and examined variation across study contexts. METHODS: We conducted a PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020-compliant, PROSPERO-registered review (CRD420251009048). PubMed, Embase, Web of Science Core Collection, CINAHL Ultimate, and Scopus were searched from January 1, 2020, through March 27, 2026. Eligible observational studies assessed eHealth literacy and reported analyzable associations with health behavior outcomes collected in 2020 or later. Health behaviors were classified as health decision-making, health-promoting, or health management behaviors. Correlation coefficients, grouped odds ratios (ORs), and continuous ORs were synthesized separately using random-effects models with Knapp-Hartung adjustment. Certainty was assessed using the Grading of Recommendations Assessment, Development, and Evaluation framework. RESULTS: In total, 19 studies were included: 10 contributed correlation coefficients, 6 grouped ORs, and 3 continuous ORs. In the correlation-based synthesis, higher eHealth literacy was associated with more favorable health behaviors (pooled r=0.43, 95% CI 0.36-0.51; 95% prediction interval 0.22-0.61), with substantial heterogeneity (I2=80.50%). In the grouped OR synthesis, higher eHealth literacy was also associated with more favorable health behaviors (pooled OR 2.12, 95% CI 1.47-3.05; 95% prediction interval 1.11-4.06), with moderate heterogeneity (I2=46.33%). In the continuous OR synthesis, all 3 studies showed positive associations, but the pooled effect was not statistically significant (pooled OR 1.07, 95% CI 0.89-1.30; 95% prediction interval 0.84-1.37), with very high heterogeneity (I2=97.98%). Subgroup analyses showed a significant difference only by geriatric status in the grouped OR synthesis. Certainty was low for the grouped OR synthesis and very low for the other 2 syntheses. CONCLUSIONS: Higher eHealth literacy was generally associated with more favorable health behaviors in the correlation-based and grouped OR syntheses, whereas evidence from the continuous OR synthesis was inconclusive. Given the predominantly cross-sectional evidence base, heterogeneity, risk-of-bias concerns, and low to very low certainty, the association should be interpreted as contextual and associative rather than causal or uniform. This review is innovative in synthesizing COVID-19 and post-COVID-19 evidence, applying a functional classification of health behaviors, and analyzing distinct effect measures separately. Unlike previous reviews that summarized the association more broadly, it avoids a single mixed pooled effect and provides a cautious, context-specific interpretation. In practice, interventions should pair eHealth literacy improvement with trustworthy digital services, clinician support, and behavior-specific conditions that help translate digital information into sustained health-related action.
10 July 2026
Read appraisal →JMIR medical education
Youth-Elder Co-Learning Model in Psychiatric Long-Term Care Education: Mixed Methods Evaluation of Communication and Empathy Outcomes
BACKGROUND: Taiwan is projected to become a "super-aged" society by 2025, leading to an increasing demand for community psychiatric long-term care (LTC). This demographic shift necessitates frontline professionals equipped with specialized communication skills and deep empathy. However, traditional didactic teaching often fails to adequately prepare students for the complex emotional and practical challenges of real-world psychiatric caregiving. OBJECTIVE: This study aimed to evaluate the effectiveness of the innovative youth-elder co-learning instructional model, which uniquely integrates micro-movie discussions and an intergenerational empathy board game, on adult learners' professional knowledge, communication competence, empathic development, and overall learning satisfaction. METHODS: A mixed methods, single-group, pre-post design was used. The educational intervention was implemented within an 18-week elective community psychiatric LTC course. Participants included 38 adult learners and continuing education students (aged 19-64 years). Notably, the majority of the cohort (n=29, 76.3%) had no prior practical experience in LTC. Quantitative data were collected using self-assessed and peer-evaluated scales for professional knowledge, communication competence, and empathy at pre-, mid-, and post-course time points, alongside an end-of-semester course student feedback survey. Qualitative data were systematically gathered through structured reflective journals and analyzed using a rigorous 6-phase thematic analysis framework. RESULTS: Students reported high course satisfaction rates, ranging from 92.4% to 95.3%. Quantitative analysis revealed a notable divergence: there were significant improvements in peer-evaluated outcomes (P<.001) and self-assessed communication competence (P=.004), but there was more conservative, statistically nonsignificant growth in self-assessed scores for professional knowledge (P=.14) and empathy (P=.09). This discrepancy likely reflects adult learners' heightened awareness of professional complexity and self-reflective humility. Furthermore, the qualitative thematic analysis uncovered the following three narrative shifts: (1) the dismantling of generational stereotypes through authentic, face-to-face interaction with real older adults; (2) an empathic awakening regarding the often-invisible burden of family caregivers, catalyzed by the micro-movies; and (3) the successful translation of theoretical nonviolent communication concepts into real-time clinical problem-solving during board game role-plays. CONCLUSIONS: The youth-elder co-learning model shows promise as an innovative, experiential pedagogical approach. By bridging theoretical frameworks with authentic intergenerational contact, the intervention supported students in translating general empathic concepts into actionable communication competencies. However, given the exploratory nature of this study and the absence of a control group, the quantitative findings must be interpreted cautiously. Future research using randomized controlled trial designs across multiple institutions is warranted to establish definitive causal impacts.
10 July 2026
Read appraisal →Brain imaging and behavior
Associations between olfactory dysfunction and structural MRI findings in Parkinson's disease: a systematic review
Olfactory dysfunction is a common and early non-motor symptom of Parkinson's disease (PD). Structural Magnetic Resonance Imaging (MRI) studies have reported changes in olfactory-related brain regions, but findings remain inconsistent. This review synthesizes MRI evidence to clarify neuroanatomical correlates of olfactory dysfunction in PD. We performed a systematic review to find the associations between olfactory dysfunction and structural MRI findings in people with PD. A systematic search in PubMed, Scopus, and Web of Science yielded 20 eligible studies. The eligible studies involved 1,092 patients with PD. Although there were inconsistent regions of interest and types of analysis, we identified structural changes in the olfactory sulcus and bulb, cortical gray matter, hippocampus, parahippocampus, and basal ganglia that are associated with olfactory dysfunction. Olfactory dysfunction in PD is linked to structural changes in primary olfactory, limbic, and cortical regions. These MRI findings may serve as early biomarkers, but standardized, longitudinal studies are needed to confirm their diagnostic or prognostic value.
10 July 2026
Read appraisal →JMIR aging
Smart Speaker-Based Applications to Support Social Connectedness in Older Adult Residents in Affordable Housing: User-Centered Design Study
BACKGROUND: Older adults in affordable housing face heightened risks of social isolation and loneliness due to limited social networks, transportation barriers, chronic conditions, and inadequate technology access. Smart speakers offer potential for enhancing social connectedness in this underserved population, yet technology interventions are rarely designed with meaningful input from older adults themselves. User-centered design (UCD) approaches can address this gap by engaging end users throughout the development process to ensure technology solutions align with their needs and living contexts. OBJECTIVE: This study aimed to engage older adults in affordable housing in an iterative UCD process to develop prototype scenarios for smart speaker-based applications that promote social connectedness while addressing safety, community-building, and wellness needs. METHODS: We conducted a 3-stage UCD study with 29 older adults (mean age 70, SD 6.8 years; 23/29, 79% African American; 20/29, 69% high school education or less) living alone in affordable housing between April 2021 and April 2022. Stage 1 included 5 focus groups (n=25) combining needs assessment discussions with rapid brainstorming activities. Stage 2 involved research team synthesis of focus group transcripts and brainstorming data to create a Design Strategies Map, development of initial prototype scenarios, 5 evaluation focus groups (n=18) to gather feedback, and iterative scenario refinement. Stage 3 comprised 4 validation focus groups (n=17) to assess refined scenarios and identify implementation recommendations. Participants included both smart speaker users (n=13) and nonusers (n=16). Data were analyzed using thematic analysis for needs assessment, content analysis for brainstorming ideas and feedback, and matrix analysis for systematic comparison across scenarios. RESULTS: Participants generated 153 ideas for smart speaker use, with Health and Safety and Daily Assistance being the most frequent categories. Analysis revealed that social connection needs were inseparable from safety concerns related to living alone. Through iterative co-design, we developed 7 prototype scenarios across 4 functional categories: Checking-In (peer and management safety verification with privacy controls), Social Companion (conversational artificial intelligence-based companionship and emotional support), Community Involvement (virtual bulletin boards and activity coordination), and Wellness Check (system-initiated monitoring of activity and behavioral patterns as health indicators with user-controlled interventions). Participants emphasized requirements for personalization, opt-in/opt-out controls, "Do-Not-Disturb" functionality, and safeguards preventing replacement of human connection. CONCLUSIONS: Older adults in affordable housing engaged in technology design and provided valuable insights that challenge assumptions about their needs and preferences. The prototype scenarios addressed the dual imperatives of social connection and safety while living alone, offering a foundation for developing technology-based applications tailored to underserved populations. Implementation should prioritize user control, privacy protection, and human-in-the-loop design ensuring that technology facilitates rather than replaces human connection and community programming, alongside consideration of user characteristics to build trust and ensure effective, sustained use of the intended technology platform.
9 July 2026
Read appraisal →Psychological medicine
Extreme heat and mental health: systematic review and qualitative investigation of risk and protective factors
BACKGROUND: Extreme hot weather poses increasing risks to mental health. Yet, factors affecting vulnerability are under-researched. This mixed-method study integrates a systematic review and qualitative investigation to identify risk and protective factors for heat-related mental health issues, leading to the co-development of a screening tool. This could inform future research and, pending validation in clinical settings, support mental health professionals in assessing vulnerability among service users. METHODS: We searched PubMed and Web of Science for publications on extreme heat, mental health, and risk/protective factors. In addition, we conducted six focus groups with 21 people with lived experience of heat and/or mental illness and 12 healthcare professionals. Transcripts were analyzed using thematic content analysis and informed the co-development of the screening tool. RESULTS: Out of 764 articles identified by the systematic review, 47 were included. Evidence emerged for age, sex, existing mental illness, ethnicity, and socioeconomic status as risk factors. However, findings were inconsistent between studies, likely due to differences in study population and methodology. Protective effects included good physical health, social support, and exposure to green spaces. Our qualitative investigation identified additional risk and protective factors related to: (1) behavioral adaptability, (2) personal heat sensitivity, and (3) disparities in heat exposure. The resulting screening tool, HEAT-MH (Heat Exposure Assessment Tool for Mental Health), contains 15 questions on previous experiences of heat, general health, and lifestyle. CONCLUSIONS: The mental health impacts of extreme heat depend on a range of risk and protective factors, including demographic, socioeconomic, health, and lifestyle characteristics.
8 July 2026
Read appraisal →PloS one
Effects of immersive virtual reality on limb motor function, balance, gait and quality of life after stroke: A systematic review and meta-analysis
Immersive virtual reality (IVR) has been increasingly integrated into stroke rehabilitation albeit the effectiveness across different outcome domains remains unclear. This study aimed to review evidence from randomized controlled trials evaluating the effects of IVR, used alone or in combination with conventional rehabilitation, on motor-, functional-, and participation-related outcomes after stroke. Ovid MEDLINE, Embase, Cochrane Library and CINAHL were screened up to April 2025 to identify eligible randomized controlled trials (RCTs). A systematic literature search was conducted. Randomized controlled trials including adults with stroke and evaluating immersive virtual reality interventions were eligible. Studies using non-immersive VR, non-randomized designs, or not reporting motor, functional, or participation outcomes were excluded. Meta-analyses were performed where data were sufficiently homogeneous; otherwise, outcomes were summarized descriptively. Risk of bias was assessed using standardized appraisal tools, and certainty of evidence was evaluated using the GRADE approach. Out of 3,574, we included 34 RCTs involving post-acute, subacute and chronic stroke survivors, with a total of1200 participants. Meta-analyses showed IVR, most commonly delivered in combination with conventional rehabilitation, to be associated with improvements in upper extremity motor impairment, particularly measured by the Fugl-Meyer Assessment Upper Extremity (FMA-UE). Small but significant effects were also found for selected gait parameters. In contrast, effects on functional performance, balance, and quality of life were inconsistent and could not be robustly quantified. The overall certainty of evidence ranged from low to very low, primarily due to heterogeneity, methodological limitations, and small sample sizes. In conclusion, IVR appears to be a promising and safe supplement to conventional stroke rehabilitation, particularly for improving motor impairment. However, evidence remains limited and inconsistent across functional outcomes. More well-designed, adequately powered trials with standardized interventions and clinically meaningful outcome measures are needed to clarify the role of IVR in stroke rehabilitation. PROSPERO (Registration number: CRD420250639792).
7 July 2026
Read appraisal →Journal of medical economics
Evaluation of nirmatrelvir/ritonavir treatment for COVID-19 on health-related outcomes: a global economic value systematic literature review.
AIMS: Nirmatrelvir/ritonavir (NMV/r)i is an antiviral drug indicated for the treatment of patients with mild to moderate coronavirus disease 2019 (COVID-19) who are at high risk of progressing to severe disease. We performed an updated economic systematic literature review (eSLR) of NMV/r to build upon evidence reported in our previous eSLR and additionally examine the health-related outcomes associated with NMV/r and any potential effect of long COVID. METHODS: A systematic search of Embase, PubMed, Cochrane, and EconLit and of conference and health technology assessment agency websites was performed to identify economic analyses published between January 2022 and October 2025. RESULTS: Of the 33 included economic evaluations, most were cost-utility analyses (n = 16) and used an analysis of a short-term decision tree with a long-term Markov model (n = 11). Several types of health-related endpoints were reported by studies, with most including hospital-related endpoints (n = 20), quality-adjusted life-years (QALYs) (n = 18), and death-related endpoints (n = 18). Positive health-related outcomes were associated with NMV/r treatment, including reductions in hospitalizations and deaths and an increase in QALYs. NMV/r treatment was also associated with a reduced number of patients with long COVID complications. LIMITATIONS: Most studies were conducted in high-income countries, were based on different COVID-19 variants, and were limited in data on the long COVID population. CONCLUSION: In addition to monetary benefits, NMV/r provides short-term and long-term health-related benefits to patients with mild to moderate COVID-19. This study provides further support for NMV/r as a cost-effective treatment option.
7 July 2026
Read appraisal →Journal of medical Internet research
Patients' Experiences of Nurse-Led eHealth Interventions for Chronic Heart Failure: Qualitative Systematic Review and Meta-Synthesis
BACKGROUND: Chronic heart failure (CHF) is a major chronic condition in the context of global population aging and is associated with high prevalence, frequent rehospitalization, and high mortality. Self-management is widely recognized as an important factor influencing prognosis and quality of life among patients with CHF. Nurse-led eHealth interventions have been increasingly used in postdischarge CHF management and have shown potential for improving adherence and health-related behaviors. However, existing studies have predominantly focused on objective outcomes, and limited systematic synthesis has examined patients' subjective experiences, which may restrict the patient-centered optimization and sustainable implementation of these interventions. OBJECTIVE: This study aimed to systematically synthesize patients' experiences of nurse-led eHealth interventions for CHF, identify facilitators and barriers to engagement and implementation, and inform the patient-centered optimization of intervention design through a qualitative systematic review and meta-synthesis. METHODS: This qualitative systematic review and meta-synthesis was conducted in accordance with the Joanna Briggs Institute methodology for qualitative systematic reviews. A comprehensive search was performed in PubMed, Web of Science, Embase, the Cochrane Library, CINAHL, CNKI, WanFang, and VIP from database inception to April 30, 2026, supplemented by manual searches of the reference lists of the included studies. Following methodological quality appraisal, the findings of the included studies were extracted and synthesized using thematic synthesis. RESULTS: A total of 23 studies involving 424 patients with CHF were included, comprising 17 qualitative studies and 6 mixed methods studies. Four synthesized themes and 12 subthemes were generated: (1) patient empowerment and enhanced self-management, (2) sense of security and continuity of care under professional support, (3) variations in acceptance and emotional responses, and (4) barriers and challenges in implementing eHealth interventions. CONCLUSIONS: Nurse-led eHealth interventions may provide important support for CHF management from the patient perspective by promoting empowerment and extending care support beyond hospital settings. The findings suggest that future intervention design should better address patient heterogeneity, technological usability, and long-term sustainability. Further efforts are needed to strengthen individualized adaptation and technological optimization to enhance the accessibility, acceptability, and patient-centered implementation of these interventions.
7 July 2026
Read appraisal →JMIR formative research
Exploring Informal Caregivers' Perception of the Olera Digital Caregiving Assistance Platform for Dementia Care: Mixed Methods Evaluation Study
BACKGROUND: Informal caregivers of people living with dementia often experience high rates of caregiver burnout while providing care. Although there are many websites and mobile apps available to help caregivers, many do not use digital tools. The Olera platform was developed to be an easily adoptable web-based support tool, connecting caregivers with long-term services and supports, financial assistance, and educational resources. The platform was developed based on the Build-Measure-Learn framework with input from caregiver needs assessments and usability studies. OBJECTIVE: This study aims to evaluate the quantitative and qualitative feedback of informal caregivers of people living with dementia on the second iteration of the Olera platform. The primary objective was to assess caregivers' acceptance of this caregiving platform. The secondary objective was to use qualitative methods to explore (1) the study cohort's challenges in daily caregiving to determine and compare them with prior literature, (2) their experience when using the Olera platform, and (3) their attitudes toward integrating artificial intelligence in caregiver services for future studies and platform development. METHODS: Caregivers were recruited through various sources and screened for eligibility through an initial survey. Participants used the platform for 4 weeks and completed a survey with an adapted Technology Acceptance Survey (TAS) and qualitative open-ended questions at the end of the testing period. TAS responses were summarized with descriptive statistics, while ANOVAs, t tests, and linear regressions were used to compare the differences in the overall TAS scores by caregiver characteristics. Qualitative feedback data on the platform's usefulness were analyzed via a thematic analysis framework approach. RESULTS: A total of 65 caregivers in the United States completed the study, with a mean age of 59.9 (SD 9.8) years. The majority were female (61/65, 95.3%), non-Hispanic or Latino White (45/65, 69.2%), and the adult child of their care recipient (42/65, 64.6%). Evaluation of the Olera platform showed a high acceptance rate, with each TAS item scoring above 5.0 and an overall TAS score of 5.83 (SD 0.85) out of 7. Higher platform use frequency was associated with higher TAS ratings in technology acceptance (F3,61=7.88, P<.001). Thematic analyses elicited the caregiving challenges, evaluation of the Olera platform, and feedback on artificial intelligence-assisted support. CONCLUSIONS: The Olera platform is an example of a beneficial web-based tool, though key features were requested to be included in the next iteration. Additionally, data supported prior findings regarding informal caregiver challenges and the insufficiency of conventional support mechanisms, indicating a need for more innovative digital solutions. Future research and development efforts using the Build-Measure-Learn approach are necessary to further iterate the platform's key features, enhance the tool, involve more informal caregivers in its improvements, and serve as a model for customizable, person-centered online care support.
6 July 2026
Read appraisal →Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
Limb apraxia in Parkinson's disease and atypical parkinsonian syndromes: a systematic review
BACKGROUND: Evidence regarding the clinical manifestations, disease-specific profiles and diagnostic significance of limb apraxia in Parkinson's disease and Atypical Parkinsonian Syndromes (APS) is limited. OBJECTIVES: The present systematic review aims to consolidate current knowledge on limb apraxia across neurodegenerative disorders, including Parkinson's disease (PD), corticobasal syndrome (CBS), Progressive Supranuclear Palsy (PSP) and Multiple System Atrophy (MSA). METHODS: A systematic literature review was conducted in accordance with PRISMA guidelines. Studies were included if they enrolled ≥ 10 patients in at least one of the patient groups and ≥ 10 control subjects with quantitative data on apraxic deficits. Risk of bias assessment was assessed. RESULTS: Twenty-two studies met inclusion criteria (PD n = 11; CBS n = 10; PSP n = 7; MSA n = 3). Across PD and APS, praxis assessment primarily involved gesture imitation, pantomime, action sequencing, actual tool use and measures of fine motor coordination. CBS demonstrated the most severe/widespread apraxic impairment, affecting both meaningless and meaningful (transitive and intransitive) gestures, as well as action sequencing and fine motor control. In PD, apraxic deficits were generally milder but shared overlapping features with CBS. Direct comparative studies between PD and CBS remain scarce. PSP was characterized by less frequent and predominantly sequence-related impairments, whereas findings in MSA were heterogeneous and less pronounced. CONCLUSIONS: Limb apraxia phenotypes differ across PD, CBS, PSP and MSA and may contribute to their differential diagnosis. Future research should adopt standardized, multimodal praxis assessment protocols in larger cohorts including all major neurodegenerative parkinsonian disorders, to facilitate the direct comparison of limb apraxia across these diseases.
4 July 2026
Read appraisal →Journal of diabetes science and technology
The Impact of Virtual Consultations on Quality of Care for Patients With Type 2 Diabetes: A Systematic Review and Meta-Analysis
BackgroundVirtual consultations (VC) have transformed healthcare delivery, offering a convenient and effective way to manage chronic conditions such as Type 2 Diabetes (T2D). This systematic review and meta-analysis evaluated the impact of VC on the quality of care provided to patients with T2D, mapping it across the six domains of the US National Academy of Medicine (NAM) quality-of-care framework (ie, effectiveness, efficiency, patient-centeredness, timeliness, safety, and equity).MethodsA systematic search was conducted in PubMed/MEDLINE, Cochrane, Embase, CINAHL, and Web of Science for the period between January 2010 and December 2024. Eligible studies involved adult T2D patients, evaluated synchronous VCs, and reported outcomes relevant to NAM quality domains. Two independent reviewers performed screening, and studies were assessed using the Mixed Methods Appraisal Tool (MMAT). A narrative synthesis was conducted for each quality domain, and a meta-analysis of HbA1c levels was performed using random-effects models.ResultsIn total, 15 studies involving 821 014 participants were included. VCs were comparable with face-to-face care in effectiveness, efficiency, patient-centeredness, and timeliness, with improvements in accessibility and patient satisfaction. Mixed results were found for safety due to limitations in physical assessments, and for equity, with older adults and those with lower digital literacy facing more challenges. The meta-analysis showed no significant difference in HbA1c reduction between VCs and face-to-face (standardized mean difference [SMD] = -0.31, 95% confidence interval [CI]: -0.71 to 0.09, P = 0.12).ConclusionVCs offer a promising alternative to in-person care, but addressing digital disparities and improving access for older adults are essential for maximizing VC potential.
2 July 2026
Read appraisal →Journal of Alzheimer's disease : JAD
Early diagnosis of Alzheimer's disease through handwriting analysis and deep learning: A review.
Alzheimer's disease (AD) is one of the most prevalent neurodegenerative disorders worldwide, requiring early identification for timely intervention and to slow disease progression. However, existing diagnostic approaches, while effective at later stages, remain limited in detecting early-stage AD. Handwriting analysis has recently emerged as a non-invasive, cost-effective, and ecologically valid digital behavioral biomarker that reflects neurocognitive impairment. This review examines the role of handwriting as a neurocognitive marker for AD, focusing on integrating deep learning methodologies to enhance early diagnostic accuracy. It also elucidates the neurocognitive mechanisms linking handwriting behavior and AD, addressing current methodological and translational challenges. We performed a PRISMA-informed structured literature search and narrative synthesis of handwriting- and drawing-based studies for detecting AD/mild cognitive impairment (MCI), including offline handwriting images and online pen-stroke kinematics captured by digital devices. Task paradigms, data dimensions, preprocessing pipelines, modeling strategies (traditional machine learning and deep learning), evaluation practices, and translational considerations were summarized, and studies were organized by detection purpose and analytic approach. Our findings show that handwriting-based models generally discriminate AD/MCI from healthy controls with accuracy exceeding 80%, while deep learning models (e.g., convolutional neural network and multimodal Transformer fusion) approach 90% in structured tasks like clock drawing and figure copying. Online kinematic markers (e.g., reduced velocity, prolonged in-air time, increased pausing, and pressure instability) recur across studies, and multimodal integration with speech, gait, or facial signals can further improve sensitivity and ecological validity, although most studies are small and single-center.
2 July 2026
Read appraisal →BMJ supportive & palliative care
Goals of care discussions in hospitalised patients: systematic review of randomised trials.
IMPORTANCE: Structured communication tools and decision aids are recommended to facilitate goals of care discussions (GOCDs) in hospitalised patients, but their impact remains unclear. OBJECTIVE: To determine the effectiveness of communication tools and decision aids on GOCDs between healthcare providers, patients and substitute decision makers. DATA SOURCES: PubMed, Web of Science, CINAHL and ClinicalTrials.gov were searched from January 2015 to July 2025, with backward citation tracking to identify additional trials. STUDY SELECTION: Randomised trials of communication tools or decision aids for hospitalised adults or substitute decision makers were eligible, regardless of primary outcomes. DATA EXTRACTION AND SYNTHESIS: Trial characteristics, primary outcomes and methodological quality were extracted and assessed using the Core Grading of Recommendations, Assessment, Development and Evaluation framework. Random-effects meta-analyses were conducted when appropriate. RESULTS: 22 trials were included. Communication tools increased GOCD documentation (OR 1.46, 95% CI 1.04 to 2.05). Effects on resuscitation preferences were inconsistent (OR 1.84, 95% CI 0.95 to 3.60). Mixed effects were observed for concordant care, psychosocial outcomes and healthcare utilisation. Evidence certainty was moderate for most outcomes; several small pilot trials were rated low due to bias, imprecision or heterogeneity. Interventions were most effective when combining structured patient-facing or clinician-facing prompts such as videos, guides or electronic health record alerts. CONCLUSIONS AND RELEVANCE: Communication tools and decision aids improve GOCD documentation in hospitalised patients but show uncertain effects on other patient-centred outcomes or healthcare utilisation. Interprofessional training and system-level support may enhance impact, and future large trials should evaluate the effectiveness of facilitated GOCDs on important patient and health system outcomes.
1 July 2026
Read appraisal →BMJ supportive & palliative care
Ethical dilemmas for palliative care nurses: systematic review
BACKGROUND: Nurses play a unique and critical role in palliative care, and it is noteworthy that nurses often encounter ethical dilemmas in this field. OBJECTIVE: This review aims to conduct a summarised synthesis of the latest research on the ethical considerations nurses faced in palliative care. METHODS: We conducted a rigorous systematic review of relevant existing studies published in high-quality English peer-reviewed journals from January 2017 to July 2023. We identified a total of 4492 articles (1029 in Web of Science, 1570 in PubMed and 1893 in Science Direct). Out of these, only 13 studies met the inclusion criteria. RESULTS: Following the thematic analysis, the ethical considerations reported in these 13 studies were grouped into three main themes and four subthemes: ethical issues in communication (ethical issues in communication with patients, ethical issues in communication with families), ethical issues in decision-making (autonomy, dignity) and moral distress in palliative care. CONCLUSION: This study elaborated on the ethical challenges faced by nurses in their communication with patients and families as well as decision-making and analysed the causes and effects of ethical distress, hoping to give a hand to ethical issues for nurses' work in palliative care.
1 July 2026
Read appraisal →American journal of medical quality : the official journal of the American College of Medical Quality
Increasing Advance Care Planning in a Community Internal Medicine Clinic
INTRODUCTION: Advance care planning (ACP) supports goal-concordant care but remains underused nationally. In resident clinics, limited continuity, short visits, and lack of standardized workflows create barriers to ACP integration. In our Community Internal Medicine resident clinic, ACP discussion was documented in 2.5% of annual physicals, and 19.6% of paneled patients had an advance directive (AD) on file. We aimed to increase ACP documentation from 2.5% to 12.5% within 1 year without increasing perceived resident workflow burden. METHODS: We conducted a resident-led quality improvement project using Lean Six Sigma methodology. Three interventions were implemented sequentially: nursing distribution of ACP brochures, an electronic medical record DotPhrase to prompt and document ACP discussions, and targeted patient portal outreach to patients without an AD before annual visits. The primary outcome was documented ACP discussion during annual physicals. Secondary outcomes included AD uploads and ACP consult referrals. The balancing measure was resident-perceived workflow burden. Charts were reviewed over 5-week periods at baseline (n = 118) and after each implementation phase (n = 132, 158, 129). RESULTS: ACP documentation increased from 2.5% at baseline to 12.2%, 17.1%, and 24.0% across sequential phases (χ2(3) = 24.74, P < 0.0001). Pairwise comparisons versus baseline were significant (all P ≤ 0.005). AD upload rate increased from 1.7 to 6.0 per month, and ACP consult referrals increased from 0.0% to 10.9%. Mean perceived workflow burden decreased from 4.9 to 3.0. CONCLUSION: Workflow-embedded interventions improved ACP documentation, AD uploads, and ACP referrals without increasing perceived resident burden.
30 June 2026
Read appraisal →Journal of medical Internet research
Operationalizing Digital Health Equity in Artificial Intelligence-Enabled Patient Decision Aids for Older Adults: Mixed Methods Study
BACKGROUND: Artificial intelligence-enabled patient decision aids (AI-PDAs) hold promise for supporting older adults with chronic diseases in accessing personalized health information, clarifying preferences, and engaging in shared decision-making. Achieving equity in their design requires attention to the complex health care and digital contexts in which these tools are used. While the Digital Health Equity Framework (DHEF) provides a conceptual foundation, practical strategies for its application remain limited. OBJECTIVE: This study aimed to identify equity-related determinants and generate actionable design strategies for applying the DHEF to AI-PDAs for older adults. METHODS: A mixed methods study was conducted. Semistructured interviews were conducted with older adults living with hypertension and/or diabetes, health care providers, and medical students to explore equity determinants relevant to AI-PDAs. In parallel, a review of reviews synthesized existing evidence on approaches to addressing these determinants. Interview findings and review findings were integrated through an iterative mapping process conducted by the research team and refined through multidisciplinary expert consultation involving medicine, public health, social services, and computer science. RESULTS: A total of 33 stakeholders were interviewed, including 15 older adults, 8 health care providers, and 10 medical students. Thirteen reviews were included in the umbrella review. The integrated synthesis identified equity determinants spanning individual, interpersonal, community, and societal levels across both the health care and digital environments, together with cross-level concerns related to algorithmic fairness. These findings informed 5 recommendations for equitable AI-PDA development: (1) co-design with end users to address their needs, (2) embrace relationship-centered design, (3) leverage community resources to improve support, (4) promote accessible and equitable artificial intelligence (AI) governance in society, and (5) enhance equitable AI through algorithmic fairness. Together, these recommendations provide practical guidance for design, pilot testing, implementation, and evaluation. CONCLUSIONS: By integrating stakeholder perspectives with synthesized review evidence, this study extends the DHEF from a primarily conceptual framework toward a more practice-oriented approach for AI-PDAs for older adults with chronic disease. Health care settings serve as a mediating sociotechnical context where AI tools may either support or constrain equitable care participation. The findings underscore the need for interdisciplinary collaboration to align technological innovation with equity-oriented design. Future work should focus on co-designed prototypes, real-world testing, and measurable equity outcomes.
30 June 2026
Read appraisal →JMIR aging
Use of Wearable Technology for Measuring and Characterizing Sedentary Behavior in People With Mild Cognitive Impairment and Dementia: Systematic Review
BACKGROUND: Sedentary behavior (SB) is a critical, modifiable risk factor for adverse health outcomes. Evidence suggests that SB is higher among individuals with cognitive impairment relative to their cognitively healthy peers. However, the nature and extent of SB across cognitive impairments remains unclear, largely due to the reliance on self-report data and the lack of synthesized evidence from more accurate methodology, such as wearable devices. Wearable device-based methodologies offer a reliable means of capturing SB in real-world settings, circumventing the recall bias inherent to self-report methods. Continuous remote monitoring of SB, via wearable devices, may provide nuanced insights important for understanding SB's contribution to cognitive impairment and health consequences. OBJECTIVE: This review aims to synthesize evidence on the volume, patterns, and variability of SB across cognitive impairment and critically appraise the wearable device-based methodology used to capture SB in this population. METHODS: Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, we searched 5 databases (Embase, MEDLINE, PsycInfo, Scopus, and Web of Science) up to January 2025 for peer-reviewed English-language studies using wearable devices to measure SB in community-dwelling or aged residential care residents aged 50 years or older with cognitive impairment (PROSPERO: CRD42024616523). Study quality was assessed using an adapted version of the National Institute of Health Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies. Data were extracted on SB outcomes (eg, volume, pattern, and variability) and methodological characteristics (eg, device type, placement, SB classification/processing, and its corresponding validation). RESULTS: From 2824 screened records, 17 studies (2016-2025) were included. Most studies (n=11, 65%) were of "good" quality (scoring ≥5 on bias assessment). Synthesis revealed inconsistent evidence for differences in SB volume across cognitive impairment. However, individuals with dementia consistently exhibited a unique SB pattern, engaging in significantly fewer but longer sedentary bouts than other forms of cognitive impairment and cognitively intact controls. All (n=17, 100%) studies used volume metrics to describe SB, followed by pattern metrics (n=7, 41%); only 1 study reported on SB variability. Methodological appraisal found significant heterogeneity: 13 different device models across 6 body placements were used. Most studies quantified SB using count-based thresholds (counts per minute), which were largely unvalidated in cognitively impaired or older adult populations. CONCLUSIONS: This review found that participants with dementia consistently exhibited a unique pattern of SB compared to other forms of cognitive impairment and healthy controls, while evidence for differences in SB volume was inconsistent. This may indicate that differences in SB volume are not inherent to dementia pathology but may be mediated by other factors, such as neuropsychiatric symptoms or environmental influences. Furthermore, methodological heterogeneity and unvalidated thresholds were observed throughout the review, highlighting a need for standardized protocols to enhance the validity and clinical applicability of future research. TRIAL REGISTRATION: PROSPERO CRD42024616523; https://www.crd.york.ac.uk/PROSPERO/view/CRD42024616523.
28 June 2026
Read appraisal →Calcified tissue international
Artificial Intelligence Approaches for Osteoporotic Fracture Risk Prediction Using Administrative Health Data: A Systematic Review
To systematically review Machine Learning (ML) fracture risk prediction models developed solely using administrative data, evaluating their development, performance, risk of bias, and concerns regarding applicability. A systematic search was conducted in PubMed, Embase, IEEE, and Web of Science following PRISMA guidelines (up to November 2025). We included studies developing or validating ML models for osteoporotic fracture risk in adults using administrative data without clinical measurements. Risk of bias and concerns for applicability were assessed using the PROBAST tool. Seven studies were included from 3435 initial records. A range of of ML models were utilized, including Random Forests, XGBoost, LASSO-regularized Logistic Regression and Neural Networks. Discriminative performance was moderate-to-good, with best Area Under the Curve (AUC) values ranging from 0.818 for osteoporotic fractures in general to 0.905 for hip fractures only. While five studies showed low risk of bias, five raised applicability concerns due to using specific subpopulations or features with database characteristics. Only two studies used external validation and none directly evaluated clinical utility of the models. ML models using solely administrative data show some promise as scalable, automated tools for fracture risk prediction without requiring manual clinical input. However, clinical implementation is hindered by limited external validation and a lack of formal utility evaluation of the models. Future research should prioritize more rigorous external validation and recalibration of the existing models, more so than the development of novel models, ensuring they are robust, interpretable, and integrable into future clinical workflows.
28 June 2026
Read appraisal →Journal of global health
Habitual physical activity and sarcopenia: a systematic review and meta-analysis of prospective cohort studies
BACKGROUND: Habitual physical activity (HPA) has been associated with a lower risk of sarcopenia by enhancing skeletal muscle protein synthesis and suppressing systemic inflammation. However, the evidence for a long-term protective association remains inconclusive. Therefore, we conducted a systematic review and meta-analysis to quantify the association between HPA and sarcopenia. METHODS: We searched PubMed, the Cochrane Library, EMBASE, Cumulative Index to Nursing and Allied Health Literature, Web of Science, and the China National Knowledge Infrastructure for prospective cohort studies on the relationship between physical activity (PA) and sarcopenia. We selected English and Chinese-language literature published before 6 October 2025, and assessed study quality using the Newcastle-Ottawa Scale. Data were statistically synthesised by calculating pooled relative risks (RRs) and 95% confidence intervals (CIs) using a random-effects model with the generic inverse-variance method. RESULTS: This meta-analysis included nine prospective cohort studies involving 21 265 participants. High levels of HPA were associated with a significantly lower risk of sarcopenia compared to the low levels (RR = 0.55; 95% CI = 0.44-0.67). This protective association remained consistent in subgroup analyses stratified by gender and by compliance with international PA guidelines. Furthermore, moderate HPA was also associated with a reduced risk compared to low HPA levels (RR = 0.73; 95% CI = 0.50-0.96). CONCLUSIONS: Our analysis indicates that moderate to high levels of HPA are independently associated with a lower risk of sarcopenia, serving as a significant protective factor. However, given the methodological heterogeneity in PA measurement, further high-quality prospective studies are needed to clarify the optimal PA dose while accounting for potential reverse causality. REGISTRATION: PROSPERO: CRD420251162529.
28 June 2026
Read appraisal →Biomedical physics & engineering express
Intelligent integrated system for dysphagia rehabilitation and assessment
Current dysphagia assessment suffers from subjectivity and reliance on complex clinical tools, while rehabilitation lacks objective feedback. The critical mechanism of respiration-swallowing coordination (RSC) for preventing aspiration is often overlooked in existing monitoring systems. This study aimed to develop and validate an intelligent integrated system for dysphagia rehabilitation and that provides a closed-loop solution for quantitative assessment and personalized rehabilitation. The system's core innovation is the tight integration of a wireless wearable hardware subsystem for synchronous swallowing sound and respiratory waveform acquisition with an intelligent software subsystem. The software incorporates novel algorithms for automated swallowing identification, respiratory cycle analysis, and real-time classification of RSC patterns into aspiration risk levels. This enables an interactive platform that closes the loop between assessment, visual feedback, and personalized audiovisual guidance for rehabilitation training. Validation included hardware performance tests and a clinical trial with 16 participants (8 healthy participants and 8 participants with dysphagia). The hardware demonstrated reliable performance, achieving a robust swallowing sound SNR (14.07 dB in a noisy environment) and showing acceptable temporal consistency in the respiratory waveform. On a 0-100 scale, the system significantly differentiated swallowing function between healthy (82.72) and dysphagic (57.25) subjects (p< 0.01). We also conducted an exploratory experiment in two dysphagic participants using audiovisual guided training. A preliminary upward trend in swallowing-coordination scores was observed after training, suggesting a potential short-term training effect of the audiovisual guidance module. The integrated system represents an innovation by seamlessly merging wearable sensing with intelligent, coordination-focused algorithms into a unified closed-loop platform. It offers a practical and effective solution for transforming dysphagia management towards objective, personalized, and intelligent rehabilitation.
27 June 2026
Read appraisal →JMIR research protocols
Exploring Non-Embodied AI-Based Digital Companions for Older Adults in Aging and Care Contexts: Protocol for a Scoping Review
BACKGROUND: Conversational artificial intelligence (AI) technologies are increasingly positioned as a response to social isolation, loneliness, and unmet psychosocial needs across health and care contexts. Non-embodied AI-based digital companions have attracted growing attention for their potential to support companionship, social interaction, communication, and psychosocial well-being among older adults, including people living with dementia. However, the evidence base remains underexplored. Terminology is inconsistently applied, systems are variably defined, and studies are distributed across disciplinary silos, limiting critical investigation of how these technologies are conceptualized, designed, and evaluated. OBJECTIVE: This study aims to map and critically synthesize the existing literature on non-embodied AI-based digital companions for older adults in aging, health, and care contexts. The review seeks to describe digital companions, examine methodological approaches, and identify research gaps. METHODS: This protocol follows the Joanna Briggs Institute methodology for scoping reviews and will be reported in accordance with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guidelines). A comprehensive search will be conducted across multiple electronic databases, including MEDLINE, APA PsycINFO, CINAHL, Scopus, Web of Science, IEEE Xplore, and ACM Digital Library, as well as selected gray literature sources. The search strategy combined terms related to digital companions, conversational AI, aging, and care contexts, including concepts related to companionship, social interaction, communication, loneliness, and psychosocial support., Eligible studies will include empirical studies involving older adults that examine non-embodied AI-based digital companions designed primarily to support companionship, social interaction, communication, or related psychosocial support in aging, health, and care contexts. Two reviewers (YC and MG) will independently conduct study selection and data charting. Data will be synthesized using descriptive statistics and narrative analysis. RESULTS: This protocol outlines a systematic approach to identifying, selecting, and synthesizing the existing evidence on non-embodied AI-based digital companions. A preliminary PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses)-style search flow for the revised database searches identified 2289 records from MEDLINE, CINAHL, and APA PsycINFO. After duplicate removal and eligibility-based removals before screening, 1978 records remained available for title and abstract screening. The full scoping review will summarize study characteristics, populations, contexts, digital companion features, and methodological trends using descriptive tables and narrative synthesis. As of June 2026, the revised database searches had been completed for MEDLINE, CINAHL, and APA PsycINFO, and title and abstract screening was underway. The full scoping review results are expected to be submitted for publication after screening, data charting, and synthesis are completed. CONCLUSIONS: This scoping review is expected to clarify conceptual boundaries, map the scope of current research, and identify knowledge gaps related to non-embodied AI-based digital companions in health, aging, and care contexts. The findings will inform future research, design, and implementation of non-embodied AI-based digital companions.
26 June 2026
Read appraisal →BMJ open
Caregiver experiences of social isolation and loneliness in chronic kidney disease: systematic review of qualitative studies.
OBJECTIVES: This qualitative systematic review aimed to describe the experiences and perspectives of loneliness and social isolation among informal caregivers of people with chronic kidney disease (CKD). METHODS: Terms for caregivers, qualitative research, loneliness and social isolation and CKD were entered into MEDLINE, Embase, CINAHL and PsycINFO and searched from inception to May 2025. Qualitative studies that described social isolation and loneliness among caregivers of people with CKD were included in this review. Study characteristics were extracted into Microsoft Excel. Qualitative data from each study were imported into HyperRESEARCH and analysed using thematic synthesis. RESULTS: We included 19 articles involving 598 caregivers of people with CKD from 28 countries. Four major themes with subthemes were identified: confined by the patient's needs (social withdrawal due to unrelenting demands, separated from communities due to treatment, torn between work and caregiving responsibilities, foregoing social outings with family and friends, restricted by patient's diet, feeling protective against infection risk); limited care assistance exacerbating isolation (inadequate familial support, absence of respite care, lacking accessible guidance from health professionals); disrupting relationships and social roles (sacrificing social needs and identity, family conflicts worsening isolation, withdrawing to avoid stigma and ridicule, grappling with hopelessness, reluctance to share struggles); and coping with support resources (connecting with other families, seeking assistance from support services, finding support through faith). CONCLUSIONS: Caregivers of people with CKD experience restricted social participation and loss of social roles and identity, which can exacerbate feelings of loneliness and social isolation. Support services are needed to prevent and address social isolation and loneliness in CKD caregivers. PROSPERO REGISTRATION NUMBER: CRD420250637194.
26 June 2026
Read appraisal →Pulmonology
Association between smoking and prognosis in idiopathic pulmonary fibrosis: A systematic review and meta-analysis
BACKGROUND: Although smoking is a well-established risk factor for idiopathic pulmonary fibrosis (IPF) development, its impact on clinical outcomes remains unclear. RESEARCH QUESTION: Is smoking associated with clinical outcomes of IPF? METHODS: A systematic search of PubMed, Embase, and the Cochrane Library was conducted to identify studies reporting associations between smoking status and IPF outcomes, including mortality, acute exacerbation (AE), lung cancer development, and baseline lung function. Summary estimates with 95% confidence intervals (CIs) were pooled using random-effects model. Subgroup analyses were conducted by study region. RESULTS: Forty-nine studies comprising 32 974 patients were included. Overall mortality did not differ by smoking status. However, significant regional differences were observed, with ever smokers demonstrating higher mortality in non - East Asian studies (Hazard ratio [HR] 1.17, 95% CI: 1.03-1.33) but lower mortality in East Asian studies (HR 0.75, 95% CI: 0.62-0.91, p-for-interaction < 0.001) compared with never smokers. Current smokers had a higher risk of developing lung cancer compared with non-current smokers (HR 1.88, 95% CI: 1.24-2.87). No significant associations were observed between smoking and the risk of AEs or baseline forced vital capacity. CONCLUSION: Smoking was not associated with overall mortality in IPF, although significant regional differences were observed.
26 June 2026
Read appraisal →Psychological medicine
Sorting the mind: cognitive enhancement through transcutaneous auricular vagus nerve stimulation: a systematic review and meta-analysis
Transcutaneous auricular vagus nerve stimulation (taVNS) is a noninvasive technique engaging vagal afferents that may enhance cognition, but results vary across domains and samples. Following PRISMA, seven databases (inception-October 2025) plus registries and gray literature were searched. Random-effects meta-analyses (REML; Hedges' g) were complemented by Bayesian hierarchical models and sensitivity analyses. Fifty-three studies were included; 30 contributed quantitative data (>1,500 participants). taVNS was associated with improved cognitive performance overall (g = 0.41, 95% CI: 0.30-0.53; I2 = 51.4%). Effects were moderate for executive functions (g = 0.46, 95% CI: 0.27-0.65; I2 = 9.5%) and cognitive flexibility/learning (g = 0.53, 95% CI: 0.32-0.75; I2 = 52.9%), and small for working memory/attention (g = 0.19, 95% CI: 0.04-0.33; I2 = 14.9%). Social cognition/emotion regulation showed larger but imprecise effects (k = 3; g = 0.80, 95% CI: 0.07-1.52; I2 = 82.1%). Clinical samples benefited similarly (k = 7; g = 0.55, 95% CI: 0.31-0.79; I2 = 29.5%), with no difference from healthy cohorts (β = -0.001, p = .994). High-intensity protocols (>1.0 mA) yielded larger effects; mode, duration, and site were not moderators. Bayesian models supported effects (P [μ > 0] ≥ 0.93). taVNS is associated with statistically significant improvements in cognitive performance, strongest for executive control and adaptive learning. We propose a Vagal Neurocognitive Integration Model linking LC-NE arousal modulation to prefrontal control. Future diagnosis-specific, adequately powered trials with multimodal neuroimaging should refine mechanisms and dose-response.
25 June 2026
Read appraisal →Behavioural brain research
Sex differences in the use of spatial strategies in humans: An updated systematic review of the last decade.
Although sex differences in spatial cognition have been widely studied, the extent to which men and women differ in their use of egocentric and allocentric spatial strategies remains unclear. This systematic review summarizes findings from 21 studies published between 2014 and 2024 that examined sex differences in spatial strategy use throughout development, adulthood, and aging. Studies were identified through searches of Scopus, Web of Science, PubMed, and PsycINFO, in accordance with PRISMA guidelines. Eligible articles examined egocentric and allocentric spatial strategies in humans and reported results by sex. Results demonstrated male advantages in allocentric strategy, as observed in thirteen studies involving children, young adults, and older adults. Men also outperformed women in several navigation outcomes, including target retrieval, route efficiency, and directional accuracy. Evidence for a female advantage in egocentric strategies was limited and inconsistent. Seven studies reported no sex differences, particularly those using small-scale, familiar, or less immersive tasks. This suggests that methodological factors and task demands strongly influence the observed effects. Age also influenced outcomes. Male advantages in allocentric strategies emerged in early childhood. Older women showed relatively preserved egocentric abilities, but greater decline in allocentric tasks. Men showed stronger hippocampal-parietal connectivity supporting allocentric processing, while women exhibited enhanced fronto-temporal connectivity. Hormonal studies highlight the context-dependent effects of testosterone and estrogen. These findings indicate that sex differences in spatial strategies are highly sensitive to task complexity, environmental familiarity, cognitive load, and individual factors. These findings emphasize the necessity of integrative models that consider developmental, neurobiological, and sociocultural contributors.
25 June 2026
Read appraisal →Biomedizinische Technik. Biomedical engineering
Arm support as hidden assistance during Sit-To-Stand movement: a systematic review
Sit-To-Stand (SitTS) training is often performed by patients who may require arm assistance to perform it successfully. This study examines the role of arms in performing the SitTS maneuver to better understand their contribution and clinical significance. This review synthesizes the reported outcome measures of arm-assisted SitTS exercise programs in people with physical impairments. Web of Science (WoS) and Scopus™ databases were searched for relevant studies up to May 2025. Inclusion and exclusion criteria were used to screen the titles and abstracts of articles identified using key search terms related to SitTS activity and arm support. Only articles that met the key terms were extracted and analyzed in this study. A total of 47 studies reported arm support measures during SitTS, either in clinical or biomechanical contexts. These studies highlighted various methods and tools used to assess the body's state during the rising movement. Joint angle and SitTS duration are clinically applicable and more easily measured compared to seat, foot, and arm forces which require specialized instrumentation. Balancing clinical measures with detailed biomechanical assessments is important in enabling practitioners and researchers to evaluate and enhance SitTS interventions that involve arm assistance, ultimately improving rehabilitation outcomes.
25 June 2026
Read appraisal →Journal of neurology
Exercise- and dietary pattern-based lifestyle interventions and neurocognitive function in adults: a Bayesian multilevel network and dose-response meta-analysis
OBJECTIVE: To systematically review the effects of lifestyle interventions focused on exercise and dietary patterns on neurocognitive function in adults, and to examine their relative effects and dose-response relationships. METHODS: PubMed, Embase, Web of Science, and the Cochrane Library were searched from database inception to March 20, 2026. A total of 23 randomized controlled trials (RCTs) involving 12,286 participants were included. Bayesian multilevel pairwise, network, and dose-response meta-analyses were conducted to evaluate overall cognition, memory, and executive function. RoB 2 and CINeMA were used to assess the credibility of the evidence. RESULTS: Combined interventions yielded small, but statistically significant cognitive benefits (overall cognition: SMD = 0.18; memory: SMD = 0.10; executive function: SMD = 0.09). These benefits were driven primarily by differences between the combined intervention and control or usual care, as well as exercise alone; compared with diet alone, the combined intervention still showed a small advantage for executive function. Network meta-analysis suggested that AE_MIND and AE_RT_MED were more beneficial for overall cognition, AE_RT_MIND and AE_RT_MED for memory, and AE_RT_CR and AE_MIND for executive function. Exploratory dose-response analyses suggested possible curvature in some spline-based models; however, comparison with simpler monotonic specifications did not provide robust evidence for a clinically interpretable non-linear or monotonic dose-response relationship. CONCLUSION: Combined lifestyle interventions may produce statistically significant but modest improvements in cognitive function in adults, with benefits varying across cognitive domains. However, current evidence remains insufficient to support a standardized precision prescription, and high-quality RCTs with long-term follow-up are needed to determine the optimal combinations and dosages.
25 June 2026
Read appraisal →BMJ global health
Lifestyle interventions for dementia prevention in low- and middle-income countries: a systematic review.
INTRODUCTION: By 2050, two-thirds of people with dementia will live in low-and-middle-income countries (LMICs). However, multimodal prevention lifestyle interventions for people at risk are being developed predominantly in higher-income countries. METHODS: We systematically reviewed randomised controlled trials (RCTs) evaluating non-pharmacological interventions in individuals with mild cognitive impairment and subjective cognitive decline in LMICs. We assessed quality using the Mixed Methods Assessment Tool, meta-analysed and synthesised evidence. RESULTS: We included 25 RCTs from six countries (most in China, n=17), involving 1304 participants. In the 15 studies with sufficient data to meta-analyse, we found significant positive effects on cognition favouring interventions (1.49 (standardised mean difference, 95% CI 1.06 to 1.93)). There was significant publication bias. We classified interventions into exercise, multidomain and arts/creative expression. Exercise (1.67, 95% CI 1.24 to 2.11, n=8) and multidomain (1.22, 95% CI 0.22 to 2.21, n=5) had replicated evidence of effectiveness. There was insufficient data to meta-analyse the arts/creative category. CONCLUSION: We propose greater consideration and investment in the development of interventions accounting for specific LMIC contexts from the outset, so they are acceptable and used by local services. PROSPERO REGISTRATION NUMBER: CRD42023403908.
24 June 2026
Read appraisal →International ophthalmology clinics
Beyond the Clinic: Home-Based Monitoring Strategies in Glaucoma Care
Glaucoma is a chronic and progressive optic neuropathy, necessitating lifelong monitoring for affected individuals. Traditional surveillance of the disease relies on evaluation of intraocular pressure (IOP), optic nerve anatomy, and visual function at the physician's office, which only provides limited snapshots of a disease characterized by dynamic physiological variation. Developments in portable ophthalmological technologies and digital health platforms have enabled home monitoring of glaucoma. This review synthesizes current evidence on the home monitoring of glaucoma, with a focus on self-tonometry and home-based visual field testing. Handheld tonometers such as iCare HOME and iCare HOME2 allow repeated, anesthesia-free self-measurement of IOP in a real-world setting. These devices have demonstrated good agreement with Goldmann applanation tonometry, the gold standard. Home tonometry captures diurnal variations of IOP, thus providing relevant insights for diagnosis and therapeutic decision-making. Home-based visual field testing (eg, tablet-based platforms, virtual reality-based testing) enables frequent, unsupervised functional assessment with repeatability and sensitivity comparable to standard automated perimetry. Increased testing frequency may facilitate earlier detection of rapid progression, particularly in the early stages of disease. Collectively, evidence suggests the feasibility and clinical relevance of these home monitoring modalities. However, patient compliance with testing schedules and cost-effectiveness remain as challenges.
24 June 2026
Read appraisal →Current opinion in anaesthesiology
Preoperative considerations for nonoperating room anesthesia
PURPOSE OF THE REVIEW: This review aims to address the unique challenges in nonoperating room anesthesia (NORA) locations, emphasizing the importance of patient selection, risk stratification, and comprehensive preoperative evaluation to ensure safe anesthetic care for increasingly complex patients. RECENT FINDINGS: The volume of NORA procedures has risen significantly, with patients often presenting higher comorbidity burdens and advanced age. Standardized protocols and validated assessment tools, such as the STOP-Bang questionnaire and a simple frailty questionnaire (e.g., FRAIL), can aid anesthesiologists in effectively stratifying risk and tailoring anesthesia plans. Challenges in NORA include logistical constraints, personnel dynamics, and environmental factors that can compromise patient safety. The integration of telemedicine and artificial intelligence into preoperative assessments shows promise in improving efficiency and safety by allowing for remote evaluations and tailored care. SUMMARY: Adequate patient selection and preoperative optimization are essential for enhancing outcomes in NORA locations. Adherence to national safety guidelines and multidisciplinary collaboration is crucial for navigating the complexities of remote anesthesia care. Future research should focus on refining preoperative screening methods and utilizing artificial intelligence to better address the unique needs of patients undergoing procedures outside traditional operating rooms.
23 June 2026
Read appraisal →Journal of robotic surgery
Robot-Assisted versus non-robotic frame-based deep brain stimulation: a systematic review and meta-analysis of targeting accuracy and clinical outcomes
Deep brain stimulation (DBS) outcomes depend on accurate lead placement. Robot-assisted DBS (RA-DBS) has been increasingly adopted, yet its advantages over conventional non-robotic frame-based DBS (NRA-DBS) regarding targeting accuracy, operative efficiency, and clinical outcomes remain debated.To compare RA-DBS versus NRA-DBS for targeting accuracy and clinical outcomes in patients undergoing DBS for movement disorders.This meta-analysis and systematic review was registered in PROSPERO (CRD420261336001) and adhered to PRISMA. Patients with mobility disorders treated with RA-DBS versus frame-based NRA-DBS were included in comparative studies (RCTs and prospective/retrospective cohorts). Targeting accuracy measures (such as target point error, vector error, and radial error) and extreme targeting outliers were the main results. Operative time measurements, perioperative problems (such as cerebral bleeding), and motor improvement (UPDRS-III) were secondary outcomes. Fixed-effects or random-effects models based on heterogeneity were used to compute pooled odds ratios (OR) and weighted mean differences (MD) with 95% confidence intervals (CI). Nine high-quality comparative studies encompassing 495 patients (227 RA-DBS vs. 268 NRA-DBS) were synthesized. RA-DBS significantly minimized target point error (MD = -0.31 mm, 95% CI: -0.61 to -0.01; P = 0.04) and significantly reduced the incidence of clinically significant targeting outliers (OR = 0.15, 95% CI: 0.04 to 0.51; P = 0.002). Comprehensive three-dimensional vector error was marginally lower in the RA-DBS cohort (MD = -0.70 mm; P = 0.05), whereas two-dimensional radial error and total operating room time, surgical duration, anesthesia time, perioperative complications, and 12-month UPDRS-III motor improvements were statistically equivalent between groups. Subgroup analyses revealed that the spatial precision advantage of RA-DBS was geographically robust across East Asian and Western cohorts, whereas chronological efficiency was heavily dependent on the specific robotic platform used.Compared with conventional frame-based stereotaxy, robot-assisted DBS significantly enhances target point precision and reduces electrode deviations. However, these technical accuracy advantages do not translate into overall clinical superiority, as both approaches yield comparable operative efficiency, perioperative safety, and mid-term motor symptom relief.
23 June 2026
Read appraisal →Biogerontology
RNA-binding proteins in aging and age-related diseases: roles, mechanisms, and a large language model analysis
Aging is a progressive and irreversible biological process that contributes to the pathogenesis of numerous age-related diseases. Elucidating the molecular mechanisms of aging is crucial for promoting healthy aging and extending healthspan. RNA-binding proteins (RBPs) are pivotal regulators of post-transcriptional gene expression and play essential roles in diverse biological processes. RBPs interact with both coding and non-coding RNAs to regulate RNA metabolism, stability, localization, and translation. Dysregulated RBP-RNA interactions have been closely associated with aging and age-related diseases. This review systematically summarizes the structural characteristics of RBPs and the evolution of methods used to study them. We focus on the molecular mechanisms of six key RBPs, namely HuR, AUF1, TTP, IGF2BP2, QKI, and LARP7, in the context of aging and age-related diseases. In addition, we discuss the regulatory functions of post-translational modifications of RBPs. Furthermore, we provide a multidimensional overview of RBP involvement in aging and age-related diseases through large language model (LLM)-based text-mining analysis. Our study provides a foundation for the comprehensive characterization of RBPs in aging and age-related diseases.
22 June 2026
Read appraisal →JMIR formative research
Clinician Decision-Making Around Offering Home Video Telehealth Visits: Qualitative Study
BACKGROUND: Use of in-home video telehealth rapidly expanded in response to the COVID-19 pandemic, including at Veterans Affairs (VA), a forerunner in telehealth. Despite this uptick, differences in use by patient age and rurality created a digital divide that persists to this day. While clinicians frequently cite patients' older age and lack of technical skills as barriers to in-home video telehealth, it remains unclear how clinicians decide whether to offer video visits to patients and to what extent these beliefs may hinder offering video visits to older adults. Gathering perspectives from clinician users of in-home video telehealth may illuminate opportunities to ensure continued access to care through solutions such as telehealth. OBJECTIVE: This study aimed to examine clinician decision-making around the offer of in-home video telehealth to understand how interprofessional clinicians determine to whom they offer in-home video telehealth and what factors (organizational, personal, or attitudinal) influence their decision. METHODS: We conducted a qualitative study by using semistructured interviews. Participants were interprofessional clinicians (N=16) employed by 11 different VA hospitals and included 1 clinical pharmacist, 6 medical doctors, 2 nurse practitioners, 1 occupational therapist, 3 psychologists, 1 physical therapist, 1 speech-language pathologist, and 1 social worker. All the participants had at least some experience using in-home video telehealth from locations across VA (the largest integrated health care system in the United States) and were interviewed over a 6-month period. Interviews focused on clinicians' use of video telehealth and the decision-making process involved in offering in-home video telehealth. We used directed content analysis with a rapid analytic approach, given the time-pressured nature of our project. RESULTS: This study revealed that clinician decision-making around offering in-home video visits is complex and influenced by several domains, namely, (1) clinician factors, including experience with video and perceived benefits of video; (2) appointment factors, including the visit's clinical goal; (3) clinician-reported patient factors, including age and willingness to try video; (4) patient social context, including caregiver availability; (5) geographical factors, such as availability of reliable high-speed internet and patient distance from the medical center; and (6) health system factors, including technical support and clinician ability to work from home. Access to in-home video telehealth may be facilitated by clinician familiarity and confidence with telehealth technology, strategies to improve patients' technical skills, and support for caregivers. Infrastructure also plays an important role, including device availability, broadband reliability, and clear protocols for matching services to video visits. CONCLUSIONS: Findings highlight the importance of clinician competency in telehealth, patient and caregiver digital readiness, and a supportive technology infrastructure to equitable in-home video care. In addition, improved guidance for specific clinical services and awareness of potential biases may enable consistent, accessible telehealth delivery for older adults and medically complex populations.
20 June 2026
Read appraisal →Journal of global health
Deep learning-based pressure injury staging: a multicentre study involving 59 hospitals
BACKGROUND: Accurate assessment of pressure injury staging is essential for guiding appropriate care, reducing patient suffering, alleviating the healthcare burden, and improving quality of life. We aimed to develop a deep learning-based model for pressure injury recognition and to translate the best-performing model into a preliminary smartphone application. METHODS: We conducted a multicentre retrospective study using pressure injury images collected from 59 hospitals. We applied to the data set three artificial intelligence-based image analysis models, Mask R-convolutional neural network (CNN) with ResNet-18, Mask R-CNN with Swin Transformer, and Segmenting Objects by Locations, version 2. We assessed model performance using standard evaluation metrics, including precision-recall curves, average recall (AR), average precision (AP), and mean average precision (mAP). The model achieving the best overall performance was selected for subsequent translation into a smartphone-based tool. RESULTS: We included a total of 1903 pressure injury images, with 1713 used for model training and 190 for validation. Among the models evaluated, Mask R-CNN with Swin Transformer demonstrated the highest overall performance (mAP = 0.894, AP50 = 0.900, AP = 0.757, AR100 = 0.792), surpassing the corresponding metrics of the other two models. The model, based on Mask R-CNN with a Swin Transformer, was integrated into a smartphone for preliminary translation. CONCLUSIONS: The proposed deep learning-based system showed promising performance in pressure injury staging and may provide meaningful support for clinical decision-making. Future studies should focus on further validation and refinement using larger and more diverse data sets to enhance their applicability in routine clinical practice.
20 June 2026
Read appraisal →Journal of neural engineering
Uncertainty-aware multi-path framework with dynamic arbitration for Alzheimer's disease MRI classification
Objective.As a progressive neurodegenerative disorder, Alzheimer's disease (AD) requires early and accurate diagnosis to delay pathological progression and improve clinical outcomes. Recently, Deep-learning methods based on structural MRI (sMRI) have been widely used for computer-aided AD diagnosis. However, reliable deployment remains challenging due to its sample-specific heterogeneity and the lack of explicit mechanisms for handling prediction uncertainty. In addition, common test-time perturbations and augmentation-based inference strategies could introduce anatomical inconsistency in medical images, leading to unstable decisions for hard cases.Approach.Accordingly, TriPathNet with Arbiter, as an uncertainty-aware multi-path framework, is proposed in the study. The framework learns spatially complementary representations via multi-view parallel encoding with metric-guided coordination. In addition, an augmentation-aware arbitration mechanism is introduced to adaptively correct predictions for high-uncertainty samples during inference.Main results.On the ADNI dataset for the AD vs. cognitively normal task, the proposed method achieves 98.85% accuracy, 99.18% sensitivity, 97.06% specificity, and 99.61% AUC, yielding satisfactory performance.Significance.The results indicate that TriPathNet with Arbiter provides an accurate and robust solution for sMRI-based computer-aided AD diagnosis, maintaining stable performance under test-time perturbations.
20 June 2026
Read appraisal →Archives of orthopaedic and trauma surgery
Association between timing of surgery and postoperative outcomes in older adults with distal femur fractures: a systematic review and meta-analysis
INTRODUCTION: The association between surgical timing and clinical outcomes in older adults with distal femur fractures remains unclear. We conducted a systematic review and meta-analysis to evaluate whether earlier surgery is associated with improved outcomes in this population. MATERIALS AND METHODS: In accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, two independent reviewers searched MEDLINE, Embase, the Cochrane Central Register of Controlled Trials, ClinicalTrials.gov, and the World Health Organization International Clinical Trials Registry Platform from inception to October 8, 2024. The primary outcome was 30-day mortality, and secondary outcomes included longer-term mortality and postoperative complications. Risk of bias was assessed using the Quality in Prognosis Studies tool, and the certainty of evidence was evaluated using the Grading of Recommendations Assessment, Development and Evaluation approach. RESULTS: Of 6,101 records screened, 16 retrospective cohort studies comprising 31,213 patients met the inclusion criteria. Early surgery was not associated with reduced 30-day mortality (adjusted odds ratio [OR]: 0.77, 95% confidence interval [CI]: 0.56-1.05; low-certainty evidence) or with longer-term mortality at 90 days (crude OR: 0.91, 95% CI: 0.51-1.60), 180 days (crude OR: 0.46, 95% CI: 0.12-1.77), or 1 year (crude OR: 0.61, 95% CI: 0.34-1.09). Similar findings were observed for postoperative complications: cardiac complications (OR: 0.71, 95% CI: 0.48-1.07), pulmonary complications (OR: 1.07, 95% CI: 0.77-1.47), and pulmonary embolism (OR: 1.14, 95% CI: 0.62-2.16). Heterogeneity across outcomes was low to moderate, and definitions of early surgery varied among studies. Overall, this meta-analysis did not demonstrate a clinically meaningful association between surgical timing and short-term clinical outcomes in older adults with distal femur fractures. Sensitivity analyses consistently suggested lower mortality with earlier surgery, likely reflecting stricter definitions of early surgery and improved control of time-related bias. CONCLUSIONS: This meta-analysis did not demonstrate a clinically meaningful association between surgical timing and short-term clinical outcomes in older adults with distal femur fractures. Given the heterogeneity in definitions and study designs, high-quality prospective studies with standardized timing are warranted.
20 June 2026
Read appraisal →Progress in biomedical engineering (Bristol, England)
Assessing the relevance of biosignal-controlled robotic rehabilitation technologies: a systematic review.
Technology is transforming rehabilitative medicine by enhancing accessibility and personalisation. Robot-assisted rehabilitation uses robotic systems for recovery from physical and neurological impairments, enabling intensive, repetitive training with real-time feedback. These systems aim to restore motor function and mobility and to increase independence in daily life, needs that are growing in light of population ageing. Rather than revisiting control algorithms or mechanical design, this review aims to investigate the combined use of bio-signals and robotic rehabilitation systems, and to discuss their potential in rehabilitation settings outside disease-specific clinical frameworks. We reviewed studies published from 2020 through early 2025 to capture recent advances in this rapidly evolving field. Our objective was to assess the relevance of these technologies to rehabilitation outcomes, such as improvements in motor function or other clinical metrics. We considered robot-assisted systems driven by biosignals such as electromyography (EMG), electroencephalography, or electrocardiography and included only studies reporting measurable rehabilitative outcomes. Following PRISMA guidelines, we searched Scopus, PubMed, and WoS databases. Of 207 records screened, 15 met the inclusion criteria. Overall, the included studies suggest that contemporary biosignal-controlled robotic rehabilitation, particularly EMG-driven approaches, may support more intensive, personalised, and engaging therapy than conventional care alone, although the current evidence remains preliminary. Preliminary findings indicate that, by closing the loop among patient, device, and therapist, these systems may reduce muscle activity and support improvements in motor performance; however, the evidence base remains heterogeneous and generally underpowered. To support future clinical adoption, the field needs adequately sized, head-to-head trials with standardised outcome measures, longer follow-up, and transparent reporting of adherence, adverse events, user experience, and implementation barriers, including training and cost. These steps will be important to further assess efficacy and evaluate usability in real-world settings.
20 June 2026
Read appraisal →Brain research bulletin
Neural substrates of apathy in Parkinson's disease: A systematic review of structural and functional neuroimaging studies
BACKGROUND: Apathy is a frequent and disabling non-motor symptom in Parkinson's disease (PD), characterized by diminished motivation, reduced goal-directed behavior, and emotional indifference. Growing evidence suggests that apathy in PD represents a distinct neuropsychiatric syndrome that may coexist with depression and global cognitive impairment, while also showing partially dissociable clinical and neural correlates. OBJECTIVE: This systematic review aimed to investigate the neural substrates of apathy in PD by synthesizing evidence from structural and functional neuroimaging studies. METHODS: Following PRISMA guidelines, we searched PubMed, Embase, Web of Science, and Scopus for studies assessing apathy in PD through neuroimaging techniques, including structural MRI, resting-state functional MRI, diffusion tensor imaging, PET/SPECT molecular imaging, and connectivity analyses. Apathy severity was evaluated using validated clinical scales, and findings were compared between PD population with and without apathy, as well as healthy controls. RESULTS: Across 16 heterogeneous studies, recurrent findings implicated structural, functional, and molecular alterations within fronto-striatal-limbic networks. However, differences in apathy measures and neuroimaging modalities limit direct comparability across studies and do not support a single unified imaging signature of apathy in PD. Apathetic PD subjects consistently exhibited reduced gray matter volume and altered spontaneous activity in the dorsolateral, inferior frontal gyri, anterior cingulate cortex (ACC), and nucleus accumbens, alongside disrupted functional and structural connectivity in fronto-limbic tracts. These alterations were significantly correlated with apathy severity and, in several studies, remained detectable after accounting for depressive symptoms or global cognitive status. Furthermore, the available findings suggest that apathy in PD may involve heterogeneous neurobiological mechanisms, including variable involvement of frontal, striatal, and limbic circuits; however, current evidence remains insufficient to support firm transmitter-based subtyping or treatment stratification. CONCLUSIONS: Current evidence suggests that apathy in PD involves partially dissociable neural correlates across prefrontal, striatal, and limbic systems. However, methodological heterogeneity across studies limits strong generalization, and future harmonized longitudinal studies are needed to clarify which findings are robust and clinically meaningful.
16 June 2026
Read appraisal →Journal of affective disorders
Brain aging in bipolar disorder using a neuroimaging and machine learning-derived metric: Findings from the ENIGMA BD Working Group.
BACKGROUND: Bipolar disorder (BD) is associated with clinical and biological markers of premature aging. In this largest study of brain age in BD to date, with 2919 participants, we compared brain-predicted age difference (brain-PAD) in individuals with BD and healthy comparison (HC) participants. Brain-PAD is a machine learning-estimated metric that quantifies the difference between an individual's predicted brain age and their chronological age, a potential clinical bio-signature of premature brain aging. Within individuals with BD, we also examined how medication and clinical characteristics were related to brain-PAD. METHODS: Age was predicted from 77 MRI measures of regional subcortical and lateral ventricle volumes, cortical thickness, and surface area for 1342 BD and 1577 HC adult participants, aged 18-75 yrs. old (μ = 37.2; SD = 12.3), from the curated ENIGMA Bipolar Disorder working group (ENIGMA-BD) and leveraging an ENIGMA machine learning model previously trained and validated using independent samples. Chronological age was subtracted from predicted age to produce an individual-level estimate known as brain-PAD. Linear mixed models (adjusting for sex and age as fixed effects and site as a random effect) were used to examine group differences and clinical associations. RESULTS: BD was associated with higher brain-PAD, compared to HC, primarily among older patients, as demonstrated by a significant age by diagnosis interaction (+0.05 [SE: 0.02] years). Individuals with BD on antiepileptic (AED) medications only (+3.20 [SE: 0.78] years) or on both AED and second-generation antipsychotics (SGA) (+3.74 [SE: 0.89] years) demonstrated greater brain-PAD compared to individuals who were not on any of the examined medications. Those taking lithium, whether alone or with AED and SGA independently, showed no difference in brain-PAD compared to individuals not taking any of the examined medications. However, individuals who were taking lithium showed lower brain-PAD compared to those on AED (-4.48 [SE: 0.84] years) or AED and SGA (-5.01 [SE:0.92] years). Individuals with a BD I subtype diagnosis had a higher brain-PAD (+1.50 [SE:0.55] years) compared to those with BDII or subtypes that are not otherwise specified (NOS). CONCLUSIONS: Results from this study suggest compounding effects of BD diagnosis and older age on brain-PAD, an ML-derived summary metric of structural alterations. Within BD, brain-PAD was differentially related to medication use, consistent with prior findings from ENIGMA-BD. Notably, AED use was generally related to more advanced brain age. Lithium use, alone or in combination with other medications, was not associated with advanced brain age, suggesting a possible neuroprotective effect of lithium. Brain-PAD as an ML-derived summary metric of structural alterations of the brain may provide clinical utility in assessing long-term holistic brain health to monitor the effectiveness of lifestyle modifications or treatments over time. LIMITATIONS: The cross-sectional nature of the study design and the limited granularity of the clinical data limit interpretation. Longitudinal studies with detailed chronicity data, medications and clinical measures overtime will improve brain-PAD modeling in BD.
15 June 2026
Read appraisal →Journal of global health
Plasma proteomic signatures predict incident benign prostatic hyperplasia: a prospective cohort study of 20 996 men
BACKGROUND: Benign prostatic hyperplasia (BPH) is a prevalent disease in elderly men. However, the plasma proteomic signatures for incident BPH are absent, hindering early prediction and risk stratification. We aimed to identify plasma proteins associated with incident BPH and the implicated biological pathways. METHODS: This prospective cohort was constructed based on the UK Biobank, enrolling 20 996 BPH-free males at baseline. We used the Olink Explore platform to determine the abundances of 2920 plasma proteins. We used Cox regression models to evaluate associations and the Extreme Gradient Boosting model to perform feature selection and predictive modelling. We performed pathway enrichment and Mendelian randomisation analyses to elucidate the molecular pathways involved and assess causality. RESULTS: During the median follow-up period of 13.38 years, 2405 incident BPH cases were recorded. Cox regression identified 92 plasma proteins significantly associated with BPH risk (false discovery rate <0.05). Extreme Gradient Boosting model prioritised a three-protein signature: TSPAN1 (hazard ratio (HR) = 1.24) and KLK3 (HR = 1.34) as risk factors, and EDA2R (HR = 0.83) as a protective factor. This three-protein panel achieved an AUC of 0.71 (95% confidence interval = 0.69-0.73) for predicting BPH onset. Enrichment analysis revealed the associated proteins were mainly involved in immune-inflammatory pathways and stromal remodelling. As revealed by Mendelian randomisation, TSPAN1 and KLK3 were causally associated with incident BPH. CONCLUSIONS: We identified a three-protein panel (TSPAN1, KLK3, EDA2R) which can predict incident BPH. The findings highlight potential targets for non-invasive risk assessment and therapy.
15 June 2026
Read appraisal →Journal of neurology, neurosurgery, and psychiatry
Clinically reported covert cerebrovascular disease and risk of neurological disease: a whole-population cohort of 367 988 people using natural language processing
BACKGROUND: The relevance of covert cerebrovascular disease (CCD) in practice is uncertain, partly because estimation of risk in whole clinical populations is difficult. Studies have had success extracting CCD from clinical text using natural language processing (NLP), though they have been limited to specific CCD phenotypes. Here, we used NLP to measure multiple clinically-reported CCD phenotypes in a large clinical cohort and estimated subsequent disease risk in health record data. METHODS: From all people with brain imaging in Scotland (2010-2018), we selected people with no prior hospitalisation for neurological disease (n=367 988). NLP of imaging reports identified: white matter hypoattenuation or hyperintensities (WMH), lacunes, cortical infarcts and cerebral atrophy. Adjusted HRs (aHRs) were estimated between each phenotype and stroke, dementia and Parkinson's disease (conditions previously associated with CCD), epilepsy and colorectal cancer (control conditions). RESULTS: For each phenotype, the aHR of stroke was WMH 1.4 (95% CI 1.3-1.4), lacunes 1.6 (1.5-1.6), cortical infarct 1.8 (1.7-1.9) and cerebral atrophy 1.1 (1.0-1.1). The aHR of dementia was WMH 1.3 (1.3-1.3), lacunes 1.0 (0.9-1.0), cortical infarct 1.1 (1.1-1.2) and cerebral atrophy 1.7 (1.7-1.8). The aHR of Parkinson's disease was WMH 1.1 (1.0-1.2), lacunes 1.1 (0.9-1.2), cortical infarct 0.7 (0.6-0.9) and cerebral atrophy 1.4 (1.3-1.5). The aHRs between CCD phenotypes and epilepsy and colorectal cancer were around the null. CONCLUSION: CCD and atrophy have implications for future disease risk and can be identified at scale using NLP of clinical reports. Prevention of neurological disease in people with CCD should be a priority for healthcare policy makers.
14 June 2026
Read appraisal →JMIR cancer
Identification of Early Signs of Mental Health Disorders in Older Survivors of Cancer Using Patient-Generated Health Data: Observational Study
BACKGROUND: Older survivors of cancer face heightened risk of depression and anxiety related to cancer experiences, fear of recurrence, and aging-related difficulties. Conventional mental health monitoring approaches, such as clinical assessments and even electronic patient-reported outcomes, are limited by recall bias, patient burden, and infrequent data collection. Emerging patient-generated health data from wearables and smart home devices offer passive, low-burden, continuous monitoring, but their ability to capture mental health risks in older survivors of cancer remains unclear. OBJECTIVE: This study aims to explore whether patient-generated health data collected in the wild, either passively or actively, can classify older survivors of cancer as having or not having signs of anxiety and depression based on Patient Health Questionnaire-4 (PHQ-4) scores and to assess the potential added value of passive monitoring modalities, such as smart plugs. METHODS: This study recruited 41 older survivors of cancer (mean age 72.3, SD 6.81 years) from the LifeChamps project. Over a 12-week period, participants were monitored using an activity tracker to measure physical activity, sleep, and physiological metrics; a smart scale to capture weight and body composition; and a smart plug to track television use as a proxy for sedentary television viewing. Mental health status was self-reported via the PHQ-4 questionnaire in a mobile app. Machine learning models were trained to classify mental health risk based on features derived from each sensor modality, both independently and in combination. RESULTS: Tree-based gradient boosting models showed good performance in classifying PHQ-4-defined mental health risk. The best-performing configuration, combining smart plug and smart scale features, achieved a mean F1-score of 0.77 (SD 0.15) and a mean area under the receiver operating characteristic curve (AUC) of 0.85 (SD 0.10) across 3 repeated train-test splits. Standalone smart plug models, based solely on passive television use patterns, achieved a mean F1-score of 0.66 (SD 0.04) and a mean AUC of 0.71 (SD 0.06), outperforming models that relied only on activity tracker data (mean F1 0.59, SD 0.2). Multimodal combinations tended to improve average performance but did not consistently yield large gains over the strongest single-modality configurations, likely reflecting adherence-related data loss for wearables and scales. Crucially, passive monitoring of television use patterns emerged as a promising behavioral proxy measure of mental health states. CONCLUSIONS: This study pioneers the use of passively collected data (eg, smart plugs) for mental health monitoring in older survivors of cancer, demonstrating their potential. Smart plugs capture behavioral patterns without user burden, with reasonable standalone performance (mean AUC 0.71, SD 0.06), positioning them as a promising low-burden modality. Future work should validate findings in larger independent cohorts and in prospective clinical workflows. Such technologies could transform monitoring for vulnerable populations, enabling scalable, inclusive care while reducing health care burdens.
14 June 2026
Read appraisal →JMIR formative research
An Interactive Digital Dashboard for Patient Monitoring and Management in a Continuity of Care Centre: Development and Preliminary Usability Evaluation Study
BACKGROUND: Clinical dashboards are becoming important tools for managing and monitoring hospitalized patients across different wards. Moreover, careful attention to design, usability, and user interaction is essential for developing effective support tools for clinicians. OBJECTIVE: This study aimed to describe the development, implementation, and preliminary evaluation of an interactive dashboard for patient monitoring and management in the Continuity of Care Centre (CCC). METHODS: We developed a dashboard according to clinicians' requests and the daily workflow of case managers. First, a CCC Data Mart was created to collect all patients' information automatically extracted from the hospital's data warehouse. However, case managers had the possibility to enter additional patient information in the dashboard using a dedicated form. Moreover, CCC physicians, nurses, and administrative staff were surveyed using 2 validated questionnaires, the System Usability Scale and the Questionnaire for User Interaction Satisfaction. The Situation Awareness Index was proposed to evaluate user awareness and task efficiency. RESULTS: The first version of the CCC dashboard presented 4 panels with different types of information, both on the individual patient and on metrics related to the overall patient population. The first panel focused on patients' data, such as demographic factors, admission, transfer, discharge wards (and their dates), etc. The importance of this panel was the possibility of viewing information collected from different sources within a single interface. The other 3 panels displayed different key performance indicators for the overall patient population and presented data both in the form of tables in the second panel and graphs in the third and fourth panels. After 3 months of daily use, a total of 15 participants, 10 nurses, 2 administrative staff members, and 3 physicians, were recruited for the dashboard evaluation. The average System Usability Scale score of the dashboard was 61.5 (SD 15.7) points, which indicates "OK to good" usability, and the median score obtained with the Questionnaire for User Interaction Satisfaction was 5.77 (IQR 4-7), with the highest results in usability (mean 6.33, SD 0) and learning (mean 6.01, SD 0.39). The overall Situation Awareness Index score was 4 points, with the highest score in "familiarity of dashboard" (mean 4.73, SD 1.66 points) and "arousal support" (mean 4.6, SD 1.8 points). CONCLUSIONS: We developed an interactive dashboard for patient monitoring and management, with positive evaluations from users across different questionnaires.
14 June 2026
Read appraisal →BMC geriatrics
The association between smart elderly care and subjective well-being among older adults in China: the mediating roles of health status and social activities
BACKGROUND: Under the initiative of China's "Internet+" strategy, smart elderly care reflects the government's capacity to bridge the digital divide and provide inclusive care services for older adults. Though studies have identified positive associations between smart elderly care and health outcomes, empirical research examining its impact on older adults' subjective well-being remains limited. METHODS: This study utilizes data from the 2021 Chinese Longitudinal Healthy Longevity Survey (CLHLS, N = 8,950), which includes multifaceted information on individuals aged 65 and above, covering their personal characteristics, health status, family information, and community elderly care services. A logit model is employed to investigate the association between smart elderly care and SWB. The mediating effects are tested using the bootstrap resampling method. To address endogeneity concerns, instrumental variable (IV) approaches including IV-Probit and 2SLS are applied as robustness checks. RESULTS: The results reveal a positive correlation between smart elderly care and older adults' subjective well-being (β = 0.101, p < 0.01). This association is partially mediated by both health status (proportion mediated: 38.47%) and social activities (proportion mediated: 2.65%). Heterogeneity analyses show that this positive association holds consistently across both rural and urban older adults, and is more pronounced among those with lower digital literacy. CONCLUSIONS: This study challenges the prevailing assumption that the "digital divide inevitably isolates the older adults from participating in a digitized society." By integrating digital technologies into older adults care services, we demonstrate that smart elderly care is positively associated with bridging technological barriers and addressing the unmet needs of aging populations. These findings further propose a synergistic mechanism-policy innovation, technological adaptation, and service optimization, to build an inclusive older adults care ecosystem, offering evidence-based insights for policymakers in aging societies to balance equity and efficiency in digital transformation.
14 June 2026
Read appraisal →Journal of medical Internet research
Experiences of Home-Dwelling Older Adults and Their Family Caregivers With Digital Health Services: Qualitative Study
BACKGROUND: As the population ages, demand for health services among older adults and caregivers increases. Digital health services help meet this demand. However, acceptance and experiences differ between older adults and caregivers. OBJECTIVE: This study aims to explore the specific experiences of home-dwelling older adults and their caregivers with digital health services, identify their willingness to use these services and the factors influencing their adoption, and identify their service needs to inform the subsequent design and optimization of digital health products and services. METHODS: From December 2023 to February 2024, a descriptive qualitative study was conducted. Researchers used purposive and maximum variation sampling to recruit 18 home-dwelling older adults and 17 family caregivers, including 10 matched dyads. Participants came from 7 community health service centers and a tertiary hospital in Hefei, China. Open-ended, semistructured, face-to-face interviews were conducted separately. Data were analyzed using conventional content analysis. RESULTS: Four main themes emerged. The first were the application characteristics, including usage situations and preferred functions. The second theme described specific service experiences, both positive and negative. The third was the influencing factors. Promoting factors included health needs, user experience, and subjective norms. Obstructive factors differed between groups and included low digital literacy, weak economic foundation, service security concerns, and lack of time. The fourth theme covered suggestions and expectations for improvement. Older adults appreciated the advantages but often had suboptimal experiences due to complex designs and threats to personal dignity. Conversely, caregivers valued efficiency but reported low use, hindered by time constraints and concerns about privacy and safety. CONCLUSIONS: This study reveals a critical gap between the potential and actual use of digital health services. Older adults face barriers with usability and digital literacy. Caregivers struggle to integrate these services and have trust issues. Future optimization requires action at several levels. Government support is needed through policies, authoritative platforms, and subsidies. Service providers should offer age-friendly, personalized design, and continuous support. Social support systems are also essential, such as digital reciprocity from family and peer help. These strategies are critical for enhancing user experience, bridging the digital divide, and enabling both older adults and their caregivers to fully benefit from digital health technologies.
14 June 2026
Read appraisal →Journal of neurology, neurosurgery, and psychiatry
On the relationships between apathy, depression and anhedonia
BACKGROUND: Apathy, depression and anhedonia are clinically overlapping constructs, which hinders diagnostic clarity and treatment development. This study aimed to comprehensively characterise these syndromes to identify a core set of non-redundant symptoms that maximally dissociate them and to investigate the psychological nature of key distinguishing features. METHODS: Data from seven datasets (N=4578) of healthy individuals and patients with major depressive disorder were analysed using the Apathy Motivation Index, Beck Depression Inventory and Snaith-Hamilton Pleasure Scale. A machine-learning algorithm identified the most informative, non-redundant items for dissociating 'pure' apathy, depression and anhedonia. The nature of emotional apathy was further investigated with follow-up studies. RESULTS: Although substantial symptom overlap existed, 'pure' syndromes were present. Factor analysis revealed a robust five-factor structure, separating depression, anhedonia and three distinct apathy domains (behavioural, social and emotional). Machine learning identified 10 core symptoms that differentiated the pure syndromes with high accuracy (area under the curve >0.90) and could also identify well the presence of each syndrome in individuals suffering from two or more syndromes. Emotional apathy negatively correlated with depression and was specifically associated with reduced affective empathy and a diminished sensitivity to the intensity of negative facial emotions, rather than with alexithymia or antidepressant-induced emotional blunting. CONCLUSIONS: Apathy, depression and anhedonia are dissociable constructs with distinct symptom signatures. Emotional apathy is a unique dimension which provides a novel target for research. A 10-item Apathy-Depression-Anhedonia Measure developed here provides a pragmatic tool for rapid, precise phenotyping to guide more personalised therapeutic strategies.
14 June 2026
Read appraisal →Journal of medical Internet research
Uptake of Clinical Decision Support Systems Among Health Care Professionals in Six European Countries and the United States: Cross-Sectional Survey Within the I-CARE4OLD Project
BACKGROUND: The use of Clinical Decision Support Systems (CDSS), such as clinical decision rules, algorithms, or machine learning-based applications, has gained attention in recent years. However, their adoption and effectiveness may vary across different health care systems and settings. For a CDSS to be adopted, it must effectively address the practical issues encountered by professionals; however, little research has been done to identify these needs and requirements. OBJECTIVE: This study aims to describe and compare the current use of various decision-support and prediction tools in long-term care for older people across health professionals from 6 European countries and the United States. METHODS: This study analyzed survey data from a CDSS pilot study in a purposive sample of health professionals working with older adults with complex chronic conditions from six European countries and the United States. The survey included participants' general background information, their current use of decision support tools, and their attitudes on the potential benefits of CDSS. About 20 participants were sampled per country. Closed responses were analyzed using correlation coefficients and regression models, while open-ended responses were clustered in a qualitative manner, categorizing each response. RESULTS: A total of 151 professionals (mean age 45.5, SD 11.6 years, 71.5%, 108/151 female) participated in the pilot study. Most participants were physicians (85/151, 56.3%) or nurses (57/151, 37.7%). About 51% (78/151) of the participants reported using CDSS, while 22.4% (34/151) used predictive CDSS, showing important variation across samples from the seven countries. The regression model for comfort with technology showed a positive association for openness to new technologies (β=0.622; P<.001), although an inverse significant association was found for age (β=-0.022; P<.001). No significant associations were found for the actual use of CDSS. Participants reported using CDSS mainly for diagnostic purposes or for guideline implementation, not aimed at prognostic information. In contrast, examples of prognostic tools were most frequently mentioned by respondents as being valuable improvements to clinical practice. CONCLUSIONS: While some countries' samples reported well-integrated digital health infrastructures and higher CDSS adoption rates, others still face challenges in implementing these. However, we found multiple examples of emerging tools, and at the same time, an important demand for predictive CDSS. Our findings highlight the need for improvement of current CDSS implementation and both development and implementation of particularly predictive CDSS.
14 June 2026
Read appraisal →JMIR cardio
Experiences of Patients With Atrial Fibrillation Using Technology to Personalize Self-Care Decision-Making: Interpretive Description Study
BACKGROUND: Atrial fibrillation (AF) is the most common sustained heart rhythm disorder and is a challenging chronic disease to manage. Patients' daily self-care decisions are associated with improved AF outcomes, quality of life, and decreased hospital use and cost. However, many patients find these real-world or naturalistic decisions difficult, often because of their inherent complexity and ambiguity, coupled with the uncertainty of AF. Intervention research using technology to support AF self-care has largely emphasized making decisions with clinicians. Patients with AF are increasingly using consumer technology; yet, little is known about the use of technology by patients with AF in independent self-care decision-making. Addressing this gap will facilitate developing interventions that better leverage technology to enhance patients' naturalistic decision-making. OBJECTIVE: This study aimed to explore the experiences of older adult patients in using technology to support self-care decision-making. METHODS: Following an interpretive descriptive qualitative approach, older adult patients with AF were recruited from 3 specialty heart function clinics in a Western Canadian province to participate in 1 of 6 facilitated virtual focus groups for 1.5 hours. Patients were asked about their self-care decision-making since AF diagnosis, their AF-specific technology use and its use in making self-care decisions, their technology motivations, benefits, constraints, and other possibilities for use. Inductive thematic analysis was used to code the transcribed data, moving from open coding to clustering of common codes into categories, looking for patterns of meaning between and across categories to iteratively arrive at main themes and subthemes. RESULTS: Thirty patients (n=15, 50% women) with AF (mean age, 73, SD 5.7 years; range 63-85 years) participated in the focus groups. Participants' experiences of using technology to make daily self-care decisions were highly variable but centered on its personalized use to meet their individualized needs, preferences, and life context. The personalizing process of technology use in decision-making was characterized by three themes: (1) beginning technology use in their own times and ways, during their AF trajectory-pre-, at the time, at some point AF post diagnosis, and could be either self-initiated and/or provider recommended or influenced; (2) developing patterns of AF self-care decision-making using technology, including establishing their personal baseline, keeping out of the danger zone, watchful waiting, and seeking decision-making support; and (3) finding the place for technology in normalizing daily life, either settling on or limiting its use to normalize life. CONCLUSIONS: Findings expand understandings of naturalistic decision-making by elucidating the personalized process of technology use in AF self-care.
13 June 2026
Read appraisal →BMC geriatrics
Components and effects of psychosocial support interventions for relatives of patients with delirium in hospitals, systematic review
BACKGROUND: Relatives of patients with delirium are often confronted with patients' challenging behaviours such as hyperactivity, aggression, or hallucinations. As a result, feelings of anxiety and depression episodes have been described. The aim is to identify psychosocial support intervention for relatives of patients with delirium in hospitals, describe components and effects, and identify research gaps. METHODS: Systematic review based on PRISMA, including psychosocial support intervention studies addressing burden and support needs of adult relatives of hospital patients with delirium. We searched the databases Medline, Web of Sience, CINAHL, Livivo, PsycINFO, and Cochrane Library in 07/2024 and reference lists. We assessed reporting using TIDieR and CReDECI2 and methodological quality using ROB-1 and the JBI tool for quasi-experimental studies. We performed narrative syntheses. RESULTS: We identified eight intervention studies addressing relatives of patients with delirium in different settings. Intervention components included provision of information using flyers, videos, web-sites, or telephone calls, and mentoring in the intensive care unit. Reporting of interventions was mostly poor. No study reported a process evaluation. Four of six studies showed a significant difference in relatives' delirium knowledge. For relatives' psychological outcomes, no clear effects were shown. Overall, study quality was limited. CONCLUSIONS: More effectiveness studies are needed with better reporting of intervention components, ideally addressing psychological aspects. Feasibility and acceptance of new interventions should be described within accompanying process evaluations. TRIAL REGISTRATION: https://osf.io/7jbsk .
13 June 2026
Read appraisal →Neurology
Acute Kidney Injury and Risk of Adverse Neurocognitive Outcomes: A Systematic Review and Meta-Analysis
BACKGROUND AND OBJECTIVES: Chronic kidney disease is a recognized risk factor for adverse neurocognitive outcomes, but the effect of acute kidney injury (AKI) on brain health remains less well defined. We conducted a systematic review and meta-analysis to evaluate associations between AKI and subsequent risk of stroke, delirium, and dementia. METHODS: Eligible studies were identified by searching Ovid MEDLINE and Embase from inception (Ovid: January 1946; Embase: January 1970) until April 2025. Studies were included if they reported quantitative estimates with measures of precision for the association between AKI and delirium, stroke, or dementia in adult populations. Two reviewers independently screened and extracted data, and study quality was assessed using standardized criteria. Study characteristics, participant demographics, and adjusted effect estimates (hazard ratios [HRs] or odds ratios [ORs]) with 95% CIs were extracted. Pooled HRs and ORs with 95% CIs were calculated using random-effects models. Heterogeneity was evaluated with the χ2 test and I2 statistic, and sources of heterogeneity were explored through prespecified subgroup analyses and meta-regression. RESULTS: We identified 49 studies comprising 11,253,825 participants with 1,279,145 events. Individuals with AKI were at increased risk of stroke (pooled adjusted HR 1.35, 95% CI 1.20-1.52), delirium (pooled adjusted OR 1.76; 1.42-2.17), and dementia (pooled adjusted HR 1.64, 1.41-1.89). A gradient of risk across increasing AKI stages was demonstrated for stroke (stage 1: HR 1.11; 1.00-1.23; combined stages 2 and 3: HR 1.57; 1.35-1.81). AKI was also associated with higher in-hospital and 90-day mortality poststroke (pooled HR 2.13, 1.56-2.90, and 4.81, 2.55-9.08, respectively) and with 90-day disability (pooled adjusted OR 1.47, 1.22-1.76). Associations between AKI and all outcomes were directionally consistent across sensitivity analyses and pooled propensity score-matched studies. DISCUSSION: In this systematic review and meta-analysis, AKI was consistently associated with increased short-term and long-term neurocognitive risk, including stroke, delirium, and dementia. These findings suggest that AKI may identify individuals vulnerable to both acute and chronic brain injury. Further studies are needed to clarify mechanisms linking AKI to brain injury and to identify strategies to mitigate neurocognitive risk in this high-risk population.
9 June 2026
Read appraisal →Neurology
Associations of Baseline Clinical Phenotypes With White Matter Hyperintensity Volume Change: A Study of 4,329 UK Biobank Participants
BACKGROUND AND OBJECTIVES: White matter hyperintensities (WMHs) relate to cognitive and physical impairment. Although WMHs typically progress over time, regression has also been observed. Our aim was to explore baseline clinical phenotypes associated with subsequent WMH volume change in the UK Biobank (UKB). METHODS: We included participants with total volume of WMHs at first and follow-up brain MRI. We assessed 107 preselected clinical phenotypes measured at baseline and preprocessed using the Phenome Scan Analysis Tool (PHESANT). We derived a measure of WMH change as the residual from a linear regression of follow-up WMH volumes on baseline WMH volumes to account for baseline WMH burden. We ran (1) linear regressions for continuous WMH change as the outcome and (2) multinomial logistic regressions comparing progression, regression, and stable groups after categorizing WMH volume change using an established percentile-based approach. Models were unadjusted, partially adjusted (age, sex, total brain tissue volume, follow-up time), or fully adjusted (further adding blood pressure; BP). We corrected p values using false discovery rate (FDR) correction. RESULTS: We included 4,329 participants (median age 52; interquartile range 12), 54.6% female, with a median follow-up of 2.3 years (range 1-7 years). Across follow-up, total brain volume decreased (mean decrease 16,058 mm3) and WMH volume increased (median increase 293 mm3; both p < 0.001). WMHs progressed in 53.9% of participants, regressed in 26.01%, and remained stable in 20%. In fully adjusted linear models, higher diastolic BP was the only baseline phenotype associated with greater WMH progression (0.10 SD higher WMH change per 1 SD higher inverse-rank normal transformed diastolic BP; 95% CI 0.05-0.15, FDR p < 0.05). In multinomial models, hip pain lasting more than 3 months (odds ratio [OR] 0.25, 95% CI 0.11-0.58) and faster walking pace (OR 0.79, 95% CI 0.66-0.95) were inversely associated with WMH progression, whereas hip pain was also inversely associated with WMH regression vs stability (OR 0.31, 95% CI 0.12-0.79; all FDR p < 0.05). DISCUSSION: We established vascular risk factors associated with subsequent WMH volume change in the studied cohort. Distinct clinical and demographic profiles characterized WMH progression, regression, and stable groups. Results suggest that vascular factors relate to WMH change but are sensitive to covariate control. Further studies should establish factors differentially and causally related to WMH progression, regression, and stability.
1 June 2026
Read appraisal →Lancet (London, England)
Efficacy and safety of intravenous prasinezumab in individuals with early-stage Parkinson's disease on stable symptomatic monotherapy (PADOVA): a phase 2b, multicentre, randomised, double-blind, placebo-controlled study
BACKGROUND: Prasinezumab has previously shown potential for reducing the progression of motor signs (Movement Disorder Society-sponsored Revision of the Unified Parkinson's Disease Rating Scale [MDS-UPDRS] Part III) in patients with early-stage Parkinson's disease who were treatment-naive or receiving monoamine oxidase type B (MAO-B) inhibitors. The aim of the PADOVA trial was to evaluate the efficacy and safety of prasinezumab in a broader population of patients receiving stable symptomatic medication. METHODS: This phase 2b, multicentre, double-blind, parallel-group, placebo-controlled, randomised, superiority trial recruited participants with early-stage Parkinson's disease (age 50-85 years, 3 months to 3 years from diagnosis, Hoehn and Yahr stage 1 or 2) on stable symptomatic medication from 110 centres in nine countries in Europe and North America. Participants were individually randomly assigned (1:1) via permuted blocks (stratified by symptomatic medication [levodopa or MAO-B inhibitor]) to intravenous prasinezumab (1500 mg) or placebo every 4 weeks for at least 76 weeks and until the target number of motor progression events was reached. Participants, investigators, and clinical assessors were masked to group assignment. The primary endpoint was time to a confirmed motor progression event (≥5-point increase in MDS-UPDRS Part III off-medication score), assessed in the full analysis set (all randomly assigned participants according to the treatment to which they were assigned). Safety and tolerability were assessed in all randomly assigned participants who received at least one dose of study drug, with participants grouped according to treatment received. The trial is registered with ClinicalTrials.gov (NCT04777331) and EudraCT (2020-004997-23), and is active, not recruiting. FINDINGS: Between May 5, 2021, and March 22, 2023, 787 individuals were screened. 586 were enrolled and randomly assigned (n=293 per group; mean age 64·2 years [SD 7·3]; 214 [37%] female and 372 [63%] male); 550 completed double-blind treatment (prasinezumab, n=277; placebo, n=273). The primary endpoint was not met; the primary analysis showed a non-significant delay in motor progression with prasinezumab versus placebo (hazard ratio 0·84 [95% CI 0·69-1·01]; p=0·066); median time to confirmed motor progression in the prasinezumab group was 61·1 weeks (95% CI 52·3-71·9), compared with 49·7 weeks (40·1-58·1) in the placebo group. The incidence of one or more serious adverse events was similar between groups (prasinezumab, 34 [12%] of 292; placebo, 34 [12%] of 290) and three recorded deaths were unrelated to the study drug (prasinezumab, n=1 [<1%]; placebo, n=2 [1%]). INTERPRETATION: Although PADOVA did not meet the primary endpoint, prespecified exploratory evidence suggests clinical activity of prasinezumab in early-stage Parkinson's disease, supporting continued investigation in the ongoing phase 3 PARAISO trial (NCT07174310). FUNDING: F Hoffmann-La Roche.
1 June 2026
Read appraisal →Human vaccines & immunotherapeutics
Safety and durability of influenza and SARS-CoV-2 antibody responses through 6 months after a single dose of mRNA-1083, a multicomponent influenza and COVID-19 vaccine, in adults ≥50 years.
Seasonal influenza and SARS-CoV-2 continue to contribute substantially to global morbidity and mortality. mRNA-1083 is an investigational multicomponent mRNA vaccine targeting seasonal influenza and SARS-CoV-2. In this phase 3, observer-blind, active-controlled trial (NCT06097273), adults (aged ≥50 y) were randomly assigned (1:1) to receive mRNA-1083 (40 µg; plus placebo) or age-appropriate licensed comparator vaccines for influenza and COVID-19. Two substudies were included: Cohort A (≥65 y) and Cohort B (50-64 y). Primary objectives were to demonstrate noninferiority of mRNA-1083 versus comparators for influenza hemagglutination inhibition (HAI) responses and SARS-CoV-2 neutralizing antibody (nAb; PsVNA) responses at Day 29 after vaccination and to evaluate safety and reactogenicity of study vaccines. Here, we report safety, reactogenicity, and immunogenicity of mRNA-1083 through Day 181 in adults aged ≥50 y. mRNA-1083 induced HAI titers against all vaccine-matched influenza strains that were maintained through 6 months and were generally comparable to, or higher than, comparator responses. SARS-CoV-2 nAb responses remained higher for mRNA-1083 than comparators through 6 months. Reactogenicity was predominantly grade 1-2 in severity, and vaccination-related unsolicited adverse events were similar between groups. No serious adverse events or deaths related to vaccination were reported through 6 months. In adults aged ≥50 y, mRNA-1083 elicited durable influenza and SARS-CoV-2 humoral immune responses with an acceptable safety profile through 6 months. These findings support the use of mRNA-1083 as a multicomponent vaccine.ClinicalTrials.gov identifier: NCT06097273 (https://clinicaltrials.gov/study/NCT06097273).
1 June 2026
Read appraisal →Radiation oncology (London, England)
HyperSightTM-CBCT based monitoring of sarcopenia during definitive radiotherapy of prostate cancer: a longitudinal feasibility study
BACKGROUND: Sarcopenia and treatment-related muscle loss are associated with increased toxicity and reduced resilience in cancer patients. HyperSight™ cone-beam CT (CBCT) provides improved soft-tissue contrast compared with conventional CBCT and may enable reliable longitudinal skeletal-muscle monitoring during image-guided radiotherapy (IGRT). This study evaluated the feasibility of HyperSight™ CBCT-based muscle assessment in prostate cancer patients and explored the clinical relevance of intratreatment muscle decline. METHODS: Eighty-eight patients with non-metastatic prostate cancer were treated on an Ethos® system with HyperSight™ CBCT. Patients received ultrahypofractionated (UHF) or normo-/moderately hypofractionated (NF/MHF) radiotherapy; 28% received androgen-deprivation therapy. Cross-sectional muscle area (CSA) and radiodensity (HU) were assessed on planning CT (PCT) and serial CBCTs (first, second, last fraction [FF, SF, LF]); HU values were z-standardized to subcutaneous fat (zHU). Agreement between PCT and CBCT was evaluated using concordance correlation coefficients (CCC), regression, and Bland-Altman analysis. Longitudinal changes were analyzed using mixed-effects models, and associations with acute genitourinary/gastrointestinal (GU/GI) toxicity (RTOG ≥ 2) using regression models. RESULTS: HyperSight™ CBCT showed excellent agreement with PCT for CSA (CCC = 0.981). The NF/MHF cohort showed a significant longitudinal CSA decline (FF-LF: -153.0 mm², p = 0.021), whereas no significant within-cohort change was observed in UHF. Muscle density (zHU) decreased significantly in both cohorts (NF/MHF: p < 0.001; UHF: p = 0.0017). Weight loss and age were independently associated with zHU change, while no independent predictors of CSA decline were identified. In the NF/MHF cohort, CSA loss > 5% was associated with acute GU toxicity in univariate analysis (OR 4.90, p = 0.033; Fisher's exact p = 0.014), but not after adjustment for age, weight loss, ECOG performance status, and ADT; no associations were found for GI toxicity or in the UHF cohort. CONCLUSIONS: HyperSight™ CBCT enables quantitative skeletal-muscle assessment during prostate radiotherapy and detects intratreatment reductions in muscle density, which may represent an early imaging marker of muscle decline. Muscle loss showed an exploratory association with acute GU toxicity in NF/MHF patients in univariate analysis, but not after adjustment. These findings support the feasibility of CBCT-based muscle monitoring and warrant further evaluation in larger cohorts. CLINICAL TRIAL NUMBER: Not applicable.
1 June 2026
Read appraisal →Lancet (London, England)
Efficacy and safety of ocrelizumab in primary progressive multiple sclerosis, including older patients and those with more advanced disease (ORATORIO-HAND): a multicentre, double-blind, randomised, placebo-controlled, phase 3b study
BACKGROUND: The ORATORIO trial showed that ocrelizumab reduced the risk of disability progression versus placebo in patients with primary progressive multiple sclerosis (PPMS). We aimed to elucidate the effect of ocrelizumab in older and more disabled patients with PPMS, particularly regarding hand function preservation. METHODS: ORATORIO-HAND was a multicentre, double-blind, randomised, placebo-controlled, phase 3b study with 138 sites across 22 countries. Patients with PPMS aged 18-65 years and Expanded Disability Status Scale (EDSS) score of 3·0-8·0 were randomly assigned 1:1 to intravenous ocrelizumab 600 mg or placebo every 6 months for 144 weeks or until a prespecified number of progression events occurred. Masking was achieved by use of a placebo solution administered in the same manner as ocrelizumab. Double-blinding across all periods was maintained through separation of investigators responsible for efficacy and safety assessments. MRI scans were evaluated by a masked central reader, and laboratory parameters that could reveal treatment allocation were masked to site personnel until the primary analysis. Two coprimary estimands were defined, with the endpoint of time to onset of 12-week composite confirmed disability progression (12W-cCDP) in 9-Hole Peg Test or EDSS evaluated in all randomly assigned patients, and the same endpoint evaluated in a subset of patients with MRI activity at baseline. This study is registered with ClinicalTrials.gov, NCT04035005 and is ongoing and not recruiting. FINDINGS: Between Aug 12, 2019, and Dec 10, 2024, of 1360 patients assessed for eligibility, 1013 were randomly assigned (ocrelizumab [n=505]; placebo [n=508]). The proportion of patients with 12W-cCDP was 165 (33%) of 505 with ocrelizumab and 205 (40%) of 508 with placebo (hazard ratio, 95% CI 0·70 0·57-0·86; relative risk reduction=30%; p=0·0007). In the MRI-active subgroup, a significant risk reduction was also observed in 12W-cCDP (risk reduction=55%; p<0·0001). The overall safety profile was similar in both groups. More infections (245 [48%] of 506 vs 226 [45%] of 506) were observed with ocrelizumab, but not after COVID-19 was excluded (38% vs 37%). Rates of serious adverse events and serious infections were similar between groups. INTERPRETATION: Ocrelizumab was superior to placebo in delaying disability progression, with stronger effect on hand function, in a broad PPMS population including older patients and those with more advanced disease, while maintaining a manageable safety profile. FUNDING: F Hoffmann-La Roche.
31 May 2026
Read appraisal →Lancet (London, England)
Comparison of [18F]flortaucipir and [18F]MK6240 for the detection of tau pathology in Alzheimer's disease (HEAD): a multicentre, prospective, cross-sectional, within-participant study
BACKGROUND: Tau PET imaging has emerged as a critical biomarker for Alzheimer's disease, informing diagnosis, staging, and therapeutic selection. We investigated whether PET tracer selection alters tau detection. METHODS: We conducted a prospective, multicentre, non-randomised, within-participant comparison of [18F]flortaucipir (Tauvid), currently used in clinical settings in the USA and Europe, and [18F]MK6240, an investigational tau PET tracer. Participants were recruited from eight north American sites and underwent tau PET, amyloid-β (Aβ) PET, and detailed cognitive assessments. Tau PET with both agents was acquired within a 45-day window. Coprimary outcomes were the discriminative accuracy for Alzheimer's disease-related cognitive impairment and the frequency of tau positivity in early medial temporal lobe (MTL) and late neocortical regions. The study is registered with ClinicalTrials.gov, NCT05361382. FINDINGS: Between March 2, 2022, and Aug 27, 2025, 775 individuals were enrolled, with 682 completing all procedures (373 [55%] female, 309 [45%] male; 38 [6%] aged 19-27 years, 214 [31%] aged 50-65 years, and 430 [63%] aged 65-89 years). 32 (5%) participants identified as Hispanic or Latino. 637 (93%) identified as White, 24 (4%) as Black or African American, 16 (2%) as Asian, and five (1%) as other. In addition, 49 (7%) individuals were identified as being from a rural area. [18F]MK6240 showed greater accuracy than [18F]flortaucipir in distinguishing Alzheimer's disease from non-Alzheimer's disease impairment (area under the curve 0·93, 95% CI 0·89-0·95 vs 0·86, 0·75-0·91; p<0·0001). Among the older adults, tau positivity status was concordant in 560 (87%) for MTL and 603 (94%) for neocortical regions. In cognitively unimpaired participants, [18F]MK6240 identified twice as many MTL-positive cases as [18F]flortaucipir (n=54 [15%] vs n=23 [6%]). Prevalence ratio in Aβ-positive was 2·43 (95% CI 1·50-3·94; p=0·0003), identifying 23 additional cases per 100. Among discordant cases, 75 (89%) were [18F]MK6240-positive only and had higher Aβ burden (p<0·0001), APOEε4 frequency (p<0·0001), and cognitive impairment (p=0·0043) than those negative on both tracers. Neocortical tau positivity was more frequent with [18F]MK6240 than with [18F]flortaucipir in cognitively impaired individuals (80 [28%] vs 46 [16%]). Prevalence ratio in Aβ-positive was 1·74 (95% CI 1·32-2·29; p<0·0001), identifying 15 additional mild cognitive impairment and 21 dementia cases per 100. INTERPRETATION: Tau PET tracer selection influences the frequency of detection of tau pathology across the ageing and Alzheimer's disease spectrum. Compared with [18F]flortaucipir, [18F]MK6240 identified more individuals with tau pathology in cognitively unimpaired and cognitively impaired individuals, with direct implications for patient stratification in clinical trials and more precise guidance for therapeutic decision-making. FUNDING: National Institute on Aging.
31 May 2026
Read appraisal →Medicine
Predicting depressive symptoms in older adults with hearing impairment development and validation of NHANES-based machine-learning models: A cross-sectional study
Depressive symptoms frequently co-occur with hearing impairment in older adults, yet available prediction tools for this high-risk subgroup remain limited in practicality and applicability. We developed and internally validated prediction models for clinically relevant depressive symptoms among older adults with hearing impairment. We analyzed cross-sectional data from 5 National Health and Nutrition Examination Survey cycles (2005-2006, 2009-2010, 2011-2012, 2015-2016, and 2017-2018). Adults aged ≥60 years with hearing impairment were identified using the 2021 World Health Organization criterion (better-ear pure-tone average at 0.5/1/2/4 kHz ≥20 dB). Depressive symptoms were defined as a Patient Health Questionnaire-9 score ≥10. Candidate predictors included sociodemographic factors, comorbidities, lifestyle behaviors, and sensory/functional limitations; missing covariates were handled using multiple imputation. Participants were randomly split into training and validation sets (7:3). Predictors were selected using the Boruta algorithm followed by least absolute shrinkage and selection operator regression, yielding 6 predictors (age, poverty-income ratio, self-rated health, sleep disorder, memory difficulty, and physical functional limitation). Logistic regression and 6 machine-learning algorithms were developed. Model performance was assessed by discrimination (area under the receiver operating characteristic curve [AUC]), calibration (calibration curves and Brier score), and clinical utility (decision-curve analysis). The analytic sample included 2123 participants, of whom 154 (7.25%) had depressive symptoms. In the validation set, AUCs across models ranged from 0.799 to 0.862. Logistic regression achieved the highest discrimination (AUC 0.862, 95% CI 0.800-0.911) with good calibration (Brier score 0.053) and favorable net benefit across clinically relevant thresholds. A nomogram, a dynamic nomogram, and an online calculator were developed to facilitate individualized risk estimation. Sensitivity analyses showed broadly consistent results, except for attenuated discrimination in the severe hearing-loss subgroup. A parsimonious 6-variable model demonstrated strong discrimination, good calibration, and potential clinical utility for risk stratification and targeted screening among older adults with hearing impairment. External validation and prospective evaluation are warranted before clinical implementation.
31 May 2026
Read appraisal →JMIR research protocols
The Effectiveness of Art Activities and Peer Group Participation on Psychological Well-Being Among Older Adult Center Residents: Protocol for a Randomized Controlled Trial
BACKGROUND: Transitioning to residential centers often induces significant psychological stress in older adults, adversely affecting their psychological well-being (PWB) and ability to age healthily. Chinese art activities and their combination with peer group participation have demonstrated efficacy in improving emotional well-being and fostering social connectivity among younger populations but have not been examined in older adult populations to date. OBJECTIVE: This study aims to assess the efficacy of Chinese art activities or their combination with peer group participation in enhancing PWB among Chinese older adults living in a residential center. METHODS: This multicenter randomized, assessor- and analyst-blinded trial uses a "max-min-con" design with 3 groups: control, Chinese art activities, and Chinese art activities plus peer group participation (1:1:1 allocation). A total of 90 participants aged 60 to 85 years and with a stay of 1 year or less will be recruited from 3 older adult centers (30 from each center) and assigned via minimized randomization to the groups. Interventions will consist of 3 sessions within a single week (Monday, Wednesday, and Friday). Baseline evaluations will be conducted before randomization to assess PWB, loneliness, happiness, relaxation, and salivary cortisol levels. Outcome evaluations will be conducted 30 minutes prior to and following each intervention session to assess immediate effects. A follow-up evaluation is planned 1 week after the final intervention to measure sustained outcomes. RESULTS: The study was funded in September 2024. Participant recruitment commenced on May 9, 2025, and ended on August 20, 2025, with the recruitment of 90 participants. Data collection ended on October 31, 2025, and data analysis is expected to conclude in April 2026, with the anticipated publication of results in 2026. CONCLUSIONS: This study is the first randomized controlled trial to evaluate the effects of Chinese art activities, either alone or combined with peer group participation, on enhancing PWB in older people and the efficacy of salivary cortisol as a biomarker for the assessment of PWB. The findings will provide empirical evidence to assist policymakers and older adult centers in selecting appropriate interventions, with the long-term potential to improve psychological health in older adults.
31 May 2026
Read appraisal →Medicine
Common biomarkers of Alzheimer disease and postoperative delirium
Alzheimer disease (AD) and Postoperative delirium (POD) may share a common mechanism, but their shared genes and potential novel therapeutic targets remains unknown. We selected datasets GSE48350, GSE4226, and GSE16759 containing AD and control samples from the GEO database for AD model development, and use the GSE63061 dataset for AD model validation. The GSE163943 dataset containing PODs and controls was used to identify common genes. Differentially expressed genes (DEGs) associated with POD and AD respectively, as well as common DEGs between 2 diseases were analyzed. Shared DEGs were assessed using gene ontology and KEGG enrichment and PPI interaction networks. Machine learning algorithms, nomogram and decision tree analyses were used to interpret top 5 genes and visualize the models. 259 DEGs in POD with 148 being up-regulated and 111 down-regulated, and 199 DEGs in AD with 25 up-regulated and 174 down-regulated were found, of which 23 were common to POD and AD. Gene ontology analysis indicated that DEGs were primarily concentrated in 9 distinct biological processes, while KEGG results revealed 3 major metabolic pathways. Five hub genes emerged as potential diagnostic biomarkers for both diseases, Bruton tyrosine kinase (BTK), NCF2, CRH, FCGR3A, and SERPINA3. The LASSO model determined that the 23 common genes could serve as biomarkers for both POD and AD patients, and the Random Forest model is the the most suitable model. POD or AD decision trees showed BTK and NCF2 were sufficient to distinguish between POD or AD patients and healthy controls. BTK and NCF2 were identified common biomarkers of POD and AD. Further investigations are required to verify correlations between gene expression levels and pathological characteristics.
31 May 2026
Read appraisal →JMIR medical informatics
Advancing Alzheimer Disease Prediction With Large Language Model-Based Linguistic Feature Analysis: Development and Validation Study
BACKGROUND: Alzheimer disease (AD) is a progressive neurodegenerative disorder with rapidly growing global prevalence. Early detection is critical for timely intervention; yet, conventional diagnostic methods remain costly and invasive. Speech-based assessment has emerged as a noninvasive alternative, as AD characteristically impairs linguistic abilities including fluency, coherence, and informational content. Recent advances in large language models (LLMs) offer new opportunities to extract structured linguistic features from transcribed speech for automated AD classification. However, existing LLM-based approaches often lack transparency and clinical interpretability, limiting their adoption in clinical workflows. OBJECTIVE: This study aims to investigate the influence of linguistic features extracted from transcribed speech, as analyzed by LLMs, on the accuracy and interpretability of AD prediction. METHODS: We propose a framework that leverages LLMs to analyze linguistic features extracted from transcribed speech for AD classification. Our approach focuses on 4 key aspects, including readability, fluency, richness of detail, and keyword relevance. To enhance classification accuracy, the framework integrates transcript embeddings with feature explanation embeddings, forming a comprehensive linguistic representation. We conducted extensive ablation studies to evaluate the contributions of individual features and benchmarked our framework against existing LLM-driven methodologies through pairwise explainability evaluations. Output stability was assessed across 3 independent pipeline runs. A fully local configuration (Llama 3 8B + nomic-embed-text) was tested to evaluate privacy-preserving deployment feasibility. Explainability was assessed via LLM-based pairwise comparison (Gemini-3.1-flash-lite) against the method of Bang et al across 54 correctly classified cases and by blinded evaluation from 2 neurologists. RESULTS: The proposed framework achieved a mean precision of 91.52%, a sensitivity of 91.08%, a specificity of 96.29%, and F1-score of 91.05% across 3 independent runs on the ADReSSo 2021 dataset, outperforming existing LLM-based approaches. A fully-local configuration (Llama 3 8B+nomic-embed-text, requiring no cloud application programming interface access) achieved an F1-score of 81.58%, demonstrating framework transferability to privacy-preserving deployment environments. Keyword relevance was the most influential feature (F1-score drop of 13.22 pp when removed). Explainability evaluations showed our method was preferred in 49 out of 54 cases via Gemini-3.1-flash-lite, with human experts preferring our method in 89 of 108 blinded assessments. CONCLUSIONS: These findings highlight that a structured linguistic feature analysis using LLMs provides a robust and interpretable framework for preliminary AD detection. Our approach offers a scalable and accessible solution that bridges artificial intelligence-driven text analysis with clinical applications, supporting early detection of cognitive decline through noninvasive assessment methods.
31 May 2026
Read appraisal →Purinergic signalling
Turning failure into success: how artificial intelligence can help personalize therapies and re-use patient data.
Despite robust preclinical evidence, many clinical trials, including several that targeted the purinergic system, fail to demonstrate efficacy in humans. Failure may stem from inability to accurately identify patient subgroups responding similarly to treatments. Here, we explore the potential of artificial intelligence to revolutionize how we group and classify patients in clinical studies. We introduce a new framework using Large Language Models-generated embeddings of detailed patient data, to create a semantic-aware latent space, enabling us to identify truly meaningful patients' clusters. Large Language Models can provide explainable groupings, giving clear reasons why certain patients respond similarly to treatments. We present an example of successful application of this approach through the re-analysis of the AMARANTH clinical trial (NCT02245737, involving ~ 2200 patients and completed in 2018) testing Lanabecestat, a BACE1 inhibitor decreasing β-amyloid production in Alzheimer's disease, for which traditional analysis reported no efficacy. As in the original trial, our simulation showed no overall benefit. However, re-analysis per patients' clusters and subjects' re-stratification by semantic similarities (shared symptom profiles, progression patterns) identified a patients' subgroup in one of the clusters showing Lanabecestat-associated slower disease worsening, thus succeeding where the full trial had failed. By making a new therapy available to at least a subset of patients with a defined disease, this new approach may help maximize the return on drug development and reduce the burden on healthcare. Moreover, it will significantly improve the precision, efficiency and interpretability of clinical trials, paving the way for a new era of personalized medical treatments.
31 May 2026
Read appraisal →Lancet (London, England)
Efficacy and safety of oral semaglutide 14 mg (flexible dose) in early-stage symptomatic Alzheimer's disease (evoke and evoke+): two phase 3, randomised, placebo-controlled trials
BACKGROUND: Evidence, including animal, clinical, and real-world studies in individuals with type 2 diabetes and/or obesity, suggests reduced risk of dementia and Alzheimer's disease after GLP-1 receptor agonist exposure. The evoke and evoke+ trials aimed to investigate the efficacy and safety of oral semaglutide in individuals with early Alzheimer's disease. METHODS: evoke and evoke+ were multicentre, randomised, double-blind, placebo-controlled phase 3 trials conducted across 566 sites in 40 countries. The trials assessed the efficacy and safety of oral semaglutide up to 14 mg once daily in participants with amyloid-confirmed Alzheimer's disease, aged 55-85 years, with mild cognitive impairment or mild dementia due to Alzheimer's disease. In evoke+, participants with significant small vessel pathology were included. Participants were randomly assigned (1:1) to once-daily semaglutide 14 mg (flexible dose) or placebo for up to 156 weeks. The primary endpoint was change in Clinical Dementia Rating-Sum of Boxes (CDR-SB) score from baseline to week 104, assessed in all randomised participants. Safety was assessed in all randomised participants and reported for those receiving at least one dose of study drug. These trials were registered at ClinicalTrials.gov (NCT04777396 and NCT04777409); both trials have been discontinued due to negative clinical outcome. FINDINGS: Between May 18, 2021, and Sept 8, 2023, 9981 participants were screened, of whom 3808 were randomly assigned; 1855 in evoke (semaglutide, n=928; placebo, n=927) and 1953 in evoke+ (semaglutide, n=976; placebo, n=977). Mean age was 72·2 years (SD 7·1), and mean CDR-SB score was 3·7 (SD 1·6) at baseline. In evoke+, 54 (2·8%) participants had small vessel pathology. In evoke and evoke+, mean changes in CDR-SB score from baseline to week 104 were 2·3 (SE 0·1) and 2·2 (0·1) with semaglutide, compared with 2·3 (0·1) and 2·1 (0·1) with placebo (estimated difference -0·08 [95% CI -0·35 to 0·20], p=0·57 in evoke and 0·10 [-0·17 to 0·38], p=0·46 in evoke+). Treatment-emergent adverse events were reported in 1729 (91·2%) of 1896 participants receiving semaglutide versus 1613 (84·8%) of 1902 receiving placebo. There were five fatalities considered treatment-related by the investigators (one in the semaglutide group and four in the placebo group). INTERPRETATION: Oral semaglutide was not efficacious in slowing clinical progression in participants with early Alzheimer's disease. Safety and tolerability of semaglutide in early Alzheimer's disease is consistent with studies in other indications. FUNDING: Novo Nordisk.
31 May 2026
Read appraisal →Journal of medical Internet research
Effects of Multicomponent Digital Health Interventions on Multidimensional Physical Activity in Older Adults: Systematic Review, Meta-Analysis, and Meta-Regression of Randomized Controlled Trials
BACKGROUND: The comprehensive effects of multicomponent digital health interventions (DHIs) on multidimensional physical activity indicators and sedentary behavior (SB) remain controversial. OBJECTIVE: This systematic review aimed to evaluate the impact of multicomponent DHIs on daily steps, moderate-to-vigorous physical activity (MVPA), light physical activity, total physical activity, and SB in older adults. METHODS: PubMed, Web of Science, Embase, The Cochrane Library, and CINAHL were searched up to February 20, 2026. Randomized controlled trials concerning multicomponent DHIs for promoting exercise behavior in older adults were included. RoB 2.0 was used to evaluate study quality. Meta-analyses were performed using the Hartung-Knapp-Sidik-Jonkman random-effects model, and 95% prediction intervals (PIs) were calculated via Nagashima adjustment to evaluate effect dispersion. The GRADE (Grading of Recommendations, Assessment, Development, and Evaluation) system was used to evaluate evidence certainty. RESULTS: A total of 26 randomized controlled trials (n=4129) were included. The results showed that multicomponent DHIs significantly improved daily steps (mean difference [MD] 822.8, 95% CI 198.3 to 1447.3 steps/d; 95% PI -1452.4 to 3098.0) and MVPA (MD 45.9, 95% CI 23.9 to 67.9 min/wk; 95% PI -9.4 to 101.2). However, the improvements in SB (MD -283.7, 95% CI -610.8 to 43.5 min/wk; 95% PI -984.5 to 417.1), total physical activity (MD 104.4; 95% CI -109.2 to 318.0 min/wk; 95% PI -444.4 to 653.2), and light physical activity (MD 39.3, 95% CI -96.2 to 174.7 min/wk; 95% PI -227.6 to 306.2) did not reach statistical significance. As some included studies combined digital tools with human support, the independent contribution of digital technology remains uncertain. PIs indicated a certain degree of dispersion across different clinical contexts. Subgroup analysis showed higher effect sizes for standalone wearables, human-assisted interventions, and populations with chronic disease risks. Meta-regression showed that effect sizes remained stable across different ages and durations. The trim-and-fill method confirmed the robustness of MVPA results. GRADE assessment indicated "moderate" certainty for MVPA and "low" for daily steps and other indicators. CONCLUSIONS: This systematic review suggests that multicomponent DHIs may serve as an effective means for enhancing daily steps and MVPA in older adults. The innovation lies in evaluating the true effect distribution of multicomponent DHIs through Hartung-Knapp-Sidik-Jonkman random-effects models and Nagashima PIs. Compared with previous studies, this review identified the impact of population characteristics and control group differences on effect estimates using PI and subgroup models, confirming that advanced age did not significantly diminish the good adaptability of older adults to DHIs. Evidence limitations include high heterogeneity, lack of long-term follow-up, and differences between objective and subjective measurement tools. In practice, priority should be given to hardware carriers with simplified interaction and integrated human support, with tailored strategies developed for different risk subgroups.
30 May 2026
Read appraisal →Journal of neurology
Artificial intelligence for gait and balance in neurological disorders: a scoping review of clinical applications and technologies
BACKGROUND: Artificial intelligence (AI) has rapidly emerged within healthcare systems and neurological rehabilitation with the potential to revolutionize clinical decision-making and therapeutic strategies. However, a comprehensive understanding of how AI is currently applied to gait and balance rehabilitation in stroke, Parkinson's disease (PD), and multiple sclerosis (MS) is still lacking. OBJECTIVE: To map the current use of AI in neurological rehabilitation, focusing on clinical purposes, geographical distribution, and applied technologies for gait and balance rehabilitation. METHODS: Following the PRISMA-ScR statement, we conducted a literature search through MEDLINE, Cochrane CENTRAL, EMBASE, and Google Scholar up to July 2025 to identify studies applying AI-based methods to gait and balance outcomes in adults with stroke, PD, or MS. Study characteristics, AI methods, validation strategies, clinical purpose, and motor outcomes were extracted and synthesized narratively. RESULTS: Eighteen studies published from 2009 to 2025 were included. Most studies were conducted in Asia (50%) and involved people with stroke (77.8%). AI was predominantly used for prognostic purposes (72.22%), such as predicting falls, gait recovery, or treatment response and diagnostic applications (33.3%). Machine learning was the most common approach (88.9%) with Random Forest, Support Vector Machine, logistic regression, and eXtreme Gradient Boosting being the most frequently applied algorithms. None of the included studies performed prospective or external validation on independent datasets. CONCLUSIONS: This scoping review provides a comprehensive overview of current AI applications, highlighting promising but still immature approaches in the neurological rehabilitation of gait and balance. However, substantial methodological limitations remain major barriers to clinical translation.
30 May 2026
Read appraisal →Behavioural brain research
Efficacy of repetitive transcranial magnetic stimulation at different frequencies for post-stroke cognitive impairment: A network meta-analysis
PURPOSE: Post-stroke cognitive impairment (PSCI) affects nearly half of stroke survivors, necessitating effective interventions. This Bayesian network meta-analysis evaluated the efficacy of various repetitive transcranial magnetic stimulation (rTMS) protocols for PSCI. METHODS: We systematically searched PubMed, Cochrane Library, and Chinese databases up to December, 2025, including 37 randomized controlled trials (2543 patients) comparing rTMS protocols with different frequencies (low/high) and sites (left/right). Outcomes were measured using the Montreal Cognitive Assessment (MoCA), Mini-Mental State Examination (MMSE), and Modified Barthel Index (MBI). RESULTS: Results indicated that high-frequency left dorsolateral prefrontal cortex stimulation (HFL-rTMS) most effectively improved MoCA scores (MD=3.38, SUCRA=87.6%), high-frequency right-sided stimulation (HFR-rTMS) ranked highest for MMSE improvement (MD=2.93, SUCRA=79.7%), and high-frequency bilateral stimulation (HFB-rTMS) yielded the greatest MBI enhancement (MD=13.47, SUCRA=89.6%). CONCLUSIONS: High-frequency rTMS demonstrated superior efficacy in cognitive and functional recovery. HFL-rTMS, HFR-rTMS, and HFB-rTMS are recommended as optimal interventions for MoCA, MMSE, and MBI outcomes, respectively. Further studies are warranted to validate stimulation parameters and expand multicenter evidence for clinical application.
30 May 2026
Read appraisal →ACS applied materials & interfaces
A High-Sensitivity MXene Tactile Sensor with Dynamic Point-Contact Networks for a Wearable Healthcare Device
The self-stacking of the MXene material can diminish its interlayer spacing and active surface area, thereby diminishing the sensitivity and response speed of pressure sensors. Here, we introduce a high-performance flexible tactile sensor utilizing a composite of MXene, polystyrene (PS) microspheres, and bacterial cellulose (BC). PS microspheres are integrated within the MXene nanosheet layers to mitigate the self-stacking. By employing an innovative point-to-point conductive network design, the self-aggregation issue of MXene materials is effectively mitigated, leading to the formation of dynamic point-contact structures among PS microspheres. This configuration notably enhances detection sensitivity within the low-pressure range to 568.8 kPa-1 (20-200 Pa). Furthermore, the incorporation of BC enhances the interfacial bonding strength through hydrogen bonding, thereby enhancing sensor stability. The sensor exhibits a rapid response time of 29 ms, a wide detection range of up to 0-50 kPa, a low limit of detection of 2 Pa, and consistent signal stability over 20,000 cycles. It has demonstrated the capability to monitor a broad spectrum of physiological activities, including joint movements, facial microexpressions, and radial pulse. Integration with wireless transmission technology enables remote health monitoring, flexible touch keyboard functionality, and human-computer interaction. Additionally, the electromyography (EMG) skin sensor based on the MXene/PS/BC (MPB) composite film accurately distinguishes muscle movements, gestures, acoustic vibrations, and facial microexpressions by detecting surface EMG signals. This comprehensive approach offers a comprehensive solution for wearable medical devices that combine high sensitivity with practicality.
30 May 2026
Read appraisal →JMIR aging
Developing Customized Personas to Capture Intrinsic Capacity Profiles and Digital Monitoring Intentions in Older Adults: Mixed Methods Study
BACKGROUND: Integrated Care for Older People (ICOPE), focused on monitoring and optimizing the intrinsic capacity (IC) of older adults, is a new model of geriatric care that is currently being accelerated globally. Digital health technologies are recommended for longitudinal IC monitoring to provide precise and timely interventions. However, little is known about the psychological intentions of engaging in digital monitoring of IC according to the profile heterogeneity of IC among older adults. OBJECTIVE: This study aims to map a set of customized personas to capture the profiles of IC and match psychological intentions that support personalized digital IC monitoring. METHODS: An explanatory sequential mixed methods study was conducted at 16 sites in Beijing, China. Older adults aged ≥60 years (n=481) were selected to complete the quantitative survey. Latent profile analysis, descriptive statistics, and logistic regression analyses were performed to cluster subgroups using Mplus (Muthén & Muthén) and SPSS (IBM Corp). A subsample of participants from each profile (n=25) was purposively sampled for semistructured interviews. An inductive-deductive content analysis was used to identify similar attributes and to affirm the personas gradually. A joint statistical and thematic visualization method was used to integrate the customized personas. RESULTS: Three profiles of IC patterns emerged: "multisubdomain decline-IC imbalance group," "multisubdomain moderate-sensory deficit group," and "multisubdomain robust-whole balance group." The distribution of latent profiles was influenced by age, education, monthly per capita household income, self-rated health, and number of chronic diseases, while positively impacting older adults' functional ability. The following customized personas were captured regarding established themes: "affects my mood-anxious evader," characterized by avoidance and anxiety, low digital interest, and perceived social isolation; "capitalize on what comes-accommodative adopter," pragmatically oriented toward disease detection, with moderate digital openness but limited self-efficacy; and "more autonomy-active improver," who exhibited proactive engagement, high digital literacy, and motivation rooted in self-management and social participation. CONCLUSIONS: This study is the first to integrate latent profile analysis with customized qualitative personas to link the heterogeneity of IC with the psychological intentions underlying digital monitoring. The resulting personas model provides an actionable framework for tailoring digital IC monitoring strategies in community-based integrated care. The findings emphasize the need to align monitoring approaches with older adults' IC characteristics, psychological readiness, digital literacy, and social support to enhance engagement in digital IC monitoring.
29 May 2026
Read appraisal →PLoS medicine
Sequential chemo-immunotherapy followed by standard versus reduced thoracic radiotherapy for older and/or frail stage III non-small-cell lung cancer: A randomized open-label cohort trial
BACKGROUND: The appropriateness of concurrent chemoradiotherapy (cCRT) for older or clinically vulnerable stage III unresectable non-small-cell lung cancer (NSCLC) patients remains contentious. Furthermore, the survival implications of de-escalating thoracic radiotherapy (RT) intensity in this population have not been conclusively elucidated. METHODS AND FINDINGS: We conducted a phase II randomized, open-label, two-cohort (non-comparative) trial at a tertiary hospital in China (NCT05557552). Between September 30, 2022 and April 30, 2024, we enrolled 56 older and/or frail patients with stage III NSCLC who were ineligible for cCRT. The primary endpoint was the 1-year progression-free survival (PFS) rate estimated using the Kaplan-Meier method. Secondary endpoints included objective response rate (ORR), overall survival (OS), and safety. In the intention-to-treat (ITT) set, which included all 56 randomized patients who received at least one dose of study treatment, the 1-year PFS was 84.3% (95% confidence interval [CI] [70.3%, 98.3%]) in the standard RT group and 70.7% (95% CI [54.3%, 87.1%]) in the reduced RT group. In the per-protocol set (53 patients), the 1-year PFS was 82.9% (95% CI [68.9%, 98.8%]) in the standard RT group and 73.4% (95% CI [58.3%, 92.4%]), with a median follow-up of 24 months. Among 56 patients in the safety analysis set, 71.4% of patients experienced grade 3/4 adverse events (AEs) in the standard RT group and 53.6% in the reduced RT group. One patient (3.6%) in the reduced RT and three patients (10.7%) in the standardized RT experienced grade 5 AEs. The main limitations are the non-comparative design, small sample size, and lack of power to establish non-inferiority or superiority. CONCLUSION: The current study suggested that reduced RT combined with sequential chemo-immunotherapy might be feasible for older/frail patients intolerant to cCRT, showing numerically similar survival outcomes. These exploratory findings warrant confirmation in larger, adequately powered randomized trials. TRIAL REGISTRATION: The trial had been registered on ClinicalTrials.gov on Sep 30, 2022. ClinicalTrials.gov NCT05557552.
29 May 2026
Read appraisal →BMJ open
Multicentre prospective cohort study to develop and validate a machine learning-based model for predicting 6-month all-cause mortality in elderly patients with advanced chronic obstructive pulmonary disease in China: study protocol
INTRODUCTION: Chronic obstructive pulmonary disease (COPD) has an unpredictable clinical course, causing difficulties in short-term mortality prediction, overtreatment and delayed palliative care. Existing prediction models are limited and lack applicability to Chinese elderly patients with advanced COPD. Given the heavy disease burden and limited palliative care in China, we designed this multicentre cohort study to develop a 6-month mortality prediction model for elderly patients with advanced COPD to aid risk stratification, timely palliative care and efficient healthcare resource allocation. METHODS AND ANALYSIS: Patient recruitment has been ongoing since May 2024 and will be completed by December 2026, with a 12-month follow-up to be completed by December 2027. Eligible patients are being enrolled, and multidimensional baseline data including demographic characteristics, clinical indicators, laboratory results, comprehensive geriatric assessment and COPD-specific prognostic factors are being systematically collected. All participants will receive 12 months of standardised follow-up (monthly for the first 6 months and quarterly thereafter) to monitor 6-month all-cause mortality (primary outcome), as well as survival duration, end-of-life healthcare utilisation and do-not-resuscitate status (secondary outcomes). After completion of data collection, we will employ multiple machine learning algorithms to develop and internally validate a 6-month mortality prediction model with pre-specified centres reserved for external validation. Model performance will be evaluated by discrimination and calibration and head-to-head comparisons with the Body Mass Index, Airflow Obstruction, Dyspnoea and Exercise Capacity (BODE) and Age, Dyspnoea and Airflow Obstruction (ADO) indices will be conducted to verify its clinical value. The findings will provide a China-specific prediction tool for elderly patients with advanced COPD to guide clinical intervention, palliative care referral and healthcare resource allocation. ETHICS AND DISSEMINATION: This study was approved by the Biomedical Ethics Review Committee of West China Hospital, Sichuan University (No. 2024-2662) and registered at ChiCTR2500100351. Informed consent is being obtained from all participants. Results will be published in peer-reviewed journals and presented at academic conferences. TRIAL REGISTRATION NUMBER: ChiCTR2500100351.
29 May 2026
Read appraisal →Progress in biomedical engineering (Bristol, England)
Wearable sensors for monitoring neurogenic dysphagia: a scoping review
Neurological disorders, such as Parkinson's disease and stroke, often lead to neurogenic dysphagia, a swallowing disorder that compromises nutrition and increases the risk of malnutrition, aspiration of food, and even death. Although instrumental assessments remain the gold standard for evaluating swallowing function, their invasiveness, cost, and reliance on specialized clinical facilities limit their routine use. A systematic search was conducted in PubMed, Scopus, IEEE Xplore, and Web of Science, including only peer-reviewed studies using non-invasive wearable devices for assessing and monitoring dysphagia in neurological disorders. The initial search identified 1669 records, and an updated search retrieved 128 additional records. After screening, 24 studies met the inclusion criteria. Accelerometers and surface electromyography sensors were the most frequently used, either alone or in multimodal configurations. Multimodal approaches generally provided more accurate and comprehensive assessments than single-sensor systems. However, validation with gold-standard methods was performed in only half of the studies, and considerable heterogeneity was observed in sensor placement, study protocols, and signal-processing strategies. Wearable sensing technologies represent a non-invasive and scalable solution for assessing neurogenic dysphagia. To enable clinical translation, future research should focus on standardized methodologies, robust validation, and the integration of artificial intelligence for more precise swallowing assessment in individuals with neurological disorders.
29 May 2026
Read appraisal →JMIR aging
e-Counseling for Fall Prevention in Older Adults: Scoping Review
BACKGROUND: Falls are a major contributor to injury, disability, and mortality among older adults worldwide. Digital health-based e-counseling has emerged as a scalable strategy to support fall prevention by addressing physical, behavioral, and cognitive risk factors. OBJECTIVE: This scoping review aimed to map the scope, characteristics, and outcomes of e-counseling interventions for fall prevention among adults aged 60 years or older. METHODS: Guided by Arksey and O'Malley's framework and reported in accordance with PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews), we searched PubMed, ScienceDirect, CINAHL, and ProQuest for studies published between January 2019 and May 2025. Eligible studies evaluated digital or telehealth-based counseling interventions for fall prevention. Data were synthesized descriptively, and methodological quality was appraised using the Mixed Methods Appraisal Tool. RESULTS: A total of 13 studies were included, comprising randomized controlled trials, feasibility studies, qualitative research, and retrospective cohorts conducted across Asia, Europe, and North America. e-Counseling modalities included virtual reality, mobile health application, teleconsultations, and wearable-triggered feedback systems. Reported outcomes included reductions in fall incidence ranging from 15% to 90.9%, improvements in balance and mobility, increased fall risk awareness, and enhanced self-efficacy. Interventions incorporating cultural tailoring and digital literacy support demonstrated higher engagement and adherence. CONCLUSIONS: e-Counseling appears to be a promising and scalable approach for fall prevention in older adults, with benefits across physical and behavioral domains. Future research should focus on long-term effectiveness, equitable access in low- and middle-income settings, and integration into routine health care systems.
29 May 2026
Read appraisal →ACS applied materials & interfaces
Boosting Piezoelectricity and Mechanical Properties of Silk Fibroin Films via Polyethylene Glycol Phase Manipulation
Piezoelectric silk fibroin (SF) shows great potential in fabricating sustainable wearable electronics toward health monitoring and human-machine interfacing, yet the processing difficulty and brittleness restrict its practical applications severely. This study proposes a simple strategy to achieve the boosted piezoelectricity and mechanical properties in electrospun regenerated silk fibroin (RSF) nanofiber mats by adding polyethylene glycol (PEG) with different molecular weights to induce the formation of β-sheets via hydrogen bonding and the regulation of PEG phase morphology. Specifically, RSFs consisting of PEG of 2000 and 1000 Da exhibit a significant improvement in the tensile strength (from 6.1 to 9.92 MPa) and elongation at break (from 5.72% to 117.58%), respectively. Meanwhile, RSFs consisting of PEG with a molecular weight of 2000 and 400 Da show a direct piezoelectric force sensitivity of 0.2 mV/N·μm and 0.14 mV/N·μm respectively, superior to that of pure RSF (0.04 mV/N·μm). The piezoelectric force sensors fabricated with RSFs show potential applications in the detection of diverse human motions, including finger tapping for Morse code, grip strength, and knee flexion. Thus, this work unveils a facile strategy to fabricate RSF-based biodegradable piezoelectric force sensors with a trade-off in processability, mechanical ductility, and piezoelectricity.
28 May 2026
Read appraisal →Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
Exercise effects on cognitive function and molecular markers in post-stroke patients with cognitive impairment: a systematic review and meta-analysis
INTRODUCTION: Exercise may improve cognitive function in stroke survivors. However, no study has yet examined the most effective exercise modalities and associated changes in biomarkers. This study aims to investigate the effects of different exercise types on cognitive function and biomarker trends, providing evidence-based guidance for early rehabilitation interventions. METHODS: Data from randomized controlled trials were collected from January 1, 2000, to March 6, 2025, using medical subject headings combined with free-text terms from PubMed, Cochrane Library, Embase, Wanfang, CNKI, CSTJ, and the China Medical Information Database. The Cochrane Risk of Bias Tool was used to assess the quality of the studies. Review Manager version 5.4.1 was employed to conduct all statistical syntheses. RESULTS: The forty-four original studies involving 4,450 patients with PSCI(Post-stroke cognitive impairment) were analyzed. Subgroup analysis by exercise intervention type revealed larger effect sizes when combining aerobic exercise with other task-based interventions(cognitive-motor dual-tasking and functional motor training) [SMD = 1.18, 95% CI (0.68, 1.68)]. Patients also showed improvements in motor function [MD = 13.98, 95% CI (10.06, 17.89)] and activities of daily living [MD = 16.78, 95% CI (12.97, 20.58)]. Concerning molecular biomarkers, serum levels of brain-derived neurotrophic factor (BDNF), glutathione (GSH), and neuron-specific enolase (NSE) increased significantly, whereas interleukin-6 (IL-6) levels decreased notably in PSCI patients following exercise therapy. CONCLUSION: Structured exercise effectively improves cognitive recovery, motor skills, and daily functioning in patients with PSCI. These clinical gains are accompanied by favorable peripheral biomarker modulations, characterized by significantly increased serum BDNF and GSH, as well as markedly decreased IL-6 levels.
28 May 2026
Read appraisal →JMIR mHealth and uHealth
Reducing Sedentary Time After Knee Replacement Using a Multicomponent mHealth Intervention: Randomized Controlled Trial
BACKGROUND: Total knee replacement (TKR) is a common surgery for end-stage knee osteoarthritis. Although reductions in pain and improvements in mobility occur after surgery, physical activity levels often do not change. Given the challenges of increasing physical activity in this population, targeting reductions in sedentary behavior may be a first step; however, no prior studies have examined the feasibility and effects of a sedentary reduction intervention after TKR. OBJECTIVE: This study examined the effects of a 2-month multicomponent mobile health sedentary reduction intervention (NEAT!2) on sedentary time in adults with TKR. METHODS: Adults (N=83; mean age 65.3, SD 9.4 years; mean BMI 32.7, SD 6.9 kg/m2; 62/83, 74.7% female; 64/83, 77.1% White) with a TKR ≤1 year ago were randomized to the NEAT!2 group (n=42, 50.6%) or the attention-matched control group (n=41, 49.4%). The NEAT!2 intervention focused on reducing sedentary time via a smartphone app designed to interrupt prolonged bouts (≥30 minutes) of sedentary behavior and through 5 coaching calls emphasizing goal setting and problem solving. The control group focused on surgery recovery via an app or website and 5 educational calls. Sedentary time, total physical activity, physical function, and pain were measured at 2 and 5 months. Linear mixed-effects models examined intervention effects and differences between groups at each time point. RESULTS: Retention was 96% and 95% at 2 and 5 months, respectively, with no differences between groups. Participants in the NEAT!2 group completed an average of 4.95 (SD 0.2) calls, used the app on an average of 40.3 (SD 13.8) days (out of 56 days), and received an average of 9.6 (SD 6.0) notifications per day. At 5 months, there was a significant increase in sit-to-stand transitions in the NEAT!2 group and a substantial decrease in the control group, resulting in a significant difference between groups (mean difference 4.06, 95% CI 0.13-7.99; P=.04); however, the NEAT!2 intervention did not result in significant effects on any of the other study outcomes at 2 or 5 months. Additionally, more days of app use were associated with greater increases in moderate-to-vigorous intensity physical activity (r=0.335; 95% CI 0.017-0.585; P=.04). CONCLUSIONS: This study highlights the challenges of reducing sitting time in adults with TKR. Future studies should explore alternative behavior change techniques across different levels of influence (eg, environmental and social) to support interventions implemented within the first year after TKR. TRIAL REGISTRATION: ClinicalTrials.gov NCT04482400; https://clinicaltrials.gov/ct2/show/NCT04482400.
28 May 2026
Read appraisal →Archives of orthopaedic and trauma surgery
Comparative analysis of the improvement in shoulder rotation in complex shoulder fractures treated by reverse shoulder arthroplasty compared to open reduction and internal fixation: a systematic review and meta-analysis
PURPOSE: To compare the clinical and functional outcomes of open reduction and internal fixation (ORIF) and reverse shoulder arthroplasty (RSA) in patients aged ≥ 65 years with complex proximal humeral fractures (PHFs). METHODS: A systematic review and meta-analysis was conducted according to the PRISMA guidelines. PubMed, Web of Science, ScienceDirect, EBSCO, and the Cochrane Library were searched for randomized controlled trials (RCTs) and cohort studies published in English without date restrictions. Eligible studies compared RSA and ORIF in elderly patients with PHFs and reported functional, radiographic, or complication outcomes. Pooled data were analyzed using a random-effects model. RESULTS: Twenty-two studies involving > 34,000 patients were included. RSA was associated with greater forward flexion and a trend toward improved abduction, whereas internal rotation favored ORIF without reaching significance. No significant differences were observed in external rotation. The functional scores (Constant-Murley, Oxford Shoulder Score) were similar, and the complication and reoperation rates did not differ significantly between the groups. CONCLUSION: RSA offers modest advantages in forward flexion compared with ORIF but does not consistently improve overall functional scores or reduce complications. RSA may be preferred in elderly patients with poor bone stock or rotator cuff pathology, whereas ORIF remains appropriate for patients in whom joint preservation is feasible. High-quality RCTs with standardized outcome reporting are needed to clarify the optimal surgical management.
28 May 2026
Read appraisal →Acta oncologica (Stockholm, Sweden)
Change in skeletal muscle mass during systemic cancer treatment: a systematic review and meta-analysis
BACKGROUND AND PURPOSE: Loss of skeletal muscle mass (SMM) is common during systemic cancer treatment, but the magnitude and variability across cancer and treatment types remain uncertain. We aimed to describe changes in SMM during systemic cancer treatment supported by pooled quantitative estimates. PATIENTS/MATERIAL AND METHODS: We systematically searched PubMed, Embase, and Web of Science until April 2025 for longitudinal studies reporting SMM during chemotherapy and/or immunotherapy (± targeted therapy) in patients with cancer (PROSPERO CRD42022308388). Standardized mean changes (SMC) were pooled in random-effects meta-analyses using the restricted maximum-likelihood estimator with Hartung-Knapp adjustment. Heterogeneity was assessed using I². Risk of bias was assessed with the NIH Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies. RESULTS: Seventy-eight studies (n = 10,502; 52% male; median age 64 years [interquartile range, IQR: 34-77]) were included. Meta-analysis across cancers showed an association between systemic cancer treatment and decline in SMM (59 studies; n = 6,373; SMC = -0.24, 95% confidence interval [CI]: -0.29 to -0.20; I² = 92%), corresponding to -5% over a median interval of 90 (IQR: 71-129) days among studies (62%) reporting assessment intervals. Declines were most pronounced during chemotherapy (± targeted therapy). INTERPRETATION: Declines in SMM are frequently observed during systemic cancer treatment, particularly during chemotherapy (± targeted therapy), although effect sizes were generally small per Cohen's thresholds. However, substantial heterogeneity limits interpretation of a single pooled estimate. Prospective studies with standardized methods are needed to clarify trajectories, mechanisms and clinical implications of SMM loss.
28 May 2026
Read appraisal →JMIR research protocols
AI-Supported, Integrative Prediction of Postoperative Delirium: Protocol for the CONFUSED Study
BACKGROUND: Postoperative delirium (POD) is a frequent and serious complication in older surgical patients, characterized by acute cognitive dysfunction and fluctuating levels of consciousness. POD is associated with prolonged hospitalization, long-term cognitive decline, reduced quality of life, and increased mortality. Despite its clinical relevance, the underlying pathophysiological mechanisms remain poorly understood, and reliable biomarkers for early prediction and prevention are lacking. OBJECTIVE: The CONFUSED study aims to identify molecular and clinical predictors of POD by integrating clinical data with proteomic, transcriptomic, and epigenetic analyses. The primary objective is to develop predictive models for POD using multimodal data. Secondary objectives include the identification of delirium-associated genes, proteins, and epigenetic signatures, as well as the exploration of patient subgroups at increased risk for POD. METHODS: CONFUSED is a prospective observational cohort study conducted at a German university hospital. Adult patients undergoing major surgery under general anesthesia will be enrolled until 100 cases of POD have been observed, which is expected to require a total sample size of approximately 200 to 300 patients. Blood samples are collected at 4 predefined time points: before premedication, immediately after surgery, and on postoperative days 2 and 5. Samples undergo comprehensive proteomic profiling, transcriptomic analysis using RNA microarrays, DNA methylation analysis, and genotyping of selected polymorphisms. Clinical data, including demographics, comorbidities, perioperative variables, medications, and delirium assessments using the Confusion Assessment Method (CAM) and CAM for the intensive care unit, are systematically recorded. Statistical analyses include univariate and multivariate methods, as well as machine learning approaches such as random forests and support vector machines, to identify relevant biomarkers and develop predictive models. The study protocol follows STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) and TRIPOD (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis) guidelines and was approved by the responsible ethics committees. RESULTS: The study was registered in the German Clinical Trials Register (DRKS00033854) on March 18, 2024. Recruitment started in January 2024 and is ongoing at the time of manuscript submission. As of now, 135 patients have been enrolled. Sample collection and laboratory analyses are ongoing. Data analysis began in January 2026, with first results anticipated in July 2026. Final data lock is anticipated after the completion of recruitment. CONCLUSIONS: By integrating multimodal molecular data with clinical parameters and applying advanced machine learning techniques, the CONFUSED study aims to improve the prediction and understanding of POD. The results are expected to support the development of personalized preventive strategies and contribute to improved perioperative care for patients at risk of POD.
26 May 2026
Read appraisal →JMIR nursing
Health Care Professionals' Perspectives on the Use of a Wearable Device for Early Detection and Continuous Vital Signs Monitoring of Acute Respiratory Infections in Nursing Homes: Qualitative Study
BACKGROUND: The growing aging population and staff shortages are placing pressure on Dutch nursing homes (NHs). These challenges have led to an increased interest in digital health technologies. Among these are wearable devices that allow for remote continuous monitoring of vital signs. An example is the Healthdot (smartQare), a wearable electronic device that continuously monitors heart rate, respiratory rate, and physical activity. In the context of acute respiratory infections (ARIs) in NHs, where initial symptoms can go unnoticed, continuous monitoring may aid in early recognition, timely intervention, and reduce staff workloads. However, little is known about how health care professionals perceive the use of continuous vital signs monitoring devices, such as the Healthdot, for this cause in NHs. OBJECTIVE: This study aims to explore the perspectives of healthcare professionals on the use of the Healthdot for early detection and monitoring of ARIs in NHs, to inform potential future implementation. METHODS: Semistructured interviews were conducted with 20 physicians, nurses, and certified nursing assistants from 4 NHs and 1 acute geriatric community hospital located in a NH. Interview transcripts were thematically analyzed to identify themes regarding their perspectives on the use of the Healthdot for monitoring ARIs in this setting. RESULTS: Five main themes were identified that related to the appropriate use of the Healthdot for NH clients and health care professionals: alignment of Healthdot use and NH clients' treatment policies, balancing safety and freedom, impact of the Healthdot on work processes, supporting rather than replacing care, and possible use during pandemics and in the future. Additionally, several preconditions for the use of the Healthdot were identified, including its usability, a support base among care staff, adequate training and guidance, communication with NH clients and their relatives, and a clear policy regarding its use. CONCLUSIONS: Given the complexity of care in NHs, where clinical care is typically balanced against quality of life and a homelike environment, physicians generally expressed reserved attitudes toward the Healthdot, highlighting the need to consider multiple factors in its implementation. Care staff were generally positive about the device. Nevertheless, tailored assessment for each individual NH client remains essential, balancing treatment goals, safety, autonomy, and person-centered care. Additionally, clear communication and alignment between health care professionals in this setting are crucial, specifically regarding their expectations of the Healthdot's role in care processes. This study offers practical guidance that may inform future implementation efforts of continuous vital sign monitoring devices in NHs.
26 May 2026
Read appraisal →Journal of rehabilitation medicine
The effect of combined motor and cognitive rehabilitation on motor performance in Parkinson's disease: a systematic review and meta-analysis
OBJECTIVE: To evaluate the effectiveness of sequential and simultaneous motor and cognitive treatment on motor functions, activities of daily living, and quality of life in people with Parkinson's disease. DESIGN: Systematic review and meta-analysis. SUBJECTS/PATIENTS: Patients with Parkinson's disease. METHODS: A search was conducted in PubMed, Embase, Scopus, Web of Science, and Cochrane Library. Functional mobility was assessed as the main outcome, and balance, gait, activities of daily living, and quality of life as secondary outcomes. Meta-analyses were conducted using mean difference or standardized mean difference with 95% confidence intervals and fixed or random effect models. Heterogeneity was explored, setting a cut-off value of I2 = 50%. RESULTS: Sixteen studies were included, with 8 -eligible for meta-analysis. Results showed that sequential combined treatment offered no clear advantage over motor rehabilitation alone for functional mobility, although a significant benefit was found for activities of daily living. Results on simultaneous combined treatment suggested improvements in quality of life, but no significant differences between treatments for functional mobility. CONCLUSION: Motor-cognitive approaches may enhance prefrontal cortex efficiency, supporting complex motor tasks and reducing the risk of falls. Future research should elucidate neural mechanisms and compare simultaneous and sequential strategies to develop personalized, multidisciplinary rehabilitation protocols.
26 May 2026
Read appraisal →Neuroscience
Therapeutic efficacy of engineered exosomes in Alzheimer's disease: A systematic review and meta-analysis of preclinical animal models
Engineered exosomes are modified extracellular vesicles designed to enhance targeting and cargo delivery, and they have been proposed as a therapeutic strategy for Alzheimer's disease. We systematically reviewed preclinical animal studies evaluating engineered exosomes, synthesized evidence from comparisons with disease models and with natural exosomes, and reported the study in accordance with the PRISMA 2020 checklist. Outcomes included spatial learning and memory assessed by the Morris water maze, amyloid beta pathology, tau phosphorylation, and neuroinflammatory markers. Random effects meta-analyses suggested that engineered exosomes improved Morris water maze performance and reduced amyloid beta burden and pro-inflammatory cytokines compared with natural exosomes, whereas evidence regarding tau phosphorylation was limited and largely qualitative, and the overall certainty of evidence was low to very low. These findings support further investigation of engineered exosomes, but conclusions should be interpreted cautiously until confirmed by rigorously designed and blinded preclinical studies and clinical trials with standardized protocols.
25 May 2026
Read appraisal →Medicine
The effects of electroacupuncture and the Otago Exercise Program in older adults with sarcopenia: A randomized controlled study
BACKGROUND: The aim of this study was to investigate the effects of a combination of electroacupuncture (EA) and the Otago Exercise Program (OEP) for sarcopenia in older individuals and provide treatment recommendations for this patient population. METHODS: In this assessor-blinded randomized controlled trial, 120 older patients with sarcopenia were randomly divided into an OEP group (n = 58) and an EA + OEP group (n = 62). The OEP group received OEP 5 times/week, with each session lasting 30 minutes, for a duration of 12 weeks. In addition to OEP treatment, the EA + OEP group was treated with EA at ST31, ST32, GB34 and ST36 for 30 minutes 3 times/week for 12 weeks. The appendicular skeletal muscle mass-height index (ASM/H2) was the primary outcome. The secondary outcomes included grip strength, 4-m gait speed and 6-min walk distance. Outcomes were measured before initiation and at 6 and 12 weeks after initiation. RESULTS: Significant differences in ASM/H2, grip strength, 4-m gait speed and 6-min walk distance were detected between the 2 groups at week 12, whereas no differences were detected at weeks 0 and 6. In the EA + OEP group, as the treatment time increased, ASM/H2, grip strength, 4-m gait speed and 6-min walk distance increased significantly. In the OEP group, as the treatment time increased, no significant difference was found in ASM/H2, whereas grip strength, 4-m gait speed and 6-min walk distance increased significantly (P < .05). CONCLUSION: In older patients with sarcopenia, EA + OEP was associated with increased skeletal muscle mass, but no such effect was found for OEP alone. Moreover, EA + OEP was superior to OEP alone in terms of improving muscle strength and walking ability. EA + OEP may be considered an effective treatment for sarcopenia in older adults.
25 May 2026
Read appraisal →Medicine
Predicting poor wrist function recovery after internal fixation of distal radius fractures: A retrospective study based on multivariate analysis and machine learning models
This study aimed to identify independent risk factors associated with poor wrist function recovery 6 months after internal fixation of distal radius fractures (DRF) and develop machine learning models for early risk prediction. This retrospective study included 328 patients who underwent internal fixation for DRF at our hospital between January 2022 and June 2025. Wrist function was evaluated using the Cooney Wrist Score at 6 months postoperatively. Poor recovery was defined as a Cooney score <75. The overall incidence of poor wrist function recovery was 21.56% (71/328). Clinical variables including demographic characteristics, comorbidities, laboratory indicators, fracture type, postoperative pain scores, and rehabilitation compliance were collected. Rehabilitation compliance referred to patients' adherence to prescribed postoperative functional training and follow-up plans. Variables with P <.05 in univariate analysis were entered into multivariate logistic regression to identify independent risk factors. These variables were subsequently used to construct random forest (RF), XGBoost, and backpropagation (BP) neural network models. Model performance was evaluated using ROC curves and classification metrics. Multivariate analysis identified low rehabilitation compliance, age ≥65 years, AO type C fractures, postoperative visual analogue scale scores >3 at 1 week, and decreased serum albumin levels as independent predictors of poor wrist function recovery. Among the machine learning models, the RF model demonstrated the best performance (AUC = 0.939, accuracy = 0.946), followed by the BP neural network (AUC = 0.924). The XGBoost model showed comparatively lower predictive performance (AUC = 0.876). Poor wrist function recovery after internal fixation of DRF is influenced by multiple clinical factors. Machine learning models based on identified independent risk factors, particularly the RF model, show strong predictive ability and may assist clinicians in early risk stratification and individualized rehabilitation planning.
25 May 2026
Read appraisal →PloS one
Timely Integration of specialist palliative care into oncology care for patients receiving radiotherapy for bone metastases: A study protocol for the TIPZO-RT Randomized Controlled Trial
BACKGROUND: Patients living with advanced cancer often benefit from palliative care. Timely referral to specialist palliative care improves quality of life and reduces potentially inappropriate end-of-life care. Despite these benefits, specialist palliative care is frequently introduced late and inconsistently. This study evaluates whether systematically offering a consultation with the hospital palliative care consultation team (PCCT) to all patients referred for radiotherapy for symptomatic bone metastases improves satisfaction with care. PATIENTS AND METHODS: The Timely Integration of Palliative Care in Oncology care for patients referred for palliative RadioTherapy (TIPZO-RT) trial follows the Trials within Cohorts design and is embedded within the PRospective Evaluation of interventional StudiEs on boNe meTastases (PRESENT+) cohort. Following cohort enrollment, 246 patients will be randomized (1:1) to either the intervention or control group. Patients in the intervention group are offered a PCCT consultation, which they may accept or refrain from. Patients in the control group are not informed about the trial and continue to receive usual care. After four weeks, patient satisfaction with care (affective behavior, EORTC Satisfaction with Cancer Care core questionnaire (EORTC PATSAT-C33)) will be compared between the groups. Secondary outcomes include symptom burden, quality of life, overall survival, and palliative care utilization. Additionally, in the intervention group, patients' experiences with the consultation are evaluated. DISCUSSION: Integrating palliative care into oncological care for patients with advanced cancer is essential to deliver comprehensive, patient-centered care that addresses physical, psychosocial and spiritual needs. This pragmatic study may provide evidence to support timely integration of specialist palliative care for all patients with bone metastases who may benefit from specialist palliative care. Using the Trials within Cohorts design, this study generates real-world evidence on the acceptance or need for a consultation with the PCCT, while minimizing disappointment or response bias, as patients in the control group are not informed. TRIAL REGISTRATION: ClinicalTrials.gov ID NCT06805396. Registered on 25-03-2025.
24 May 2026
Read appraisal →Calcified tissue international
Performance of Large Language Models in Answering Osteoporosis-Related Frequently Asked Questions: A Systematic Comparative Evaluation Based on International Associations
To systematically evaluate and compare the performance of large language models (LLMs) in answering osteoporosis-related frequently asked questions (FAQs) derived from international osteoporosis-related associations. A standardized question bank was constructed based on FAQs summarized from three international osteoporosis-related associations. Six LLMs were prompted to generate responses to all questions under uniform conditions. Two osteoporosis experts independently and blindly evaluated all responses using 5-point Likert scales for accuracy and comprehensiveness. Inter-rater reliability was assessed using Cohen's κ coefficient. Nonparametric statistical analyses, including Kruskal-Wallis tests with Dunn's post hoc comparisons and Wilcoxon signed-rank tests, were performed to compare models' performance. A total of 528 responses were generated from 88 association-based questions. Inter-rater agreement was good for both accuracy and comprehensiveness (κ = 0.668 and 0.702, respectively). Significant differences were observed among models, with Claude Sonnet 4.5 demonstrating the highest overall performance on association-based FAQs. Although most models showed solid medical knowledge and basic reasoning ability, substantial variability was observed across question types and prompt formulations. Current LLMs demonstrate promising potential as auxiliary tools for osteoporosis-related health management. However, it is necessary to optimize the strategies for integrating medical knowledge and designing prompts, so as to better improve the accessibility of its clinical application.
24 May 2026
Read appraisal →Medicine
Nomogram and machine learning models for predicting the risk of delirium in ICU patients with NSTEMI
Delirium is a frequent and clinically consequential complication among patients admitted to the intensive care unit (ICU). Early risk stratification in patients with non-ST-segment elevation myocardial infarction (NSTEMI) remains challenging. We aimed to develop and validate prediction models for ICU delirium in NSTEMI patients using Boruta-based feature selection and machine learning (ML) approaches, and to compare ML performance with a nomogram model. We retrospectively identified adult ICU patients (≥ 18 years) diagnosed with NSTEMI in the Medical Information Mart for Intensive Care IV database, and included only those with no delirium within the first 24 hours of admission and no prior history of delirium. A total of 392 patients were included (mean age 72.2 ± 12.6 years; 64% male) and categorized into a delirium group (n = 201) and a non-delirium group (n = 191). Clinical characteristics and laboratory indices were extracted. Participants were randomly split into a training cohort (70%) and a validation cohort (30%). The Boruta algorithm was applied in the training cohort for feature selection and identification of relevant predictors. A nomogram was constructed using Boruta-selected variables. In parallel, the same features were incorporated into ML models; model discrimination and clinical utility were evaluated using receiver operating characteristic curves, calibration curves, and decision curve analysis. SHapley Additive exPlanations were used to quantify and visualize feature contributions for the best-performing model. Boruta identified 9 key variables: systolic blood pressure, serum calcium, pulse oximetry, serum sodium, creatinine, serum chloride, anion gap, blood urea nitrogen, and body temperature. In the validation cohort, CatBoost achieved the best discrimination among ML models, with an area under the receiver operating characteristic curve of 0.743 (95% CI = 0.653-0.833). The nomogram demonstrated lower discrimination (validation area under the curve = 0.61). SHapley Additive exPlanations analyses indicated that the Boruta-selected variables contributed differentially to delirium prediction, supporting model interpretability. In ICU patients with NSTEMI, a Boruta-informed CatBoost model showed moderate predictive performance and outperformed a nomogram constructed from the same predictors. This interpretable ML approach may facilitate early identification of patients at high risk of delirium and support timely preventive strategies.
24 May 2026
Read appraisal →Archives of osteoporosis
Management of osteoporosis in older men: a systematic review of randomized trials of pharmacological and non-pharmacological strategies
INTRODUCTION: Osteoporosis in older men is often underdiagnosed and undertreated, increasing the risk of fractures and functional decline. Despite its clinical relevance, evidence specific to this population remains limited. This systematic review evaluated the effectiveness of pharmacological and non-pharmacological interventions in reducing fracture risk and improving bone mineral density (BMD) in older men with primary or secondary osteoporosis. METHODS: Searches were conducted in PubMed, EMBASE, Scopus, Cochrane Library, Web of Science, SciELO, PEDro, and LILACS, in addition to gray literature sources. Randomized controlled trials (RCTs) evaluating pharmacological and non-pharmacological interventions were included. Analyses were performed using RStudio (v. 4.3.3), and risk of bias was assessed using the RoB 2 tool. Results Twelve RCTs were included (10 pharmacological, one nutritional, and one exercise-based). The mean age was 60.9 ± 5.2 years, with baseline T-scores of - 2.9 ± 0.3 at the lumbar spine and - 2.7 ± 0.4 at the femoral neck. Bisphosphonates and teriparatide showed modest improvements in BMD, while effects on fracture risk were inconsistent. Resistance exercise (60-90 min/day) and L-carnitine supplementation were associated with BMD benefits, although evidence was limited by methodological constraints. Risk of bias varied across studies. CONCLUSION: Pharmacological therapies improve BMD in older men; however, evidence remains insufficient to demonstrate consistent fracture risk reduction. Non-pharmacological interventions show potential benefits but are supported by limited evidence. Larger randomized trials with clinically relevant outcomes are needed.
24 May 2026
Read appraisal →Medicine
Global, regional, and national burden of clavicle, scapula, or humerus fracture in 204 countries and territories, 1990 to 2021: A systematic analysis from the Global Burden of Disease Study 2021
Clavicle, scapula, or humerus fracture represent a major public health challenge, yet their epidemiology and health burden remain poorly characterized. Using the Global Burden of Disease 2021 framework, we quantified incidence, prevalence, and years lived with disability (YLDs) of clavicle, scapula, or humerus fractures across 204 countries from 1990 to 2021. Estimates were stratified by age, sex, region, and socio-demographic index (SDI), and reported with 95% uncertainty intervals (UIs) from Bayesian models. Globally, the age-standardized incidence rate (ASIR) declined from 276.68 (95% UI: 219.06-346.71) per 100,000 person-years in 1990 to 220.94 (95% UI: 172.60-280.99) per 100,000 person-years in 2021, with an estimated annual percentage change of -0.78% (95% UI: -0.82% to -0.73%). High SDI regions showed the greatest ASIR (300.30 per 100,000 person-years), age-standardized prevalence rate (112.03 per 100,000 person-years), and age-standardized YLDs rate (3.72 per 100,000 person-years). In 2021, Eastern Europe had the highest ASIR (465.06 per 100,000 person-years), while Australasia led in age-standardized prevalence rate (151.42 per 100,000 person-years) and age-standardized YLDs rate (5.04 per 100,000 person-years). China ranked first in absolute prevalence (1.21 million, 95% UI: 1.04-1.39 million) and YLDs (40,235, 95% UI: 24,471-61,196). Despite declines in age-standardized rates, absolute cases increased substantially, highlighting demographic and regional disparities. Priority populations for targeted prevention include women aged ≥60 years and residents of high-SDI regions, as these strata accounted for the highest burden across incidence/prevalence/YLD rates.
24 May 2026
Read appraisal →Journal of neurology
External sensory cueing on gait in Parkinson's disease: a systematic review and network meta-analysis
OBJECTIVE: This systematic review and network meta-analysis compares the individual and synergistic efficacy of external sensory cueing modalities to establish a treatment hierarchy for optimizing spatiotemporal gait parameters in individuals with Parkinson's disease (PD). METHODS: A systematic literature search was conducted across Scopus, PubMed, Cochrane CENTRAL, and ClinicalTrials.gov. Randomized controlled trials (RCTs) evaluating visual, auditory, somatosensory, or combined cueing modalities in individuals with PD were eligible. Trials were required to compare at least two active cueing modalities or an active modality against a control condition and report gait velocity and/or stride length. RESULTS: Thirty-three RCTs (n = 1118 participants) were included. For gait velocity, somatosensory (MD = 0.12 m/s; 95% CI 0.04-0.19) and visual cues (MD = 0.11 m/s; 95% CI 0.02-0.20) demonstrated significant improvements over control. For stride length, visual (MD = 11.46 cm; 95% CI 6.40-16.51), somatosensory (MD = 10.74 cm; 95% CI 7.04-14.44), and auditory cues (MD = 5.25 cm; 95% CI 0.43-10.06) all yielded statistically significant benefits. P-score rankings identified somatosensory cues as the top-ranked modality for gait velocity (P-score = 0.71) and visual cues for stride length (P-score = 0.83). Global heterogeneity was substantial (I2 = 64-90%), and no significant publication bias was detected. CONCLUSIONS: External sensory cueing improves spatiotemporal gait parameters in PD, with somatosensory and visual cues emerging as the most effective modalities for enhancing gait velocity and stride length. Both modalities outperformed standalone auditory stimulation, providing a data-driven hierarchy to guide evidence-based cueing selection in clinical practice.
24 May 2026
Read appraisal →Studies in health technology and informatics
Design and Evaluation of an Episodic Guideline-Driven Decision Support Engine
Clinical guidelines (GLs) standardize care but are complex to implement. Most clinical decision support systems (CDSSs) assume continuous use, which does not reflect real-world episodic workflows. We developed and evaluated e-Picard, a CDSS providing GL-based recommendations for episodic, on-demand consultations, enabling prospective decision support informed by retrospective quality assessment. At runtime, e-Picard analyzes offline patient data, computes fuzzy-logic-based compliance, identifies missed actions and generates context-specific recommendations. The system was applied to longitudinal geriatric data managed under pressure ulcers (PU) and diabetes (DM) GLs. Manual technical validation using 3,110 PU and 12,538 DM data instances (43 PU and 82 DM patients) achieved ≥99% correctness and up to 98% completeness. Retrospective simulation on 1,000 patients per domain (57,860 PU and 100,940 DM data instances) demonstrated potential adherence improvements from 68%-69% to 89%-97% for PU and from 14%-15% to 60%-87% for DM, with higher consultation frequencies increasing compliance and reducing care variability. These results demonstrate that episodic CDSSs can deliver accurate, context-aware support even under intermittent use.
24 May 2026
Read appraisal →Journal of ethnopharmacology
Efficacy and safety of medicine-food homology Shenqi paste in older adults with diabetic sarcopenia: A randomized, double-blind, placebo-controlled trial
ETHNOPHARMACOLOGICAL RELEVANCE: Diabetic sarcopenia (DS), an emerging diabetes complication, poses an increasing challenge to the elderly population. Shenqi paste (SQP) consists of 8 medicine-food homology substances: Astragalus membranaceus (Fisch.) Bge., Codonopsis pilosula (Franch.) Nannf., Polygonatum sibiricum Red., Dioscorea opposita Thunb., Poria cocos (Schw.) Wolf, Eucommia ulmoides Oliv., Hordeum vulgare L., and Ophiopogon japonicus (L.f.) Ker Gawl. SQP has shown potential benefits for sarcopenia in clinical practice. Its efficacy and safety in DS have not been validated in a randomized controlled trial. AIM OF THE STUDY: This study aimed to evaluate the efficacy, safety, and limitations, and provide insights into methods and strategies for utilizing SQP in DS management. MATERIALS AND METHODS: In this randomized, double-blind, placebo-controlled trial, community-dwelling older adults (≥60 years) with DS and spleen-kidney deficiency syndrome were allocated 1:1 to receive either SQP or placebo paste for 12 weeks. Primary outcomes were skeletal muscle mass index (SMI), handgrip strength, and five-time chair stand test (FCST) duration. Secondary outcomes included health-related scales and biochemical indicators. Assessments were conducted at the baseline (T0), week 6 (T1), and week 12 (T2). Safety evaluation included blood routine, liver and kidney function tests. Intention-to-treat analysis was applied to the primary outcomes data from all participants, with per-protocol analysis for sensitivity. Generalized estimating equations with covariate adjustment and t-tests or Wilcoxon rank-sum tests were used for statistical analysis. RESULTS: Of 90 randomized participants, the SQP group showed greater improvements in handgrip strength (T2: interact p < 0.001), SMI (T2: interact p < 0.001), and FCST (T2: interact p < 0.001) versus control. Secondary outcome measures of 74 participants, including frailty, nutrition, physical activity, sleep quality, depression, and TCM syndrome scores, significantly improved (p < 0.05), with significant fasting blood glucose reduction (T2: interact p = 0.018) and C-reactive protein decrease (T2: interact p < 0.001). There was no statistically significant difference in the change in the difference between the two groups in fasting insulin, glycated hemoglobin and insulin resistance index before and after intervention (p > 0.05). Within-group analysis demonstrated significant reductions of SQP group in inflammatory cytokines, and sarcopenia-related biomarkers (p < 0.001) post-intervention. No serious adverse events occurred. CONCLUSION: A 12-week SQP intervention enhanced muscle mass, strength, function, and metabolic and inflammatory profiles in older adults with DS, with good tolerability, supporting its potential as a safe, complementary therapy.
23 May 2026
Read appraisal →Lancet (London, England)
Safety and efficacy of astegolimab for COPD with frequent exacerbations regardless of baseline blood eosinophil counts (ALIENTO and ARNASA): randomised, double-blind, placebo-controlled, phase 2b and 3 trials
BACKGROUND: Interleukin-33 and its receptor, ST2, are implicated in neutrophilic and eosinophilic inflammation during chronic obstructive pulmonary disease (COPD) exacerbations. We aimed to assess the efficacy and safety of astegolimab, an anti-ST2 human IgG2 monoclonal antibody, which were evaluated in two COPD pivotal trials. METHODS: In two randomised, double-blind, placebo-controlled trials (phase 2b ALIENTO and phase 3 ARNASA), current or former smokers with COPD and a history of frequent exacerbations, irrespective of baseline blood eosinophils, were randomly assigned (1:1:1; stratification by smoking status and region) to receive subcutaneous astegolimab 476 mg every 2 weeks, every 4 weeks, or placebo, alongside optimised inhaled maintenance therapy over 52 weeks. The primary endpoint (analysed in participants receiving one or more doses) was annualised rate of moderate or severe exacerbations. Missing data were considered similar to data from other participants in the same treatment group with the same baseline characteristics. The trials were registered with ClinicalTrials.gov (NCT05037929 and NCT05595642). FINDINGS: In ALIENTO, 1301 participants (astegolimab every 2 weeks, n=433; astegolimab every 4 weeks, n=437; and placebo, n=431) initiated treatment between Oct 5, 2021, and Feb 19, 2024. In ARNASA, 1375 participants (astegolimab every 2 weeks, n=459; astegolimab every 4 weeks, n=459; and placebo, n=457) initiated treatment between Jan 9, 2023, and June 25, 2024. Adjusted rate ratios versus placebo for the primary endpoint were 0·85 (95% CI 0·72-1·00; p=0·049) for astegolimab every 2 weeks and 0·93 (0·79-1·10; p=0·38) for astegolimab every 4 weeks in ALIENTO, and 0·85 (0·72-1·01; p=0·068) for astegolimab every 2 weeks and 0·82 (0·70-0·98; p=0·024) for astegolimab every 4 weeks in ARNASA. Adverse events were balanced between treatments, with most participants experiencing one or more adverse events (1093 [84·0%] of 1301 participants in ALIENTO and 1176 [85·5%] of 1375 in ARNASA). The most common non-COPD adverse event was nasopharyngitis in ALIENTO and upper respiratory chest infection in ARNASA. Deaths occurred in 40 (3·1%) of 1301 participants in ALIENTO and in 44 (3·2%) of 1375 participants in ARNASA, and were balanced across treatment groups. Across both trials, a total of three deaths (0·1%) were considered to be related to treatment by investigators. INTERPRETATION: In ALIENTO, astegolimab every 2 weeks was associated with a lower annual rate of exacerbations versus placebo in patients with COPD and a history of frequent exacerbations. In ARNASA, these findings did not meet statistical significance. Together, these findings suggest a role for targeting the ST2/IL-33 pathway to reduce the frequency of COPD exacerbations in patients with limited treatment options. FUNDING: Genentech, a member of the Roche Group, and F Hoffmann-La Roche.
23 May 2026
Read appraisal →JMIR human factors
Support, Monitoring, and Reminder Technology for Mild Dementia (SMART4MD) for People With Mild Cognitive Impairment and Their Informal Caregivers: Cost-Effectiveness Analysis
BACKGROUND: Support, Monitoring, and Reminder Technology for Mild Dementia (SMART4MD), a customized tablet app, was developed to improve or maintain the quality of life of people with mild cognitive impairment (MCI) and their informal caregivers. OBJECTIVE: This study conducts an 18-month economic evaluation of the SMART4MD app, in addition to standard care, compared with standard care alone in Sweden and Spain, from a health care provider perspective. METHODS: In a pragmatic randomized controlled trial, people with MCI and their informal caregivers were randomized to the intervention and control groups. Health care costs, quality-adjusted life-years (QALYs), and incremental cost-effectiveness ratios (ICERs) were measured in 345 Swedish people with MCI and their informal caregivers, and in 347 Spanish people with MCI. RESULTS: The analysis showed higher incremental costs and lower QALYs for Swedish people with MCI than for controls, whereas higher incremental costs and higher QALYs were observed for Spanish people with MCI. The intervention was not found to be cost-effective for Swedish informal caregivers, with an ICER of €78,000/QALY (€1=US $1.16). CONCLUSIONS: The differing findings regarding cost-effectiveness for people with MCI in Sweden and Spain highlight the need for further research with extended follow-up, ideally involving a larger sample size and conducted across different national contexts. TRIAL REGISTRATION: ClinicalTrials.gov NCT03325699; https://clinicaltrials.gov/ct2/show/NCT03325699.
23 May 2026
Read appraisal →PloS one
Comparison between two different concentrations of ropivacaine in pericapsular nerve group block for patients undergoing total hip arthroplasty: A randomized clinical trial
Effective pain control during spinal anesthesia positioning remains a clinical challenge in elderly patients undergoing total hip arthroplasty. The pericapsular nerve group block, a novel ultrasound-guided regional anesthesia technique, has shown promise for alleviating hip pain, yet the optimal concentration of local anesthetics for this approach remains unclear. In this randomized, observer-blinded clinical trial involving 102 patients, we compared the analgesic efficacy of two concentrations of ropivacaine (0.375% vs. 0.5%) administered via pericapsular nerve group block prior to spinal anesthesia. Patients receiving 0.5% ropivacaine reported significantly lower pain scores during positioning and demonstrated prolonged postoperative analgesia up to 48 hours, without a corresponding increase in adverse events. Both concentrations improved positioning quality, reduced opioid requirements, and enhanced recovery compared to controls who received standard intravenous analgesic supplementation (including preoperative midazolam and ketorolac, and postoperative sufentanil PCA), though the higher concentration yielded modest additional benefits. These findings suggest that a 0.5% ropivacaine pericapsular nerve group block may offer superior perioperative analgesia for hip arthroplasty without compromising safety, while highlighting the need to balance analgesic potency with potential motor effects. This study informs optimization of nerve block protocols to improve perioperative outcomes in a high-risk surgical population. This study informs optimization of nerve block protocols to improve perioperative outcomes in a high-risk surgical population. The trial was registered with the Chinese Clinical Trial Registry (registration number: ChiCTR2400089440).
23 May 2026
Read appraisal →Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
Prediction and diagnosis of post-stroke epilepsy using artificial intelligence approaches: a systematic review and meta-analysis
INTRODUCTION: Post-stroke epilepsy (PSE) is a common complication following a stroke and is a major cause of epilepsy in the elderly. Artificial intelligence (AI) is currently developing rapidly in the medical field and has a promising outlook in disease diagnosis, treatment, and prognosis. METHODS: We screened five studies that fully met the requirements from the PubMed, Web of Science, and EMBASE databases using relevant search terms such as PSE and AI, and analyzed the role of AI in predicting and diagnosing PSE in these studies. RESULTS: The results showed that the sensitivity of AI in predicting and diagnosing PSE was 88% (95% CI 0.78-0.94), and the specificity was 83% (95% CI 0.79-0.86). The area under the summary receiver operating characteristic (SROC) curve was 0.90 (95% CI 0.87-0.92). CONCLUSION: These results indicate that using AI to predict and assist in diagnosing PSE demonstrates high specificity and sensitivity, and has certain prospects in the future auxiliary diagnosis of PSE.
23 May 2026
Read appraisal →Journal of medical systems
Advances in Technology-Assisted Visual Rehabilitation Therapies: Scoping Review
Visual impairment (VI) is a growing condition associated with aging, neurological diseases, and chronic eye conditions. In 2023, more than 2.2 billion people experienced VI, and at least 1 billion could have been prevented or remain untreated. Visual rehabilitation (VR) is essential for optimizing residual vision and promoting functional independence; however, only a minority have access to these services. In recent years, the development of technologies such as artificial intelligence (AI), mobile applications, and virtual reality (VRt) has transformed VR, offering accessible and personalized alternatives. This scoping review aimed to map and categorize the available evidence on technology-assisted visual rehabilitation therapies, identifying the types of technologies used, their applications, and reported clinical and functional outcomes. A scoping review was conducted following the Joanna Briggs Institute (JBI) methodology and PRISMA-ScR guidelines to map the literature published between 2015 and 2025 on technology-assisted VR therapies in adults with VI worldwide. Twenty-five studies, mainly from developed countries, were included. Interventions included videoconferencing, computerized systems, portable devices, VRt, artificial vision (AV), and smart sensors. Overall, digital technologies were shown to improve visual function, autonomy, and quality of life, with high patient satisfaction and adherence. Telerehabilitation (TR), understood as the remote delivery of visual rehabilitation services through videoconferencing and digital monitoring platforms, has emerged as a viable and cost-effective strategy. In contrast, immersive virtual reality technologies (VRt), head-mounted display systems, and multisensory integration devices have demonstrated potential to stimulate neuroplasticity and enhance functional visual outcomes.Telerehabilitation platforms, immersive virtual reality systems, portable electronic vision enhancement devices, artificial vision technologies, and sensor-based monitoring tools are emerging as promising interventions. These technologies have demonstrated improvements in visual acuity, reading performance, visual field sensitivity, patient adherence, and functional autonomy, although most studies remain small-scale and geographically concentrated in high-income settings.
23 May 2026
Read appraisal →Journal of neurology
Effectiveness of telemedicine-based rehabilitation after stroke: a systematic review
BACKGROUND: Access to timely post-acute stroke rehabilitation remains inconsistent, contributing to persistent disability despite advances in acute stroke care. Telemedicine has been proposed as a strategy to improve continuity and access to rehabilitation services, but its effectiveness across functional domains remains incompletely defined. OBJECTIVE: To evaluate the effectiveness of telemedicine-delivered post-acute follow-up and rehabilitation on functional recovery and disability outcomes among adult stroke survivors. METHODS: A systematic review was conducted in accordance with PRISMA guidelines. PubMed, Embase, Scopus, and Web of Science were searched for studies published between January 2015 and December 2025. Eligible studies included adult stroke populations receiving telemedicine-based post-acute follow-up or rehabilitation and reporting functional, motor, balance, quality-of-life, or feasibility outcomes. Randomized controlled trials and observational studies were included. Risk of bias was assessed using validated tools, and findings were synthesized narratively without quantitative pooling due to heterogeneity. RESULTS: Twenty-eight studies comprising approximately 1237 participants were included. Telerehabilitation was associated with improvements in activities of daily living, although between-group differences varied across studies. Activities of daily living improved post-discharge (Barthel Index + 23.3 vs + 9.3; p < 0.001), and Functional Independence Measure scores increased in tele-CIMT and wearable-assisted interventions. Upper limb performance improved (WMFT time - 2.28 s; Box and Block Test + 5.57 vs 1.0; Nine-Hole Peg Test - 71.6 s; Jebsen-Taylor - 26.6 s). Spasticity decreased (MAS) with wrist improvements maintained at 6 months. Mobility and balance also improved, with intervention groups walking farther (6MWT 141.6 ± 8.7 m vs 129.5 ± 7.1 m; p < 0.001) and showing comparable improvements in BBS outcomes across groups. Technology-assisted telerehabilitation programs were more frequently associated with statistically significant improvements; however, these findings were not consistently superior to standard rehabilitation across all studies. Most randomized trials had some concerns for bias, and half of non-randomized studies were rated as serious risk, limiting overall certainty. CONCLUSIONS: Telemedicine-based post-acute stroke rehabilitation is feasible and associated with functional improvements comparable to in-person care. Reported adverse events were infrequent and generally minor, though safety outcomes were inconsistently measured across studies. High feasibility, adherence, and patient satisfaction support its role as a scalable component of post-stroke rehabilitation pathways.
23 May 2026
Read appraisal →BMJ evidence-based medicine
Tramadol versus placebo for chronic pain: a systematic review with meta-analysis and trial sequential analysis
OBJECTIVES: The objective of our study was to assess the benefits and harms of tramadol vs placebo in adults with chronic pain. DESIGN: The research method was a systematic review of randomised clinical trials with meta-analysis. The review followed the Trial Sequential Analysis and the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach. DATA SOURCES: The Cochrane Library, MEDLINE, Embase, Science Citation Index and BIOSIS were searched for trials published from inception to 6 February 2025. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: Studies were eligible for inclusion if they were published and unpublished randomised clinical trials comparing tramadol vs placebo in adults with any type of chronic pain. Risk of bias was assessed according to the Cochrane Handbook for Systematic Reviews of Interventions. MAIN OUTCOME MEASURES: The main outcome measures were pain level, adverse events, quality of life, dependence, abuse and depressive symptoms. RESULTS: We included 19 randomised placebo-controlled clinical trials enrolling 6506 participants. All outcome results were at high risk of bias. Meta-analysis and Trial Sequential Analysis showed evidence of a beneficial effect of tramadol on chronic pain (mean difference numerical rating scale (NRS) -0.93 points; 97.5% CI -1.26 to -0.60; p<0.0001; low certainty of evidence). However, the effect size was below our predefined minimal important difference of 1.0 point on NRS. Beta binomial regression showed evidence of a harmful effect of tramadol on serious adverse events (OR 2.13; 97.5% CI 1.29 to 3.51; p=0.001; moderate certainty of evidence), mainly driven by a higher proportion of cardiac events and neoplasms. It was not possible to conduct a meta-analysis of the quality of life due to a lack of data. Meta-analysis and Trial Sequential Analysis showed that tramadol increased the risk of several non-serious adverse events including nausea (number needed to harm (NNH) 7), dizziness (NNH 8), constipation (NNH 9), and somnolence (NNH 13) (all very low certainty of evidence). CONCLUSION: Tramadol may have a slight effect on reducing chronic pain levels (low certainty of evidence) while likely increasing the risk of both serious (moderate certainty of evidence) and non-serious adverse events (very low certainty of evidence). The potential harms associated with tramadol use for pain management likely outweigh its limited benefits.
23 May 2026
Read appraisal →JMIR mHealth and uHealth
mHealth-Supported Exercise Rehabilitation to Reverse Frailty After Autologous Transplantation in Multiple Myeloma: Randomized Controlled Trial
BACKGROUND: Frailty is highly prevalent in survivors of multiple myeloma (MM) after autologous hematopoietic cell transplantation and is associated with poor functional recovery and adverse clinical outcomes. Although exercise is known to improve physical function, traditional center-based rehabilitation models are often inaccessible to this population during early posttransplant recovery. Mobile health (mHealth)-supported exercise may offer a scalable alternative; however, evidence in hematologic malignancies remains limited. OBJECTIVE: This study aimed to evaluate the effects of a 16-week mHealth-supported exercise rehabilitation program on frailty phenotype and physical function in survivors of MM within 180 days after autologous hematopoietic cell transplantation. METHODS: In this single-center randomized controlled trial, participants who self-reported as prefrail or frail were randomized 1:1 to an mHealth-supported exercise group (n=16) or usual care control (n=16). Remote assessments were conducted at baseline (week 0), midpoint (week 9), and follow-up (week 17). The intervention consisted of 8 weeks of supervised tele-exercise (3 sessions/week, 50 minutes/session), followed by 8 weeks of independent home-based exercise using the same mHealth platform. Exercise intensity was prescribed using a repetitions-in-reserve-based rating of perceived exertion approach with symptom-guided progression. The primary outcome was change in the 5-component Fried frailty phenotype score (0-5). Secondary outcomes included Short Physical Performance Battery components, chair stand time, gait speed, and handgrip strength. Intention-to-treat analyses were conducted using generalized estimating equations to evaluate between-group differences over time. RESULTS: Participants had a mean age of 64.6 (SD 7.1) years and were enrolled a mean of 136 (SD 36.3) days posttransplant. At baseline, 94% (30/32) of participants were classified as frail. Adherence to the supervised sessions was 85% (326/384 sessions), and adherence during the unsupervised phase was 78% (298/384 sessions). The exercise group demonstrated a significantly greater reduction in frailty score compared with control from baseline to week 17 (P<.001). Between-group difference estimates showed a clinically meaningful improvement favoring exercise at both week 9 and week 17 (P<.001). Chair stand time improved significantly in the exercise group compared with control, with faster completion times observed at week 9 and sustained through week 17 (P=.002). Improvements in other Short Physical Performance Battery components and handgrip strength favored the exercise group but did not reach statistical significance. No serious adverse events occurred. CONCLUSIONS: A 16-week mHealth-supported, progressively prescribed exercise rehabilitation program was feasible, safe, and effective in reversing frailty phenotype and improving functional mobility in survivors of MM early after autologous transplantation. This approach provides a scalable model for delivering structured rehabilitation during a high-risk recovery window. Larger trials incorporating attention-matched controls and longer follow-up are warranted. TRIAL REGISTRATION: ClinicalTrials.gov NCT05142371; https://clinicaltrials.gov/study/NCT05142371.
22 May 2026
Read appraisal →Surgical and radiologic anatomy : SRA
Proximal femoral morphometry and its association with hip fracture patterns: a systematic review with meta-analysis
PURPOSES: Proximal femoral fractures (PFFs) represent a major cause of morbidity and mortality in the elderly population. Beyond bone mineral density, proximal femoral (PF) and hip joint (HJ) morphometry have been implicated in fracture susceptibility and fracture pattern. However, the relative contribution of individual geometric parameters remains incompletely defined. The purpose of this study was to systematically evaluate the association between PF and HJ morphometry and hip fracture (HF) patterns, and to assess the predictive value of radiographic morphometric parameters derived from standard anteroposterior radiographs. MATERIALS AND METHODS: A systematic review with meta-analysis was conducted in accordance with PRISMA guidelines. PubMed and MEDLINE databases were searched up to September 2025 for observational studies reporting radiographic morphometric parameters in patients with unilateral PFFs. Statistical meta-analysis was performed with random-effect models to compare intracapsular-ICF/subcapital-SCF fractures with intertrochanteric (ITF) and extracapsular (ECF) fracture patterns. RESULTS: Twenty-two studies comprising 4184 patients (77.8% female; pooled mean age 77.4 years) were included. Meta-analysis using a random-effects model revealed that ITF were significantly associated with a smaller femoral head diameter (p < 0.001) and increased medial neck cortex thickness (p = 0.021) compared with ICF. ECF demonstrated significantly longer femoral neck axis length (p = 0.012), increased horizontal offset (p = 0.048), smaller absolute offset (p = 0.026) and shorter hip axis length (p = 0.026) than ICFs. Angular parameters provided the most robust stratification. ITFs and ECFs exhibited significantly lower (more varus) neck-shaft angles (p < 0.001) and significantly higher Wiberg angles (p < 0.001) compared with the ICF group. CONCLUSIONS: PF and HJ morphometry are associated with distinct HF patterns, independently of bone mineral density. Simple linear and angular measurements obtained from standard anteroposterior radiographs may assist fracture pattern stratification, risk assessment, and preoperative planning in elderly patients.
22 May 2026
Read appraisal →PloS one
Effect of an herbal supplement on quality of life in participants with insomnia: A randomized placebo controlled cross-over pilot trial
BACKGROUND: Insomnia is a common sleep disorder linked to poor quality of life. Patients with insomnia frequently rely on herbal supplements to improve sleep and quality of life; however, most supplements do not undergo rigorous testing to determine their effectiveness. The primary objective of the pilot trial was to test the effectiveness of the herbal supplement on sleep and quality of life. METHODS: In a double-blind, randomized, placebo-controlled, crossover pilot trial, participants were assigned study drinks consisting of the herbal supplement containing extracts from chicory and green tea or placebo for 7 days. This was followed by a crossover to the other arm after 2 weeks of a washout period. Participants were healthy individuals of the age of 50 years or more with insomnia. Study measures included subjective assessments of sleep, daytime sleepiness, fatigue, and quality of life via questionnaires. Habitual sleep patterns and sleep architecture were objectively evaluated by a 7-day accelerometry and polysomnography. RESULTS: Ten participants (9 women; age 59 ± 6 y; BMI 30 ± 10 kg/m2) with insomnia severity index of 21 ± 4 and wake after sleep onset (WASO) of 75 ± 33 min were enrolled in the study. The impact of placebo and supplement was different for measures related to daytime sleepiness and physical fatigue but not quality of life. No within and between treatment differences were observed for any objective measures of habitual sleep patterns or sleep architecture. Within group analysis showed improvement in subjective sleep quality, daytime sleepiness, and general fatigue after 7-day consumption of the placebo drink, but not after consumption of herbal supplement. There were no adverse events related to study participation. CONCLUSION: Compared to placebo drink, daily consumption of the herbal supplement for 7 days did not improve any aspect of quality of life or sleep in our pilot study participants with insomnia. Well controlled clinical trials are needed to confirm the claims related to supplements.
22 May 2026
Read appraisal →BMJ (Clinical research ed.)
Calcium, vitamin D, or combined supplementation to prevent fractures and falls: systematic review and meta-analysis
OBJECTIVE: To assess the effect of calcium, vitamin D, or combined supplementation on fractures and falls in adults. DESIGN: Systematic review and meta-analysis. DATA SOURCES: Trials included in systematic reviews from 2014, three databases (Medline, Embase, CENTRAL) to 19 February 2025, clinical trial registries, abstracts from scientific meetings, and references from included studies. ELIGIBILITY CRITERIA: Randomised controlled trials comparing calcium, vitamin D, or combined supplementation with placebo or no treatment in adults (≥18 years) not receiving drug treatment for osteoporosis. DATA EXTRACTION AND SYNTHESIS: The primary outcome was the risk of any fracture. Secondary outcomes included the risk of hip fracture, non-vertebral fracture, vertebral fracture, and falling, as well as the total number of falls. Pairs of reviewers independently screened trials, extracted data, and assessed risk of bias using the second version of Cochrane's risk of bias tool. Findings were synthesised using random effects meta-analyses and appraised using Grading of Recommendations Assessment, Development and Evaluation, with application of thresholds for absolute effects considered important. RESULTS: This review included 69 trials involving 153 902 participants. Participants in most of the trials were community dwelling (87%) and not at high risk of fractures or falls (73%). For the primary outcome of any fracture, little to no effect was found from use of calcium supplements (11 trials, 9067 participants; risk ratio 0.91, 95% confidence interval 0.81 to 1.01; moderate certainty), vitamin D supplements (36 trials, 92 045 participants; 1.00, 0.95 to 1.06; high certainty), or combined supplementation (15 trials, 51 126 participants; 0.91, 0.84 to 0.99; high certainty). Calcium, vitamin D, or combined supplementation appeared to have little to no effect on other fracture and fall outcomes, based largely on moderate to high certainty of evidence. The findings remained robust after an extensive exploration of heterogeneity across multiple subgroup analyses. Evidence for high risk patients or those requiring residential care was limited for many outcomes for calcium monotherapy and for combined supplementation. CONCLUSION: Based on absolute risk reductions and thresholds considered clinically meaningful, this review found little to no benefits from use of calcium, vitamin D, or combined supplementation on the prevention of fractures and falls. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42023483915.
22 May 2026
Read appraisal →JMIR formative research
Comparing Stakeholders' Perspectives on Parkinson Disease Management and Digital Technologies: Exploratory International Survey
BACKGROUND: Parkinson disease (PD) is a progressive neurodegenerative disorder that poses complex challenges for persons with PD, informal caregivers, and health care professionals. With growing interest in digital and predictive artificial intelligence (AI) tools for disease management, understanding the needs and digital readiness of these stakeholder groups is crucial. OBJECTIVE: This work aims to (1) identify digital practices for PD management among persons with PD, at-risk individuals, caregivers, and health care professionals; (2) compare these practices across groups; (3) explore stakeholder desires for AI-based tools; and (4) assess alignments and gaps to inform tailored AI solutions. METHODS: An anonymous cross-sectional online survey of an exploratory nature was distributed (from December 2024 to October 2025) in 5 languages and completed by 255 respondents. Descriptive statistics summarized responses to 41 questions, including stakeholder-specific items. χ2 tests were performed to examine stakeholder differences in desired AI features. RESULTS: Interest in predictive AI was high across stakeholder groups. Symptom tracking was the most desired feature (selected by more than 76% of the respondents), and personalized treatment recommendations came second for both persons with PD and health care professionals; however, stakeholder priorities diverged in other areas. Health care professionals rated improving patient and informal caregiver engagement as significantly more important than persons with PD did, χ21 (n=205)=34.78, P<.001, and Cramer V=0.41. Despite considerable interest, the reported use of digital tools was limited, as most persons with PD did not use symptom-tracking apps or wearables, nor were they currently monitoring their condition, although many expressed intentions to begin. CONCLUSIONS: While predictive AI tools were viewed positively across groups, there were significant gaps in stakeholder preferences, highlighting the importance of tailored, context-aware design. Early diagnosis was not prioritized by persons with PD or health care professionals, likely reflecting the complexity of diagnosing PD in the absence of disease-modifying therapies. Coupled with the emphasis placed on preventive lifestyle guidance by persons with PD and those at risk, this highlights the importance of actionability in AI-based monitoring and prediction. Such actionability may also enhance perceived relevance and uptake, given that reported interest in digital health tools and self-tracking exceeded actual use. These findings offer early-stage insight to guide the development of future AI-based solutions for PD.
21 May 2026
Read appraisal →Scientific reports
Use of machine learning and voice for multiclass classification of Parkinson's disease, chronic obstructive pulmonary disease, and healthy controls.
Parkinson's disease (PD) and chronic obstructive pulmonary disease (COPD) are prevalent conditions with substantial impact on quality of life and health care systems. Both disorders affect voice production through different physiological mechanisms, yet neither condition has a widely adopted objective biomarker for routine clinical use. Voice analysis has emerged as a non-invasive digital biomarker candidate, but existing studies have largely focused on binary classification within a single disorder or language. This study aimed to evaluate whether an unified multiclass machine learning (ML) framework applied to sustained vowel "a" phonation can discriminate between PD, COPD, and healthy controls (HC) across linguistically distinct cohorts. Sustained vowel recordings were analyzed from Swedish speaking individuals with COPD and HC, and English-speaking individuals with PD and HC, collected under comparable mobile recording conditions. Acoustic features included baseline voice measures and Mel Frequency Cepstral Coefficients. A soft voting ML framework integrating support vector machine, random forest, CatBoost, and light gradient boosting classifiers was trained using nested cross validation with hyperparameter optimization. Data were partitioned at the participant level into a development cohort and an independent test cohort. Model performance was evaluated using accuracy, macro averaged precision, recall, F1 score, receiver operating characteristic analysis, and confusion matrices. Model interpretability was assessed using Shapley additive explanations and vowel space analysis. The final soft voting classifier achieved robust multiclass discrimination on the participant disjoint independent test set, with an overall accuracy of 0.842 and a macro averaged F1 score of 0.839. Classification performance differed across groups, with the highest performance observed for PD, intermediate performance for HC, and lower performance for COPD. Misclassifications occurred primarily between HC and COPD, while confusion between PD and COPD was minimal. Feature attribution analysis revealed class dependent relevance patterns, and vowel space analysis demonstrated subtle but consistent group level differences. These findings demonstrate the feasibility of using an explainable soft voting machine learning framework applied to sustained vowel phonation to distinguish between neurologically and respiratory driven voice impairments across linguistic contexts. The study supports voice as a promising digital biomarker modality for multiclass clinical discrimination using mobile recordings.
21 May 2026
Read appraisal →Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
Parkinson disease severity detection based On OPtFuzNet with fused features
BACKGROUND: Parkinson's disease (PD) is a progressive neurodegenerative disorder affecting millions of people worldwide. It severely impairs patients' mobility. For effective treatment strategies, it is essential to determine the severity of the disease at an early stage. METHODS: In this study, an optimized feature-fusion framework is developed to detect the severity of Parkinson's disease using gait data via an Optimized Fuzzy Neural Network (OPtFuzNet). This feature-fusion framework can be categorized into three stages: pre-processing, feature extraction, and classification. In the pre-processing stage, a median filter is employed to reduce noise present in the gait images. During feature extraction, local and global features of the gait images are captured using SAE and IDCN, respectively. Subsequently, these features are fused and fed into the OPtFuzNet classifier, where parameter optimization is performed using IGWO-Lévy. RESULTS: The proposed model is evaluated on the GAIT-IT and GAIT-IST benchmark datasets using standard metrics, achieving superior performance with accuracies of 98.08% and 98.12%, respectively. Furthermore, a feature importance analysis is conducted to identify the most influential gait characteristics that contribute to determining the severity of the impairment, thereby enhancing the clinical interpretability of the model.The proposed model is evaluated on the GAIT-IT and GAIT-IST benchmark datasets using standard metrics, achieving superior performance with accuracies of 98.08% and 98.12%, respectively. Furthermore, a feature importance analysis is conducted to identify the most influential gait characteristics that contribute to determining the severity of the impairment, thereby enhancing the clinical interpretability of the model. CONCLUSION: These results validate the efficacy of the proposed feature fusion and optimization strategy in identifying discriminative gait patterns associated with the severity of Parkinson's disease. However, as the datasets are based on simulated gait patterns rather than real clinical data, further validation on real-world datasets is necessary for practical applicability.
20 May 2026
Read appraisal →Journal of the American Heart Association
Association of Edoxaban Pharmacokinetics With Bleeding in Older Patients With Atrial Fibrillation Treated With Low-Dose Edoxaban: A Post Hoc Analysis of a Randomized Controlled Trial
BACKGROUND: Edoxaban is a direct oral anticoagulant used for stroke prevention in patients with atrial fibrillation (AF) and treatment of venous thromboembolism. We examined the relationship between trough edoxaban concentrations (E-trough) and prothrombin time (PT) and major bleeding in very old Japanese patients with atrial fibrillation and high bleeding risk. METHODS: In this post hoc analysis of the multicenter, randomized, double-blind, placebo-controlled ELDERCARE-AF (Study of DU-176b Aged 80 Years or Older) trial, patients were randomly assigned 1:1 to edoxaban 15 mg or placebo once daily. After 8 weeks of treatment, E-trough was determined and the incidence of major bleeding examined in each quartile. The incidence of major bleeding by PT was also examined. RESULTS: Data were obtained from 427 patients. E-trough (ng/mL) was ≤9.24 in the first quartile (107 patients), >9.24 to ≤13.6 in the second (111 patients), >13.6 to ≤21.6 in the third (103 patients), and >21.6 in the fourth (106 patients). Older age, lower body weight, lower creatinine clearance, and the presence of congestive heart failure were independent predictors of higher E-trough. Higher E-trough was associated with greater incidence of major bleeding (0.8%, 1.9%, 3.4%, and 4.7%/year, respectively, P=0.0408). There was a significant positive correlation between E-trough and PT (r=0.426, P<0.0001). Significantly more major bleeding events occurred in the longer (>13 seconds) versus shorter (≤13 seconds) PT subgroups (4.61% versus 0.98%/year, P=0.0060). CONCLUSIONS: Several factors were associated with higher E-trough and increased risk of major bleeding events. PT >13 seconds may be a useful predictor of the development of major bleeding. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT02801669.
20 May 2026
Read appraisal →JAMA
Using Serious Games to Increase the Implementation of Trauma Triage Guidelines: A Randomized Clinical Trial
IMPORTANCE: Low-cost methods that improve physician adherence to clinical guidelines are lacking. Trauma triage exemplifies time-sensitive conditions where guideline adherence remains at 50% or less despite stakeholder efforts, particularly when treating older adults. OBJECTIVE: To determine whether a theory-based serious game (a purpose-driven video game) improves emergency physician adherence to trauma triage guidelines for older adults. DESIGN, SETTING, AND PARTICIPANTS: This randomized clinical trial recruited 800 emergency physicians responsible for the triage of Medicare fee-for-service injured patients (≥65 years of age) at emergency departments within nontrauma centers in the US. Physician enrollment began November 27, 2023, and concluded February 7, 2024. Data collection began January 1, 2024 (or the date of enrollment), and ended February 6, 2025. The patients were treated during the 1 year after physician enrollment. INTERVENTIONS: Game-based training on a tablet for an initial session of 2 hours and then a 20-minute session each quarter (4 doses; n = 400 physicians) vs usual education (n = 400 physicians). MAIN OUTCOMES AND MEASURES: The primary outcome was undertriage (defined as the proportion of severely injured patients not transferred to trauma centers) during the year after randomization. The secondary outcomes were overtriage (defined as the proportion of transferred patients with minor injuries) and a composite clinical outcome of 30-day mortality or hospital readmission. Medicare fee-for-service claims were analyzed using mixed-effects regression models. RESULTS: Of the 800 physicians, the length of experience was a median of 10 (IQR, 6-17) years in practice, 71% were male (563/789), and 750 (94%) had completed Advanced Trauma Life Support. The physicians worked at 1147 hospitals and treated 41 073 injured Medicare patients, of whom 1738 (4.2%) had severe injuries. Of the physicians in the game-based training (intervention) group, 99% (397/399) received 1 dose and 67% (268/399) received 4 doses. Physicians in the game-based training group had a lower proportion of severely injured older adults who were undertriaged (49%; 402/819) compared with the usual education (control) group (57%; 527/919) (model-adjusted difference, -7% [95% CI, -13% to -0.8%]; P = .02). The model-adjusted difference did not differ for the outcomes of overtriage (-3% [95% CI, -6% to 1%]; P = .14) and the composite outcome (-0.4% [95% CI, -5% to 4%]; P = .87). CONCLUSIONS AND RELEVANCE: Among physicians responsible for the triage of patients in emergency departments of nontrauma centers, use of a theory-based serious game decreased the proportion of severely injured patients who were undertriaged. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT06063434.
20 May 2026
Read appraisal →BMC public health
Interventions against loneliness and social isolation in older adults- a systematic review.
BACKGROUND: Loneliness is a growing health and social challenge. The COVID-19 pandemic boosted feelings of loneliness for many people. To combat loneliness, a wide range of interventions have been developed and evaluated for their efficacy and effectiveness. As a result, many new interventions to alleviate loneliness have been developed, especially technological interventions. Therefore, the objective of this study was to conduct a comprehensive and up-to-date systematic review to identify interventions aimed at loneliness and social isolation among adults, 60 years and older published during or post-pandemic. METHODS: A comprehensive literature search was conducted for studies between 2020 and 2024 across five online databases: MEDLINE via PubMed, Web of Science, Scopus, Cochrane Library, and CINAHL. Title and abstract screening, critical appraisal of the studies, and data extraction were performed by two independent reviewers. A narrative approach was adopted to assess and integrate the diverse findings from the research. RESULTS: Ultimately, 79 studies were included in this systematic review. The results are structured based on the categorization of the interventions, which includes differentiation between analogue interventions, technological interventions and multicomponent (analogue and technological) interventions. The effectiveness of analogue interventions, particularly community-based interventions such as group meetings, social participation programs, and educational or psychological interventions, tended to be superior to that of technological interventions. CONCLUSIONS: The combination of analogue and technological interventions in particular produced promising results regarding a decrease of loneliness. Specific interventions must be tailored to the target group and setting and regularly reevaluated. The aim now should be to implement these interventions comprehensively and monitor their effectiveness over several years. Future research should focus on differentiating the circumstances under which various forms of intervention are effective. TRIAL REGISTRATION: PROSPERO systematic review registration: CRD42024538755.
20 May 2026
Read appraisal →PloS one
Facilitating the measurement and treatment of Behavioral and Psychological Symptoms of Dementia (BPSD) and understanding caregiver burden using wearable devices in Rural Taiwan-Protocol for a dyadic feasibility pilot study
INTRODUCTION: Alzheimer's disease (AD) prevalence rises with societal ageing. In clinical care, behavioral and psychological symptoms of dementia (BPSD)-including depression, agitation/aggression, apathy, and sleep disturbance-worsen patients' quality of life and substantially increase caregiver burden, more significantly than the cognitive symptoms. Standard BPSD assessments rely on caregiver-rated questionnaires that are cross-sectional and may be biased when caregivers are themselves older adults. Device-based measures (e.g., research-grade wrist actigraphy) can provide objective longitudinal data and novel features. In parallel, therapeutic wearables may improve sleep and mood in adults, and might improve BPSD if accepted by people living with dementia. METHODS: This dyadic pilot study will recruit 20 participants (n = 10 AD patients; n = 10 caregivers) from outpatient services and affiliated day-care/dementia hubs in rural Taiwan. Participants will wear Geneactiv continuously for 8 weeks and Re-Timer ≥30 min/day for 4 weeks. Device-based data will be processed with GGIR, a well validated R-package designed for processing accelerometer data. Questionnaire assessments include Pittsburgh Sleep Quality Index (PSQI), Neuropsychiatry Inventory Questionnaire (NPI-Q), Caregiver Burden Inventory (CBI), and a semi-structured interview based on the Taiwanese version of Quebec User Evaluation of Satisfaction with Assistive Technology (T-QUEST) at prespecified timepoints. DISCUSSION: Wearable devices may facilitate the measurement and treatment of specific BPSD, as well as reduce caregiver burden. If proven feasible even in rural Taiwan where both digital and health literacy and resources are limited, this model will inform how device-based dementia care model can be considered and applied in the context of global ageing.
19 May 2026
Read appraisal →Journal of medical Internet research
Efficacy of Digital Speech Therapy for Poststroke Dysarthria: Randomized Noninferiority Trial
BACKGROUND: Poststroke dysarthria, a common speech impairment, affects up to half of all stroke survivors, often reducing their ability to communicate, and adversely affecting their quality of life. Although conventional speech therapy for poststroke dysarthria is effective, access is often limited by time and geographical constraints. Here, digital speech therapy may serve as a remotely deliverable alternative for selected patients. However, few trials have assessed its efficacy, safety, and usability. OBJECTIVE: This study aimed to evaluate whether a smartphone-based speech therapy app is noninferior to conventional workbook-based therapy in improving speech intelligibility among individuals with poststroke dysarthria. METHODS: This single-blind, randomized controlled, noninferiority trial was performed at 3 hospitals in South Korea. Adults (≥19 y) with poststroke dysarthria who were cognitively intact, without aphasia, and able to use a smartphone were eligible. Participants were enrolled between July 20, 2023, and April 15, 2024. Participants were randomly assigned (1:1), stratified by stroke phase, using a block randomization method, to receive either a smartphone-based digital therapy app or a conventional workbook-based therapy for 4 weeks. The primary outcome was speech intelligibility (0-100 perceptual rating) after the intervention. Primary analysis was intention-to-treat using analysis of covariance. A noninferiority margin of 19 points was pre-defined. RESULTS: A total of 73 participants were enrolled (median age 62.00 years). Among them, 38 were assigned to the digital speech therapy group and 35 to the control group. Intelligibility scores improved from 80.48 (SD 18.92) to 92.08 (SD 12.38) in the intervention group, and from 80.94 (SD 16.74) to 88.11 (SD 18.06) in the control group. The adjusted between-group difference was 4.49 (95% CI 0.61-8.37), and the lower bound of the 95% CI was above the prespecified noninferiority margin (-19), which supported noninferiority. No significant between-group differences were observed in the secondary outcomes related to speech function or psychological status. The system usability score was 89.6, and adherence in the digital speech therapy group was 64.6% based on app logs, with no treatment-related adverse events. CONCLUSIONS: Digital speech therapy was noninferior to conventional workbook-based therapy in improving speech intelligibility and was feasible across acute to early subacute and chronic stroke phases in cognitively intact stroke survivors with predominantly mild-to-moderate dysarthria. However, feasibility and efficacy in older stroke survivors with cognitive deficits or co-occurring aphasia, or in those unable to use smartphones, remain to be established.
19 May 2026
Read appraisal →Drug discoveries & therapeutics
Trends in the prescription of constipation medications in Japan (fiscal years 2019-2023): A nationwide baseline study prior to the 2023 clinical guidelines.
Constipation is a common gastrointestinal disorder that markedly affects health and quality of life. In Japan, magnesium-based and contact laxatives have long been widely prescribed. Since the launch of lubiprostone in November 2012, several new agents with novel mechanisms have been introduced, and their use has increased. However, to our knowledge, no nationwide study has examined their use. This study aimed to analyze nationwide trends and regional differences in the prescription of medications for constipation. We used data from the Japanese National Health Insurance Claims and Specific Health Checkup Database Open Data from fiscal years (FYs) 2019 to 2023, covering the period when all currently available constipation medications were on the market in Japan, and prior to the inclusion of the drug selection flowchart in the Evidence-Based Clinical Guidelines for Chronic Constipation 2023. Prescription trends were evaluated using defined daily doses per 1,000 inhabitants per day (DID), and regional variations were assessed using standardized claim ratios (SCRs). The DID for contact laxatives decreased during the study period, whereas the DID for other constipation medications increased. Magnesium oxide showed the smallest regional variation, whereas polyethylene glycol and linaclotide showed the largest variations in FY 2019 and FY 2023, respectively. Throughout the 5-year period, the highest SCRs for contact laxatives were observed in Akita, Iwate, and Aomori prefectures. Our findings provide a comprehensive nationwide picture of evolving constipation treatment patterns and regional variations in Japan, establishing baseline data for evaluating the impact of guideline dissemination and evidence accumulation on clinical practice.
18 May 2026
Read appraisal →Journal of molecular neuroscience : MN
Transcriptomic Analysis and Multiple Machine Learning Approaches Identify ZDHHC20 and Its Highly Correlated Gene AK5 as Biomarkers in Multiple System Atrophy
Multiple system atrophy (MSA) is a fatal neurodegenerative disorder lacking effective diagnostic tools. While protein palmitoylation is crucial for neuronal function, its specific role in MSA pathogenesis remains unexplored. We integrated bulk and single-nucleus RNA sequencing (snRNA-seq) data from postmortem MSA brain tissues. Eight machine learning algorithms were utilized to screen palmitoylation-related genes. Downstream analyses, including functional enrichment, cellular deconvolution, and pseudotime trajectory inference, were then conducted. ZDHHC20 and its highly correlated gene, AK5, were identified as hub genes. Both demonstrated significant downregulation in MSA, particularly within the cerebellar white matter. Functional enrichment analysis linked this expression pattern to mitochondrial dysfunction and impaired energy metabolism. Furthermore, snRNA-seq revealed that ZDHHC20 and AK5 are predominantly expressed in oligodendrocytes and exhibit lower expression levels along the developmental trajectory in MSA compared to healthy controls. ZDHHC20 and AK5 represent promising biomarkers for MSA. These findings provide new insights into the diagnosis and treatment of MSA.
18 May 2026
Read appraisal →Journal of the International Society of Sports Nutrition
Creatine monohydrate for lean mass, strength, and bone density in postmenopausal women: a systematic review and meta-analysis
BACKGROUND: Menopause is accompanied by accelerated losses in muscle mass and strength and declining bone density. Whether creatine monohydrate benefits postmenopausal women are uncertain. METHODS: We systematically reviewed randomized, placebo-controlled trials examining creatine supplementation, with or without resistance training (RT), in postmenopausal women. MEDLINE, Embase, Scopus, Web of Science, SPORTDiscus, and Cochrane CENTRAL were searched from 2000 to August 2025, supplemented by trial registries and reference-list screening. Eligible studies included postmenopausal women aged ≥40-45 years, intervention durations ≥6 weeks for primary analyses, and outcomes including DXA-derived lean mass, one-repetition maximum (1RM) strength, bone mineral density, physical function, and safety. Dual screening, duplicate extraction, and Cochrane RoB 2 assessment were performed. Random-effects meta-analysis used the Paule-Mandel estimator for τ² with Hartung-Knapp-Sidik-Jonkman adjustment. Heterogeneity (τ², I²), 95% prediction intervals, subgroup analyses by RT status, exploratory dose/duration meta-regression, small-study effects, and GRADE certainty were assessed. RESULTS: Seven RCTs (n = 608 randomized; duration 12-104 weeks, median 38 weeks) enrolled postmenopausal women (mean age ≈ 62 y). Lean mass (k = 5; n = 338) favored creatine: mean difference (MD) + 0.37 kg (95% CI + 0.05 to + 0.69; I² = 25%; τ² = 0.01; 95% PI -0.10 to + 0.84). Leg-press 1RM (k = 3; n = 111) improved with creatine: MD + 7.5 kg (95% CI + 2.2 to + 12.8; I² = 0%). Benefits were evident when creatine ≥ 5 g·day⁻¹ was combined with RT; trials using ≤ 3 g·day⁻¹ without RT showed no measurable effect. Bone density was unchanged overall. Adverse events were mild and similar to placebo; renal indices were unchanged. Risk of bias was mostly "some concerns;" one large, preregistered, double-blind RCT was at low risk. CONCLUSIONS: In postmenopausal women, creatine, particularly ≥ 5 g·day⁻¹ with RT, yields small but meaningful gains in lean mass and strength without evidence of harm. Effects on bone density remain unclear.Registration: This review was not prospectively registered. De-identified data and supplementary materials were deposited on OSF after completion of the analysis (DOI: 10.17605/OSF.IO/BVTRZ).
18 May 2026
Read appraisal →Computers in biology and medicine
LADNET: An MRI-based deep learning approach for Alzheimer's disease detection.
Alzheimer's disease (AD) is considered a leading form of dementia, found in the majority of individuals, and it accounts for the highest prevalence of all the types of dementia. It is a gradually progressing condition which can begin with mild memory impairment and may lead to the inability to engage in meaningful conversations in the future. In addition to that, it is also capable of reacting appropriately to the environment around it. According to official data, there has been an increase in reported cases of AD-related mortality in recent years, along with rising rates of the disease itself. Therefore, it is beneficial for patients to be diagnosed with AD as early as possible in order to maximize their chance of survival. In medical field, deep learning (DL) is employed in many applications, especially the identification of Alzheimer's disease, has shown considerable success. Based on the fact that these methods are capable of acquiring and extracting features from extensive datasets autonomously, they are highly suitable for analyzing intricate medical images with the aid of these methods. Design the Lightweight Alzheimer's Disease Net (LADNET) model for the current task. Accurate detection of AD using magnetic resonance imaging (MRI)-based scans is presented in this work. The model classifies the three dementia types, namely Mild Dementia (MD), Moderate Dementia (MDD), and Very Mild Dementia (VMD), while also accurately categorizing the non-demented (ND) category based on the severity of the condition. We measure the performance of the model we recommend against a publicly available Kaggle dataset that contains over six thousand images from four different types of images. LADNET model achieves 99.4% accuracy and 99% AUC, outperforming existing methods. With a lightweight design of approximately 1.2 million parameters and fast inference (4.2 ms per image), LADNET demonstrates strong potential for practical clinical deployment, where it could help extract biomarker information from conventional MRIs, potentially reducing patient burden and diagnostic costs.
17 May 2026
Read appraisal →Journal of affective disorders
Biological age acceleration associated with mental and behavioural disorders: Evidence from the UK biobank cohort.
BACKGROUND: Mental and behavioural disorders may accelerate biological ageing, but comprehensive evidence integrating observational and causal approaches is lacking. METHODS: We performed observational and Mendelian randomisation (MR) analyses. First, 502,411 UK Biobank participants (78,674 with ICD-10 Mental and Behavioural Disorders) were propensity score matched (1:1). Biological Age Acceleration (BAA = biological age - chronological age) was regressed on mental and behavioural disorder status, and robustness checks were conducted using heteroscedasticity-robust and cluster-robust standard errors. Second, two-sample MR assessed causal effects of genetic liability to six mental and behavioural disorders on four ageing biomarkers, using strong genetic instruments (F > 10) and validated through sensitivity analyses including MR-Egger, Cochran's Q, and leave-one-out tests. RESULTS: Ever having a diagnosis of a mental and behavioural disorder was associated with higher BAA (β = 0.261, 95% CI: 0.229-0.293, P < 0.001). Strongest associations were observed for disorders of psychological development (β = 0.717), organic mental and behavioural disorders (β = 0.395), and mental and behavioural disorders due to psychoactive substance use (β = 0.338). BAA increased with comorbidity count (P-trend <0.001). MR indicated potential causal effects of depression (OR = 5.12 for frailty index), insomnia (OR = 2.04 for frailty index), and anxiety (OR = 1.06 for frailty index) on accelerated ageing, with no evidence of horizontal pleiotropy (MR-Egger intercept P > 0.05). Bipolar disorder was associated with slower epigenetic ageing (OR = 0.71 for GrimAge). Alcohol use disorder increased facial ageing (OR = 1.02). Schizophrenia showed no significant associations. CONCLUSIONS: Mental and behavioural disorders are observationally and robustly linked to accelerated biological ageing, with a dose-response by comorbidity. Genetic evidence supports potential causal roles for depression, insomnia, and anxiety in promoting ageing, while bipolar disorder may relate to slower epigenetic ageing. Biological ageing should be considered in the long-term management of mental health.
17 May 2026
Read appraisal →European journal of cancer (Oxford, England : 1990)
Aflibercept and 5-FU vs. FOLFOX as 1st line treatment for older adults or frail elderly patients with metastatic colorectal cancer - The randomized phase 2 AIO / IKF ELDERLY trial (AIO-KRK-0117 / IKF 629)
BACKGROUND: Fluoropyrimidines + angiogenesis inhibitor or dose-reduced doublets are used for (frail) elderly patients with metastatic colorectal cancer (mCRC). This non-comparative trial evaluated two 1st line regimens for patients not eligible for full-dose combinations. METHODS: This open-label, multicenter, phase II study randomized untreated mCRC patients ≥ 70 years ± frailty 1:1 to dose reduced mFOLFOX7 in Arm A or aflibercept (4 mg/kg) plus dose reduced mLV5FU2 in Arm B. Primary endpoint was the 6-months PFS rate (PFS@6), secondary endpoints included safety, overall survival (OS), overall response rate (ORR), patient reported outcomes and overall treatment utility (OTU). FINDINGS: The full analysis population comprised 120 patients (62 Arm A, 58 Arm B; 63% male), with a median age of 78.5 years. 82% frail patients were included. PFS@6 was 68% [95%CI 56%;80%] in Arm A and 46% [32%;59%] in Arm B. Median PFS was 7.9 and 5.5 months, median OS was 20.4 and 19.0 months and ORR was 47% and 22% in Arm A and B, respectively. At 3 months, 52% in Arm A and 35% in Arm B had a good OTU. Most frequent grade ≥ 3 AEs in Arm B were hypertension (41%) and proteinuria (10%). More patients in arm B had treatment-related SAEs grade ≥ 3 (17% in arm B and 5% in arm A). INTERPRETATION: Both regimens were feasible in this (frail) elderly population. Dose-reduced mFOLFOX7 and, to a lesser extent, 5-FU/aflibercept showed good PFS and 3-month OTU. Although most patients were regarded frail, survival rates were promising, most likely due to effective further-line treatments.
17 May 2026
Read appraisal →JMIR mHealth and uHealth
WhatsApp-Supported Teledentistry to Reinforce Oral Health Promotion Among Older Adults Residing in Rural and Urban Areas: Randomized Controlled Trial
BACKGROUND: Access to oral health promotion for older adults is globally limited, especially in rural, low- and middle-income settings. Digital research often lacks theoretical foundation and focuses primarily on younger cohorts, yielding few randomized trials evaluating accessible tools for oral health education in older adults. OBJECTIVE: This study aimed to develop a telehealth reinforcement strategy for oral health promotion to improve knowledge, attitudes, and self-efficacy in community-dwelling older adults. METHODS: A single-center, parallel-group randomized controlled trial was conducted in 4 municipalities (2 urban and 2 rural) in La Araucanía, Chile. Eligible participants were functionally independent adults aged ≥60 years with smartphone and internet access; those with cognitive impairment, complete edentulism, or inability to use WhatsApp were excluded. Participants were recruited from regional databases and assessed using the Geriatric Dental Specialties Tele-platform, a teledentistry tool for older adults. Participants were randomized (1:1) to face-to-face instruction (comparator) or the same instruction plus 2 weeks of social cognitive theory-informed telehealth reinforcement (4 validated videos via WhatsApp). Clinicians and statistical advisors were blinded. Primary outcomes (oral health knowledge, attitudes, and self-efficacy) were measured via telephone-administered questionnaires at baseline and 6 weeks post intervention. Secondary outcomes included acceptability and self-reported behaviors. Analyses included hypothesis testing, multiple correspondence analysis, and k-means clustering. RESULTS: A total of 120 older adults were randomized (comparator: n=59; telehealth: n=61), with 103 analyzed (comparator: n=51; telehealth: n=52). Both groups showed substantial within-group improvements in oral health knowledge (comparator: Cohen d=0.93, 95% CI 0.52-1.34; P<.001; telehealth: Cohen d=1.07, 95% CI 0.66-1.48; P<.001) and self-efficacy (comparator: r=0.59, 95% CI 0.38-0.74; P<.001; telehealth: r=0.62, 95% CI 0.43-0.77; P<.001). In per-protocol analysis, telehealth improved dental caries knowledge (P=.03) and attitudes (P=.004), with no between-group differences in other domains (P>.05). In intention-to-treat analysis, telehealth showed a significant between-group difference for attitudes only (adjusted mean difference=0.91, 95% CI 0.34-1.48; P=.002), with no differences for overall oral health knowledge (P=.11) or self-efficacy (P=.59). Exploratory analyses indicated only the rural telehealth subgroup showed significant gains in attitudes (P=.003) and flossing (P<.001). Clustering suggested greater improvements among participants with higher baseline needs, predominantly rural, with fewer teeth. Telehealth demonstrated acceptability across multiple indicators (>80% for most measures) with no clinical adverse events; minor video-access issues occurred. CONCLUSIONS: Telehealth reinforcement provided significant advantages in oral health attitudes compared with face-to-face instruction. The intervention was acceptable and showed benefits among older adults with higher preventive needs, commonly seen in rural settings. By integrating theory-informed strategies into a familiar digital platform, this study adds evidence from rural and urban contexts, extending prior work on mobile oral health. It offers insights to address service gaps in underserved areas and highlights potential for feasible, context-aligned implementation. Future research should evaluate long-term effects, adaptability, and cost-effectiveness. TRIAL REGISTRATION: ClinicalTrials.gov NCT05917548; https://clinicaltrials.gov/study/NCT05917548.
17 May 2026
Read appraisal →Computer methods and programs in biomedicine
Integrating multimodal data and deep learning for functional assessment and rehabilitation prediction after cerebral hemorrhage
BACKGROUND: Intracerebral hemorrhage (ICH) is a leading cause of long-term disability, particularly in China. Post-stroke motor recovery exhibits considerable heterogeneity, presenting substantial challenges for clinicians in establishing realistic goals. While integrative AI paradigms have shown success in other complex medical domains, existing models often rely on single-modality data, limiting their predictive accuracy and clinical utility. This study aims to develop and validate a multimodal predictive model integrating CT imaging, clinical data, and rehabilitation assessments to simultaneously predict motor recovery and global rehabilitation outcomes following cerebral hemorrhage. METHODS: We conducted a retrospective study involving 739 patients (315 for motor function prediction and 424 for rehabilitation outcome assessment) who received rehabilitation therapy after cerebral hemorrhage. To predict motor function, we constructed a late-fusion deep learning model leveraging 3D-DenseNet for CT neuroimaging and Multi-Layer Perceptron (MLP) for clinical and laboratory features. To predict rehabilitation outcomes, a Gradient Boosting Decision Tree (GBDT) model was developed and validated using 5-fold and 10-fold cross-validation, comparing it against other machine learning algorithms, including SVR, Random Forest and AdaBoost. Model performance was assessed using metrics including AUC and R². Additionally, univariate and multivariate regression analysis were performed to identify significant factors influencing motor recovery and rehabilitation outcomes. RESULTS: A total of 739 patients were included. The multimodal fusion model achieved an AUC of 0.856 (95 % CI: 0.741-0.971) and an F1 score of 0.897 (95 % CI: 0.819-0.975), significantly outperforming the imaging-only (AUC: 0.833) and clinical-only (AUC: 0.749) models. For rehabilitation outcome prediction, the GBDT model achieved an R2 of 0.849 (95 % CI: 0.803-0.887), demonstrating superior stability and accuracy over other models. Additionally, multivariate analysis revealed that serum albumin (ALB), neutrophil percentage (NEUT%), triglycerides (TG), and thrombin time (TT) were independent predictors of motor recovery, while age, admission mBI, and time to start rehabilitation significantly influenced functional outcomes. CONCLUSION: This study confirms that a multimodal deep learning framework integrating routinely available CT imaging and clinical biomarkers provides high predictive value for simultaneously forecasting motor recovery and global functional outcomes after ICH. This proof-of-concept approach offers a reproducible, data-driven tool for early risk stratification, facilitating the formulation of individualized rehabilitation strategies and optimizing resource allocation in clinical workflows.
17 May 2026
Read appraisal →Journal of affective disorders
Sex differences in cognitive function trajectories and influencing factors in older adults: A machine learning study based on CHARLS and HRS
BACKGROUND: Identify the influencing factors that respectively affect the differences in cognitive function trajectories between China and the United States, and explore the reasons for gender and transnational differences. METHODS: Five waves of data from the China Health and Retirement Longitudinal Study (CHARLS) and the Health and Retirement Study (HRS) were utilized, and Latent Class Growth Models (LCGMs) was employed to identify cognitive trajectories. The optimal machine learning (ML) model was adopted and the Boruta algorithm was used to screen the variables. The SHAP value is used to explain the ranking of feature importance. RESULTS: The XGBoost model performs best among the six ML models. Education was the top influencing factor across groups. Family factors mattered more in China, while U.S. medical and endowment insurance had stronger effects. Men were more affected by external social networks; women were influenced by family responsibilities and community environments. CONCLUSION: Differences were linked to public services, social roles, and traditional culture. These results support the development of tailored interventions targeting gender-specific and culture pathways at the individual, family, society and community levels, offering actionable insights for promoting cognitive health in aging populations globally.
16 May 2026
Read appraisal →Thorax
Measurement properties of the sit-to-stand test in community-dwelling people with chronic obstructive pulmonary disease: a COSMIN systematic review
BACKGROUND: The sit-to-stand (STS) test can assess physical function in people with chronic obstructive pulmonary disease (COPD); however, there are multiple versions. No study has used current guidelines to assess the measurement properties of the STS tests in people with COPD. METHODS: We conducted a systematic review using current COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) guidelines. Full text peer-reviewed publications were included if they assessed the measurement properties (validity, reliability and/or responsiveness) of at least one STS test among community-dwelling people with COPD. We searched six databases and imported results into Covidence where title/abstract screening and full text selection was completed independently and in duplicate. Extraction was conducted independently and in duplicate using the COSMIN extraction file. We assessed study risk of bias (very good, adequate, doubtful or inadequate), measurement property quality (sufficient, indeterminate or insufficient) and overall certainty of evidence (high, moderate, low or very low) using the COSMIN recommended tools. RESULTS: We assessed 2577 titles/abstracts and 102 full texts for inclusion; 30 publications met eligibility. Seven unique STS tests were located with the most common being the 1 min STS test (n=14, 39%), 5-repetition STS test (n=10, 28%) and the 30 s STS test (n=8, 22%). Where assessed, reliability was sufficient for the 1 min, the 5-repetition and the 30 s STS tests. Only the 1 min STS test had high-quality evidence of sufficient construct validity, while the 30 s STS test was the sole test with at least moderate quality evidence of sufficient responsiveness. CONCLUSIONS: The 1 min STS test has the most robust measurement properties for cross-sectional assessments while the 30 s STS test is more robust to assess change.
16 May 2026
Read appraisal →Journal of neurology
The effects of computerized cognitive training on cognitive function in patients with mild cognitive impairment: a systematic review and meta-analysis
BACKGROUND: We sought to quantify the effects of computerized cognitive training (CCT) on patients with mild cognitive impairment (MCI), to inform the development of clinical non-pharmacological intervention strategies. METHODS: We searched PubMed, Cochrane Library, Embase, and Web of Science databases from 2007 to February 2026 for randomized controlled trials (RCTs) of CCT in patients with MCI. Outcomes including global cognition, specific cognitive domains, psychosocial functioning, and activities of daily living were analyzed separately by stata 18 and Review Manager 5.4. RESULTS: We included 30 studies and 2056 participants. Our meta-analysis revealed that, compared with control conditions, CCT led to a significant moderate improvement in overall cognitive function, standardized mean difference (SMD) = 0.62, 95% confidence interval (CI) = 0.43-0.81; I2 = 73%. Subgroup analysis showed that intervention characteristics-including session duration, training device, total intervention length, weekly frequency, sample size, and assessment tools-yielded similar improvements in global cognition, with no statistically significant differences between subgroups. These findings indicate that the cognitive benefits of CCT are consistent and robust. CONCLUSION: This systematic review and meta-analysis shows CCT significantly improves global cognitive function in MCI patients, with stable benefits across subgroups. Despite clear overall effects, high heterogeneity, varying training protocols and assessment tools exist. Further large, long-term RCTs are needed to clarify CCT's long-term effects and support its standardized clinical application. TRIAL REGISTRATION: PROSPERO International prospective register of systematic reviews CRD420261328762; https://www.crd.york.ac.uk/PROSPERO/view/CRD420261328762 .
15 May 2026
Read appraisal →Journal of medical Internet research
Machine Learning and Deep Learning Models for Predicting Future Falls in Community-Dwelling Older Adults: Systematic Review and Meta-Analysis of Longitudinal Evidence
BACKGROUND: Machine learning (ML) and deep learning (DL) show promise for fall risk prediction, but prior reviews focused mainly on real-time fall detection, in-hospital falls, or conventional statistical models. The performance of ML-DL-based models for predicting future falls in community-dwelling older adults remains unclear. OBJECTIVE: This study aimed to review ML-DL studies for predicting future falls among community-dwelling older adults and meta-analyze discrimination where feasible. METHODS: Six databases were searched from inception to September 23, 2024, with updates on August 31, 2025, and February 28, 2026. We included longitudinal studies developing or validating ML-DL models to predict future falls in community-dwelling adults aged ≥60 years and excluded real-time detection, simulated or no fall, and inpatient studies. Risk of bias was assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST). Areas under the curve (AUCs) were meta-analyzed using Hartung-Knapp-Sidik-Jonkman random-effects models with 95% CIs. Heterogeneity, 95% prediction intervals (PIs), sensitivity analyses, and subgroup analyses were conducted. RESULTS: After screening 10,253 records, 28 (0.3%) studies were included; 18 (64.3%) focused on general older adults. Prediction horizons ranged from 3 months to 7 years, and fall incidence ranged from 1.6% to 46.6%. Twenty-three (82.1%) studies applied ML, and 5 (17.9%) studies used DL. Input modalities included text (n=18, 64.3%), sensor (n=5, 17.9%), image (n=1, 3.6%), and multimodal data (n=4, 14.3%). Common predictors included age, sex, fall history, depression, and basic daily activities. Only one model underwent external validation. Calibration reporting was sparse. All models were rated at high risk of bias. Ten models were meta-analyzed, yielding a pooled AUC of 0.79 (95% CI 0.69-0.87) with extreme heterogeneity (τ2=0.64; τ=0.80; I2=99.8%; Q=4128.99). The confidence-distribution bootstrap PI was 0.20 to 0.99, indicating substantial uncertainty in expected performance across new populations. Subgroup analyses indicated moderation by sample size and population type, with higher discrimination in specific populations than in general samples; however, the specific population subgroup included only 2 studies. Although all participants were community dwelling, some cohorts were recruited through clinically enriched pathways rather than general community sampling. CONCLUSIONS: ML-DL models show potential for identifying community-dwelling older adults at elevated future fall risk; however, wide PIs, limited external validation, and high risk of bias suggest real-world performance may be optimistic. The pooled AUC should be interpreted as a summary of reported discrimination under study-specific conditions, predominantly from internally validated, high-risk-of-bias models, rather than as a robust estimate of transportable real-world performance. This review extends prior reviews by focusing on community-dwelling settings and by integrating PROBAST, Hartung-Knapp-Sidik-Jonkman meta-analysis, PIs, and modality-specific synthesis to evaluate both discrimination and uncertainty. Findings support the use of ML-DL models for proactive fall prevention while emphasizing the need for validation and context-specific implementation.
15 May 2026
Read appraisal →Computers in biology and medicine
Dual-model deep learning for Alzheimer's prognostication
Disease-modifying therapies for Alzheimer's disease demand precise timing decisions, yet current predictive models require longitudinal clinical observations and provide no uncertainty quantification-rendering them impractical at the critical first-visit encounter when treatment decisions must be made. We developed PROGRESS (PRognostic Generalization from REsting Static Signatures), a dual-model deep learning framework that transforms a single baseline cerebrospinal fluid (CSF) biomarker assessment into actionable prognostic estimates without requiring prior clinical history. The framework addresses two complementary clinical questions: a probabilistic trajectory network predicts individualized cognitive decline parameters with calibrated uncertainty bounds that achieve near-nominal coverage, enabling honest prognostic communication rather than false precision; and a deep survival model estimates time-to-conversion from mild cognitive impairment to dementia. Using data from over 3000 participants across 43 Alzheimer's Disease Research Centers in the National Alzheimer's Coordinating Center database, PROGRESS substantially outperforms existing approaches including Cox proportional hazards, Random Survival Forests, and gradient boosting methods for survival prediction. Risk stratification identifies patient groups with seven-fold differences in conversion rates, enabling clinically meaningful treatment prioritization. Leave-one-center-out validation demonstrates robust generalizability, with survival discrimination remaining strong across all held-out clinical sites despite heterogeneous measurement conditions spanning four decades of assay technologies. By combining superior survival prediction with trustworthy trajectory uncertainty quantification, PROGRESS bridges the gap between biomarker measurement and personalized clinical decision-making-providing the prognostic timeline that current staging approaches cannot offer.
15 May 2026
Read appraisal →BMJ open
Prediction of inpatient falls and key predictors using machine learning applied to electronic health records: a retrospective cohort study in a tertiary hospital in Türkiye
OBJECTIVE: Traditional fall risk tools are often inaccurate and burdensome. This study aims to develop a predictive model for inpatient falls and identify the most influential variables using machine learning applied to electronic health record data. DESIGN: A retrospective cohort study. SETTING: A large tertiary university hospital in Türkiye. PARTICIPANTS: Adult patients (≥18 years) hospitalised in a university hospital between January 2017 and June 2023. PRIMARY OUTCOME MEASURES: Occurrence of inpatient falls recorded in incident reporting systems. RESULTS: A total of 518 fallers were identified and compared with 3121 non-fallers. Fallers were significantly older (median 68.5 vs 64 years, p<0.001), had longer hospital stays (16 vs 12 days, p<0.001) and higher comorbidity burden (p<0.001). They were also more likely to receive high fall-risk medications (86% vs 76%, p<0.001) and had a higher prevalence of mental disorders (26.3% vs 17.2%, p<0.001). The random forest quantile classifier demonstrated the highest discrimination (area under the curve (AUC) 0.821, 95% CI 0.781 to 0.861), with balanced sensitivity (0.799, 95% CI 0.734 to 0.867) and specificity (0.722, 95% CI 0.691 to 0.755). Pairwise comparisons using DeLong's test showed significantly higher AUC compared with several models (adjusted p=0.001), while differences with eXtreme Gradient Boosting were not significant (p=0.240). Key predictors included comorbidity burden, age, number of fall-risk-increasing medications and laboratory variables such as eosinophil and basophil counts, blood urea nitrogen, sodium and red blood cell count. CONCLUSION: Machine learning models using electronic health data can predict inpatient falls and reveal key risk factors. The random forest quantile classifier offers a promising approach for improving fall risk prediction in imbalanced clinical datasets.
15 May 2026
Read appraisal →Neurology. Clinical practice
Clinical Meaningfulness of Donanemab in Early Symptomatic Alzheimer Disease: Data From the Randomized Phase 3 TRAILBLAZER-ALZ 2 Trial
BACKGROUND AND OBJECTIVES: Understanding the meaningfulness of clinical trial outcomes is essential for people living with Alzheimer disease (AD) and their clinicians to make evidence-based shared treatment decisions in real-world clinical care. Donanemab, a monoclonal antibody targeting the insoluble form of β-amyloid found in plaques, significantly slows cognitive and functional decline of AD in participants with mild cognitive impairment (MCI) or mild AD-related dementia. This analysis reviews the efficacy of donanemab across various clinical outcome assessments, using both published data and new complementary analyses to provide context on its potential benefits for patients and caregivers. METHODS: We present findings from prespecified and post hoc analyses from the TRAILBLAZER-ALZ 2 trial. Clinical outcomes assessed were Integrated AD Rating Scale (iADRS), comprising the 13-item AD Assessment Scale-Cognitive Subscale (ADAS-Cog13) and AD Cooperative Study-Instrumental Activities of Daily Living (ADCS-iADL); Clinical Dementia Rating (CDR)-Sum of Boxes (CDR-SB) for clinical severity and individual cognitive and functional domains; CDR-Global for clinical severity stage progression; meaningful within-patient change (MWPC); and ADCS-Activities of Daily Living dependence score. RESULTS: Donanemab reduced the risk of progression from MCI to mild AD by 33% (hazard ratio [HR] = 0.67; 95% CI 0.52-0.87; p = 0.003) and from mild to moderate AD by 50% (HR = 0.50; 95% CI 0.33-0.78; p = 0.002). In addition, donanemab reduced MWPC risk over 76 weeks by 38% for CDR-SB (HR = 0.62; 95% CI 0.52-0.75; p < 0.001) and 30% for iADRS (HR = 0.70; 95% CI 0.58-0.84; p < 0.001). Donanemab-treated participants exhibited significant slowing of clinical progression across multiple ADAS-Cog13 and ADCS-iADL items and all CDR-SB cognitive and functional domains. Donanemab also slowed progression of dependence least-squares mean change difference, -0.14 [95% CI -0.24 to -0.04; p = 0.007]), representing 23% slowing of progression (95% CI 6.17%-40.32%), and reduced risk of progression to requiring in-home support by 27% (HR = 0.74; 95% CI 0.59-0.91; p = 0.005). DISCUSSION: These results add to the evidence and further support clinically meaningful donanemab-mediated effects on cognition and function for patients and their caregivers and may aid communication of realistic treatment expectations and informed decision-making. TRIAL REGISTRATION INFORMATION: ClinicalTrials.gov NCT04437511. Submitted: June 17, 2020; First patient enrolled: June 19, 2020. clinicaltrials.gov/study/NCT04437511 EudraCT Number 2020-000077-25. Start date of recruitment: June 19, 2020. clinicaltrialsregister.eu/ctr-search/trial/2020-000077-25/results.
15 May 2026
Read appraisal →JMIR aging
Leveraging Multimodal Large Language Models for Fall Risk Reduction in Older Adults in the Home: Proposed Model Design
This research letter proposes a novel model design leveraging natively multimodal large language models to identify fall risks and generate visualizations of recommended home environmental modifications, aiming to improve the accessibility and impact of personalized fall prevention advice for older adults. Through a pilot rating study, this work demonstrates that multimodal large language models can generate safe and actionable advice to reduce fall risk in lived spaces of older adults, and also generate realistic edits based on original images. While this concept needs further testing and clinical comparison, it highlights a promising avenue for further innovation of fall prevention tactics.
15 May 2026
Read appraisal →PloS one
Emotion-focused therapy and forgiveness in the older population: Protocol for a feasibility randomized controlled trial
Over recent decades, Emotion-Focused Therapy, aging, and forgiveness have garnered significant attention in the field of psychology. However, there is a lack of studies on Emotion-Focused Therapy and forgiveness specifically tailored for older adults. This article presents a protocol designed to assess the feasibility and acceptability of a randomized controlled trial of Emotion-Focused Therapy aimed at resolving emotional injuries in individuals aged 65 and older, within the context of interpersonal offenses. Feasibility will be evaluated through recruitment and retention rates, as well as adherence to the intervention protocol. Acceptability will be assessed based on perceived treatment credibility, participants' expectations regarding outcomes, levels of engagement, and overall satisfaction with the intervention. The study is structured as a two-arm, parallel-group randomized trial with a waiting list control. We propose recruiting a sample of 70 participants, randomly assigned to either an immediate intervention group, which will receive Emotion-Focused Therapy over twelve weekly sessions, or a control group that will receive the same therapy after a twelve-week waiting period. Data will be collected in the beginning, middle, and at the end of therapy, and in two planned follow-ups (three and six months after therapy). Once this protocol is implemented, if the therapy proves to be feasible, acceptable, and shows promising results, the findings will inform a large-scale randomized clinical trial to advance the understanding of this treatment for individuals aged 65 + .
15 May 2026
Read appraisal →Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
When bad turns good: a systematic review on cholesterol and LDL in longitudinal patient cohorts with Parkinson's disease
BACKGROUND: While the aetiology of Parkinson's disease (PD) involves genetic and environmental factors, emerging evidence has suggested a surprising link between lipid metabolism - particularly serum total cholesterol (TC) and low density lipoprotein cholesterol (LDL-C) - and the pathogenesis of PD. Cholesterol plays a vital role in neuronal membrane integrity, myelination, and synaptic function. However, its peripheral concentration and regulatory mechanisms in the central nervous system (CNS), remain incompletely understood in the context of PD. OBJECTIVE: This review aims to systematically examine the current literature on the relationship between TC and LDL-C with the risk and progression of PD in longitudinal patient cohorts. METHODS: A systematic literature search was conducted using PubMed with the keywords "Parkinson's disease" AND "cholesterol" and "Parkinson's disease" AND "LDL." Inclusion criteria encompassed English-language longitudinal human studies published before October 2024, with data on cholesterol levels in relation to PD incidence or progression. Fourteen studies met the eligibility criteria. RESULTS: Twelve of the 14 included studies reported an inverse association between lower serum total cholesterol (TC) and low-density lipoprotein cholesterol (LDL-C) levels and increased risk for PD onset as well as faster disease progression. Notably, this pattern was not uniform across all subgroups: age, sex, BMI, and statin use modulated the strength and direction of associations. CONCLUSION: Despite some heterogeneity across studies, there is growing evidence that lower TC and LDL-C may be associated with a higher risk and more rapid progression of PD. Future research should focus on mechanistic studies and stratified analyses to clarify whether and how cholesterol modulation could contribute to neuroprotective strategies in PD.
15 May 2026
Read appraisal →Fluids and barriers of the CNS
Cognitive assessment methods and outcomes following shunt surgery in idiopathic normal pressure hydrocephalus (iNPH): a systematic review and meta-analysis
INTRODUCTION: Core cognitive deficits in iNPH include slowed information processing, psychomotor slowing and executive dysfunction. However, cognitive outcomes following shunt surgery are not well understood. This review synthesised evidence on cognitive assessment methods and outcomes following shunt surgery in iNPH. METHODS: PubMed, Scopus, PsycINFO and Web of Science were searched for peer-reviewed studies including adults with iNPH who underwent shunt surgery and had within-subject cognitive evaluations pre- and post-operatively. Key data were extracted and study quality was assessed. Random-effects meta-analyses were performed on pooled baseline and post-shunt difference scores for frequently reported cognitive tests with comparable outcome data. RESULTS: Of 1,876 records, 195 met the inclusion criteria, comprising 11,445 patients. Cognitive evaluation methods ranged from subjective reports and NPH grading scales to brief screening tools and comprehensive test batteries. Over 193 distinct tests were reported and 54.4% of studies did not formally assess any core iNPH cognitive deficits. Post-shunt improvement rates, follow-up times and criteria for defining improvement varied widely. Eighty-five studies contributed data to meta-analyses of ten outcomes. Pooled estimates indicated post-shunt cognitive improvement, with Trail Making Test-A, Grooved Pegboard-Dominant and Trail Making Test-B showing changes exceeding thresholds for clinically significant improvement. CONCLUSIONS: Cognitive assessment in iNPH is highly heterogeneous and frequently omits core domains, limiting detection of treatment effects. When domain-relevant cognitive measures are used, shunt surgery is associated with statistically and clinically significant cognitive improvement. These findings highlight the need for standardised iNPH-appropriate cognitive evaluation tools with validated criteria for detecting clinically meaningful change and have direct implications for clinical assessment, interpretation of shunt response and the selection of cognitive endpoints in future interventional studies.
15 May 2026
Read appraisal →Hernia : the journal of hernias and abdominal wall surgery
Obturator hernia in adults: a systematic review of CT diagnosis and surgical management
PURPOSE: Obturator hernia is a rare but life-threatening pelvic floor hernia occurring predominantly in elderly, underweight women and is associated with delayed diagnosis and high perioperative mortality. This systematic review evaluated the diagnostic performance of computed tomography (CT), factors associated with bowel resection, and short-term outcomes of open versus laparoscopic repair in adults. METHODS: A systematic review was conducted per PRISMA 2020. PubMed/MEDLINE, Scopus, and Web of Science were searched from January 2004 to January 2026. Studies reporting at least three surgically confirmed adult cases were eligible. Three consecutive institutional cases were incorporated as an illustrative clinical adjunct. RESULTS: Twenty-two studies including 876 patients were analysed. The mean age across included studies was 79.7 years; 91.5% were female. Reported preoperative CT sensitivity across included studies was 93.2% (range 66.7-100%). Bowel resection was required in 31.6% and was associated with higher 30-day mortality (19.6% vs. 3.9%). Laparoscopic repair appeared to be associated with lower wound morbidity, shorter hospital stay (5.3 vs. 12.6 days), and lower recurrence (0% vs. 2.8%) in selected patient cohorts, although case-mix differences preclude direct comparison. Mesh was used in 44.3% overall. In three institutional cases (all elderly cachectic women presenting emergently), one underwent TAPP and two underwent open midline laparotomy; primary suture repair was performed in all owing to contamination, bowel compromise, or extreme frailty. CONCLUSION: CT remains the diagnostic cornerstone in obturator hernia. Surgical strategy should be individualised based on haemodynamic status, contamination, and frailty. Reported outcomes represent descriptive trends rather than pooled statistical estimates due to heterogeneity of available data.
14 May 2026
Read appraisal →JMIR aging
Self-Identified Age Cohorts and Personal Experience Sharing in Pseudonymous Online Spaces: Natural Language Processing Analysis of Reddit
BACKGROUND: While older adults' social media use has been widely studied for its instrumental benefits, such as accessing health information or maintaining family ties, research has largely focused on identity-based platforms that mirror offline social networks, leaving pseudonymous, interest-driven environments such as Reddit underexplored. Although older adults actively participate in these spaces to share personal narratives and engage beyond their existing social roles, the literature has yet to center their own voices, with most existing work focusing on caregivers or younger users discussing older adults rather than older adults speaking for themselves. OBJECTIVE: Integrating a life course perspective with the concept of subjective age cohort, this study examines how Reddit users who explicitly self-identify as being in their 50s, 60s, or 70s engage in self-expression and personal experience sharing within pseudonymous online spaces. METHODS: Using data collected via the Reddit application programming interface, this study analyzed posts and comments from 848 self-identified older Reddit users, including 488 future older adults in their 50s (57.5%) and 360 current older adults in their 60s and 70s (42.5%) identified through age-based flairs in the subreddit r/AskOldPeople. A fine-tuned large language model (BLOOM-560m; F1-score=0.96) classified 4,055,275 sentences into three personal experience domains: (1) health and wellness, (2) personal relationships and identity, and (3) professional and financial life. Chi-square analysis compared domain distributions across age groups. BERTopic topic modeling identified thematic patterns within each domain. RESULTS: Of 569,107 personal experience sentences identified, personal relationships and identity comprised the largest share (n=268,212, 47.1%), followed by professional and financial (n=186,768, 32.8%) and health and wellness (n=114,127, 20.1%). Chi-square analysis revealed significant between-group differences (χ22=34.7; P<.001): current older adults shared proportionally more about health and wellness, whereas future older adults shared more about relationships and identity. Topic modeling further revealed qualitatively distinct emphases within each domain. Future older adults' posts frequently discussed menopause, depression, and friendships, while current older adults more frequently addressed chronic illness, aging, and financial security. Both groups used pseudonymity to disclose sensitive or stigmatized topics. CONCLUSIONS: This study demonstrates that older adults' personal experience sharing on pseudonymous platforms is shaped by the simultaneous interplay of life course stage, age cohort background, and platform affordances, rather than by any single factor. Pseudonymous environments such as Reddit enable disclosures of health vulnerabilities, contested social identities, and emotional experiences that are structurally discouraged on identity-based networks, and this has direct implications for how digital inclusion is conceptualized and practiced. Beyond operational literacy, supporting older adults' expressive inclusion in interest-based, pseudonymous online communities represents a meaningful yet underaddressed dimension of digital participation policy.
14 May 2026
Read appraisal →Molecular neurobiology
Gut Microbiota Dysbiosis and Neuroinflammation in Alzheimer's Disease: a Systematic Review of Mechanistic Insights
This systematic review aims to identify microbial species associated with Alzheimer's disease and clarify how they influence Alzheimer's disease pathology, including immune system interactions and blood-brain barrier integrity. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, 18 studies were analyzed, revealing significant differences in gut microbiota composition between patients with Alzheimer's disease and controls. Key findings include reduced beneficial bacteria, such as Faecalibacterium prausnitzii and Roseburia hominis and increased pro-inflammatory species like Escherichia coli. Mechanistically, Escherichia coli produces lipopolysaccharides, triggering the release of pro-inflammatory cytokines such as tumor necrosis factor-alpha. This response correlates with elevated YKL-40 levels and reduced beneficial bacteria like Turicibacter. Additionally, reductions in butyrate-producing bacteria impair blood-brain barrier integrity, while bile acid metabolism disruptions impair signaling pathways, exacerbating amyloid-beta aggregation. These findings highlight the interplay between gut microbiota-derived metabolites and neuroinflammation, suggesting that targeting the gut microbiota for novel diagnostic and therapeutic approaches in Alzheimer's disease.
14 May 2026
Read appraisal →Experimental brain research
Subthalamic nucleus deep brain stimulation and associations with attention and processing speed in Parkinson's disease
Although subthalamic nucleus deep brain stimulation (STN DBS) is an effective treatment for the motor symptoms of Parkinson's disease (PD), its effects on cognitive functions are controversial. This cross-sectional study aimed to compare attention and processing speed in PD patients with STN-DBS and those receiving medical therapy, using a multimodal assessment combining P300 event-related potentials and neuropsychological tests. The study included 20 PD patients who underwent STN DBS and 17 PD patients who were followed with medical treatment. Demographic and clinical data for all patients were recorded. Cognitive functions were assessed using the P300 potential, the Stroop test (ST), and the Serial Digit Learning Test (SDLT). P300 potential latency and amplitude values, as well as neuropsychometric test results, were statistically compared between the two groups. P300 latency was shorter in the DBS-treated group than in the non-DBS group (p = 0.004). Performance times on Part III of the ST were shorter in the DBS-treated group (p = 0.03). In addition, a higher proportion of patients in the DBS-treated group performed normally on Part I of the ST compared to the non-DBS group (p = 0.03). There was no significant difference between the two groups in SDLT results. Shorter P300 latency and subtle differences in ST performance suggest a potential association between STN-DBS and the cognitive domains of attention and processing speed. Combining neuropsychological and electrophysiological assessments could provide a more comprehensive evaluation, and future large-scale, longitudinal studies are needed to clarify domain-specific cognitive effects.
14 May 2026
Read appraisal →Biology of sex differences
Importance of integrating biological sex and age analyses in health research
Research findings in human, animal and cell populations may be influenced by biological sex and age. The traditional statistical approach to sex and/or age differences that are discovered in research data is to consider them as confounders that interfere with the ability to make accurate estimations and thus need to be controlled for. This article provides examples of how combining sexes or controlling for sex and/or age may lead to inaccurate findings and overlook important insights. Rather than treating these variables as confounding factors, they should be viewed as variables of importance to the research question. We contend that data should be analyzed according to sex/age as the primary analysis, while controlling for sex and age should be conducted as a secondary analysis. This topic is increasingly important as data are made publicly available and deep learning models/artificial intelligence are used to analyze large volumes of data. Mechanistic insights will continue to be lost if prompt action is not taken to report data according to sex and age and to analyze data by sex and age in health research.
14 May 2026
Read appraisal →European journal of heart failure
Finerenone, polypharmacy, and clinical outcomes in heart failure: pre-specified analysis from the FINEARTS-HF trial
AIMS: Patients with heart failure with mildly reduced or preserved ejection fraction (HFmrEF/HFpEF) have a high comorbidity burden, which may necessitate numerous medications. Patients and clinicians may be hesitant about initiating another medication, especially among those already experiencing polypharmacy. This pre-specified analysis sought to examine the efficacy and safety of adding finerenone based on the concomitant medication number. METHODS: In the FINEARTS-HF trial, baseline medication use was collected in all 6001 participants with HFmrEF/HFpEF. Clinical outcomes and treatment effects were assessed by the categories of total medication count ('non-polypharmacy': ≤4 medications; 'polypharmacy': 5-9 medications; and 'hyper-polypharmacy': ≥10 medications) and as continuous variables. The primary outcome was a composite of cardiovascular death and total HF events. RESULTS: Overall (age: 72 ± 10 years; 46% women), the total number of medications at baseline ranged from 0 to 29 (mean: 8.4 ± 3.6), with 3588 (60%) meeting polypharmacy and 1878 (31%) meeting hyper-polypharmacy. Incidence rates for the primary outcome increased across medication categories: non-polypharmacy, 10.2; polypharmacy, 12.3; and hyper-polypharmacy, 26.1 per 100 person-years. The treatment benefits of finerenone in reducing the primary outcome were consistent across the spectrum of total medication count (Pinteraction = 0.94). Any serious adverse events and study drug discontinuation were not more frequent with finerenone vs placebo, regardless of polypharmacy categories. CONCLUSION: In FINEARTS-HF, >90% of patients with HFmrEF/HFpEF met the criteria for polypharmacy or hyper-polypharmacy, and these patients faced excess risks of cardiovascular events. Finerenone safely reduced cardiovascular death and total HF events across a broad range of baseline medication use.
14 May 2026
Read appraisal →Journal of medical Internet research
Effectiveness of Digital Health Interventions in Older Adults With Frailty and Sarcopenia: Systematic Review and Meta-Analysis of Randomized Controlled Trials
BACKGROUND: Frailty and sarcopenia represent substantial global health challenges, frequently diminishing patients' quality of life through impaired muscle function and physical performance. Digital health interventions (DHIs) have shown promise in mitigating these conditions among older adults. However, outcomes of such interventions in this demographic are inconsistent, and a thorough synthesis of existing evidence is lacking. OBJECTIVE: This study aimed to evaluate the effectiveness of DHIs in older adults with frailty and sarcopenia. METHODS: A comprehensive search of PubMed, Web of Science, MEDLINE, Embase, and Cochrane Library was conducted from their inception until January 2026 to identify randomized controlled trials. Meta-analyses were performed using R software (R Foundation for Statistical Computing). Study quality was evaluated using the revised Cochrane Risk of Bias Tool 2.0 (Cochrane Collaboration), and evidence certainty was assessed using GRADE (Grading of Recommendations, Assessment, Development, and Evaluation). RESULTS: From 3506 records, 16 studies were included. DHIs significantly improved total skeletal muscle mass (weighted mean difference [WMD] 1.01, 95% CI 0.08-1.94, 95% prediction interval [PI] -0.95 to 2.96), gait speed (WMD 0.09, 95% CI 0.03-0.15, 95% PI -0.1 to 0.26), Timed Up and Go Test (TUGT: WMD -0.52, 95% CI -1.02 to -0.03, 95% PI -1.93 to 0.85), 30-second Chair Stand Test (30CST: WMD 2.19, 95% CI 0.89-3.48, 95% PI -1.59 to 5.66), balance (standardized mean difference [SMD] 0.61, 95% CI 0-1.21, 95% PI -0.94 to 2.13), and quality of life (SMD 0.16, 95% CI 0.05-0.27, 95% PI 0.04-0.28). No significant improvements were observed in Appendicular Skeletal Muscle Mass Index (ASMI), grip strength, 6-minute walk test (6MWT), 2-minute walk test (2MWT), Short Physical Performance Battery (SPPB), or BMI. Although the pooled effect was favorable, the wide 95% PI suggests substantial between-study heterogeneity. Subgroup analysis stratified by intervention duration revealed significant intersubgroup differences in ASMI (χ²₁=9.93; P=.0016), indicating interventions lasting ≥12 weeks were more effective for improving ASMI (WMD 0.28, 95% CI 0.06-0.50, 95% PI -0.30 to 0.83). Subgroup analysis stratified by intervention type showed significant intersubgroup differences in balance (χ²₃=9.89; P=.0195), with exergame-based interventions showing significant effects (SMD 0.83, 95% CI 0.26-1.40). CONCLUSIONS: This systematic review is the first to quantify the disease-specific efficacy of DHIs in improving muscle function, physical performance, and quality of life among older adults with frailty and sarcopenia, demonstrating their unique value as a scalable complementary approach. By overcoming geographical and resource constraints, DHIs support underserved populations. However, low evidence quality and heterogeneity warrant cautious interpretation. The 95% PIs indicate that actual effects may vary with population characteristics and implementation contexts. Nonetheless, DHIs represent a promising and cost-effective strategy for service expansion. Future high-quality studies are needed to better understand their effectiveness and implementation across settings.
13 May 2026
Read appraisal →Journal of chemical information and modeling
MechMemDyn: Coupling Frustration Analysis with Membrane Dynamics to Target the TREM2-DAP12 Complex Interface
The transmembrane complex formed by TREM2 (Triggering Receptor Expressed on Myeloid cells 2) and DAP12 (DNAX-activating protein of 12 kDa) constitutes a pivotal therapeutic target for Alzheimer's disease. However, the intrinsic plasticity of its interface poses a formidable challenge for structure-based discovery of protein-protein interaction stabilizers (PPI stabilizers). Conventional approaches, ranging from static molecular docking to geometric deep learning, fail to capture the subtle interfacial energetics required for stabilizing this assembly, often leading to erroneous activity predictions. Here, we present MechMemDyn, a novel predictive framework that uniquely integrates protein frustration analysis with membrane-embedded molecular dynamics (MD) simulations. To our knowledge, this is the first systematic application of frustration analysis to rationalize PPI-stabilizing activity and guide ligand design. By mapping the local frustration landscape, we identify critical, minimally frustrated contact networks at the protein interface, which are essential for stabilizing the PPI. We demonstrate that the ability of a ligand to dampen distance fluctuations within these key networks, a metric rooted in thermodynamic rigor, correlates strongly with experimental potency. This method outperforms conventional static docking, AI-driven dynamic docking approaches, and standard molecular simulations, providing a more accurate and reproducible basis for cross-ligand comparison. This work not only resolves the intractability of the TREM2-DAP12 complex but also establishes a physics-driven paradigm for targeting dynamic transmembrane interfaces via frustration-optimized PPI stabilizers.
13 May 2026
Read appraisal →Medical science monitor : international medical journal of experimental and clinical research
Improved Cardiac Function and Glycemic Control in Elderly Diabetic Patients Through Structured Case Management After CABG
BACKGROUND Elderly patients with both coronary heart disease (CHD) and diabetes mellitus (DM) undergoing off-pump coronary artery bypass grafting (CABG) face high risks of perioperative glucose fluctuations and impaired recovery. We evaluated the impact of a structured, multidisciplinary case management model on cardiac function and glycemic control in this population. MATERIAL AND METHODS This single-center, randomized controlled trial enrolled 168 elderly (≥65 years) patients with CHD and type 2 DM scheduled for off-pump CABG. Patients were randomized (1: 1) to a control group (CG, n=84; routine care) or an observation group (OG, n=84). The OG received routine care plus a 6-month multidisciplinary intervention including individualized cardiac rehabilitation, modified Mediterranean diet, psychological support, and intensified follow-up. The primary endpoint was 6-month left ventricular ejection fraction (LVEF); HbA1c was a key secondary endpoint. RESULTS All 168 randomized patients were analyzed. At 6 months, the observation group demonstrated significantly superior primary outcomes. LVEF was significantly higher in the OG (mean difference: 6.2%, P<0.001), and HbA1c was significantly lower (mean difference: 1.3%, P<0.001). The intervention group also showed significant improvements in secondary endpoints, including reduced left ventricular diameters (LVESD, LVEDD), an improved lipid profile (P<0.05), longer 6-minute walk test distance, and higher Diabetes-Specific Quality of Life (DSQL) and Activity of Daily Living (ADL) scores (P<0.0001). CONCLUSIONS Structured, multidisciplinary case management significantly improved cardiac function, metabolic control, and functional capacity in elderly diabetic patients after off-pump CABG, validating its efficacy for this high-risk group.
13 May 2026
Read appraisal →Vaccine
The immune response of the mRNA vaccine CS-2034 as a heterologous booster before and after breakthrough infection: A follow-up study.
BACKGROUND: The emergence of highly mutated SARS-CoV-2 variants challenges vaccine protection. Evaluating long-term immune protection from boosters is crucial. The experiment compares neutralizing antibody dynamics and ACE2-RBD binding inhibition induced by mRNA and inactivated vaccine boosters, and assesses the impact of breakthrough infections and age on immune responses. METHODS: This study is based on a previous randomized, double-blind, parallel controlled trial. Healthy participants aged ≥18 who had received ≥3 inactivated COVID-19 vaccine doses. Volunteers (32 aged 18-59 and 32 ≥ 60) were stratified by age and vaccine type (24 mRNA, 8 inactivated in each age group). Pregnant women, those with severe chronic diseases or a history of allergies are excluded. Neutralizing antibodies were measured via microneutralization assay, and ACE2-RBD binding inhibition by high-sensitivity electrochemiluminescence. The study assessed changes in antibody levels and ACE2-RBD binding inhibition pre- and post-infection across age groups to evaluate vaccine efficacy and durability. (Registed number: ChiCTR2200064575). RESULTS: The neutralizing antibody titers from mRNA vaccine showing stronger and more consistent responses against the original strain and variants like XBB and BA.5. After breakthrough infections, mRNA-vaccinated individuals show a significant and lasting boost in antibodies, while the increase in inactivated vaccine recipients is modest. By age, mRNA-vaccinated people over 60 experience a faster antibody decline post-vaccination, whereas the inactivated vaccine group sees an initial rise at three months before declining. For ACE2-RBD binding inhibition, mRNA vaccines maintain higher early-phase rates, while inactivated vaccines show a delayed rise at three months followed by decline. CONCLUSIONS: mRNA vaccines induce stronger neutralizing antibodies and broader immunity than inactivated vaccines. Breakthrough infections boost antibody levels more in mRNA recipients than in inactivated vaccine recipients. Antibody decline is faster with mRNA vaccines in people over 60, while inactivated vaccines show a modest but weaker antibody increase against variants in the elderly.
13 May 2026
Read appraisal →Neurosurgical review
Deep brain stimulation for freezing of gait in Parkinson's disease: An updated systematic review, meta-analysis, and a single-center clinical experience.
Freezing of gait is a disabling and treatment-resistant manifestation of Parkinson's disease (PD). The effectiveness of deep brain stimulation (DBS) for freezing of gait remains inconsistent across stimulation targets, frequencies, and medication states. We conducted a systematic review and meta-analysis following PRISMA guidelines to examine how DBS affects freezing of gait in patients with PD. We searched Medline, Scopus, Web of Science, and Cochrane up to September 28, 2025. For synthesis, we combined mean differences and 95% confidence intervals for the Freezing of Gait Questionnaire (FOG-Q) and the Unified Parkinson's Disease Rating Scale (UPDRS) part III across different medication and stimulation settings to calculate the final effect size. Thirty-one studies with 905 patients were included. Of these, 21 provided FOG-Q data, and all reported UPDRS-III results. DBS led to a modest decrease in FOG-Q scores (mean difference [MD] = - 2.99; 95% CI = - 5.69 to - 0.29). The biggest improvement in FOG was seen when stimulation was used while patients were off medication (Med-OFF/Stim-OFF vs. Med-OFF/Stim-ON: MD - 5.88; 95% CI - 9.28 to - 2.47). Stimulation during the medication-ON state had smaller effects (Med-ON/Stim-OFF vs. Med-ON/Stim-ON: MD - 2.65; 95% CI - 4.99 to - 0.32), and there was no significant benefit when comparing Med-ON/Stim-OFF to Med-OFF/Stim-ON (MD - 0.70; 95% CI - 3.88 to 2.48). UPDRS-III scores improved substantially in the medication-OFF state with stimulation (MD - 14.35; 95% CI - 17.39 to - 11.32). High-frequency stimulation targeting the subthalamic nucleus provided more consistent benefits, yet substantial variation persisted across studies. The results of our small cohort showed significant improvement in FOG-Q and UPDRS-III (P values = 0.034, 0.022, respectively). DBS improves freezing of gait primarily in the medication-OFF state, with greater effects observed using high-frequency stimulation and subthalamic nucleus targets. Significant heterogeneity and limited data for alternative targets warrant cautious interpretation and further controlled studies.
13 May 2026
Read appraisal →Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
Efficacy and safety of GLP-1 receptor agonists in Parkinson's disease: a systematic review and meta-analysis of randomized clinical trials
BACKGROUND: Parkinson's disease (PD) is a progressive neurodegenerative disorder with no proven disease-modifying therapies to date. Because changes in cerebral glucose metabolism and insulin resistance have been linked to PD pathophysiology, glucagon-like peptide-1 receptor agonists (GLP-1RAs), widely used for diabetes, have been investigated as potential neuroprotective treatments. METHODS: This study systematically assessed the efficacy and safety of GLP-1RAs in PD through a systematic review and meta-analysis of randomized controlled trials identified in PubMed, Embase, and the Cochrane Library. The primary outcomes were motor function improvements measured by the MDS-UPDRS Part III in both on- and off-medication states at study endpoints and at intermediate timepoints of interest. Secondary outcomes included MDS-UPDRS Parts I, II, and IV, quality of life assessed by the PDQ-39, levodopa equivalent daily dose (LEDD), and the occurrence of adverse events. RESULTS: The meta-analysis found no statistically significant difference in favor of GLP-1RAs over placebo for motors and non-motors outcomes, except for PDQ-39 (MD: - 0.75; 95% CI: [- 1.34, - 0.17], P = 0.01). Regarding safety, GLP-1RAs were associated with a higher incidence of adverse events, especially gastrointestinal effects such as nausea, vomiting, and constipation. CONCLUSIONS: Overall, current evidence does not demonstrate consistent clinical benefit of using GLP-1RAs for treating motor or non-motor symptoms in PD nor support GLP-1RAs as disease-modifying therapy, underscoring the need for further research.
13 May 2026
Read appraisal →Journal of medical Internet research
Efficacy of Technology-Based Interventions on the Reduction of Loneliness: Systematic Review and Meta-Analysis
BACKGROUND: Loneliness is a widespread public health concern linked to increased risks of health problems. As populations age, the demand for effective interventions to mitigate loneliness continues to grow. OBJECTIVE: This meta-analysis aimed to examine the effectiveness of technology-based interventions in reducing loneliness. METHODS: A systematic literature search was conducted in Web of Science, PsycInfo, PubMed, Scopus, Google Scholar, Embase, and the Cochrane Library in August 2024. We included randomized controlled trials that examined the effectiveness of technology-based interventions compared with any control group in reducing loneliness across all age groups. Nonrandomized studies, qualitative research, and studies lacking sufficient statistical data for effect size calculation were excluded. After screening 1089 records, 7 studies involving 580 participants met the inclusion criteria. Data were extracted by 3 independent reviewers, with discrepancies resolved by a fourth reviewer. The risk of bias was assessed using the Cochrane risk-of-bias tool. A random-effects model was used to synthesize effect estimates, with standardized mean differences as the primary effect size metric. Heterogeneity was assessed using the Q statistic and I² index, and a prediction interval was calculated to estimate the expected range of true effects. RESULTS: We found a small and statistically nonsignificant overall effect of technology-based interventions on loneliness (pooled standardized mean difference=-0.21, 95% CI -0.59 to 0.17; 95% prediction interval -1.14 to 0.63). Substantial between-study variability was present (I²=57%; τ²=0.08; τ=0.28), and the prediction interval indicated that true effects in future studies may range from substantial reductions to moderate increases in loneliness. Differences between intervention types could not be examined due to the limited number of eligible studies. The Egger test showed significant funnel plot asymmetry (t5=-5.47; P=.003). However, since the trim-and-fill method identified no missing studies, the asymmetry is unlikely to be fully explained by publication bias. CONCLUSIONS: Unlike previous reviews, which focused primarily on older populations or included nonrandomized studies, this meta-analysis provides a rigorous synthesis of only randomized controlled trials across the lifespan. Our findings do not provide evidence for a reliable reduction in loneliness following technology-based interventions. Moderate heterogeneity indicates that effects differ across studies, and the limited number of eligible trials prevented the analysis of potential moderators. By incorporating robust prediction intervals and a broader age demographic, this study offers a more comprehensive view of the variability of intervention outcomes compared with existing literature. The wide prediction interval suggests that intervention effects may vary across settings. In practice, technology-based approaches should be viewed as supportive tools that require careful tailoring rather than universal solutions to loneliness.
12 May 2026
Read appraisal →PloS one
Effects of deconstructed Tai Chi step training combined with conventional rehabilitation on lower limb function in brunnstrom stage III stroke patients: A randomized controlled trial
OBJECTIVE: To evaluate whether a deconstructed Tai Chi stepping protocol adapted for patients with Brunnstrom Stage III stroke, when combined with conventional rehabilitation, improves lower limb motor function, walking ability, and joint mobility compared with conventional rehabilitation plus limb synergy training. METHODS: In this assessor-blinded randomized controlled trial, 52 patients with subacute stroke (Brunnstrom Stage III, ≤ 6 months post-stroke) were randomized in a 1:1 ratio to an experimental group (conventional rehabilitation plus adapted Tai Chi stepping training) or a control group (conventional rehabilitation plus limb synergy training). Both interventions were delivered 5 days per week for 8 weeks. Fifty participants completed the study and were included in the final per-protocol analysis (25 per group). Outcomes were assessed at baseline and after intervention. The primary outcomes were the Fugl-Meyer Assessment for Lower Extremity (FMA-LE) and Holden Walking Function Classification. Secondary outcomes were hip, knee, and ankle range of motion (ROM). RESULTS: Both groups improved after treatment, with greater improvement observed in the experimental group. FMA-LE scores increased by 5.6 ± 1.5 points in the experimental group and 1.8 ± 0.8 points in the control group (p < 0.001, Cohen's d = 1.32). In addition, 92% (23/25) of participants in the experimental group achieved Holden Grades II-III compared with 60% (15/25) in the control group (p < 0.001). The experimental group also showed larger gains in joint ROM, including hip flexion (+19.9° vs. + 4.2°), knee external rotation (+9.9° vs. + 3.7°), and ankle dorsiflexion (+7.6° vs. + 1.3°) (all p < 0.001). CONCLUSION: Deconstructed Tai Chi stepping training combined with conventional rehabilitation was associated with greater improvement in lower limb motor function, walking ability, and joint mobility than conventional rehabilitation plus limb synergy training in patients with Brunnstrom Stage III stroke. This stage-specific protocol may represent a practical adjunct to stroke rehabilitation, although confirmation in larger trials is still needed. CLINICAL TRIAL REGISTRATION: International Traditional Medicine Clinical Trial Registry (ITMCTR; a WHO ICTRP Primary Registry), registration number ITMCTR2025000972.
12 May 2026
Read appraisal →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
A systematic review of the assessment model for palliative care needs in cancer patients
BACKGROUND: Timely palliative care can reduce the disease burden and improve quality of life in patients with cancer. Although several studies have developed assessment models for palliative care needs in cancer patients, the quality and clinical applicability of these models remain unclear. OBJECTIVE: To systematically review existing assessment models for palliative care needs in patients with cancer, with a focus on their characteristics, predictors, risk of bias, and applicability. METHODS: A systematic search was conducted in PubMed, Cochrane Library, Embase, Web of Science, CINAHL, Scopus, China National Knowledge Infrastructure (CNKI) through September 12, 2025. Data extraction and evaluation were rigorously performed by two researchers based on the Prediction Model Risk of Bias Assessment Tool (PROBAST). RESULTS: A total of 5714 articles were identified, and eight studies were included, which covered 24 models for assessing palliative care needs. The sample size of the included studies ranged from 179 to 54,628, with areas under the curve ranging from 0.724 to 0.998. The models in all the included studies encompassed four categories of predictive factors: general demographic data, symptom/functional assessments, laboratory indicators, and treatment status. Five studies were rated as having a high risk of bias, primarily due to high risks associated with participants and conclusions, with generally low applicability. CONCLUSION: Existing models demonstrate potential for identifying patients with cancer who have increased palliative care needs using routinely collected clinical data. Commonly included predictors were symptom burden, functional status, laboratory parameters, treatment-related factors, and demographic characteristics. However, the overall body of evidence is constrained by a substantial risk of bias, particularly arising from inappropriate data sources, limited sample sizes, suboptimal handling of continuous variables, insufficient reporting of missing data, and the lack of robust internal or external validation. In addition, many models adopted mortality-based surrogate outcomes rather than clinically meaningful indicators of palliative care needs. Therefore, the currently available models should be interpreted with caution, and further high-quality model development and external validation are required before they can support broader routine clinical implementation. Future research should prioritize clinically actionable outcomes and incorporate patient-, caregiver-, and family-level factors to improve the relevance of these models for referral decisions and care planning.
12 May 2026
Read appraisal →Monaldi archives for chest disease = Archivio Monaldi per le malattie del torace
Prevalence of urinary incontinence in patients with chronic cough: a systematic review
Chronic cough-related diseases increase the risk of urinary incontinence (UI) due to repeated intra-abdominal pressure affecting the pelvic floor. Existing studies focus on specific diseases rather than UI as a broader consequence. To determine the prevalence of UI in patients with chronic cough-related diseases and compare it with the prevalence in healthy populations. A systematic review was conducted using Medline, Embase, Cochrane, OVID, Scopus, ProQuest, PEDro, and EBSCO. Two independent reviewers screened studies using predefined criteria in a Microsoft Excel spreadsheet. Only prevalence studies were included. Data were extracted, synthesized, and assessed using the GRADE approach for evidence quality and the STROBE checklist for reporting quality. Prevalence estimates varied widely based on demographic characteristics. UI prevalence ranged from 2.2% to 45% in pediatric patients, 30.4% to 74% in adult women, and 2.4% to 39% in adult men. The quality of evidence was low, while reporting quality was acceptable. UI is a common complication in patients with chronic cough, with a higher prevalence than in healthy populations. Due to the stigma surrounding urogenital disorders, clinicians should actively inquire about UI during patient history-taking and refer affected individuals for appropriate treatment.
11 May 2026
Read appraisal →AJNR. American journal of neuroradiology
Hippocampal Subfield Susceptibility Alterations in Mild Cognitive Impairment Revealed by 7T MRI
BACKGROUND AND PURPOSE: Hippocampal magnetic susceptibility alterations may serve as early neurodegenerative biomarkers of Alzheimer disease (AD), but subfield-level evidence in mild cognitive impairment (MCI) remains limited. This study uses submillimeter high-resolution quantitative susceptibility mapping (QSM) to investigate hippocampal subfield susceptibility changes in MCI. MATERIALS AND METHODS: Thirteen individuals with MCI and 13 cognitively normal controls (NC) underwent 7T MRI scans including MP2RAGE and multiecho gradient echo imaging (for QSM). The visibility of hippocampal strata radiatum, lacunosum, and moleculare (SRLM) was assessed using a visual rating scale. Hippocampal subfields (Cornu Ammonis 1 [CA1], CA2, CA3, dentate gyrus [DG], and subiculums) were segmented, and magnetic susceptibility values (χ) were extracted for each subfield. The susceptibility difference between the DG and other subfields was also calculated. Associations between subfield susceptibility measures and cognitive performance were then assessed. RESULTS: Qualitative analysis revealed a distinct hyperintense curved pattern in the hippocampus on the 7T QSM image, corresponding to the SRLM. In NC, this pattern was clearly visible exhibiting consistently delineated boundaries, while it appeared blurred or absent in most subjects with MCI. The visual rating results showed that NC had significantly higher scores than subjects with MCI (Hedgesg = 1.43, 95% confidence interval) [95% CI, 0.60-2.35; P = .003], with most NC rating 3-4 and most subjects with MCI rating 1-2. Quantitative analysis of hippocampal subfield susceptibility revealed significantly elevated susceptibility [χ] in the left DG in those with MCI compared with NC (Hedgeg = 0.88, 95% CI, 0.10-1.72; P = .03), leading to reduced susceptibility contrast between the SRLM and adjacent CA1-CA3 or DG. Furthermore, regional differences of susceptibility between key subfields (left CA1 versus DG and subiculum versus DG) were significantly reduced in MCI, reflecting a loss of subfield contrast. These quantitative changes were significantly associated with lower cognitive performance, even when controlling for age. CONCLUSIONS: 7T QSM reveals susceptibility changes in hippocampal substructures in individuals with MCI, providing a potential early biomarker for AD.
11 May 2026
Read appraisal →JMIR formative research
At-Home Sleep Electroencephalography Assessment in Young and Older Adults Using a Novel Wireless Soft Electronics Sleep Monitoring System: Experimental Study
BACKGROUND: Sleep quality declines with age and is a known contributor to multiple chronic health conditions, including Alzheimer disease. Emerging evidence suggests that certain electroencephalography (EEG) neural signatures measured during sleep may be predictive of cognitive decline in older adults. Sleep EEG signals are traditionally measured using bulky, rigid, and uncomfortable equipment in an unfamiliar laboratory setting, which can negatively impact sleep signals. Due to these limitations, sleep EEG data acquisition is typically limited to a single night. OBJECTIVE: This study aimed to validate our recently developed portable, skin-like EEG monitoring patch for 7 nights in the home environment in a pilot sample of young and older adults by evaluating usability and acceptance, and replicating age-related differences in sleep architecture observed in the polysomnography literature. METHODS: Eighteen young adults and 18 cognitively unimpaired older adults without sleep disorders were enrolled (data from 11 young adults and 12 older adults were included in the analyses) in a 7-night study during which they wore novel, gel-free, wireless, ultrathin, skin-conforming, sleep monitoring, fabric-based patches. These patches were self-applied to the forehead and face for optimal usability and comfort. The patches incorporate laser-cut mesh electrodes with low-profile electronics (including a rechargeable battery and amplifier) and transmit EEG signals to a participant-controlled, Bluetooth-enabled, tablet-based data acquisition app. An automated algorithm was used to stage sleep and assess microarchitecture features from the EEG commonly impacted for each participant. Averages across nights were computed for these sleep features for each participant. RESULTS: Young and older adults reported that the sleep patch was easy to use and comfortable to wear. There was no loss of signal power over 7 nights of wear across participants (retained-data signal-to-noise ratio over the 7-d period: young adult, mean 20.69, SD 12.78, maximum 52.13, minimum 5.19; older adult, mean 22.10, SD 9.39, maximum 49.96, minimum 13.79). Most datasets not retained were lost due to poor reference electrode adhesion on the nose (75/101, 74% of lost datasets in young adults and 57/88, 65% in older adults). Trained sleep technologists verified that the retained datasets were of sufficient quality to be scored without difficulty. Expected age-group differences in sleep features were observed, including age-related reductions in stage N3 sleep (young adult, mean 18.55, SD 6.70; older adult, mean 10.40, SD 6.43; Mann-Whitney U=42.0; P=.01) and reduced sleep spindle density (young adult, mean 2.92, SD 2.24; older adult, mean 0.94, SD 1.33; Mann-Whitney U=45.0; P=.006). CONCLUSIONS: This study demonstrates that our novel, comfortable, wearable patch can reliably measure physiological sleep data over multiple nights at home in adults across the lifespan, thereby making multinight sleep assessment in cognitive aging studies and clinical research more accessible than traditional polysomnography. In future studies, the small, lightweight system, which is highly scalable, can be shipped inexpensively to participants' homes, making this technology and research accessible to individuals who may have difficulty traveling or who are hesitant to travel to a laboratory or clinic.
11 May 2026
Read appraisal →JMIR research protocols
Investigating the Implementation and Impact of AI-Assisted Fall Prevention in Hospitals: Protocol for a Multicenter, Multimethod Observational Study in Sweden (SAFE)
BACKGROUND: Artificial intelligence (AI) has the potential to enhance patient safety, particularly in the prevention of in-hospital falls. Recent advances in sensor-based AI systems allow for the analysis of complex, multimodal data to generate real-time alerts, enabling health care professionals to intervene before a fall occurs. By shifting from reactive responses to proactive risk management, these technologies may enable reductions in fall incidence and improvements in care outcomes. As a result, hospitals across Europe are increasingly adopting such systems. Nevertheless, empirical evidence concerning their routine implementation remains limited, particularly concerning their impact on patient safety, clinical workflows, and the usage of health care resources. Addressing these gaps is essential for effective and sustainable integration into hospital care. OBJECTIVE: This paper outlines the protocol for the multicenter, multimethod project SAFE (Safe AI-Assisted Fall Prevention Through Evidence), which investigates the implementation and impact of AI-assisted fall prevention in Swedish hospitals. METHODS: The research project is a collaboration between Halmstad University and hospitals in the Västra Götaland Region (VGR) and will, during 2026-2028, investigate an ongoing large-scale AI system implementation in VGR hospitals, covering up to 2400 patient beds. Using surveys, interviews, observations, and a retrospective study, it will track the implementation and impact over time. Two learning laboratories involving patients, their relatives, and health care professionals will be conducted to codevelop strategies for the implementation of AI-assisted fall prevention. RESULTS: The project will provide evidence-based insights and practical guidance on AI-assisted fall prevention. The findings will be relevant not only to patients, health care professionals, and hospital organizations, but also to policymakers and stakeholders involved in the digital transformation of health care. CONCLUSIONS: Although VGR serves as the primary research setting, the project's results will inform future similar initiatives in Sweden and offer transferable lessons for other health care systems internationally. This study will contribute to the evidence base on AI-assisted fall prevention in health care, supporting the responsible and scalable integration of such systems across diverse health care environments.
11 May 2026
Read appraisal →AJNR. American journal of neuroradiology
Comparative Analysis of Artificial Intelligence-Based Quantification versus Visual Rating of Enlarged Perivascular Spaces in the Multi-Ethnic Study of Atherosclerosis Cohort
BACKGROUND AND PURPOSE: Enlarged perivascular spaces (PVS) are fluid-filled spaces surrounding small cerebral vessels. Current PVS research has been limited by the absence of efficient and scalable quantification tools. We compared visual ratings versus artificial intelligence (AI)-based quantification in identifying associations of PVS with vascular risk factors and cognitive performance. MATERIALS AND METHODS: This cross-sectional study included 235 participants from the Multi-Ethnic Study of Atherosclerosis who had undergone brain MRI and had available both visual ratings and AI-derived PVS quantification. Visual ratings were performed by an expert neuroradiologist (K.D.H.) using a semiquantitative scale. AI-derived counts were based on a fully automated deep learning algorithm. Visual and AI-derived counts were grouped into 4 a priori-defined anatomic locations: basal ganglia, frontoparietal cerebrum, midbrain, and cerebellum. The relationships of PVS counts with demographic characteristics, vascular risk factors, and global and domain-specific cognitive scores were examined using ordinal logistic regression (for ordinal categoric outcomes) and linear regression (for continuous outcomes) models. RESULTS: Mean age (SD) was 72.1 (6.8) years; 95 (40%) participants were men; and 126 (54%) self-reported as black. Means (SD) of AI-derived regional PVS counts were 63.7 (24.6) for basal ganglia, 414.9 (167.5) for frontoparietal cerebrum, and 9.8 (4.4) for midbrain. On visual ratings, the most prevalent count category for each region was 11-20 for basal ganglia (40%), 21-40 (31%) for frontoparietal cerebrum, and 1-5 (83%) for midbrain. For basal ganglia PVS, while both methods were associated with older age and white race/ethnicity, AI-derived counts exhibited additional associations with higher systolic blood pressure (β, 0.20; 95% CI, 0.05-0.36) and diabetes (unstandardized β coefficient [β], 11.51; 95% CI, 3.48-19.55), as well as poorer global cognition (β, -0.012; 95% CI, -0.023 to -0.0004), delayed memory (β, -0.005; 95% CI, -0.010 to -0.0005), and attention/processing speed (β, -0.005; 95% CI, -0.009 to -0.001) cognitive performance. CONCLUSIONS: In this cross-sectional study, AI-derived PVS quantification was more sensitive in detecting associations with vascular risk factors and cognitive outcomes than traditional visual ratings. AI-based quantification may aid in the analysis of large-scale epidemiologic data, advancing PVS research.
11 May 2026
Read appraisal →JMIR formative research
Older Adults' Experiences Navigating Setup of Digital Health Technology: Implementation Report
BACKGROUND: Digital health and connected technologies may support better health outcomes among older adults, including those with multiple chronic conditions or low engagement in health behaviors. However, initial experiences with technology, including during unboxing, setup, and first use, can influence emotional reactions and perceptions and can ultimately determine sustained, meaningful use. Older adults with low technology experience or poor health may be particularly vulnerable to frustration, stress, or abandonment of devices when early interactions are negative. OBJECTIVE: The purpose of this implementation study was to closely observe the initial engagements with a telehealth treatment app and connected blood pressure monitor (BPM) among a group of older adults with low prior technology use and reported low health behavior engagement. The goal was to identify setup "pain points" that may influence initial impressions and intention to use the technology over time. METHODS: A total of 24 older adults (aged ≥65 years) were recruited for a 4-week trial of a telehealth app. Participants were provided with a box containing a tablet preloaded with the app, paper instructions, and a BPM and cuff. Researchers first conducted in-home ethnographic interviews with participants to observe the unboxing and setup process, documenting experiences with reading instructions, using the BPM, and engaging with customer support. Weekly check-in calls and a final exit interview captured ongoing experiences and likelihood of continued use. Interview recordings were transcribed and independently coded, guided by the unified theory of acceptance and use of technology. RESULTS: Most of the sample were White (20/24, 83%) and female (14/24, 58%). Negative experiences with the app's customer support were the top challenge for participants, with representatives providing confusing steps or conflicting terminology. Other common challenges were understanding instructions, connecting to Bluetooth, and correctly using the BPM. While 67% (16/24) of the participants indicated that they were likely or very likely to continue to use the app after the study ended at the end of week 1, this number dropped to 54% (13/24) by the end of the 4 weeks. Participants who reported lower technology self-efficacy at the beginning of the study also experienced frustration, anxiety, and embarrassment as friction with the setup process continued. CONCLUSIONS: First impressions of digital health apps play a critical role in influencing older adults' emotions and perceptions regarding the technology and may impact the likelihood of longer-term engagement. Those with lower technology self-efficacy are particularly susceptible to experiencing negative emotions such as frustration, stress, or shame. Mobile health apps and interventions targeting older adults should incorporate simplified instructions with clear, consistent terminology and well-trained customer support staff to improve the onboarding experience.
11 May 2026
Read appraisal →Palliative & supportive care
Relational-temporal dignity at the end of life: Ethical foundations for the DiRePal model in the algorithmic age
OBJECTIVES: The increasing presence of artificial intelligence (AI), electronic patient-reported outcomes (ePROMs), and digital infrastructures in palliative care is transforming how clinical encounters are organized and how suffering is interpreted. These technological shifts heighten the risk of relational compression and a reduction of dignity to measurable outputs. This paper proposes the DiRePal model (Relational-Temporal Dignity in Palliative Care) as a philosophical framework to re-examine dignity beyond coherent narrative identity or autonomy-centered ethics, emphasizing relational presence, temporal sensitivity, and structural conditions of care. METHODS: A philosophical-ethical analysis informed by narrative identity (P. Ricoeur), ethics of alterity (E. Levinas), capabilities theory (M. Nussbaum), and care ethics (J. Tronto). Critical readings of dignity frameworks, AI ethics, and digital health literature were synthesized to develop a relational-temporal account of dignity and 2 operational concepts: the temporal dignity indicator and the architecture of prudence. RESULTS: While digital tools can enhance communication and support anticipatory care, they also risk reducing patients to data profiles, narrowing listening practices, and eroding opportunities for narrative, silence, and relational presence. The DiRePal model reframes dignity as a fluctuating, co-constructed achievement that depends on temporal attentiveness, ethical listening, institutional conditions, and prudent integration of AI and ePROMs. It further expands dignity to include post-biographical dimensions such as memory, grievability, and digital legacy. SIGNIFICANCE OF RESULTS: End-of-life care in the algorithmic age requires an ethics that recognizes dignity as relational, temporal, and structurally mediated. The DiRePal model offers clinicians and institutions a conceptual grammar to resist technological reductionism, protect time for presence, and safeguard the narrative and post-biographical continuity of persons whose voices may be fragmented, vulnerable, or digitally extended.
11 May 2026
Read appraisal →PloS one
Association between hemoglobin dynamic trajectories and 28-day mortality in elderly patients with sepsis: A retrospective cohort study using the MIMIC-IV database
BACKGROUND: Anemia is a common complication in sepsis and is associated with poor prognosis. However, there are few studies on the dynamic changes in hemoglobin (Hb) levels and their relationship with prognosis in elderly patients with sepsis. This study aims to retrospectively investigate the relationship between Hb trajectories and 28-day mortality in this population. METHODS: This retrospective observational study used data from 4,961 elderly patients with sepsis extracted from the MIMIC-IV database(2008-2019). Hb trajectories were identified using a Joint Latent Class Model (JLCM) for longitudinal pattern classification. Following validation of proportional hazards assumptions (Schoenfeld residual test), we assessed the prognostic relationship between Hb trajectories and 28-day mortality using multivariable Cox regression models and Kaplan-Meier survival curves. Sensitivity analyses included sex-stratified multivariable Cox regression, subgroup analysis, and E-value assessment. RESULTS: Hb trajectories were classified into three patterns: Class 1 with persistently low levels, Class 2 with medium levels and a gradual decline, and Class 3 with a rapid decline from higher levels. Using Class 2 as the reference, multivariable Cox regression showed that Class 1 (HR = 1.38, 95% CI = 1.18-1.62, p < 0.001) and Class 3 (HR = 1.21, 95% CI = 1.03-1.42, p = 0.020) were associated with higher 28-day mortality. Kaplan-Meier curves indicated the lowest survival probability in Class 1 and the highest in Class 2. Gender-specific analysis revealed consistent trajectory patterns between males and females, but higher Hb values in males. The predictive power of the rapid decline pattern for early mortality was stronger in males. CONCLUSION: The Hb trajectories in elderly sepsis patients are significantly associated with early mortality. Persistently low levels and rapid declines in Hb are indicators of poor prognosis.
10 May 2026
Read appraisal →JMIR human factors
Identifying Skill and Usability Barriers to Digital Health Tool Use Among Older Adult Patients in US Safety Net Clinics: Mixed Methods Study
BACKGROUND: Despite their benefits, digital health tools often face adoption barriers because of the digital divide. Identifying the fundamental user skills required to effectively navigate these tools and the usability barriers is essential to addressing disparities in use. OBJECTIVE: This study aimed to identify the skill and usability barriers to using digital health tools. METHODS: This study included English-, Spanish-, or Cantonese-speaking patients, aged ≥50 years, who received care at an urban safety net health system in the United States. Participants completed a survey examining sociodemographic characteristics and digital health tool use and were observed and video recorded as they navigated four digital health care tasks: (1) launch a video visit, (2) visit a health website through a URL, (3) log in to the patient portal, and (4) sign up for a patient portal account. Participants who could not independently perform the tasks received additional support. Tasks were conducted in English, while instructions and additional assistance were provided in each participant's preferred language. Video recordings were thematically coded to identify the fundamental skills needed for effective digital tool use and usability barriers in the design of digital tools. We examined whether task independence was associated with participant demographics and thematic categories using Kruskal-Wallis, χ2, and Fisher exact tests. RESULTS: In total, 74% (34/46), 52% (31/60), 71% (44/62), and 70% (43/61) of participants (N=64) independently completed digital tasks 1, 2, 3, and 4, respectively. Older age, minoritized races and ethnicities, non-English language preference, lower educational attainment, access to cellular data only or no internet access, and lack of a portal account were associated with a higher likelihood of requiring assistance or being unsuccessful at completing each task (P<.001, except for older age [P=.004]). The qualitative coding of video recordings identified 3, 4, and 6 categories of typing, navigation, and human-computer interaction (HCI) skills, respectively, as fundamental skills required to independently complete digital tasks. χ2 and Fisher exact tests indicated significant associations between most typing, navigation, and HCI categories and independent task completion. We coded usability barriers as one of 6 learnability challenges or 3 operability challenges. CONCLUSIONS: This study identified that independent use of digital health tools requires fundamental typing, navigation, or HCI skills as well as high usability of digital tools. The inclusion of 4 different digital tasks added specificity to the type of skills and usability considerations necessary to ensure accessibility of digital health tools to diverse older adults. This study underscores the need for vendors to cocreate digital health tools with historically excluded end users in mind. As health care systems expand digital tool adoption, they must distinguish fundamental skill gaps from usability barriers, as each may require different intervention strategies.
10 May 2026
Read appraisal →JMIR mHealth and uHealth
mHealth-Based Gamification Interventions to Promote Health Among Older Adults: Scoping Review
BACKGROUND: Healthy aging has emerged as a global priority. However, older adults' participation in health promotion programs remains low, and traditional health promotion models have achieved limited success in fostering sustained engagement among this population. Mobile health (mHealth)-based gamification interventions offer a promising way to address these challenges. However, no published reviews support or oppose the use of mHealth-based gamification interventions as health promotion strategies in older adults. OBJECTIVE: The study aimed to identify mHealth interventions using gamification to promote health among older adults. METHODS: Our scoping review was conducted following the Joanna Briggs Institute recommendations for scoping reviews and Arksey and O'Malley's framework. The process followed PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines and PRISMA-S (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Literature Search Extension) checklist. A comprehensive literature search was conducted across 8 databases: PubMed, Scopus, Web of Science, Embase, Cochrane Library, CINAHL, PsycARTICLES, and IEEE Xplore Digital Library, from their inception to December 10, 2025. Two reviewers independently screened titles, abstracts, and full texts via Rayyan, with disagreements resolved by a third reviewer. RESULTS: This scoping review identified 11 studies. Only 1 article was published before 2022. The interventions were found to improve enjoyment and motivation (n=5), cognitive function (n=3), physical activity (n=2), and digital literacy (n=2). Individual studies also reported improvements in mental health (n=1) and adherence (n=1), a reduction in suicidal ideation (n=1), improvements in physical function (n=1), the promotion of social engagement (n=1), and the identification of mild cognitive impairment (n=1). Game elements used were ranked by frequency as progress, challenges, goals, levels, reward, sensation, storytelling or narration, leaderboard, surprise, and avatar. No research was found to use the game element of "social sharing." mHealth types included augmented and virtual reality-based training systems, wearable devices, mobile phones, tablets, and Windows platforms and devices. Notably, only 4 studies applied theoretical frameworks, and 3 omitted the concrete approach to gamification. CONCLUSIONS: As the first scoping review to identify and map mHealth-based gamification interventions for older adults, this study highlights their potential as an innovative approach to health promotion. By systematically synthesizing evidence regarding intervention designs, gamification strategies, and preliminary health outcomes, it establishes a foundation for future inquiry. However, this review is limited by the small number of included studies, precluding broad generalizations. Future research should assess long-term impacts, integrate theoretical frameworks, establish reporting guidelines, design personalized social-interactive interventions, and expand to broader health domains. Ultimately, these insights provide targeted guidance for developing age-appropriate digital health solutions, contributing to the realization of active aging.
10 May 2026
Read appraisal →PloS one
Reducing annotation burden in medical imaging with ADGNET: A semi-supervised deep learning strategy
We propose ADGNET, a semi-supervised framework for Alzheimer's disease (AD) diagnosis that jointly optimizes image reconstruction and classification through shared feature representations. The architecture integrates a residual backbone with attention modulation for dynamic feature selection, an encoder-decoder reconstruction branch for unsupervised representation learning, and a classification branch with focal loss to address class imbalance. This dual-task design enables effective feature learning from limited annotations. On two public MRI datasets-KACD (2D, 6,400 images) and ROAD (3D, 532 scans)-ADGNET achieves average performance improvements of 4.1% and 7.2% over state-of-the-art methods (ResNeXt WSL, SimCLR) across six metrics. Interpretability analysis using Grad-CAM and attention visualization confirms that the model focuses on clinically relevant neuroanatomical structures, particularly the hippocampus and temporal lobes, with strong correlation to established AD pathology (r = 0.67, p < 0.001). These results validate the model's exceptional generalization capability and feature representation effectiveness across multi-modal medical imaging data, offering an efficient solution for few-shot medical image analysis.
10 May 2026
Read appraisal →Archives of orthopaedic and trauma surgery
Healthcare utilization following hip fractures based on social vulnerability status in the US: an analysis of 2016-2020 nationwide readmissions data
INTRODUCTION: Social vulnerability (SV) influences rehabilitation and postoperative care for patients with hip fracture. However, most previous work relies on area-level measures that overlook interindividual variation. The recent adoption of ICD-10 Z-codes allows clinical identification of patient-level SV and may offer a better understanding of its impact. This study aimed to evaluate healthcare utilization, including readmissions, discharge disposition, and length of stay (LOS) in surgically treated hip fracture patients with and without clinically acknowledged SV. METHODS: Adults surgically treated for hip fracture between 2016 and 2020 were included from the Nationwide Readmissions Database. SV was defined as having at least one documented relevant ICD-10 Z-code. Primary outcome measures included complications, LOS, discharge disposition, and 30- and 90-day readmissions, stratified by SV and evaluated using chi-square analyses. Multivariable logistic regression assessed long LOS (≥ 5 days) and discharge to home, adjusting for age, insurance/income status, and substance use. RESULTS: Patients with SV were younger (35.6% with SV vs. 50.1% without SV were 81+), had a lower median household income (38.8% with SV vs. 25.7% without SV were in the lowest quartile), and were more often insured by Medicaid (19.3% vs. 3.8%). Alcohol/drug use disorders were significantly more prevalent in patients with SV (18.5% vs. 4.5%). SV was associated with 47% higher odds for long LOS (1.47, 1.41-1.54) and 23% higher odds for discharge to home (1.23, 1.16-1.30) but comparable 90-day readmissions (21.2% vs. 19.8%). CONCLUSION: Among surgically treated hip fracture patients, SV was associated with higher odds of long LOS and discharge to home but no meaningful difference in readmissions. The small number of patients with clinically documented SV highlights the limited reporting by healthcare workers. This analysis of a nationwide all-payer database highlights the need to identify these higher risk patients and implement appropriate care pathways to reduce healthcare utilization.
10 May 2026
Read appraisal →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
Modified Frailty Index-11 (mFI-11) measured frailty as a predictor of post-transplant outcomes in non-Hodgkin's lymphoma patients undergoing hematopoietic stem cell transplantation: a retrospective analysis of National Inpatient Sample database
BACKGROUND: Hematopoietic stem cell transplantation (HSCT) is a critical treatment for non-Hodgkin lymphoma (NHL) but involves significant post-transplant risks. Frailty, measured by the Modified Frailty Index-11 (mFI-11), may influence outcomes. METHODS: We analyzed NHL patients undergoing autologous or allogeneic HSCT from the Nationwide Inpatient Sample (2016-2020). Frailty was defined by mFI-11, and outcomes included in-hospital mortality, unfavorable discharge, prolonged length of stay (LOS), complications, and costs. Multivariable regression models with stratification by age and Charlson Comorbidity Index were applied. RESULTS: Of 2807 patients (ages 18-85), frail patients were older and had more comorbidities. In autologous HSCT, frailty was linked to higher mortality (2.02% vs. 0.27%, p < 0.001), increased unfavorable discharges (19.75% vs. 10.80%, p < 0.0001), and prolonged LOS (15.92% vs. 7.75%, p < 0.0001). Similar trends were observed in the allogeneic cohort. CONCLUSIONS: Frailty predicts worse post-transplant outcomes in NHL patients undergoing HSCT, emphasizing the need for pre-transplant frailty screening.
10 May 2026
Read appraisal →European journal of heart failure
Efficacy of finerenone in patients with heart failure and mildly reduced or preserved ejection fraction: a prespecified analysis of heart rate and heart rhythm in the FINEARTS-HF trial
AIMS: The association between heart rate (HR) and clinical outcomes is well understood in patients with heart failure with reduced ejection fraction (HFrEF) but less clear in those with HFmrEF/HFpEF, especially among individuals with atrial fibrillation (AF). In a prespecified analysis of the FINEARTS-HF trial, we examined the association between baseline HR and clinical outcomes by heart rhythm and evaluated finerenone's effect across the spectrum of HR. METHODS: The primary outcome was a composite of cardiovascular death and total (first and recurrent) HF events. Heart rhythm (sinus rhythm or AF) was determined from the baseline ECG. Patients with pacemaker rhythm or missing HR/rhythm data were excluded. RESULTS: Among patients with sinus rhythm (SR n = 3497; 62%), higher baseline HR was associated with a higher incidence rate for the primary outcome. In patients with AF (n = 2190; 38%), no association between HR and outcomes was observed. The effect of finerenone on the primary outcome was consistent across the HR spectrum, regardless of rhythm (P for interaction = 0.96 in SR; 0.49 in AF). In patients with SR, there was no significant HR change with finerenone versus placebo. In AF patients, finerenone led to a small but statistically significant HR reduction: a placebo-corrected decrease of 1.35 bpm (95% CI: 0.41-2.29) from baseline to 12 months. CONCLUSIONS: Among patients with HFpEF/HFmrEF in FINEARTS-HF, higher baseline HR was associated with a higher risk of the primary outcome in patients with SR but not in those with AF. Finerenone's effect on the primary outcome was consistent across the HR spectrum, irrespective of rhythm. TRIAL REGISTRATION: ClinicalTrials.gov NCT04435626.
10 May 2026
Read appraisal →Journal of physiology and biochemistry
Effects of acute, subacute, and chronic exercise on plasma s-Klotho levels: a systematic review and meta-analysis
Exercise exerts beneficial effects on multiple physiological systems, influencing biomarkers associated with aging processes and chronic diseases. This systematic review and meta-analysis aimed to evaluate the acute, subacute, and chronic effects of exercise on s-Klotho levels in healthy individuals and patients with chronic diseases. A comprehensive search was conducted in electronic databases. Included studies were randomized and non-randomized studies assessing the effects of aerobic, resistance, or combined exercise on s-Klotho levels. Interventions were classified as acute (single session), subacute (< 12 weeks), or chronic (≥ 12 weeks). Risk of bias was assessed using RoB 2 and ROBINS-I tools, and quality of evidence was evaluated with the GRADE framework. Forty-one studies were included in the qualitative synthesis, and 30 in the meta-analysis, comprising 2,765 participants (18-85 years). Twenty-five involved healthy individuals, while 21 included patients with chronic diseases. Acute and subacute aerobic exercise increased s-Klotho levels in healthy individuals (SMD 0.69; 95%CI 0.41-0.97) and diseased populations (SMD 0.62; 95%CI 0.11-1.12). Chronic exercise (12-36 weeks) showed significant increases in healthy (SMD 0.57; 95%CI 0.33-0.82) and diseased populations (SMD 1.51; 95%CI 0.87-2.16). Resistance exercise thrice weekly demonstrated the highest effect (SMD 1.60; 95%CI 0.81-2.38). Most studies had high risk of bias, with quality of evidence ranging from very low to moderate. Exercise significantly increases s-Klotho levels, with acute and subacute aerobic sessions benefiting healthy and diseased populations, while chronic resistance exercise elicited greater responses in diseased populations. Despite promising findings, low methodological quality suggests cautious interpretation.
10 May 2026
Read appraisal →Current osteoporosis reports
The Use of Statistical Shape and Appearance Modelling for Predicting Osteoporotic Fracture Risk
PURPOSE OF REVIEW: Osteoporotic fractures remain a major cause of morbidity and mortality worldwide. Current clinical assessment metrics (e.g., bone mineral density) are limited in their ability to identify fracture risk and bone strength. Statistical Shape and Appearance Modelling (SSAM) offers a method to quantify anatomical geometry and density patterns. This review examines advancements in SSAM for osteoporosis research. RECENT FINDINGS: Recent literature demonstrates that SSAM can capture detailed bone geometry and internal density distribution. Increasingly, these models are combined with computational analytics, including finite element analysis and machine learning, to assess the mechanical and structural behavior of bone. SSAM provides a robust quantitative framework for bone research. Notably, SSAM is used to reconstruct 3D subjects from clinical 2D images for biomechanical evaluation. Although clinical adoption remains limited by generalizability, the advancement of deep learning and complex SSAM pipelines supports its potential for osteoporosis screening and fracture risk prediction.
10 May 2026
Read appraisal →Sleep & breathing = Schlaf & Atmung
What is the most effective non-pharmacological treatment for poor sleep quality in Chronic obstructive pulmonary disease patients? a systematic review.
BACKGROUND: Chronic obstructive pulmonary disease (COPD) features poor sleep quality due to nocturnal dyspnea and psychological distress, leading to increased hospital admissions and reduced quality of life. Considering patient preference and existing evidence, non-pharmacological intervention, i.e. cognitive behavioral therapy for insomnia (CBT-I), on sleep for COPD patients had its potential efficacy. However, current guidelines do not include sleep management as standard care, and there is a lack of comprehensive reviews on this topic. This systematic review hence evaluated the effectiveness of non-pharmacological treatments for poor sleep in COPD patients. METHODS: Literature identification was conducted through MEDLINE, CINHAL PLUS, AMED and ScienceDirect under PRISMA guidance. Randomized controlled trials, cohort studies and cross-sectional studies regarding poor sleep quality among COPD population were retrieved. Abstract, title and full text screening were performed by the author and assessed by a second reviewer. Risk of bias and quality appraisal was done with JBI scale. RESULTS: 12 articles (783 patients) were identified, including 9 randomized controlled trials, 1 cohort study and 2 cross-sectional studies. The risk of bias (quality) was ranked: low (high) for 3, moderate (moderate) for 7 and high (low) for 2. Several interventions were identified (number of study): CBT-I (2) and cognitive behavioral therapy (1), pulmonary rehabilitation (PR) (2), progressive muscle relaxation technique (PMRT) (2), non-invasive ventilation (NIV) (3), relaxation exercise (RE) (1) and therapeutic touch (TT) (1). Significant improvement in sleep quality was reported in PMRT, TT and RE. CONCLUSION: PMRT, TT and RE showed improvement in sleep quality in COPD patients yet unable to rank their effectiveness as heterogeneous outcome measures across studies. No substantial evidence was capable to demonstrate effectiveness of CBT-I, CBT, PR and NIV to explicit COPD patients. Moreover, future research including combination of non-pharmacological treatments is needed.
10 May 2026
Read appraisal →Neuropsychologia
Sparsity and memory constraints interact with training sequence to bias learning of associative maps.
Cognitive maps support inference and planning by representing associations between experiences encoded in memory. These map-like representations are thought to carry information not only about directly observed links but also about longer paths. The ability to make judgments based on multi-step associations varies with one's experience in an environment and with changes in memory abilities across the lifespan. However, it remains unclear exactly how representations of associative structure are influenced by learning curricula and memory constraints. Prior studies have suggested a tradeoff: memory representations can either be more integrated to improve inference, or more separated to recall distinct direct associations. Whether overlapping associations are experienced nearby in time (interleaved) or spaced apart (blocked) can bias memory representations toward integration or separation. However, key recent findings about how blocked versus interleaved experience bias integration or separation have been inconsistent. Here, we introduce a computational framework that helps reconcile these apparent discrepancies. Using neural network simulations of three separate memory-guided inference tasks, we show that variations in memory capacity and the sparsity of neural codes interact with learning sequence to shape network representations. Specifically, blocked learning promotes integration when memory capacity is low, while interleaved learning promotes integration when memory capacity is high. Integration is especially likely to result from representations formed when neural codes are both sparse and distributed. These results offer a principled computational account of how flexible, map-like representations can arise from experience and suggest avenues for individualized memory interventions to improve inference, generalization, and planning.
10 May 2026
Read appraisal →Carbohydrate polymers
Multifunctional starch-based eutectogels with high mechanical properties, adhesion, self-healing, fatigue resistance and degradability for flexible wearable sensing
Flexible wearable sensors have developed rapidly due to the urgent demands for human motion monitoring. Eutectogel-based soft materials have exhibited great potential for wearable sensing applications. However, it is still a challenge to develop multifunctional eutectogels with excellent mechanical properties, adhesion, self-healing, fatigue resistance and degradability. In this work, a multifunctional starch-based eutectogel was developed by a one-pot and in-situ photo-polymerization method. The eutectogel had excellent stretchability (2710%), high strength (5.43 MPa), toughness (94.38 MJ/m3), Young's modulus (10.23 MPa), adhesion, fatigue resistance (1000 cycles), degradability and a certain degree of self-healing ability. These properties were closely correlated to the synergistic effect among the amylopectin, polyacrylic acid and choline chloride via hydrogen-bond and electrostatic interactions. The mechanical properties of eutectogel can be adjusted by changing the starch content. A wearable sensor based on the eutectogel was realized accurate and stable monitoring of human joint motions. This study addressed the limitations of the dehydration and single function of the traditional hydrogels, and provides a novel way for the development of the multifunctional bio-based gels for next-generation flexible wearable devices.
9 May 2026
Read appraisal →Asian journal of psychiatry
Machine learning prediction model for delirium after heart valve replacement with cardiopulmonary bypass: A large-scale cohort study
OBJECTIVE: Postoperative delirium (POD) is a common and severe complication following heart valve replacement (HVR) with cardiopulmonary bypass (CPB), associated with poor outcomes. Accurate early prediction is crucial for targeted prevention. This study aimed to develop and externally validate machine learning (ML) models for predicting POD in this high-risk population. METHODS: We conducted a retrospective cohort study involving 1076 adult patients who underwent HVR with CPB between January 2018 and December 2022. POD was assessed using the CAM-ICU. Perioperative factors were analyzed to develop a traditional logistic regression-based nomogram and four ML models. The best-performing model was then tested on an independent external validation cohort of 281 patients. RESULTS: The incidence of POD was 25.1% (270/1076) and was associated with significantly increased mortality (11.9% vs. 0.2%, P < 0.001) and prolonged hospitalization. In the internal test set, the Random Forest (RF) model demonstrated the highest predictive performance with an AUROC of 0.854 (95% CI, 0.764-0.945), outperforming the traditional nomogram (AUROC: 0.754). The RF model maintained robust performance in the external validation cohort, achieving an AUROC of 0.793. Model interpretation using SHAP analysis identified postoperative awakening time (PAT), postoperative mechanical ventilation time (PMVT), and postoperative reintubation (POR) as the most influential predictors. CONCLUSIONS: Machine learning models, particularly Random Forest, provide a robust and generalizable tool for the early prediction of POD after HVR. These models outperform traditional statistical approaches and offer interpretable insights, highlighting PAT as a critical, modifiable target for perioperative management to mitigate POD risk.
9 May 2026
Read appraisal →International journal of medical informatics
Development of an interpretable machine learning model for predicting 4-year chronic kidney disease risk in elderly hypertensive patients
INTRODUCTION: Age and hypertension are key drivers of renal impairment, predisposing older hypertensive adults to faster kidney function decline and higher mortality. We aim to develop an interpretable machinelearning model to predict 4-year chronic kidney disease (CKD) risk in this population. METHODS: Our study incorporated 4,142 hypertensive patients from the Health and Retirement Study (HRS) 2010 and 2012 cohorts for model development and internal validation, with additional temporal validation performed within the HRS 2006 and 2008 cohorts. External validation was conducted using three distinct subcohorts derived from the China Health and Retirement Longitudinal Study (CHARLS) database. Feature selection was implemented through an integrated LASSO-Boruta algorithm, followed by model construction using eight machine learning approaches. Discriminative performance was rigorously evaluated through multiple metrics, including receiver operating characteristic (ROC) curve analysis, accuracy, sensitivity, specificity, and Brier score. The optimal model underwent interpretability analysis via SHapley Additive exPlanations (SHAP) to elucidate decision-making mechanisms and was subsequently deployed as a web-based clinical prediction tool. RESULTS: Using a combined LASSO-Boruta strategy, we identified nine routinely available predictors for model development. In the training set, SVM achieved the highest AUC (0.735), closely followed by XGBoost (0.734); notably, in the temporal validation cohort, XGBoost was the only model with an AUC > 0.700 (0.702). Overall performance metrics derived from confusion matrices, together with Brier scores, suggested that XGBoost provided a favorable balance between sensitivity and specificity while maintaining acceptable probabilistic calibration. Calibration curves further suggested that XGBoost showed relatively stable agreement between predicted and observed risks across datasets, supporting its selection for subsequent SHAP-based interpretation and web deployment; SHAP identified age as the leading contributor to CKD risk. CONCLUSIONS: We developed an interpretable model using routine clinical indicators to predict 4-year CKD risk in elderly hypertensive adults, with applicability across Asian and Caucasian populations.
9 May 2026
Read appraisal →Abdominal radiology (New York)
Advancing osteoporosis opportunistic screening: multicenter validation of a deep learning algorithm using abdominal CT scans
OBJECTIVES: To develop and do multicenter validation on an algorithm that screens for osteoporosis from abdominal CTs. METHODS: This is a diagnostic accuracy study with retrospective data from January 2022 to July 2022 consisting of two steps: a segmentation step of the lumbar vertebral bodies, involving outpatient non-contrast abdominal CTs from Diagnósticos da América S/A (Dasa), and a multicenter validation step incorporating data from four additional institutions. The segmentation employed a 2D UNet with a ResNet34 backbone. We determined the Pearson correlation coefficient (r) between the mean of the slices' mean attenuations (MSMA) on CT scans against the bone mineral density (BMD) on recent DEXA scans, calculated AUCs and performance metrics for osteoporosis prediction, including 95% confidence intervals, and evaluated calibration plots. RESULTS: The multicenter validation included 504 participants (median age, 66 years, interquartile range 56-72; 388 women). A linear regression analysis showed an r of 0.62 (95% CI, 0.56-0.67) between MSMA (HU) and BMD (g/cm2). The AUCs (95% CI) for distinguishing between normal and osteoporosis were 0.96 (0.89, 1.0) for the internal dataset and 0.82 (0.74, 0.89) for the external dataset, and the performance metrics (95% CI), for a globally optimized threshold of 202.6 HU, were 100% sensitivity (44, 100) and 91% specificity (84, 95) for internal data and 79% sensitivity (61, 90) and 81% specificity (76, 84) for external sites. MSMA was independently associated with osteoporosis on a mixed-effects logistic regression analysis. The model showed good calibration, with Brier score of 0.054. CONCLUSION: We developed and performed a multicenter validation of a DL model for osteoporosis prediction on CT.
6 May 2026
Read appraisal →European radiology
Enhancing vertebral fracture prediction using multitask deep learning computed tomography imaging of bone and muscle
OBJECTIVES: To develop and externally validate a computed tomography (CT)-based multitask learning model to predict fracture risk. MATERIALS AND METHODS: This study was conducted in two parts, using a multitasking learning approach. We developed a cross-sectional vertebral fracture (VF) detection model using abdominal CT scans of 2553 patients aged 50-80 years. Then, we leveraged this detection model within a multitask learning framework to develop a longitudinal VF prediction model over a 5-year follow-up period. External testing was performed on 1506 patients from two independent hospitals. The performance was compared between the single-task and multitask models, bone-only and bone+muscle images, and image-only and clinical models. RESULTS: For the cross-sectional fracture detection model, the mean age of the patients was 76.2 years, and 66.7% were female. In the classification task for detection of VF, the model using both bone and muscle showed an area under the receiver operating characteristic curve (AUROC) of 0.82 in the development set and 0.80 in the external test sets. Using multitask learning, the bone + muscle image model showed a c-index of 0.68 and had superior performance than the bone-only model in the external test set for 2-year, 3-year, and 5-year AUROCs (0.79 vs. 0.75, 0.71 vs. 0.68, and 0.71 vs. 0.68, respectively, all p < 0.01). Also, the multitask model significantly outperformed the Fracture Risk Assessment Tool (FRAX) (c-index: 0.68 vs. 0.66, p < 0.01). CONCLUSION: The CT-based multitask learning model integrating both bone and muscle data showed superior predictive performance for VFs compared with models using bone images only and traditional clinical models. KEY POINTS: Question Vertebral fracture risk remains underestimated in many individuals undergoing CT scans for other reasons, highlighting the need for improved opportunistic prediction tools. Findings A multitask deep learning model integrating both bone and muscle features from CT scans demonstrated superior performance compared to bone-only and traditional clinical models, including FRAX. Clinical relevance The proposed model enables accurate vertebral fracture risk prediction using routinely acquired CT scans, facilitating early identification and intervention without the need for additional tests.
6 May 2026
Read appraisal →Journal of applied gerontology : the official journal of the Southern Gerontological Society
Anomaly Detection Technologies for Dementia Care: Monitoring Goals, Sensor Applications, and Trade-offs in Home-Based Solutions-A Narrative Review
Anomaly detection technologies are increasingly used to monitor people living with dementia (PLwD) in home settings, addressing critical behaviors such as wandering, sleep disturbances, and agitation. This narrative review examines technologies used for detecting behavioral anomalies, the activities they monitor, and the trade-offs between their benefits and limitations. A systematic search across MEDLINE, IEEE Xplore, ACM Digital Library, and Web of Science identified 78 studies, categorized through thematic analysis. Three primary motivations emerged: early diagnosis, safety monitoring, and reducing caregiver stress while promoting autonomy. Technologies include GPS tracking, wearables, environmental sensors, and smart home systems, each with benefits like real-time alerts and non-intrusive monitoring but also challenges such as user compliance, false positives, and privacy concerns. While these systems enhance safety and autonomy, improving sensor accuracy, integrating AI for personalized interventions, and addressing ethical concerns are essential for long-term effectiveness and supporting the well-being of both PLwD and caregivers.
5 May 2026
Read appraisal →International journal of psychophysiology : official journal of the International Organization of Psychophysiology
Task-evoked brain network dynamics underlying cognitive control using source-localized EEG and machine learning
OBJECTIVE: Functional connectivity (FC) coordinates brain activity during cognitive tasks, yet the influence of demographic variables and health factors on task-related dynamic FC remains underexplored. We examined FC within and between seven core brain networks: Default Mode (DMN), Ventral Attention (VAN), Somatomotor (SMN), Dorsal Attention (DAN), Frontoparietal (FPN), Visual (VIN), and Limbic (LIN) during a modified Eriksen flanker task. We aimed to identify distinct network configurations linked to varying cognitive control demands and assess the influence of demographic and health factors. METHODS: Secondary analyses of source-localized EEG data from 255 adults (mean age 38.97 ± 11.65 years; BMI 30.21 ± 9.29 kg/m2) were conducted. A hidden Markov model extracted spatiotemporal dynamics, quantifying within- and between-network correlations. Extreme Gradient Boosting with recursive feature elimination identified connectivity metrics predicting reaction time (RT) of high performers (>80% accuracy). Regression models assessed effects of age, sex, BMI, and income on task-related FC. RESULTS: Classification accurately predicted RT in both congruent (AUC = 0.81) and incongruent (AUC = 0.86) trials. Key FC features included VAN-LIN, VIN-VAN, and SMN-DAN for congruent trials; SMN-DMN, LIN-FPN, and VIN-DAN for incongruent trials. Age was linked to reduced FC, while higher BMI showed modest positive FC associations in LIN-FPN and VIN-DAN. Sex and income were not significant predictors of RT or FC. CONCLUSIONS: These findings reveal the relevance of specific dynamic network interactions in cognitive control and highlight the need to consider age and BMI as contributors to brain connectivity during task performance.
4 May 2026
Read appraisal →IEEE transactions on bio-medical engineering
Extending Multiscale Characterization of Heart Rate Variability via Deep Learning for Mortality Risk Prediction
OBJECTIVE: To improve mortality risk prediction from heart rate variability (HRV) signals by capturing nonlinear scaling patterns often overlooked by traditional linear analyses. METHODS: This study combines detrended moving average (DMA) analysis with convolutional neural networks (CNNs). DMA curves were computed from 2-hour overlapping windows of 24-hour Holter ECG recordings in 916 survivors and 70 nonsurvivors. A CNN was trained to extract features from these curves and benchmarked against models using traditional HRV and clinical features. RESULTS: The CNN achieved an ROC-AUC of 0.72 and an adjusted hazard ratio of 2.129 for daytime recordings, outperforming standard models. Two patient groups emerged based on DMA scaling patterns. Group 1, with dominant short-term scaling, exhibited reduced slopes in nonsurvivors, suggesting impaired autonomic adaptability. Group 2 showed earlier transitions between short- and long-term behavior, where reduced long-term slopes more strongly predicted mortality. Integrated gradients analysis identified key timescales in the DMA curve driving model predictions. CONCLUSION: DMA combined with CNNs enhances HRV-based mortality risk stratification and reveals distinct physiological scaling patterns associated with survival outcomes. SIGNIFICANCE: This study highlights the potential of DMA and CNNs in improving mortality risk stratification and providing mechanistic insights into HRV dynamics, with implications for personalized health monitoring.
4 May 2026
Read appraisal →Journal of affective disorders
Plasma metabolomics identifies lipid mediators linking depression and cognitive decline in late-life depression.
BACKGROUND: Late-life depression (LLD) features recurrent episodes and frequently co-exists with cognitive impairment, which predicts worse outcomes and progression to dementia. Evidence indicates a bidirectional depression-cognition relationship, but objective biological tools to capture severity and this interplay are scarce. METHODS: This study compared 110 patients with LLD between depressive episodes and remission phases. Based on untargeted liquid chromatography-mass spectrometry (LC-MS) metabolomics analysis of plasma samples, we identified key metabolites and developed four predictive machine learning models (GLMNet, LDA, Naive Bayes, and cTree). Additionally, Spearman rank correlation analysis and mediation analysis were conducted to further investigate the relationships and mediating effects of the key metabolites. RESULTS: The diagnostic model based on key metabolites selected by the random forest algorithm showed good discriminatory performance in distinguishing LLD Episodes (AUC = 0.824). Tridecanoylcarnitine (Car(13:0)), PC(P-16:0/22:6), and SM(d18:1/22:0) were significantly downregulated during the depressive episode. Tridecanoylcarnitine (Car(13:0)) negatively correlated with depressive severity (p < 0.001) and positively with Mini-Mental State Examination scores. PC(P-16:0/22:6) was associated with both emotional and cognitive impairments. Mediation analysis supported that Tridecanoylcarnitine (Car(13:0)) and PC(P-16:0/22:6) partially mediated the depression-cognition relationship, explaining 22.0 % and 26.9 % of the variance, respectively (p < 0.05). CONCLUSION: This study reveals specific lipid metabolic dysregulation in LLD and identifies key metabolites significantly associated with both depressive severity and cognitive function. It further supports their mediating role in the comorbidity between depression and cognitive impairment. These metabolites may serve as potential targets for simultaneously regulating depression and cognition. The machine learning model developed provides a new auxiliary tool for the objective assessment of LLD.
4 May 2026
Read appraisal →PloS one
Interpretable multivariate survival models: Improving predictions for conversion from mild cognitive impairment to Alzheimer's disease via data fusion and machine learning
Accurately predicting which individuals with mild cognitive impairment (MCI) will progress to Alzheimer's disease (AD) can improve patient care. This study examines the role of quantitative MRI (qMRI), cognitive evaluations, apolipoprotein [Formula: see text]4 (APOE [Formula: see text]4), and cerebrospinal fluid (CSF) biomarkers in Cox survival models to predict progression from MCI to AD. Data from 564 participants in the ADNI study, who transitioned from MCI to AD, were analyzed. The data set included 330 features encompassing qMRI, cognitive assessments, CSF biomarkers, and APOE [Formula: see text]4 status. Advanced machine learning (ML) methods were applied to evaluate the importance of these data sources, select relevant features, and develop interpretable Cox survival models within a cross-validation framework. The top optimized model achieved a sensitivity of 0.69, 95% CI [0.63, 0.76], and a specificity of 0.87, 95% CI [0.83, 0.90], and used all data sources. The results demonstrated that combining qMRI features with cognitive assessments, CSF biomarkers, and APOE [Formula: see text]4 status, analyzed using the BSWiMS model, resulted in a substantial improvement in the ability to predict progression from MCI to AD, achieving 81% precision and 87% specificity. These results exceed those obtained with other models evaluated. Finally, biomarker analysis showed that cognitive scores are the most relevant features to predict conversion, followed by CSF and qMRI biomarkers. These findings highlight the value of integrating multiple data sources in highly interpretable Cox survival models for the early identification of individuals at risk for AD.
4 May 2026
Read appraisal →Polish archives of internal medicine
Systematic review and meta-analysis of the safety and efficacy of transvenous lead extraction in octogenarian patients
INTRODUCTION: As the global population ages and the use of cardiac implantable electronic devices (CIEDs) increases, transvenous lead extraction (TLE) is being performed more frequently in elderly patients. Concerns remain regarding its safety and efficacy in octogenarians, and age‑specific recommendations are limited. OBJECTIVES: We aimed to assess procedural success, complication rates, and mortality associated with TLE in patients aged 80 years or older in comparison with younger individuals. PATIENTS AND METHODS: A systematic search of PubMed, Cochrane CENTRAL, SciELO, and ScienceDirect was conducted up to December 2024. Studies reporting TLE outcomes in octogenarians and younger patients were included according to predefined criteria. A meta‑analysis was performed to compare procedural success, complications, and mortality between the age groups. RESULTS: Seventeen studies including 15 984 patients were analyzed. Complete lead extraction success was 96.7% in octogenarians and 96.2% in the patients aged below 80 years. No significant differences were observed in major or minor procedural complications. In‑hospital mortality was low and comparable between the groups. Although long‑term mortality was higher in the octogenarians, this reflected greater baseline comorbidity and frailty rather than increased procedural risk. None of the included studies demonstrated higher periprocedural danger of TLE in elderly patients. CONCLUSIONS: TLE is safe and effective in patients aged 80 years and older, with outcomes comparable to younger individuals. Advanced age alone should not preclude referral for lead extraction.
3 May 2026
Read appraisal →JMIR aging
The Effectiveness of Digital Interventions to Increase Preventive Care Uptake in Older Adults: Systematic Review
BACKGROUND: Older adults face increasing health risks associated with aging and chronic disease; yet, uptake of recommended clinical preventive services remains low. Digital health interventions have the potential to enhance access and engagement, but their effectiveness in older adult populations remains unclear. OBJECTIVE: This systematic review aimed to examine the range and types of digital clinical preventive service interventions and assess their impact on preventive care uptake among older adults. METHODS: We conducted a systematic review of peer-reviewed research literature published since 2014. Eligible studies included experimental and quasi-experimental designs evaluating digital interventions targeting community-dwelling adults aged 60 years and older. Interventions focused on high-priority preventive services, including cancer screening and adult immunizations. Data were extracted using a standardized form and synthesized narratively due to heterogeneity in study designs and outcomes. RESULTS: In total, 24 studies involving over 1.3 million participants from 11 countries were included. Interventions used a range of digital tools, including telephone calls, SMS text messages, patient portals, and video-based education. While some digital and automated interventions demonstrated modest improvements in preventive services uptake, results were mixed. Interventions incorporating personalized elements (eg, tailored telephone counseling or in-person education) were generally more effective than generic, automated communications. Few studies reported on digital literacy support or intervention reach, and engagement with digital platforms was often low. CONCLUSIONS: Digital interventions can support modest improvements in preventive services uptake among older adults, particularly when personalized or combined with human interaction. However, assumptions of digital fluency and limited reporting on engagement constrain generalizability. Future research should prioritize inclusive design, detailed reporting, and strategies that address digital equity to better support older adult populations.
3 May 2026
Read appraisal →World journal of urology
Thulium fiber laser versus Holmium laser enucleation of the prostate: 24-month outcomes from a prospective randomized non-inferiority trial
PURPOSE: To compare 24-month outcomes of Thulium fiber laser enucleation of the prostate (ThuFLEP) and Holmium laser enucleation of the prostate (HoLEP). METHODS: A 24-month follow-up analysis of a prospective randomized trial including 150 patients (ThuFLEP n = 74, HoLEP n = 76) was conducted. Primary outcome was non-inferior International Prostate Symptom Score (IPSS) and quality of life (QoL) reduction. Secondary outcomes included urinary continence, medication use reduction, erectile and ejaculatory function, and late adverse events. RESULTS: At 24 months, non-inferiority of ThuFLEP regarding IPSS and QoL was confirmed (p = 0.03). Median IPSS improved from 21 preoperatively to 3 after ThuFLEP and from 20 to 4 after HoLEP (p < 0.001), with no significant intergroup differences at any time point (all p > 0.1). Median QoL improved from 4 to 1 in both groups (p < 0.001), likewise without significant differences at any time point (all p > 0.3). Continence rates improved from 74.0% preoperatively to 90.0%, pad usage decreased from 89.8% to 25.4% and ICIQ-SF decreased from 5 to 3 points (all p < 0.001), without intergroup differences (all p > 0.1). Use of medication for lower urinary tract symptoms decreased from 89.4% to 7.7% (p < 0.001). Ejaculatory function declined, while ejaculation-related bother remained unchanged and erectile function improved modestly, all without intergroup differences (all p > 0.1). Three late adverse events occurred exclusively in the ThuFLEP group, without statistical significance (p > 0.1). CONCLUSION: ThuFLEP remains non-inferior to HoLEP for patient-reported functional outcomes at 24 months and demonstrates comparable safety, continence, medication use reduction, and sexual function results over time. Both techniques represent effective, durable and safe surgical options for treating benign prostatic obstruction. TRIAL REGISTRATION NUMBER: DRKS00032699 (18.09.2023, retrospectively registered).
3 May 2026
Read appraisal →ACS applied materials & interfaces
Fingerprint-Inspired Recyclable Electronic Skin for Non-Contact Urinary Incontinence Monitoring and Sensing
The development of high-performance flexible electronics faces a fundamental challenge: balancing sophisticated multimodal sensing and end-of-life environmental sustainability. Drawing inspiration from the human fingerprint, we present a microelectronic-printing-based arch-shaped sensor that resolves this conflict by integrating dual-mode (contact/non-contact) sensing with inherently recyclable components. The device, fabricated through high-resolution printing of liquid metal circuits (with line widths below 200 μm) on functionally tailored substrates, exhibits a sensitivity of 3.07 kPa-1 and a response time under 60 ms. It operates across a broad pressure range (0-15 kPa) in contact mode and senses proximity up to 5 cm. This robust performance enables an integrated wireless system for encrypted communication, gesture-trajectory tracking, and dynamic gesture recognition with 92.67% classification accuracy. Notably, we demonstrate a closed-loop lifecycle for the key functional materials, achieving 86.2% recovery and direct reuse of the liquid metal conductor. This work provides a versatile design strategy that simultaneously meets the demands for performance, functionality, and sustainability, offering a practical platform for future green and intelligent wearable technologies.
3 May 2026
Read appraisal →Journal of medical Internet research
Effects of a Soft Robotic Exoskeleton for Gait Training on Clinical Outcomes in Patients With Parkinson Disease: Randomized Controlled Pilot Study
BACKGROUND: Balance and gait disorders in Parkinson disease (PD) impair motor function and quality of life. OBJECTIVE: Evidence on soft exoskeleton robots (SERs) for PD rehabilitation is limited. This study evaluated the impact of SERs on motor dysfunction in PD. METHODS: A total of 56 people with PD (July 2023 to May 2024) were randomized to 2 groups: the control group (n=25, 44.6%) received conventional rehabilitation, and the experimental group (n=31, 55.4%) received conventional rehabilitation combined with SER training (ChiCTR2500111990). Training occurred 5 times per week for 20 minutes each session over 4 weeks. Primary outcomes included gait speed and stride length, while secondary outcomes assessed the percentage of swing phase, ankle joint range of motion, Unified Parkinson Disease Rating Scale total and motor scores, and Montreal Cognitive Assessment. Paired sample t tests (2-tailed) were used for within-group pre- and postintervention comparisons, and independent sample t tests (2-tailed) were used for between-group comparisons. Correlation analyses were conducted between gait parameters and improvements in ankle mobility. RESULTS: After 4 weeks, the experimental group showed significant improvements in gait and balance. Specifically, left stride length increased by a mean of 0.15 (SD 0.16; 95% CI 0.09-0.21) m (P<.001), right stride length by a mean of 0.15 (SD 0.15; 95% CI 0.10-0.21) m (P<.001), left ankle dorsiflexion by a mean of 2.84 (SD 1.46; 95% CI 2.32-3.36) degrees (P<.001), left swing phase percentage by a mean of 1.56% (SD 3.05%; 95% CI 0.44-2.68; P=.01), and right swing phase percentage by a mean of 1.6% (SD 2.72%; 95% CI 0.62-2.62; P=.002). The Unified Parkinson Disease Rating Scale Part III total score decreased by a mean of 2.80 (SD 3.98) points, and balance subscale scores decreased by a mean of 0.40 (0.58) points (P<.001). Montreal Cognitive Assessment scores increased by a mean of 1.23 (1.23; 95% CI 0.77-1.68) points (P<.01), and Barthel Index scores increased by a mean of 6.84 (7.14; 95% CI 4.22-9.46) points (P<.001). Other measures such as balance reaction time, reaction speed, maximum movement distance, and movement direction control showed significant improvement (P<.01). Compared to the control group, the experimental group demonstrated greater improvements in gait speed (P=.04), balance reaction time (P=.04), and maximum movement distance (P=.048). Correlation analysis revealed that improvements in left ankle dorsiflexion were positively correlated with improvements in gait speed, stride length, and swing phase duration (P<.05). CONCLUSIONS: SER-assisted training significantly improves gait, balance, and PD symptoms. Our work integrates multidimensional assessments (gait analysis, balance metrics, and clinical scales) and reveals that gains in ankle mobility directly correlate with gait improvements, suggesting a key mechanism. This study contributes by establishing SER as an effective adjunct to conventional therapy, supported by comprehensive quantitative data.
3 May 2026
Read appraisal →JMIR research protocols
Geriatric Telemanagement of Health Conditions (GET HEALTH) in Nursing Home Residents Recently Discharged From the Hospital: Protocol for a Before-After Study
BACKGROUND: Nursing home (NH) residents have a high burden of multimorbidity and disability and are frequently hospitalized. Comprehensive geriatric assessment might reduce hospitalization. However, few geriatricians are working in NHs. Technology might be used to overcome this problem. OBJECTIVE: This study aims to verify if a geriatric telemanagement model improves patient outcomes in NH residents recently discharged from the hospital. METHODS: Clinical outcomes of NH residents recently discharged from the hospital are compared before and after the implementation of GTM, where a geriatrician evaluates each patient during televisits with NH staff and discusses the case with the general practitioner. NH residents aged ≥70 years readmitted to NHs after hospitalization in the 3 years prior to the COVID-19 pandemic (2017-2019) represent the control group enrolled in the retrospective preintervention phase. The primary outcome of the study is the hospital readmission rate during 6-month follow-up. Health technology assessment evaluates the cost-effectiveness of model implementation. RESULTS: A total of 333 NH residents were included in the preintervention retrospective study. Data collection started in February 2022 and was completed in February 2023. A total of 104 NH residents were recruited in the intervention study, from March 2024 to March 2025. Data analysis is currently in progress. CONCLUSIONS: Currently, NHs in Italy are striving to manage the complexity of older patients who are often hospitalized. A geriatric telemanagement model might reduce hospitalizations, and therefore negative outcomes, as well as health care costs in this vulnerable population.
3 May 2026
Read appraisal →Biomedical physics & engineering express
A novel machine learning approach for prediction of postoperative delirium using multi-domain features of high-density EEG
Background.Postoperative delirium (POD) poses a significant risk to patients, and accurate prediction of POD can provide guidance for positive interventions. Although many studies have applied machine learning (ML) to electronic health records to predict POD, there has been a lack of studies utilizing electroencephalogram (EEG) data to accurately predict POD.Methods.This retrospective study included 142 patients from two hospitals, among whom 33 developed POD. We extracted multi-domain features from high-density EEG (HD-EEG), screened 39 key features using recursive feature elimination, and analyzed the feature space structure using linear discriminant analysis (LDA). Four ML models, namely support vector machine (SVM), logistic regression (LR), random forest (RF), and gradient boosting machine, were compared. The dataset was divided using 5-fold cross-validation for model training and testing.Results.Compared to the other three models, SVM (linear kernel) performed best in predicting POD, achieving a classification accuracy of 95.71%. Alpha (8-12 Hz) and theta (4-8 Hz) powers, combined with nonlinear dynamics, critically contributed to the model. Furthermore, the performance of the linear models (SVM and LR) was significantly (p< 0.001) better than that of the nonlinear models, with LDA confirming strong feature space separability (Jensen-Shannon divergence = 0.69).Conclusions.HD-EEG data can be used to establish high-quality ML models for POD predictions. Multidomain features enable more comprehensive integration of EEG information related to delirium. The feature space structure significantly affects the performance of the prediction model, with the linear model offering distinct advantages in predicting POD.
2 May 2026
Read appraisal →Molecular neurobiology
Establishing a Diagnostic Model Based on Plasma CDR1as and Alpha-Synuclein for Mild Cognitive Impairment in Type 2 Diabetes
Emerging evidence implicates cerebellar degeneration-related protein 1 antisense transcript (CDR1as) and alpha-synuclein (α-Syn) in diabetes and cognitive decline, suggesting their potential to serve as biomarkers. This study aimed to evaluate their diagnostic efficacy for type 2 diabetes mellitus-associated mild cognitive impairment (T2DM-MCI) and establish a corresponding diagnostic model using machine learning. T2DM-MCI-associated differentially expressed genes were identified from the hippocampus using the Gene Expression Omnibus and predictive databases. The study involved 529 T2DM patients in two phases (training set = 408, test set = 121). Levels of plasma CDR1as, α-Syn, Aβ1-40/1-42, and ApoE genotype were detected. Three diagnostic models were identified using least absolute shrinkage and selection operator (LASSO) and logistic regression. Their performance was assessed using the area under the curve (AUC), net reclassification index (NRI), and integrated discrimination improvement (IDI). The best-performing model was selected from nine machine learning algorithms. Personalized risk assessment was achieved with the SHapley Additive exPlanations (SHAP). Bio-informatics analysis identified CDR1as and α-Syn as potential biomarkers. Their inclusion in multi-modal panels markedly improved diagnostic accuracy. The Random Forest (RF) model outperformed eight other machine learning classifiers (AUC = 0.975, 95% CI: 0.946-0.999) with the lowest Brier score (0.054). When evaluated on a test set, the RF model maintained robust performance (AUC = 0.901, accuracy = 0.850). An online diagnostic tool was established ( https://www.xsmartanalysis.com/model/list/predict/model/html?mid=6734&symbol=816GyBh9ag1Wr9367341 ). The study preliminarily identified CDR1as and α-Syn as promising biomarkers for T2DM-MCI. Integrating these markers with conventional blood tests showed potential clinical utility for improving diagnostic precision.
2 May 2026
Read appraisal →Neurology
Association of MRI-Visible Perivascular Spaces With Longitudinal Cognitive Decline Over a Decade
BACKGROUND AND OBJECTIVES: Cerebral small vessel disease (SVD) is the most common vascular contributor to dementia. SVD markers often coexist, contributing to difficulty assessing their independent contributions to cognitive domains. MRI-visible perivascular spaces (PVSs) are an emerging SVD marker visualized by MRI. We previously showed that basal ganglia PVSs cross-sectionally contribute to worse cognition, independent of other SVD markers. To further characterize the clinical relevance of PVS, we studied its role as a unique SVD marker of longitudinal cognitive decline. METHODS: Participants without stroke or dementia were included in the Vanderbilt Memory and Aging Project, a longitudinal observational cohort study based in Nashville, TN. Participants completed 3T MRI at study entry to measure SVD burden (PVS volume fraction, white matter hyperintensities volume, lacune counts, and cerebral microbleeds counts). PVS volumes were segmented using a deep learning algorithm. Participants underwent comprehensive serial neuropsychological testing over an 11-year follow-up period (mean follow-up = 4.9 ± 3.1 years). Each SVD marker was related to longitudinal neuropsychological performances using a linear mixed-effects model adjusting for age, sex, race/ethnicity, education, baseline cognitive status, apolipoprotein E-ε4 presence, Framingham Stroke Risk Profile, and intracranial volume. Head-to-head comparisons simultaneously tested multiple statistically significant SVD markers. RESULTS: Among participants (n = 750, age 68 ± 9 years, 52% female), higher basal ganglia PVS burden at baseline was associated with worse longitudinal performances in Boston Naming Test (β = -29.63; 95% CI -56.66 to -2.60), Animal Naming (β = -33.09; 95% CI -65.66 to -0.51), Wechsler Adult Intelligence Scale IV Coding (β = -86.36; 95% CI -150.9 to -21.82), executive function composite (β = -9.51; 95% CI -14.33 to -4.68), Hooper Visual Organization Test (β = -26.06; 95% CI -50.53 to -1.59), and episodic memory composite (β = -7.05; 95% CI -11.9 to -2.21). In head-to-head comparisons, basal ganglia PVS remained independent associations with executive function composite (β = -7.47; 95% CI -12.84 to -2.10) and Hooper Visual Organization Test (β = -22.11; 95% CI -42.38 to -1.85). DISCUSSION: Basal ganglia PVS burden independently contributes to worse longitudinal executive function and visuospatial skills independent of other SVD markers, highlighting PVS as an emerging marker of domain-specific cognitive decline in aging. Although causation cannot be established, findings further support PVS as a vascular contributor to deep brain structure damage underlying cognitive decline over time.
28 Apr 2026
Read appraisal →Scientific reports
LASSO-HHO two-stage hybrid gene selection framework for accurate Alzheimer's disease diagnosis
Selecting informative genes is essential for building accurate and efficient diagnostic models, especially for high-dimensional gene expression data such as those related to Alzheimer's disease. These datasets typically involve thousands of genes with limited samples, increasing the risk of overfitting and computational complexity. To address this, we propose a LASSO-HHO Gene Selection (LHGS) framework. LASSO is first applied to reduce dimensionality by filtering irrelevant genes. A conditional HHO-based optimization stage is then applied only when the LASSO-selected subset does not achieve sufficient accuracy or remains relatively large. Otherwise, the LASSO-selected features are directly used without further optimization. Experimental results show that the proposed method reduces the number of selected genes by up to 99.9% while maintaining high performance. The experimental results indicate that 100% accuracy can be achieved on specific datasets. In particular, GSE48350 and GSE36980 achieved 100% accuracy using LASSO alone, whereas GSE118553 and GSE132903 required the full LHGS framework to achieve the same performance. The framework also improves computational efficiency within a consistent experimental setup by reducing the optimization search space after LASSO filtering. Overall, LHGS provides a practical and efficient solution for gene selection in high-dimensional biomedical data.
28 Apr 2026
Read appraisal →JAAPA : official journal of the American Academy of Physician Assistants
Barriers to hearing aid adoption in hearing loss
More than one-fifth of the US population has hearing loss, a condition associated with higher risk of falls, depression, isolation, cognitive decline, dementia, and all-cause mortality. Treatment of hearing loss has been shown to correlate with decreased risk of cognitive decline, dementia, and mortality, as well as an improved quality of life. Surprisingly, however, fewer than 1 in 10 Americans with hearing loss use hearing aids. As the prevalence and burden of hearing loss grow, clinicians must understand factors influencing hearing aid use to increase treatment rates. This article reviews key barriers to hearing aid adoption-including low hearing health knowledge, inadequate screening, stigma, cost, and difficulty of use-and discusses recent changes that may influence these barriers.
27 Apr 2026
Read appraisal →ACS sensors
Wearable Flexible Acoustic Alarm Device Based on MXene/Laser-Induced Graphene@Polyimide Thermoacoustic Film
Flexible acoustic devices have advantages of high flexibility and large sound output area, capable of adhering to the surface of the skin or clothing, which shows great potential in the field of wearable electronics. However, the design of acoustic material structures and their functional applications are still difficult. Here, we fabricated a porous laser-induced graphene (LIG) microarray layer on a polyimide (PI) film by laser direct writing. Subsequently, we uniformly coated the few-layer MXene nanosheets onto the LIG surface to obtain an MXene/LIG@PI film featuring low specific heat capacity per unit area, large interlayer spacing, excellent electrical conductivity, and high thermal conductivity. Meanwhile, the MXene/LIG@PI-based acoustic device composed of flexible copper electrodes exhibits excellent acoustic performance with a high sound pressure level of 77.6 dB and maintained a stable acoustic spectrum as the frequency increased from 20.0 Hz to 20.0 kHz. Finally, the portable acoustic alarm device composed of the piezoelectric module and the MXene/LIG@PI-based acoustic module was designed, which can be worn on various parts of the human body clothing, to provide audible alarm reminders in case of accidental falls or drops during outdoor activities. This study provides a novel approach for designing new flexible acoustic materials and devices.
26 Apr 2026
Read appraisal →Reviews in the neurosciences
Microstructural alterations of the amygdala in neurodegenerative and neuroinflammatory disorders: insights from diffusion tensor imaging
Diffusion tensor imaging (DTI) is a valuable method for evaluating microstructural changes in the amygdala associated with neurodegenerative and neuroinflammatory disorders. This systematic review examines amygdala microstructural alterations in Alzheimer's disease (AD), Parkinson's disease (PD), amyotrophic lateral sclerosis (ALS), dementia with Lewy bodies (DLB), and multiple sclerosis (MS) using DTI metrics. Following PRISMA 2020 guidelines, we searched PubMed, Scopus, and Web of Science databases through August 2025, identifying 4,442 records. After screening and eligibility assessment, 13 studies were included, comprising 1,412 patients and 1,146 healthy controls. Due to sample heterogeneity and lack of standardized effect size measures, meta-analysis was not performed. Across disorders, elevated mean diffusivity (MD) emerged as the most consistent finding, present in 100 % of AD patients and observed in all examined conditions. Reduced fractional anisotropy (FA) was the second most frequent alteration, with 36.6 % of AD patients showing decreased FA. In MS, increased radial diffusivity (RD) was prominent, while longitudinal DLB studies revealed progressive free water (FW) increases. These DTI-based microstructural changes often preceded volumetric atrophy and correlated with clinical severity. Our findings demonstrate that DTI metrics, particularly MD and FA, serve as sensitive markers of amygdala pathology across neurodegenerative diseases and may facilitate early diagnosis, disease monitoring, and differential diagnosis of these conditions.
26 Apr 2026
Read appraisal →JMIR aging
The Associations Between Digital Exclusion and Physical or Cognitive Function in Middle-Aged and Older Adults: Systematic Review and Meta-Analysis
BACKGROUND: Digital exclusion posed a significant challenge, especially in middle-aged and older adults, which affected their health outcomes. However, the evidence regarding the associations of digital exclusion on physical or cognitive function outcomes was controversial, and no systematic review had been performed to synthesize the pooled associations. OBJECTIVE: This study aimed to explore the relationship between digital exclusion and physical or cognitive function in middle-aged and older adults. METHODS: We conducted a systematic review and meta-analysis of cohort and cross-sectional studies, including Chinese or English publications retrieved from PubMed, Embase, Web of Science, PsycINFO, Scopus, CNKI, and Wanfang databases up to August 31, 2024. The risk of bias was evaluated using the Newcastle-Ottawa Scale (NOS). The pooled effect size was calculated based on odds ratios (ORs), hazard ratios, risk ratios, and 95% CIs. This study was registered on PROSPERO (CRD42024585459). RESULTS: Nineteen studies met the inclusion criteria, including 13 cohort studies and 6 cross-sectional studies, which had moderate-to-low risk of bias. The pooled analysis indicated that digital exclusion had prospective associations with decreased basic activities of daily living (incidence rate ratio 1.35, 95% CI 1.12-1.64, I2=94.7%) and instrumental activities of daily living (incidence rate ratio 1.46, 95% CI 1.13-1.89, I2=96.2%), or cross-sectional association with activities of daily living (OR 1.23, 95% CI 0.41-3.73, I2=91%), with no statistical significance in the prospective association with frailty (OR 1.21, 95% CI 0.92-1.59, I2=95.2%). There were prospective associations between digital exclusion and dementia (hazard ratio 1.78, 95% CI 1.43-2.22, I2=0%), decreased Mini-Mental State Examination scores (OR 1.96, 95% CI 1.39-2.75, I2=0%), as well as cross-sectional associations with Mini-Mental State Examination scores (OR 2.90, 95% CI 2.07-4.07, I2=0%), and no statistical significance in the prospective association with cognitive impairment (risk ratio 2.08, 95% CI 0.98-4.44, I2=78.2%). CONCLUSIONS: Our findings indicated the negative associations of digital exclusion with physical and cognitive functions. Future research and clinical practice should consider designing digital interventions and services that match the physical and cognitive capacities and preferences of middle-aged and older adults, thereby improving digital engagement and reducing the harms associated with digital exclusion. Policies should focus on expanding access, reducing financial barriers, and improving digital literacy. However, due to the presence of heterogeneity and publication bias, the results should be interpreted cautiously.
26 Apr 2026
Read appraisal →American journal of physical medicine & rehabilitation
Optimal Strategy of Resistance Training Combined With Other Rehabilitation Interventions for Lower-Limb Dysfunction in Stroke Patients: A Systematic Review and Network Meta-Analysis
OBJECTIVE: Stroke frequently results in lower-limb dysfunction, severely limiting independence. This study compared the effectiveness of resistance training alone and resistance training combined with other rehabilitation modalities in improving poststroke lower-limb function. DESIGN: A systematic review and Bayesian network meta-analysis were conducted following PRISMA guidelines. Randomized controlled trials involving resistance training alone or in combination with other therapies versus daily care or resistance training alone were included. The primary outcomes were balance, lower-limb motor function, and walking ability. RESULTS: Forty-seven trials with 2448 participants were analyzed. Resistance training combined with electrical stimulation therapy produced the greatest improvements in balance and lower-limb motor function, while resistance training combined with stretching training showed the best enhancement in walking ability. Surface Under the Cumulative Ranking Curve rankings indicated resistance training plus electrical stimulation therapy as most effective for balance (91.13%) and lower-limb motor function (79.70%), and resistance training plus stretching training for walking ability (96.34%). CONCLUSIONS: Resistance training remains fundamental in stroke rehabilitation. Integrating resistance training with targeted adjunctive training particularly electrical stimulation therapy or stretching training yields superior therapeutic outcomes and supports individualized, evidence-based strategies for optimizing lower-limb recovery after stroke.
25 Apr 2026
Read appraisal →Biomedical physics & engineering express
A graph deep learning method for diagnosis of Parkinson's disease using brain functional connectivity features
Early and precise identification of Parkinson's disease (PD) is crucial for clinical intervention. Resting-state functional magnetic resonance imaging (rs-fMRI) provides a valuable approach for revealing PD-related differences in brain functional connectivity (FC). However, existing methods often focus solely on characterizing the spatial topology of FC while neglecting its time-varying dynamic fluctuations. Furthermore, they frequently exhibit limited generalization capability when dealing with small sample sizes, and their decision-making mechanisms lack interpretability. To address these limitations, this study proposes an interpretable graph convolutional network framework. This framework integrates both static and dynamic FC information to capture both the stable topological structure and the dynamic temporal characteristics of brain networks. Simultaneously, it models population relationships by constructing an inter-subject similarity graph to enhance the model's representational capacity. Additionally, this study incorporates interpretability analysis techniques to deeply dissect the model's decision-making mechanism and identify key brain regions critical for classification. Results demonstrate that the proposed model achieves superior performance in PD classification tasks and exhibits good generalization ability. More importantly, by interpreting the model's decisions, key brain regions associated with PD discrimination were successfully identified. This study provides an effective computational framework for PD identification and offers new insights into understanding its pathological mechanisms.
25 Apr 2026
Read appraisal →The Journal of clinical endocrinology and metabolism
Health and adverse events associated with extended oral bisphosphonates among postmenopausal women: a systematic review
BACKGROUND: International guidelines recommend the use of oral bisphosphonates (oBP) for up to 5 years in the treatment of postmenopausal osteoporosis. However, benefits and risks of extending oBP therapy beyond this duration remain uncertain. AIM: To determine the health outcomes (bone mineral density [BMD], fracture risk) and adverse effects (atypical fracture, osteonecrosis of the jaw [ONJ]) of extended oBP therapy beyond 5 years among postmenopausal women. METHODOLOGY: A systematic search of Medline©, EMBASE©, and CINAHL© was conducted from inception to 15 March 2024. Interventional and observational studies published in English were included. Two authors independently performed screening, quality appraisal using the NIH Quality Appraisal tool, and data extraction. The review followed PRISMA guidelines and was registered on PROSPERO (CRD42024475332). High heterogeneity precluded meta-analysis. RESULTS: Eleven studies (4 controlled trials, 7 observational) were included: 3 rated as good quality, 7 as fair, and 1 as poor. Extended oBP therapy beyond 5 years increased BMD at the hip (3/3 studies), femoral neck (3/3 studies) and lumbar spine (5/5 studies). Prolonged oBP use reduced clinical vertebral fracture risk (1/6 studies) but increased the risk of atypical fractures (3/3 studies) and incomplete atypical femoral fractures (1/1 study). None of the included studies assessed or reported ONJ. CONCLUSION: Substantial heterogeneity and limited high-quality evidence preclude definitive conclusions regarding extended oBP use in postmenopausal women. While extended treatment may improve bone density and reduce vertebral fractures, it could also increase atypical fractures. Future high-quality studies are required to inform decisions regarding optimal treatment duration.
25 Apr 2026
Read appraisal →Renal failure
Decoding the renal-cochlear axis: explainable machine learning and phenotype clustering reveal high-risk hearing loss subtypes in CKD
This study develops a dual-level machine learning framework for risk stratification and phenotyping of hearing loss (HL) in patients with chronic kidney disease (CKD) using data from the National Health and Nutrition Examination Survey (NHANES). From a cohort of 3,402 CKD patients, feature selection via univariate and multivariate logistic regression identified key predictors, which were used to construct predictive models with nine machine learning algorithms. The eXtreme Gradient Boosting (XGBoost) model demonstrated superior performance, with mean area under the curve (AUC) values of 0.984 (training), 0.984 (validation), and 0.939 (testing). SHapley Additive exPlanations (SHAP) interpretation identified age as the predominant risk determinant. Subsequent Gaussian mixture modeling (GMM) clustered patients into two distinct subtypes: a low-risk subgroup (n = 1,075) with a 1.58% HL prevalence and a high-risk subgroup (n = 2,316) characterized by older age, elevated blood urea nitrogen and bicarbonate levels, and a 48.2% HL prevalence. A classifier trained on these subtypes achieved discrimination (AUC = 0.99974). A clinically a web-based tool was also developed based on the six most influential features. The findings establish a dual-level predictive framework integrating explainable ML and unsupervised clustering for HL risk assessment in CKD. This approach provides a robust strategy for the precision screening of high-risk subpopulations and supports the integration of hearing assessments into routine CKD.
24 Apr 2026
Read appraisal →PloS one
Understanding older adults' perception, acceptance, and adoption of smart home technologies
Smart home technologies have increased in popularity and affordability in recent years, however, there is limited research on their adoption specifically among older adults. This study aims to uncover incentives and barriers to the adoption of smart home components, given the impact of COVID-19 on older adults' quality of life (QoL). For this purpose, online audio-recorded interviews were conducted with 21 participants aged between 65 and 90 years from the UK, Malta, and Pakistan. Participants were shown various smart home technology images and videos and asked for their perception, existing knowledge, and current use. The corresponding audio recordings were transcribed and thematically analysed. Findings revealed that older adults experienced a decline in QoL during COVID-19, accompanied by an increased reliance on digital technologies. While participants recognised the potential of smart home devices to enhance independence and well-being, their knowledge of available options was limited. Key barriers included affordability, privacy and trust concerns, compatibility and integration issues, and environmental and social considerations. Cross-cultural differences emerged, with European participants reporting higher familiarity than those in Pakistan, particularly rural areas where availability was limited. These results highlight the need for designers and policymakers to improve affordability, usability, and cultural adaptability in order to support older adults' acceptance and meaningful adoption of smart home technologies.
23 Apr 2026
Read appraisal →Medical education online
Medical students' attitudes towards older persons - a systematic review and meta-analysis
OBJECTIVE: Ageism negatively affects older people's health, well-being, and quality of care. Identifying ageism and attitudes toward older adults may help identify medical students motivated to work with a growing older population. We conducted a systematic review of interventions to reduce ageism among medical students. METHODS: We conducted a systematic literature retrieval in Medline, the Cochrane Library, Epistemonikos and PubMed via Ovid, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses, from database inception to August 23rd, 2024. We searched for studies examining medical students' attitudes towards older persons, including interventions to influence these attitudes. RESULTS: The systematic review included 21 studies. Study samples ranged between 29 and 415, with a pooled total of 3940 medical students, the majority conducted in the USA. The meta-analysis of students' attitudes before and after all interventions included a total of 2004 students, yielding a non-significant standardized mean difference (SMD) of 0, 12 (95% CI -0, 01 to 0, 26). The meta-analysis of students' attitudes before and after all interventions (35-37, 39, 41, 45, 48, 50), after removing UCLA-GAS and R-GAS, included a total of 1344 students with a standardized mean difference of 0, 23 (95% CI 0, 06 to 0, 40). Analysis of empathy-based teaching interventions included 1652 students, and showed a statistically significant effect, SMD 0, 18 (95% CI 0, 01 to 0, 34). Analysis of 352 students receiving traditional teaching methods yielded no effect on ageism, with an SMD -0, 13 (95% CI -0, 81 to 0, 54). High heterogeneity (I2 75-85%) affects our confidence in the effect estimates. CONCLUSION: Empathy-based interventions seem to improve medical students' attitudes towards older persons. Traditional teaching methods may increase negative attitudes. Considering the high heterogeneity, the results should be interpreted cautiously. Adding empathy-based components to medical curricula could combat ageism. More studies are needed to examine whether the results of studies from North America are valid globally.
23 Apr 2026
Read appraisal →Journal of Korean medical science
Comparison of the Hearing Benefits and User Satisfaction With Hearing Aids and Personal Sound Amplification Products: A Multicenter Prospective Randomized Crossover Trial
BACKGROUND: While hearing aids (HAs) remain the primary intervention for hearing loss, their high cost creates barriers to access. Personal sound amplification products (PSAPs) are a more affordable alternative, but few studies have compared their efficacy to those of HAs. This multicenter, prospective, randomized, crossover trial was conducted to compare audiologic performance and user satisfaction between HAs and PSAPs to evaluate PSAP potential as alternatives for hearing rehabilitation. METHODS: A total of 73 participants with sensorineural hearing loss was enrolled. Participants used one hearing device (HA or PSAP) for 3 months before changing to the other for 3 months. Outcome measures were pure-tone audiometry, word recognition score, and Hearing in Noise Test score. Subjective satisfaction was measured using standardized questionnaires-the Abbreviated Profile of Hearing Aid Benefit and the International Outcome Inventory for Hearing Aids-and two custom surveys. RESULTS: HAs showed superior performance in high-frequency functional gain, speech perception in noise, and user satisfaction compared to PSAPs. Premium HAs demonstrated better performance than basic HAs, while basic HAs significantly outperformed both high-end and basic PSAPs. No significant differences were found between high-end and basic PSAPs. CONCLUSION: The superior audiologic performance of and user satisfaction with HAs compared to PSAPs support their continued preference for hearing rehabilitation. Although PSAPs offer a more affordable option, they may not meet the needs of individuals with hearing loss, particularly in terms of high-frequency gain and speech perception. TRIAL REGISTRATION: iCreaT Identifier: C220078.
23 Apr 2026
Read appraisal →The aging male : the official journal of the International Society for the Study of the Aging Male
The effect of dutasteride on storage symptoms in men with lower urinary tract symptoms: a REDUCE trial analysis
BACKGROUND: Dutasteride is used to treat voiding symptoms; its impact on storage symptoms is unclear. METHODS: We reviewed 8122 men in the placebo and dutasteride arms from the REDUCE trial. Lower urinary tract symptoms outcomes were measured with international prostate symptom score and subscores. RESULTS: At 2-year follow-up, dutasteride significantly reduced the total International Prostate Symptom Score and voiding subscore versus placebo (both p < 0.001), but not storage symptoms (p = 0.09) or nocturia (p = 0.2). By 4 years, improvements in the total score and voiding subscores remained, but now extended to storage symptoms (p < 0.001) and nocturia (p = 0.003), though the impact on storage symptoms was modest (0.62 points improvement). CONCLUSION: While dutasteride reduces storage symptoms, it takes several years, and the overall effect is modest and unlikely to be clinically meaningful. Timely, comprehensive lower urinary tract symptoms management may require additional therapy.
22 Apr 2026
Read appraisal →Annals of medicine
Comparative evaluation of oxidative stress biomarkers F2-isoprostanes and 8-OHdG in Parkinson's disease and Type 2 Diabetes Mellitus: a systematic review and meta-analysis of human studies
BACKGROUND: Oxidative stress is central to type 2 diabetes mellitus (T2DM) and Parkinson's disease (PD). However, the utility of biomarkers for lipid peroxidation (F2-isoprostanes) and DNA damage (8-OHdG) in the comorbidity of PD and T2DM remains unclear. METHODS: We conducted a systematic review and meta-analysis of 54 unique studies of human subjects aged ≥ 50 years (n = 7,521: 3,522 with T2DM, 722 with PD, and 3,277 controls), measuring biomarkers in serum, plasma, or leukocytes. Mixed-effects models quantified standardized differences (Hedges' g) across subgroups. RESULTS: In T2DM, F2-isoprostanes (g = 1.60, 95% CI: 0.95-2.25) and 8-OHdG (g = 2.64, 95% CI: 2.13-3.14) were markedly elevated (p < 0.001). Stronger effects were observed in younger cohorts and serum/plasma samples, with complications like nephropathy exhibiting extreme oxidative stress (g = 5.24). In PD, 8-OHdG was moderately elevated (g = 0.78, 95% CI: 0.18-1.39; p = 0.011), particularly in randomized controlled trials and plasma samples, whereas F2-isoprostanes were not significantly elevated (g = 0.47, 95% CI: -0.43-1.38). High heterogeneity in T2DM (I2 > 90%) reflected methodological variability. CONCLUSION: Distinct profiles - both markers elevated in T2DM but only 8-OHdG in PD - underscore 8-OHdG's potential in PD-T2DM comorbidity. Future research should focus on standardized assays, multi-compartmental or multi-modal sampling, and longitudinal studies to clarify mechanisms and therapeutic targets.
21 Apr 2026
Read appraisal →Medicine
Effect of parecoxib on postoperative anxiety and cognitive function after pancreaticoduodenectomy: A clinical trial
BACKGROUND: After pancreaticoduodenectomy, perioperative neurocognitive disorders (PND) are a common complication. Parecoxib, a selective cyclooxygenase-2 inhibitor, is used for its analgesic and antiinflammatory effects, but its impact on postoperative anxiety and cognitive function is not clear. METHODS: The patients undergoing laparoscopic pancreaticoduodenectomy at the Second Hospital of Hebei Medical University were enrolled in the study from November 1, 2020 to September 30, 2022 in this randomized controlled trial. Patients were randomly divided into Group P who received 20 mg of parecoxib intravenously 10 minutes before anesthesia and at the end of surgery and Group C who received an equal volume of normal saline at the same times. Primary outcomes included the effect of parecoxib on anxiety, cognitive impairment, and postoperative intestinal function recovery, with the incidence of PND calculated using the Z-value method. Secondary outcomes included pain control, length of hospital stay, postoperative recovery, complication rates, and analgesic use. RESULTS: 80 patients were enrolled and randomly divided into 2 groups of 40 equally: there were no significant differences in postoperative flatus passage, defecation time, hospital stay, or complication rates (P > .05). In terms of postoperative anxiety, the experimental group had significantly lower State Anxiety Inventory scores at 24 hours postoperatively (T2) (P < .05). Cognitive function, measured by Montreal Cognitive Assessment scores, did not show significant differences; however, cognitive decline was less pronounced in the experimental group. PND rates were 5% (2/40) in the experimental group versus 12.5% (5/40) in the control group, suggesting a possible clinical tendency toward reduced incidence, although the difference did not reach statistical significance. The rates of PND at 24 hours (T2) and 7 days (T3) postoperatively were 10% (4/40) and 5% (2/40) for the experimental group, compared to 15% (6/40) and 12.5% (5/40) for the control group using the Z-value method, Although the between-group differences did not reach statistical significance, a lower incidence was observed in the parecoxib group, suggesting a possible clinical tendency. CONCLUSION: Parecoxib, when used with pancreaticoduodenectomy, alleviates postoperative anxiety and neurocognitive disorders without affecting postoperative intestinal function recovery or increasing the incidence of postoperative complications.
19 Apr 2026
Read appraisal →JMIR aging
"Living Inside the Walls": Systematic Review and Qualitative Meta-Synthesis
BACKGROUND: Social isolation is a prevalent issue among older people in long-term care facilities (LTCFs), with profound negative impacts on their quality of life and mental health. However, the authentic experiences and underlying mechanisms of social isolation among older people in LTCFs remain understudied. A nuanced understanding of these experiences is essential for designing targeted nursing interventions. OBJECTIVE: This study aimed to systematically review and synthesize qualitative evidence to explore the experiences of social isolation among older adults in LTCFs and analyze them in terms of causes, mechanisms, outcomes, and strategies. METHODS: A systematic review and qualitative meta-synthesis were conducted. We searched PubMed, Web of Science, Embase, PsycInfo, Cochrane Library, CINAHL, Scopus, Chinese Biomedical Literature Database, CNKI, Wanfang Data, and Weipu (VIP) with no restriction on the start year, up to February 2025. Two reviewers (WL and ZM) independently screened studies, extracted data using a Microsoft Excel spreadsheet, and assessed study quality with the Joanna Briggs Institute Critical Appraisal Checklist for Qualitative Research. Qualitative data synthesis was performed following Thomas and Harden's thematic and content analysis method, including line-by-line coding of participant quotes, organizing codes into descriptive themes, and developing analytical themes. RESULTS: A total of 4856 papers were retrieved through systematic searches, and 14 qualitative studies met the inclusion criteria. Four overarching analytical themes were identified: (1) root cause: person-environment mismatch (encompassing 4 interrelated dimensions: imbalance between declining individual capacity and insufficient environmental support, negative self-perception paired with inadequate positive environmental feedback, personal biases conflicting with homogeneous institutional social ecology, and disrupted social roles due to rigid institutional management); (2) overt behavioral patterns: proactive isolation, defensive isolation, and adaptive isolation; (3) inner emotional experiences: feelings of confinement, sorrow, and weariness; and (4) strategies for restoring person-environment match: active engagement in interaction, rational acceptance of coexistence, and passive avoidance and withdrawal. Notably, person-environment mismatch was confirmed as the core mechanism driving the onset and persistence of social isolation in this population. CONCLUSIONS: Person-environment mismatch is the fundamental driver of social isolation among older adults in LTCFs. Interventions should prioritize restoring person-environment alignment-including optimizing physical environments, reconstructing supportive social environments, and enhancing psychological empowerment. Future studies should focus on developing tools to quantify person-environment matching degrees in LTCFs and conduct longitudinal evaluations of targeted interventions to further reduce social isolation and promote active social participation among older people.
18 Apr 2026
Read appraisal →JMIR aging
Leveraging Digital Health Technologies to Assess Older Adults' Frailty and Nutritional Status: Two Cross-Sectional Studies
BACKGROUND: With the rising prevalence of aging worldwide, there is a necessity for reliable and frequent assessments of older individuals' health status to manage and potentially prevent age-related complications. Digital health technologies (DHTs), such as wearable devices, provide an opportunity to gather objective, continuous, and unobtrusive measurements, enabling effective health management in everyday life. OBJECTIVE: We aimed to evaluate the relationship between digital endpoints and frailty, nutritional status, and patient-reported outcomes (PROs) in older individuals, as well as quantified their compliance and comfort with DHTs. METHODS: In two cross-sectional studies, namely the Geriatric Anorexia Studies 1 and 2, including three in-clinic visits and 2-3 weeks of at-home monitoring, 94 participants (mean age 72.98 years, SD 6.28 years) were stratified based on their frailty status (n=39, 41%, nonfrail; n=45, 48%, prefrail; and n=10, 11%, frail) and nutritional status (n=70, 74%, with normal nutrition and n=24, 26%, at risk for malnutrition), as assessed in a clinical setting using the Fried Frailty Score and the Simplified Nutritional Appetite Questionnaire, respectively. We remotely monitored older adults using different DHTs. At home, participants were monitored with a wrist accelerometer for physical activity, continuous glucose monitoring (CGM) for glucose concentration, a digital body scale for weight and body composition, and a digital nutritional scale for meal tracking. Compliance with devices was assessed via wear time and correct at-home usage, while comfort was evaluated using questionnaires. The association between digital endpoints and frailty/nutritional status was investigated via linear regression, followed by ANOVA, and the relationship between digital endpoints collected at home and PROs was evaluated via Spearman's ρ. Weight and body composition were also assessed during in-clinic visits with a research-grade scale, used to validate the at-home digital body scale measurements, via the intraclass correlation coefficient, Pearson's R, and Bland-Altman plots with mean bias. RESULTS: Physical activity digital endpoints collected at home were significantly different across frailty and nutritional groups and significantly correlated with self-reported appetite, fatigue, and physical function (eg, for mean daily activity in the maximum 60 minutes of activity, ρ=0.28, -0.23, and 0.47, respectively). More than 80% of participants reported that all devices were mostly to very acceptable to wear/use: 95% (88/93) for the body scale, 86% (43/50) for CGM, 80% (40/50) for the nutrition scale, and 91% (84/93) for the wrist accelerometer. Compliance ranged from 60% of monitoring days for the digital nutrition scale to 90% for the wrist accelerometer. CONCLUSIONS: Physical activity digital endpoints show clear prognostic significance for frailty and malnutrition in older individuals and reflect self-reported measures. DHTs can be reliably deployed at home for older individuals to measure their physical activity, weight and body composition, glucose concentration, and meal intakes, thus enabling patient-centric, data-centric clinical trials. TRIAL REGISTRATION: ClinicalTrials.gov NCT04858932; https://clinicaltrials.gov/study/NCT04858932; ClinicalTrials.gov NCT05211973; https://clinicaltrials.gov/study/NCT05211973.
18 Apr 2026
Read appraisal →Medicine
Publication trends of dexmedetomidine and perioperative cognitive dysfunction: A bibliometric analysis from 1995 to 2024
INTRODUCTION: Dexmedetomidine (Dex) has shown potential in mitigating perioperative cognitive dysfunction (POCD). This study aimed to systematically evaluate the research hotspots and development trends in Dex and POCD using bibliometric analysis. METHODS: A systematic search was conducted on the Web of Science Core Collection database to identify publications related to Dex and POCD from 1995 to 2024. Bibliometric analysis was performed using VOSviewer (V 1.6.20), CiteSpace (V 6.3.R1), and R V4.3.3. RESULTS: A total of 585 articles on Dex and POCD between 1995 and 2024 were analyzed. These articles involved 1688 authors from 1857 institutions across 140 countries or regions, published in 240 journals, and citing 12,881 sources. The annual growth rate of publications in this field was 16.21%, with a peak of 91 articles in 2021. China led in the number of articles (n = 379), multiple country publications (MCP = 29), and total citations = 4989). The USA ranked second with 74 articles, 18 MCPs, and 2608 citations, followed by South Korea with 29 articles and 533 citations. Harvard University published the most articles (n = 51). Wang Dong-Xin was the most influential author, with an h-index of 7. Anesthesiology led the high-impact journals with an H-index of 11. The keyword analysis identified "neuroinflammation," "risk," "injury," and "efficacy" as the latest research themes. CONCLUSION: This bibliometric analysis provides a comprehensive overview of the research trends regarding Dex and POCD. Future research should continue to explore the mechanisms, efficacy, and safety of Dex in preventing POCD.
18 Apr 2026
Read appraisal →Thorax
Multidisciplinary, non-pharmacological breathlessness intervention service for patients with moderately severe to severe COPD: a randomised controlled trial
BACKGROUND: Chronic obstructive pulmonary disease (COPD) is an often-progressive respiratory disease associated with disabling breathlessness. Breathlessness intervention services (BIS), which coach patients to self-manage breathlessness using non-pharmacological strategies, are effective in a variety of populations, including those with cancer and serious respiratory disease. This study aimed to compare the impact of the Westmead Breathlessness Service in people with moderate to severe COPD. METHODS: We analysed 113 participants randomised (1:1) with moderate/severe COPD (forced expiratory volume in 1 s (FEV1)/forced vital capacity <0.70 and FEV1 ≤60% predicted) and disabling breathlessness (modified Medical Research Council (mMRC) Breathlessness Score ≥2) to either an 8-week intervention involving breathing techniques, handheld fan use, exercise, energy conservation, dietetic advice (n=54) or 8-week wait-list control group (n=59). The primary outcome was change in Chronic Respiratory Questionnaire (CRQ) Mastery of breathlessness subscale. Secondary outcomes included change in other CRQ subscales (Fatigue, Emotion and Dyspnoea), exertional breathlessness intensity/unpleasantness (0-10 Numerical Rating Scale Score), anxiety and depression. Difference in change over 8 weeks between groups was compared using ANCOVA; p<0.05 statistically significant. FINDINGS: Participants were aged 70.9 (±8.5) years, 50% female, mean FEV1 =0.8 L (±0.3 L; 34% predicted), mMRC Breathlessness Score 3 (IQR 3-4). CRQ-Mastery improved following intervention compared with control (between-group difference 0.5 units; 95% CI 0.2 to 0.8; p=0.0262) using modified intention-to-treat analysis. Better CRQ-Dyspnoea and CRQ-Fatigue were seen in the intervention group (between-group difference-CRQ-Dyspnoea 0.4 units; CI 0.1 to 0.7; p=0.005, and CRQ-Fatigue 0.4 units; CI 0.1 to 0.7; p=0.014). Exertional breathlessness intensity (difference -0.8 units; CI -1.4 to -0.2; p=0.013) and breathlessness unpleasantness (difference -1.2 units; CI -1.7 to -0.6; p=0.001) also improved. INTERPRETATION: An 8-week BIS improved CRQ-Mastery, Dyspnoea and Fatigue, exertional breathlessness intensity and unpleasantness in people with severe COPD.
17 Apr 2026
Read appraisal →Acta orthopaedica
Intraoperative single-dose methadone significantly affects postoperative morphine consumption in older patients with a hip fracture: the MetaHip randomized controlled trial
BACKGROUND AND PURPOSE: Effective pain management in older patients with a hip fracture is critical for postoperative recovery. Our primary objective was to compare intraoperative methadone with placebo on postoperative morphine consumption over 72 hours. METHODS: Patients aged ≥ 60 years with hip fractures were randomized to receive methadone (0.10 mg/kg) or placebo intraoperatively. The primary outcome was postoperative morphine consumption measured in 24-hour intervals over 72 hours. Secondary outcomes included pain scores, time to mobilization, and discharge. Harms were assessed as adverse and serious adverse events. RESULTS: 129 patients were included. The primary endpoint analysis demonstrated that postoperative morphine consumption over 72 hours differed significantly between groups (likelihood-ratio test, P = 0.02). Model-based estimates suggested lower morphine consumption in the methadone group at 0-24 hours (least square mean [LSM] 7.1 [SE 1.2] vs placebo 10.1 [SE 1.7] mg) and at 24-48 hours (4.1 [SE 0.8] vs placebo 5.3 [SE 0.9] mg). At 48-72 hours, the model suggested lower morphine consumption in the placebo group (3.2 [SE 0.6] vs methadone 4.6 [SE 0.9] mg). Secondary outcomes were similar between groups, except that time to hospital discharge was longer in the methadone group (LSM 5.6 vs 4.5 days; mean difference -1.3 days, 95% confidence interval -2.3 to -o.4; P < 0.01). Harms appeared comparable, although the low event rate precluded formal analysis. CONCLUSION: A single intraoperative dose of methadone significantly alters postoperative morphine consumption over 72 hours after hip fracture surgery, without major safety concerns.
17 Apr 2026
Read appraisal →The Cochrane database of systematic reviews
Amyloid-beta-targeting monoclonal antibodies for people with mild cognitive impairment or mild dementia due to Alzheimer's disease
RATIONALE: Alzheimer's disease is a neurodegenerative disorder and the most common cause of dementia. Aggregated amyloid-beta protein deposits are implicated in its pathogenesis. Amyloid-beta-targeting monoclonal antibodies (sometimes represented as Aβ-mAbs) are potentially disease-modifying for Alzheimer's disease: through the clearance of amyloid in the brain, they may slow cognitive and functional decline. OBJECTIVES: To assess the clinical benefits and harms of amyloid-beta-targeting monoclonal antibodies aducanumab, bapineuzumab, crenezumab, donanemab, gantenerumab, lecanemab, ponezumab, remternetug, and solanezumab in people with mild cognitive impairment or mild dementia due to Alzheimer's disease. SEARCH METHODS: We searched CENTRAL, MEDLINE (PubMed), Embase, and two clinical trials registries (Clinicaltrials.gov and WHO International Clinical Trials Registry Platform), and we undertook reference checking and citation research. The most recent search date was 7 August 2025. ELIGIBILITY CRITERIA: We included randomised controlled trials (RCTs) that lasted at least 12 months and compared amyloid-beta-targeting monoclonal antibodies with placebo or no treatment in people with mild cognitive impairment or mild dementia due to Alzheimer's disease. We included both parallel-group and cluster designs. OUTCOMES: Our outcomes of critical importance were: cognitive function; dementia severity; functional ability; any amyloid-related imaging abnormality (ARIA), which includes oedema (E) and haemorrhage (H); any symptomatic ARIA E and H; symptomatic brain haemorrhage; serious adverse events; and any-cause mortality. We analysed data at 12, 18, 24, and over 24 months of treatment. RISK OF BIAS: We used the Cochrane risk of bias tool RoB 2 to assess the risk of bias in outcomes of critical importance. SYNTHESIS METHODS: We meta-analysed results for each outcome within each comparison using the inverse variance method and the random-effects model. We used GRADE to assess the certainty of evidence for each outcome as very low, low, moderate, or high. INCLUDED STUDIES: Overall, we included 17 studies with 20,342 participants. The mean age of participants in the studies ranged from 70 to 74 years. Seven studies enroled only participants with mild dementia, and one study enroled only participants with mild cognitive impairment. The remaining studies included a mixed population. The mean duration of participants' cognitive impairment ranged from 17 to 52 months. The 17 studies assessed seven different amyloid-beta-targeting monoclonal antibodies: aducanumab (n = 3), bapineuzumab (n = 4), crenezumab (n = 2), donanemab (n = 1), gantenerumab (n = 4), lecanemab (n = 1), and solanezumab (n = 2). All used placebo as a comparison. Eleven studies lasted 18 months, four lasted 24 months, and two lasted more than 24 months. All studies were funded by the pharmaceutical industry. SYNTHESIS OF RESULTS: Below, we report the results of the studies at 18 months. Cognitive function Compared to placebo, amyloid-beta-targeting monoclonal antibodies probably result in little to no difference in cognitive function as measured by the ADAS-Cog (Alzheimer's Disease Assessment Scale-Cognitive) scale (standardised mean difference (SMD) -0.11, 95% confidence interval (CI) -0.16 to -0.06; 13 studies, 9895 participants; moderate certainty). Dementia severity Amyloid-beta-targeting monoclonal antibodies may result in little to no difference in dementia severity as measured by the CDR-SB (Clincal Dementia Rating Sum of Boxes) scale (SMD -0.12, 95% CI -0.24 to -0.00; 9 studies, 8053 participants; low certainty). Functional ability Amyloid-beta-targeting monoclonal antibodies probably result in little to no difference in functional ability as measured on the ADCS-ADL (Alzheimer's Disease Cooperative Study - Activities of Daily Living) scale (SMD 0.09, 95% CI 0.03 to 0.16; 3 studies, 3478 participants; moderate certainty) and may result in a small increase in functional ability if measured with the ADCS-iADL (Alzheimer's Disease Cooperative Study-Instrumental Activities of Daily Living) scale (SMD 0.21, 95% CI 0.10 to 0.32; 1 study, 1252 participants; low certainty) or ADCS-ADL-MCI (Alzheimer's Disease Cooperative Study - Activities of Daily Living for Mild Cognitive Impairment) scale (SMD 0.23, 95% CI 0.12 to 0.33; 4 studies, 2802 participants; low certainty). Adverse events Amyloid-beta-targeting monoclonal antibodies probably result in a small increase in the occurrence of any ARIA E (ARD (absolute risk difference) 107 more per 1000, 95% CI 77 more to 148 more; 11 studies, 13,595 participants; moderate certainty) and probably little to no difference in symptomatic ARIA E (ARD 29 more per 1000, 95% CI 22 more to 38 more; 2 studies, 3522 participants; moderate certainty) or symptomatic ARIA H (ARD 4 more per 1000, 95% CI 1 fewer to 31 more; 1 study, 1795 participants; moderate certainty). Three studies assessing any ARIA H showed heterogeneous results (I2 = 81%), which prevented pooled analysis. At 18 months, amyloid-beta-targeting monoclonal antibodies do not increase serious adverse events (ARD 6 more events per 1000, 95% CI 10 fewer to 26 more; 9 studies, 11,904 participants; high certainty) or overall mortality (ARD 2 more events per 1000, 95% CI 3 fewer to 11 more; 7 studies, 9733 participants; high certainty). We judged the overall risk of bias as low for the outcomes of serious adverse events and mortality. We had some concerns about the overall risk of bias for efficacy outcomes, mainly due to the risk of functional unblinding (i.e. participants and investigators correctly guessing whether a participant is receiving the active drug or placebo because of noticeable side effects). AUTHORS' CONCLUSIONS: The effect of amyloid-beta-targeting monoclonal antibodies on cognitive function and dementia severity at 18 months in people with mild cognitive impairment or mild dementia due to Alzheimer's disease is trivial, while on functional ability, it is small at best. Amyloid-beta-targeting monoclonal antibodies increase the risk of amyloid-related imaging abnormalities. Both desirable outcomes and adverse events were inconsistently reported in the studies included in the review. Successful removal of amyloid from the brain does not seem to be associated with clinically meaningful effects in people with mild cognitive impairment or mild dementia due to Alzheimer's disease. Future research on disease-modifying treatments for Alzheimer's disease should focus on other mechanisms of action. FUNDING: This Cochrane review was funded in part by the Drug and Medical Devices Governance Area, Regione Emilia-Romagna, Bologna, Italy. The publication of this article was supported by "Ricerca Corrente" funding from the Italian Ministry of Health. REGISTRATION: Protocol (2025): PROSPERO registration number CRD420251114325.
17 Apr 2026
Read appraisal →JMIR medical informatics
Research on Risk Transfer Pathways for Lung Cancer Among Middle-Aged and Older Individuals Using Deep Reinforcement Learning: Retrospective Cohort Study
BACKGROUND: Nowadays, lung cancer has a significantly high incidence rate worldwide. The mortality rate of lung cancer continues to rise; it is more common in middle-aged and older individuals and poses a great threat to human health. OBJECTIVE: This study aimed to assess the lung cancer risk among middle-aged and older individuals in a timely manner and to establish an efficient pathway for the risk transfer. METHODS: We proposed a deep reinforcement learning model based on deep Q-network (DQN) to explore the risk transfer pathway for lung cancer among middle-aged and older individuals. Risk stratification of lung cancer occurrence was deduced through deep neural network. The DQN model was developed using the Health and Retirement Study cohort for model training and internal validation. We also used the China Health and Retirement Longitudinal Study cohort for model external validation. Transfer simulation of multiple pathways in different cycles was calculated in a stratified risk groups-leveraged DQN model. RESULTS: We developed and evaluated the DQN method for optimizing the risk transfer pathway among middle-aged and older individuals, with accuracy ranging from 0.917 (95% CI 0.896-0.928) to 0.949 (95% CI 0.909-0.961) and area under curve ranging from 0.906 (95% CI 0.887-0.933) to 0.927 (95% CI 0.893-0.938). External validation was conducted to assess the model's effectiveness and availability. A total of 8780 and 3763 samples from the Health and Retirement Study were used for model training and internal testing, respectively, and 16,442 samples from the China Health and Retirement Longitudinal Study were used for external validation. The results showed that DQN models illuminated the optimal risk transfer pathways for stratified risk groups. Lung cancer incidence in the high risk group had declined by 68.2% through risk transfer of model-based simulation, which had declined by 56.9% in the medium risk group. Through simulative intervention and risk transition deduced from the DQN model, lung cancer incidences of the high risk, medium risk, and low risk groups were obviously decreased. CONCLUSIONS: A DQN-based deep reinforcement learning model was proposed and validated to develop and simulate a risk transfer pathway of lung cancer among middle-aged and older individuals. Risk stratification supplied an effective foundation for lung cancer risk transition.
17 Apr 2026
Read appraisal →Scientific reports
Predicting post-stroke functional outcome using explainable machine learning and integrated data
Functional outcome after acute ischemic stroke (AIS) varies widely, and existing prognostic scores may not capture complex relationships. We evaluated a diverse set of clinical characteristics and blood biomarkers with multiple machine learning models to predict 3-month functional outcome after AIS, and used explainable artificial intelligence to identify key drivers of performance. Models were trained on 506 patients aged 18-69 years with AIS enrolled at four stroke units in western Sweden. We compared extreme gradient boosting, multilayer perceptron (MLP), and L1- and L2-regularized logistic regression. Feature importance was assessed with Shapley additive global explanations. Of the 506 patients, 105 had an unfavorable outcome (modified Rankin Scale score > 2). All models showed high area under the curve (AUROC, 0.900-0.906). The MLP achieved the highest precision-recall performance (AUPRC, 0.773 ± 0.080) and sensitivity (0.655 ± 0.096), though with lower specificity (0.920 ± 0.035). Stroke severity (NIH Stroke Scale score) was the dominant predictor across models. Among biomarkers, brain-derived tau (BD-tau) was most informative, followed by inflammation-related plasma proteins. In conclusion, machine learning accurately predicted functional outcome after AIS. BD-tau and inflammation-related proteins contributed predictive information above stroke severity, suggesting a potential for blood biomarkers to enhance individualized prognostication after AIS.
17 Apr 2026
Read appraisal →Journal of the American College of Cardiology
Risk-Guided Atrial Fibrillation Screening With Artificial Intelligence-Enabled Electrocardiogram Models: A VITAL-AF Trial Analysis
BACKGROUND: Screening for atrial fibrillation (AF) may lead to earlier detection and initiation of preventive measures. Current AF screening approaches using a guideline age-based threshold of ≥65 years have shown limited yield. OBJECTIVES: In an AF screening trial, we assessed whether the screening effect was larger among individuals at elevated AF risk using validated clinical and electrocardiogram (ECG)-based artificial intelligence (AI) risk models. METHODS: VITAL-AF was a cluster-randomized trial of patients aged ≥65 years treated at 1 of 16 primary care practices affiliated with Massachusetts General Hospital. Patients randomized to a screening practice were screened using a single-lead ECG. Among VITAL-AF participants without prevalent AF with at least one 12-lead ECG within 3 years before enrollment, we estimated AF risk using 3 validated models derived outside of VITAL-AF: the Cohorts of Heart and Aging Research in Genomic Epidemiology-AF (CHARGE-AF) clinical score, an AI-based model using a 12-lead ECG alone (ECG-AI), and a model combining ECG-AI and CHARGE-AF (CH-AI). Two-year incident AF discrimination was assessed by the time-dependent area under the receiver-operating characteristic curve (AUROC) and average precision. AF screening effect was defined as the difference in 2-year incident AF diagnosis rate (per 100 person-years) in screening vs control across AF risk deciles. RESULTS: Of 30,630 VITAL-AF participants without prevalent AF, 16,937 had pretrial ECG and clinical data. Each score discriminated 2-year AF risk according to AUROC (CHARGE-AF: 0.711 [95% CI: 0.671-0.749]; ECG-AI: 0.784 [95% CI: 0.743-0.819]; CH-AI: 0.788 [95% CI: 0.754-0.824]) and average precision (0.0952 [95% CI: 0.0836-0.112]; 0.132 [95% CI: 0.113-0.157]; 0.133 [95% CI: 0.117-0.159]). An AF screening effect was observed in the top decile of CH-AI (AF diagnosis rate in screening 10.07/100 person-years [95% 8.28-11.87] vs 7.76 [95% 6.30-9.21] in control, P < 0.05), corresponding to a difference in AF diagnosis rate of 2.32/100 person-years (95% CI: 0.01-4.63) and number-needed-to-screen of 43 per year. CONCLUSIONS: Use of ECG-based AI and clinical factors identified individuals at particularly high risk for AF who may benefit from screening. Findings suggest a trade-off between increasing AF screening efficiency and decreasing population coverage (ie, restriction of the screening pool). Future studies are needed to determine whether a risk-based approach is optimal or whether consideration of additional clinical- and systems-level factors (eg, access, health care system engagement) can further refine AF screening strategies. (Screening for Atrial Fibrillation Among Older Patients in Primary Care Clinics [VITAL-AF]; NCT03515057).
16 Apr 2026
Read appraisal →Journal of medical Internet research
Digital Technologies and Biomarkers for Locomotor Capacity Assessment in Older Adults: Systematic Review
BACKGROUND: Locomotor capacity, encompassing endurance, balance, muscle strength, muscle function, muscle power, and joint function of the body, is a key determinant of functional ability in older adults. Assessment tools based on digital technologies for objectively assessing locomotor capacity are increasingly being developed, but their reliability, validity, and clinical potential remain underexplored. OBJECTIVE: This systematic review aims to evaluate the current state of digital technologies, assess their validity and reliability for assessing locomotor capacity, and facilitate their effective implementation in clinical settings. METHODS: Systematic literature searches were performed in 6 electronic databases from inception to March 7, 2025. Citation lists from the included studies and gray literature from Google Scholar were additionally searched. Studies focusing on the reliability and validity of digital technologies for assessing locomotor capacity in general older adults were included. Standardized forms were used to extract information on study characteristics, participant demographics, digital technology details, and validity and reliability results. Methodological quality assessment and rating of measurement properties were conducted in accordance with the COSMIN (Consensus-Based Standards for the Selection of Health Measurement Instruments) guidelines. RESULTS: A total of 14 studies were included, of which 13 assessed balance using inertial measurement units, smartphones, balance boards, and force plates, and 1 assessed muscle power using smartphones. Fifty-one digital biomarkers were identified, including 47 for balance and 4 for muscle power assessment. Test-retest reliability coefficients ranged from 0.016 to 0.97, and validity was context specific. Overall, 13 studies demonstrated sufficient test-retest reliability and validity, whereas 1 study was rated as insufficient for convergent validity. Methodological quality was rated as "doubtful" or "inadequate" in 11 studies. CONCLUSIONS: This review provides a comprehensive summary of digital technologies for assessing locomotor capacity in older adults and identifies 51 digital biomarkers with generally acceptable reliability and validity. Unlike previous studies that focused on specific sensor types or disease-specific populations, this review integrates evidence across technologies within general older populations, providing insights into the clinical application potential of digital biomarkers as well as the key translational barriers limiting their real-world implementation. Specifically, existing digital technologies show considerable promise for early detection of functional decline, longitudinal monitoring, and informing personalized interventions. However, their clinical applicability remains constrained by limited assessment of certain locomotor components and by methodological shortcomings across current studies. Future research should prioritize rigorous, high-quality investigations that expand evaluation to a broader range of locomotor components in real-world settings while developing age-friendly tools with enhanced clinical interpretability.
16 Apr 2026
Read appraisal →Neurology
Outcomes After Minor Ischemic Stroke in Older Patients Treated With IV Thrombolysis vs Standard of Care in the TEMPO-2 Trial
BACKGROUND AND OBJECTIVES: There are few high-quality data on thrombolysis outcomes in the oldest patients, especially for minor stroke. We examined safety and efficacy outcomes of thrombolysis in patients older than 80 years, compared with those ≤80 years, in the Tenecteplase Versus Standard of care for Minor Ischaemic Stroke With Proven Occlusion (TEMPO-2) trial. METHODS: In this post hoc analysis of the TEMPO-2 randomized controlled trial, we compared outcomes and adverse events in patients with minor stroke and symptomatic intracranial occlusion or focal perfusion lesion within 12 hours of symptom onset, assigned to tenecteplase vs nonthrombolytic standard of care, in those >80 years and ≤80 years. The primary outcome was responder analysis of the 90-day modified Rankin Scale (responder was mRS = 0-1 if premorbid mRS = 0-1, 0-2 if premorbid mRS = 2). Secondary outcomes included neurologic recovery according to the NIH Stroke Scale (NIHSS) at 5 days/discharge, vessel recanalization, Lawton Instrumental Activities of Daily Living Scale, EuroQol-5 Dimension (EQ-5D), and adverse events. We used mixed-effects Poisson, ordinal logistic, and quantile regression as appropriate, adjusted for sex, baseline NIHSS, and treatment assignment as fixed-effects and enrolling site as random effects. RESULTS: Among 884 patients in the intention-to-treat analysis (208 [23.5%] >80 years, 365 [41.6%] female). Significant interactions between age and treatment were observed for mRS responder, mRS 0-1, return to baseline function, EQ-5D index, and vessel recanalization. Patients >80 years generally fared worse with tenecteplase for mRS outcomes (mRS 0-1 in 49 [46.2%] vs 61 [59.8%] with control, adjusted risk ratio [aRR] 0.83, 95% CI 0.72-0.97), while among patients ≤80 years, there was no significant difference (249 [76.4%] vs 260 [74.7%], aRR 1.01, 95% CI 0.98-1.03). However, in both age groups, patients were more likely to achieve recanalization of visible occlusions (aRR ≤80 years 2.06, 95% CI 1.60-2.65, >80 years 2.77, 95% CI 2.21-3.47) and NIHSS = 0 at 5 days/discharge when assigned to tenecteplase (aRR ≤80 years 1.14, 95% CI 1.04-1.26, >80 years 1.21, 95% CI 1.10-1.33). Serious adverse events (SAEs) were more frequent with tenecteplase among patients >80 years (risk ratio 2.29, 95% CI 1.27-4.13), particularly hemorrhages and stroke progression/recurrence (any extra/intracranial hemorrhage in 5 [4.7%] vs 0 with standard of care, p = 0.026). DISCUSSION: Older patients (>80 years) with minor stroke and visible occlusion or perfusion lesion assigned to tenecteplase were more likely to achieve recanalization of occlusions and short-term neurologic recovery, as were younger patients, but they experienced worse 90-day outcomes with more frequent hemorrhagic and stroke progression/recurrence-related SAEs. Overall, these results, while post hoc, do not support thrombolysis for minor strokes in older patients. TRIAL REGISTRATION INFORMATION: ClinicalTrials.gov: NCT02398656. CLASSIFICATION OF EVIDENCE: This study provides Class III evidence that in patients >80 years with minor stroke and visible occlusion or perfusion lesion, IV thrombolysis with tenecteplase is associated with worse functional outcomes at 90 days compared with nonthrombolytic standard of care therapies.
15 Apr 2026
Read appraisal →Food & function
The effect of anthocyanins through diet and supplementation on cognitive function in older adults: a multi-centre randomised controlled clinical trial
Anthocyanins, a flavonoid subclass present in certain blue, purple and red fruits and vegetables, have potential neuroprotective properties due to their anti-inflammatory, antioxidant, and signalling effects. Clinical and epidemiological studies of anthocyanins provide promising evidence for attenuation of memory loss and improved cognitive function. We investigated the effects of anthocyanins, provided through diet or supplementation, on cognitive function and related parameters. A multi-centre, 24-week randomised, parallel 3-arm clinical trial was conducted in participants aged 60-85 years with self-reported memory complaints and scoring ≤13 on the Memory Index Score within the Telephone Montreal Cognitive Assessment (T-MoCA). Participants were randomised to high anthocyanin diet, freeze-dried product derived from blackcurrants (250 mg anthocyanins per day), or control (placebo). The primary outcome was auditory episodic memory functioning. Secondary outcomes were additional cognitive functions, subjective memory complaints and self-reported depression symptoms, blood pressure, inflammatory biomarkers, lipid profile, vascular and microvascular endothelial function tests. Mixed linear modelling evaluated changes over time using both intention-to-treat and completer analyses. Of 110 participants enrolled (mean age 69.2 years SD 6.7; 36 males, 74 females), 94 completed the study. There was no treatment × time effect on any primary or secondary outcomes. We postulate that the potential anti-inflammatory, antioxidant, and signalling effects of anthocyanins were insufficient to significantly modulate any outcomes due to participants' overall good health and lack of relevant cognitive decline. We recommend future interventional studies focus on subjects with diagnosed cognitive impairment, such as MCI and dementia, and those with more severe comorbidities, particularly chronic inflammatory conditions. Trial Sponsor: National Health and Medical Research Centre (NHMRC) Dementia Collaborative Research Centre.
15 Apr 2026
Read appraisal →Blood advances
HCT Pre-App program: a telemedicine-based prehabilitation for allogeneic hematopoietic cell transplant candidates
Frailty has emerged as a key component of allo-HCT candidate assessment and is closely associated with transplant outcomes. Exercise-based prehabilitation may improve transplant candidacy, yet real-world implementation is limited. Our institution implemented a structured Frailty Program (FP) to assess frailty and introduce prehabilitation interventions. This study describes the program's evolution and evaluates the impact of prehabilitation. Between April 2021 and April 2025, 185 consecutive allo-HCT candidates were included in 3 sequential FP phases. In the first phase (No-Prehab, n = 76), patients underwent frailty assessment only. In the second phase (Pilot-Prehab, n = 59), patients received a home-based, nonsupervised prehabilitation program. In the third phase (Tele-Prehab, n = 50), patients participated in the HCT Pre-App Program, a structured digital telemedicine intervention supervised by rehabilitation physicians. Frailty was assessed at first consultation and HCT admission using the HCT Frailty Scale. Median prehabilitation duration was 6 weeks. Adherence was high across frailty groups (76%-88%), with no adverse events. Prehabilitation improved frailty status: fit patients increased from 22% to 42% in the Pilot-Prehab cohort (P = .009) and from 34% to 56% in the Tele-Prehab cohort (P = .001). Tele-Prehab independently increased the odds of being fit at admission (odds ratio [OR], 3.86; P = .001) and reduced frailty incidence (OR, 0.17; P = .031). One-year OS and NRM were comparable across cohorts (OS, 74.5%, 84.5%, and 78.1%; P = .367) with a trend toward lower NRM among prehabilitated patients (NRM, 14.6%, 5.1%, and 5.6%; P = .075). Frailty is dynamic in allo-HCT candidates, and results support how home-based digital prehabilitation improves fitness before transplantation.
15 Apr 2026
Read appraisal →JMIR aging
Web-Based Personalized Machine Learning Recommendations to Enhance Shared Decision-Making in Prostate-Specific Antigen Screening: Randomized Controlled Trial
BACKGROUND: Prostate‑specific antigen (PSA) screening involves complex trade‑offs between early detection and the risks of overdiagnosis. For older adults (aged ≥50 years), shared decision‑making (SDM) is often hindered by limited health literacy, sensory or cognitive impairments, and multimorbidity, which complicate risk comprehension. Traditional decision aids provide foundational knowledge but are often nonpersonalized. Machine learning (ML) may offer individualized recommendations, yet the psychological and behavioral effects of ML‑assisted SDM in geriatric populations remain poorly characterized. OBJECTIVE: This study aimed to develop and evaluate a web‑based, ML‑driven decision aid integrated into an SDM workflow to provide personalized PSA screening recommendations and to assess its effects on decisional conflict (primary outcome), state anxiety, and decision satisfaction among middle‑aged and older men. METHODS: The study followed a 2‑stage design. First, a model establishment group (n=507) was used to train and evaluate 6 ML algorithms based on clinical and values‑clarification data. A random forest model was selected for its superior performance (mean area under the curve 0.933, SD 0.350; 95% CI 0.902-0.963). Second, a randomized controlled trial was conducted with 367 participants (mean age 64.34, SD 10.30 years) randomly assigned 1:1 to the ML suggestion group (MLSG; n=185) or the control group (CG; n=182). Both groups received video‑based education, counseling, and values clarification; only the MLSG received an ML‑generated "second opinion" recommendation. Primary and secondary outcomes were assessed using the Decisional Conflict Scale (DCS), Spielberger State‑Trait Anxiety Inventory (STAI), and Satisfaction with Decision scale. RESULTS: In the randomized controlled trial (n=367), the MLSG reported significantly lower decisional conflict than the CG (total DCS score: mean difference [MD] -3.77, 95% CI -5.55 to -1.99; Cohen d=-0.44; P<.001). The MLSG reported greater perceived support (DCS7: adjusted P=.03), more adequate advice (DCS9: adjusted P<.001), and higher decision confidence (DCS10: adjusted P=.03; DCS11: adjusted P<.001). Regarding psychological well‑being, although total anxiety scores did not differ, the MLSG reported reduced worry (STAI item 6: MD -0.98, 95% CI -1.20 to -0.76; d=-0.89; adjusted P<.001) and increased calmness (STAI item 1: MD 0.30, 95% CI 0.06-0.54; d=0.25; adjusted P=.01). Decision satisfaction was higher in the MLSG across all items (total Satisfaction with Decision score: MD -7.38, 95% CI -8.54 to -6.18; P<.001). Behavioral choices were strongly influenced by the ML recommendation: participants in the MLSG who received an "accept" recommendation were more likely to select "accept" (34/67, 50.7%) than those in the CG (44/182, 24.2%; P<.001). When the system suggested "not now," only 17.8% (21/118) chose "accept," which was lower than in the CG. CONCLUSIONS: Integrating personalized ML recommendations into SDM workflows provides emotional scaffolding for older men, reducing decisional distress and enhancing confidence without undermining autonomy. By addressing geriatric‑specific vulnerabilities through a facilitated digital interface, this ML‑driven approach complements traditional clinical consultations. These findings support the scalable integration of artificial intelligence-assisted decision support to foster patient‑centered care in aging populations.
15 Apr 2026
Read appraisal →JMIR aging
Personalized Digital Care Program Allocation for Older Adults: Reinforcement Learning-Based Simulation Study
BACKGROUND: As the demand for innovative older adult care grows alongside a shortage of care workers, personalization is key to optimizing services and enhancing long-term sustainability. This study proposes an adaptive reinforcement learning (RL)-based framework to promote precision digital care by dynamically assigning care programs based on individuals' unique characteristics and evolving needs. Its effectiveness was evaluated through simulation-based experiments comparing multiple allocation methods within an artificial intelligence (AI)-powered care call service for older adults. OBJECTIVE: This study aimed to develop and evaluate an RL-based model for personalizing digital care program allocation to optimize care engagement and health outcomes among low-income older adults living alone. METHODS: We developed the framework by using contextual bandits, specifically Thompson Sampling, to maximize user outcomes. Four program allocation strategies were tested using a synthetic dataset of user features and program attributes. Simulations were conducted over multiple iterations to evaluate how the model adapts over time and optimizes program assignments compared with static methods. RESULTS: Four program allocation methods were compared across 100 simulation runs (n=3000 assignments per run) using 2 datasets (AI Call: n=1196; Community Health Survey [CHS]: n=72,812): (1) systematic allocation (baseline), (2) single best program based on population average, (3) idealized personalized delivery (theoretical upper bound), and (4) precision digital care using Thompson Sampling. Precision digital care outperformed baseline and population-average approaches, achieving outcomes comparable to the theoretical upper bound. Compared to systematic allocation, call success rates increased by 84.2% (AI Call) and 54.4% (CHS), Patient Health Questionnaire-2 depression scores decreased by 32.1% (AI Call) and 41.4% (CHS), and self-reported health scores improved by 19% (AI Call) and 22% (CHS). It also showed improved learning efficiency, refining assignments dynamically as it learned from user responses. CONCLUSIONS: Our findings emphasize the importance of personalization in digital care. We plan to refine and validate the model through a publicly funded AI care program for community-dwelling, low-income older adults living alone in the Republic of Korea. RL offers a scalable and effective approach to advance precision digital care delivery and support future innovations in aging services.
14 Apr 2026
Read appraisal →Neurology
Type 1 Diabetes and Incident Dementia: An Analysis in the All of Us Cohort
BACKGROUND AND OBJECTIVES: Although diabetes mellitus (DM) is a well-established determinant of dementia risk, most studies have evaluated type 2 DM (T2DM) or any DM. The influence of type 1 DM (T1DM) on dementia risk remains unclear. We evaluated associations of T1DM and T2DM, separately, with incident dementia using linked electronic health records (EHRs). METHODS: This prospective cohort study used previously collected survey and EHR data from the All of Us (AoU) cohort, a convenience sample of US adults. Eligible participants were 50 years or older and completed baseline surveys. Enrollment began in 2017, with data available through October 2023, along with records before enrollment. Mean follow-up from baseline was 2.4 years. We developed an algorithm to distinguish DM type based on count of T1DM encounters. This algorithm was validated against 2 reference measures: self-reported diabetes type and C-peptide values. Using AoU data, we classified participants as having no DM, T1DM, or T2DM. We ascertained incident dementia using ICD-9, ICD-10, and Systematized Nomenclature of Medicine codes in participants' EHRs. We estimated hazard ratios (HRs) and 95% CIs for the association of diabetes type with incident dementia using Cox proportional hazards models. RESULTS: Among 283,772 participants (mean [SD] age 64.62 [8.96] years; 56.7% women), 60.3% identified as non-Hispanic White and 13.3% as Hispanic/Latino. Optimal DM classification algorithm cutoffs varied by reference standard: self-reported diabetes: ≥1 T1DM EHR encounter (sensitivity: 0.59; specificity: 0.90) and C-peptide: ≥3 T1DM EHR encounters (sensitivity: 0.76; specificity: 0.79). Defining T1DM as having ≥1 T1DM encounter, 5,442 participants had T1DM. Compared with those without DM, participants with T1DM had higher dementia incidence (sociodemographic-adjusted HR 2.82; 95% CI 2.28-3.48) and those with T2DM also had elevated risk (sociodemographic-adjusted HR 2.08; 95% CI 2.87-2.31). Results were similar across sex, race, and ethnicity strata. DISCUSSION: In AoU, DM was associated with elevated dementia risk, with the highest risk among those with T1DM. These findings highlight the need to better understand mechanisms tying T1DM to dementia.
14 Apr 2026
Read appraisal →JMIR medical informatics
Responsible AI for Predicting Delayed Hospital Discharge Among Older Adults: Development and Evaluation Study for Balancing Accuracy, Equity, and Explainability
BACKGROUND: Amid growing demands and constrained health care resources, effective hospital bed capacity management is crucial. Delayed hospital discharge, where patients remain in the hospital beyond the need for acute care, strains resources, affects patient outcomes, and reduces system efficiency. Predicting such delays facilitates early interventions to avert them and alleviate burdens on patients, care partners, hospitals, and the broader health care system. OBJECTIVE: This study aimed to develop comprehensive predictive analytics for delayed discharges among older adults using explainable machine learning to boost transparency and interpretability, while integrating fairness to mitigate algorithmic biases. METHODS: Leveraging longitudinal data from over 2 decades in Ontario, Canada, we applied extreme gradient boosting and logistic regression models to predict delayed discharges within 90 days post-acute care. Data preprocessing included a 2-year look-back for clinical histories and balanced sampling to address class imbalance. Model performance was assessed via area under the receiver operating characteristic curve, calibration, and clinical utility. Fairness was evaluated across sex, urban or rural residence, and residential instability using several threshold-free metrics. Explainability was examined at the global model level (via partial dependence plots and permutation feature importance) and locally (via Shapley Additive Explanations, breakdown, and ceteris paribus methods), with principal component analysis used to cluster key features for high-risk patients. RESULTS: The extreme gradient boosting model outperformed logistic regression, achieving an area under the receiver operating characteristic curve of 0.82 on the test set, with acceptable within-group and cross-group ranking fairness across subgroups. Explainability clustering analyses identified functional and cognitive declines (eg, care support needs, dementia, and mobility issues) and regional disparities as primary drivers of high-risk predictions. Bias mitigation improved calibration parity, especially when stratifying by residential instability, underscoring the trade-offs policymakers must weigh between accuracy, fairness, and explainability. CONCLUSIONS: This study demonstrates the potential of responsible artificial intelligence in health care, emphasizing the need to balance predictive accuracy, equity, and interpretability. It uncovers systemic gaps and offers actionable insights for enhanced discharge planning, resource optimization, and equitable care delivery.
14 Apr 2026
Read appraisal →Lancet (London, England)
Deferral of percutaneous coronary intervention in patients undergoing transcatheter aortic valve implantation (PRO-TAVI): an investigator-initiated, multicentre, open-label, non-inferiority, randomised controlled trial
BACKGROUND: Coronary artery disease is common in patients undergoing transcatheter aortic valve implantation (TAVI). We aimed to assess whether deferral of percutaneous coronary intervention (PCI) is non-inferior to routine PCI before TAVI in patients with coronary artery disease. METHODS: In this investigator-initiated, open-label, randomised controlled trial, done at 12 hospitals in the Netherlands, TAVI patients with coronary artery disease were randomly assigned in a 1:1 ratio to deferral of PCI or PCI before TAVI. Randomisation was done by use of a web-based system with random block sizes of 2 and 4, and stratification by presence of coronary artery disease involving proximal left anterior descending artery. The primary endpoint was a composite of all-cause mortality, myocardial infarction, stroke, and major bleeding at 1 year. Non-inferiority testing was done in the intention-to-treat population against the prespecified margin of 11 percentage points. The study is registered with ClinicalTrials.gov (NCT05078619) and long-term follow-up is ongoing. FINDINGS: Between Oct 7, 2021, and Nov 19, 2024, 466 patients were enrolled: 233 were assigned to deferral of PCI and 233 to PCI before TAVI. Median age was 81 years (IQR 78-84), and 166 (36%) of 466 patients were female. The primary endpoint occurred in 56 (24%) of 233 patients in the deferral group as compared with 60 (26%) of 233 patients in the PCI group (rate difference -1·7% [95% CI -9·5 to 6·2]; hazard ratio 0·89 [95% CI 0·62-1·28]; p=0·0008 for non-inferiority; p=0·68 for superiority). INTERPRETATION: In patients with coronary artery disease undergoing TAVI, deferral of PCI was non-inferior to PCI before TAVI for the 1-year composite of all-cause mortality, myocardial infarction, stroke, and major bleeding. These findings suggest that an initial conservative strategy can be appropriate in selected patients, although patient-tailored treatment decisions remain essential. FUNDING: ZonMw.
14 Apr 2026
Read appraisal →Biomacromolecules
Highly Flexible, Adhesive, Antimicrobial, and Self-Powered Hydrogel Strain Sensor for Human Motion Monitoring
Piezoelectric hydrogel sensors are a recent innovation that combines the flexibility of hydrogels with the self-powering of piezoelectric materials. Here, we present a self-powered hydrogel strain sensor by incorporating piezoelectric Na0.5Bi0.5TiO3 nanoparticles into a dual-network hydrogel composed of poly(vinyl alcohol), acrylamide, and 2-acrylamido-2-methyl-1-propanesulfonic acid. The hydrogel exhibits excellent mechanical properties, achieving a tensile strength of 1.28 MPa, an elongation at break of 1127%, and strong tissue adhesion (55.97 kPa on porcine skin). The sensor delivers a maximum output voltage of 237.4 mV, a sensitivity of 17.99 mV under small compressive strain, and a broad tensile strain detection range of 30 to 400%. It reliably monitors both subtle physiological activities (facial expression, swallowing, pulse) and large joint movements. Moreover, it exhibits notable antibacterial performance via ultrasound-driven piezocatalysis, reducing bacterial viability to below 1.5%. This tough, adhesive, and antibacterial piezoelectric hydrogel sensor holds significant promise for wearable electronics and human-machine interfaces.
14 Apr 2026
Read appraisal →Journal of speech, language, and hearing research : JSLHR
A Speech-Segregation Algorithm for Spatial Hearing Aids to Operate With Multiple Sound Sources
PURPOSE: Hearing-aid users often face challenges in noisy environments due to time, level, and spectral cues being compromised by current generation hearing aids. This study explored a physiologically based speech-segregation algorithm that selectively removes or attenuates unwanted sound sources. METHOD: In our previously developed localization algorithm, the time-frequency responses after a unique normalization approach always reside inside the unit circle of the model space. Given the "sparseness" property of daily sound, the model forms distinct clusters that correspond to the source locations. In the present study, the localization model was adapted to segregate speech by using a binary mask to remove the cluster of unwanted sound. The speech target was one of 200 intelligible sentences. The interfering sound was time-reversed sentences in a random sequence spoken by the same speaker. Automatic speech recognition transcribed the sound mixture before and after the segregation algorithm. RESULTS: When both target and noise were located at the front, applying a hard mask (i.e., 1 or 0) almost perfectly removed the energy of noise. When the sound sources moved to the side or back with smaller angular separations, clusters were less distinguishable, leading to worse intelligibility performance. Applying a soft mask (i.e., 1 or 0.2) instead showed slightly lower performance for the front but improved performance for the back and side. CONCLUSION: Our algorithm performs localization and segregation in a combined and straightforward manner, potentially for spatial hearing aids to function better in challenging listening environments.
12 Apr 2026
Read appraisal →Medicine
Prognostic factors for mortality in age-stratified elderly sepsis patients: A retrospective cohort study based on the MIMIC-IV database
Sepsis represents a major cause of death among elderly patients; however, prognostic factors specific to different age strata within this population remain insufficiently characterized. This retrospective cohort study aimed to identify early predictors of mortality in elderly sepsis patients using the MIMIC-IV database. We included 13,461 sepsis patients aged ≥60 years, stratified into younger-old (60-74 years; n = 7618) and older-old (75-90 years; n = 5843) groups. Propensity score matching was applied to balance baseline characteristics, yielding 4657 matched patients per group. Multivariable logistic regression, receiver operating characteristic analysis, and Kaplan-Meier survival curves were employed to assess clinical and biochemical markers for 28-day mortality and to identify age-specific prognostic indicators. These findings underscore the importance of age-stratified prognostic models to guide targeted clinical interventions and potentially improve outcomes in elderly patients with sepsis. Lactate dehydrogenase (LDH) and the PaO2/FiO2 ratio emerged as key mortality predictors in the younger-old group, whereas urea nitrogen and hypoalbuminemia were predominant in the older-old group. Urine output served as a common predictor across both strata, with oliguria associated with a 2.5-fold increase in mortality risk among older patients. While a combination of predictors enhanced prognostic accuracy in younger-old patients, this was not observed in the older-old group, suggesting a greater influence of frailty and comorbidities in advanced age.
12 Apr 2026
Read appraisal →Heart (British Cardiac Society)
Parsimonious machine learning model to predict 1-year mortality and procedural futility after transcatheter aortic valve replacement
BACKGROUND: Current risk scores inadequately predict long-term mortality after transcatheter aortic valve replacement (TAVR), limiting their ability to guide decisions around procedural futility. We aimed to develop and externally validate a machine learning (ML) model using only preprocedural variables to predict 1-year all-cause mortality. METHODS: An ML model was trained on a retrospective cohort of 1025 TAVR patients using 52 clinical and echocardiographic variables. Feature selection and model tuning were performed via a multiobjective evolutionary algorithm to optimise predictive performance and model simplicity. The final model used 13 preprocedural variables and was externally validated in an independent cohort of 270 patients. Performance was compared with European System for Cardiac Operative Risk Evaluation II (EuroSCORE II), FRANCE-2 and TAVI2-SCORE using the area under the curve (AUC), calibration and net reclassification improvement (NRI). RESULTS: The ML model demonstrated excellent discrimination, with AUCs of 0.81 in the discovery cohort and 0.84 in the external validation cohort. This exceeded the performance of EuroSCORE II (AUC: 0.61 and 0.70), FRANCE-2 (0.52 and 0.58) and TAVI2-SCORE (0.56 and 0.64). Calibration plots showed strong agreement between predicted and observed risks. NRI in the test set compared with FRANCE-2 was 0.62 (95% CI: 0.49 to 0.75); compared with TAVI2-SCORE, it was 0.36 (95% CI: 0.14 to 0.61). The final model incorporated age, atrial fibrillation, creatinine, haemoglobin, pulmonary function, frailty markers (Katz Index, poor mobility) and tricuspid regurgitation. Misclassification analysis revealed that most errors were clustered near the decision threshold, with no evidence of systematic bias. Performance was consistent across subgroups and robust to temporal and institutional variation. CONCLUSION: This externally validated ML model, using 13 routinely available variables, significantly outperforms existing risk scores in predicting 1-year mortality post-TAVR. Its simplicity and generalisability support its potential use in real-world clinical decision-making to identify patients at high risk of procedural futility.
12 Apr 2026
Read appraisal →Nature communications
Exposome-wide patterns predict brain health in aging
Promoting brain health is vital for well-being and reducing healthcare burdens. Brain health as measured with the Brain Age Gap (BAG) - the difference between chronological and predicted brain age- relates to many factors. However, a holistic view, integrating the range of factors an individual brain is exposed to, is missing for understanding how the exposome shapes brain health. After computing BAG as an indicator of grey matter (GM) health, we predicted it using machine learning based on 261 exposome variables (spanning biomedical, environmental, lifestyle, socio-affective, and early life domains) in UK Biobank participants. Exposome data can predict GM health with factors pertaining to cardiovascular and bone health, along with alcohol and smoking, nutrition and diabetes showing greater contribution to the prediction. In such domains, life period and duration of exposure appeared crucial. These findings call for early prevention in cardiovascular and metabolic health to promote life-long brain health.
12 Apr 2026
Read appraisal →Vaccine
Impact of pharmacy-based interventions on vaccination coverage among vulnerable and underserved populations: a systematic review and meta-analysis
BACKGROUND: Vaccination coverage remains suboptimal among vulnerable and underserved adult populations. We investigated the effectiveness of pharmacy-based interventions in improving vaccine uptake in high-risk groups and the most effective strategies. METHODS: A systematic search (PubMed/MEDLINE, Embase, Scopus; 26 July 2025) identified experimental and observational studies evaluating pharmacy-led vaccination interventions targeting adults ≥65 years or individuals aged 18-64 years with chronic conditions or limited healthcare access. The primary outcome was vaccination coverage. Odds ratios (ORs) were pooled using fixed- and random-effects models. Sensitivity analyses were conducted by intervention type and setting. Risk of bias was assessed using ROBINS-I, RoB 2, and NOS tools. RESULTS: A total of 35 studies were included, encompassing over 1.3 million participants. In pre-post comparisons, pharmacy-based interventions nearly doubled vaccination uptake (random-effects OR = 1.88; 95% CI: 1.68-2.12). In case-control analyses, individuals exposed to pharmacist interventions were almost five times more likely to be vaccinated (random-effects OR = 4.87; 95% CI: 2.23-10.64). Multicomponent strategies and direct vaccine administration by pharmacists produced the most consistent improvements. CONCLUSIONS: Pharmacist-led vaccination interventions significantly increase vaccine uptake among vulnerable adults. The most effective approaches involved pharmacists included eligibility assessment, counselling, administration, and documentation, integrated within routine care workflows.
11 Apr 2026
Read appraisal →JMIR aging
Facilitators and Barriers to Digital Self-Management in Older Adults With Depression: COM-B and Theoretical Domain Framework Qualitative Study
BACKGROUND: Depression in older adults presents unique challenges in self-management. Digital tools, such as mobile health (mHealth) apps, have the potential to support this population. This study explored the facilitators and barriers to digital self-management in older adults with depression to inform the design of effective mHealth apps. OBJECTIVE: This study aims to explore the facilitators and barriers to digital self-management in older patients with depression with the aim of informing the design and development of mHealth apps for older adults. METHODS: A purposive sampling method was used to recruit 25 older patients with depression from July to September 2024. Semistructured interviews were conducted to capture real-life experiences. Directed content analysis ensured objective and accurate data interpretation, and the Capability, Opportunity, Motivation, and Behavior (COM-B) model and the Theoretical Domains Framework were applied to identify facilitators and barriers related to behavior. RESULTS: Six themes were identified based on the COM-B model and the Theoretical Domains Framework: perception of illness and accumulation of personal experience; dual challenges of cognitive abilities and physical limitations; integration of digital technologies and acceptance differences; social influences and access to and utilization of support resources; environmental constraints and accommodations; and intertwined influences of beliefs, emotions, and motivation. A total of 13 barriers and 11 facilitators were identified. CONCLUSIONS: Digital self-management in older adults with depression is complex and influenced by multiple interrelated factors. Effective mHealth apps must integrate the cognitive, emotional, and social contexts of patients to provide user-friendly, personalized solutions.
11 Apr 2026
Read appraisal →JMIR aging
Global, Regional, and National Burden of Falls Among Older Adults Aged 65 Years and Above: Secondary Data Analysis of the Global Burden of Disease Study 2021
BACKGROUND: Falls are a leading cause of injury, disability, and death among older adults, posing significant public health challenges. However, comprehensive global analyses of fall-related burdens in older populations remain scarce. OBJECTIVE: This study aimed to explore the patterns and distribution of the global, regional, and national burden of falls among adults aged 65 years and older. METHODS: Data from the Global Burden of Disease study 2021 were used to assess the overall, disability, and mortality burden of falls among adults aged 65 years and older. Age-standardized rates of disability-adjusted life years (DALYs), years lived with disability (YLDs), and years of life lost were calculated to compare burdens across countries. Health inequalities were evaluated via the slope index of inequality and the concentration index. Frontier analysis identified optimal burden levels by sociodemographic index (SDI). Bayesian age-period-cohort models projected trends up to 2050. RESULTS: DALY age-standardized rates showed a U-shaped distribution across SDI regions: lower-SDI countries faced higher mortality burdens, while higher-SDI countries had elevated disability burdens. Despite an increase in absolute overall burden inequality from 1990 to 2021, absolute inequalities in YLDs and years of life lost declined, with DALYs and YLDs exhibiting relatively more balanced distributions. Frontier analysis pinpointed countries with the greatest burden reduction potential. Projections suggest decreasing overall and mortality burdens by 2050 but rising disability burdens. CONCLUSIONS: Higher- and lower-SDI countries face distinct fall-related challenges. Reducing cross-national health inequalities and closing gaps between the observed burden and the optimal burden level achievable at a similar SDI level are critical. Despite projected declines in the overall burden (DALYs), the rising disability burden (YLDs) could present evolving challenges, potentially underscoring the importance of proactive preparedness.
11 Apr 2026
Read appraisal →Journal of epidemiology and community health
Utilisation of in-person and telehealth care among US adults with loneliness and social isolation
BACKGROUND: Telehealth has become a critical tool for improving access to care among populations facing physical barriers to in-person care. However, its potential role in delivering care to adults with social barriers remains underexplored. We examined whether adults with loneliness or social isolation are more likely to use telehealth care versus in-person care compared to adults without loneliness or social isolation. METHODS: This cross-sectional study analysed data for US adults in the 2021 Medical Expenditure Panel Survey. Loneliness and social isolation were measured using the UCLA Loneliness Scale and the Berkman-Syme Social Network Index. Three indicators of healthcare utilisation were used as outcomes: in-person care only, telehealth care only and a combination of both. RESULTS: Among adults experiencing loneliness and social isolation, the likelihood of using only in-person care was lower by 4.1 (95% CI -6.1 to -2.1) and 2.8 (95% CI -5 to -0.6) percentage points. In contrast, the likelihood of using only telehealth care was higher by 2.6 (1.1-4.2) and 5.4 (3.9-6.9). The likelihood of using both in-person and telehealth care was also higher by 2.3 (0.7-3.9) and 5.2 (3.7-6.7), but telehealth was used more frequently than in-person care. Furthermore, telehealth utilisation for mental health services was higher among adults experiencing loneliness or social isolation. CONCLUSION: Compared with adults who are not lonely or socially isolated, adults experiencing loneliness and social isolation are more likely to use telehealth and less likely to use in-person care. Telehealth may serve as a viable alternative for individuals facing social challenges in accessing in-person services.
11 Apr 2026
Read appraisal →Scientific data
Continuous observation of Parkinsonian symptoms using symptom diaries & wearable accelerometry
Treatment adjustments in Parkinson's Disease (PD) are often based on clinical evaluations at single time points which are insufficient to adequately assess real-life motor fluctuations. Patient-written symptom diaries on the other hand are highly subjective and require well-educated and adherent patients to provide reliable results. Wearable accelerometry might provide a reliable, objective, and continuous diagnostic method to assess PD motor symptoms & fluctuations. However, large datasets of simultaneous sensor data and symptom diaries are needed for such method development and validation. We here provide a well-described, open-science dataset of simultaneous, bilateral, wrist-worn accelerometry and symptom diary data from 66 participants (41 male, 25 female) with PD. On average, participants provided data for 6.0 consecutive days resulting in a total of 393.8 days for the dataset as a whole. Symptom diaries include data on kinetic state, tremor, freezing of gait, falls, and PD-related medication intake. Further demographic information is also provided. This dataset will support the development and validation of accelerometry-based approaches to assessing motor symptoms and fluctuations in PD.
11 Apr 2026
Read appraisal →Neurology
Neurovascular Unit Dysfunction and Cerebral Small Vessel Disease in Parkinson Disease: A Systematic Review and a Meta Analysis
BACKGROUND AND OBJECTIVES: Parkinson disease (PD) pathogenesis remains incompletely understood; beyond nigrostriatal loss, nondopaminergic mechanisms including neurovascular unit dysfunction may contribute to disability. Cerebral small vessel disease (CSVD) burden reflects neurovascular dysfunction in the form of white matter hyperintensities (WMH), lacunes, cerebral microbleeds (CMB), and enlarged perivascular spaces (ePVS). In PD cohorts, CSVD burden correlates with worse motor and gait scores. The aim of the study was to explore whether patients with PD exhibit greater CSVD burden than healthy controls (HC). METHODS: We conducted a PRISMA-conformant systematic review and meta-analysis of studies including adults with idiopathic PD and a HC group that presented data comparing CSVD burden in these 2 groups. Six databases (MEDLINE, Embase, CINAHL Plus, CENTRAL, Scopus, and Web of Science) were searched on May 14 and 15, 2024. Two reviewers independently screened records, extracted data, and assessed risk of bias, with discrepancies resolved by consensus. Continuous outcomes were pooled as standardized mean differences (SMD); dichotomous outcomes as odds ratios (ORs). We evaluated small-study effects for pooled analyses with 10 or more studies using funnel plots and Egger regression test. RESULTS: We examined 13,403 records. Forty-six studies (45 cross-sectional) met inclusion criteria, totaling 3,817 PD and 2,593 HC (mean ages: 66.9 and 66.5, respectively). WMH volume (k = 21) was higher in PD (SMD 0.36, 95% CI 0.11-0.62). Visual ratings also indicated higher WMH in PD: global (k = 14) SMD 0.27 (95% CI 0.08-0.46); periventricular (k = 11) SMD 0.32 (95% CI 0.12-0.51); deep (k = 8) SMD 0.20 (95% CI 0.09-0.31). Differences in CMB (k = 6; OR 1.18, 95% CI 0.57-2.42) and lacunes (k = 4; OR 1.48, 95% CI 0.58-3.78) were not significant. ePVS results were heterogeneous but trended toward greater burden in PD, most notably in the midbrain (k = 3; SMD 1.80, 0.15-3.44). Overall evidence quality was rated as low, reflecting the observational nature of the included studies. DISCUSSION: Our analysis showed PD to be associated with greater WMH burden and increased midbrain ePVS. Pooled differences in CMB and lacunes were not significant. Substantial heterogeneity and cross-sectional designs limit certainty; standardized imaging and prospective cohorts are needed to define mechanisms and clinical implications.
10 Apr 2026
Read appraisal →Blood
A decade of ibrutinib for CLL with and without TP53 aberration: final report on an investigator-sponsored phase 2 study
Bruton tyrosine kinase inhibitors improve outcomes for patients with chronic lymphocytic leukemia (CLL). Long-term data with continuous therapy are limited. With a median follow-up of 10.0 years, we report final results on 84 patients with TP53 aberrations (deletion of chromosomal arm 17p or TP53 mutation) or ≥65 years of age treated with 420 mg of single-agent ibrutinib daily until progression or unacceptable toxicity. A total of 52 (61.9%) patients were previously untreated; 56 (66.7%) had unmutated immunoglobulin heavy-chain variable region; and 53 (63.1%) had TP53 aberrations, including 34 treatment naïve patients. As of 31July 2024, 9 (10.7%) patients continued ibrutinib, 39 (46.4%) discontinued ibrutinib for progressive disease, 31 (36.9%) for adverse events, and 5 (5.9%) withdrew consent. The median progression-free survival (PFS) was 7.2 years; median overall survival (OS) was not reached. In patients with and without TP53 aberrations, median PFS was 5.6 years and not reached, and 10-year OS was 51.3% and 75.3%, respectively. The estimated 10-year PFS and OS for patients with TP53-aberrant CLL treated in first line was 38.6% and 65.7%, respectively. Minimal residual disease (MRD) was quantified by peripheral blood flow cytometry annually. Undetectable MRD (uMRD; at 10-4) was achieved in 13 (15.5%) patients after a median of 5 years. Twelve patients maintained uMRD, the longest observation ongoing at 8.0 years. Seventeen (42.5%) patients with best response of high MRD (>10-2) remained progression-free for >5 years. These results highlight durable benefits and deepening responses with ibrutinib, including in high-risk CLL. Whether patients maintaining uMRD for years can safely discontinue therapy should be assessed prospectively. This trial was registered at www.clinicaltrials.gov as NCT01500733.
10 Apr 2026
Read appraisal →European journal of orthopaedic surgery & traumatology : orthopedie traumatologie
Memantine as a pain management strategy post-total knee arthroplasty: a randomized controlled trial
INTRODUCTION: Total knee arthroplasty (TKA) provides significant pain relief and improved function for patients with knee osteoarthritis. Despite its effectiveness, postoperative pain remains a major challenge. Effective management of this acute postoperative pain is critical for enhancing recovery. The study investigates the effectiveness of memantine in reducing postoperative pain in patients undergoing TKA. METHODS: This double-blind, randomized controlled trial aimed to evaluate the efficacy of memantine for managing postoperative pain after TKA. Conducted from April to September 2022, eligible participants over 18 years with ASA grades I or II were recruited. Exclusions included patients with substance abuse histories, chronic opioid use, significant comorbidities, or contraindications to memantine. Participants were randomly assigned to receive either memantine (20 mg daily preoperatively for three days and 10 mg daily postoperatively for two weeks) or a placebo. Both groups received standard analgesics during hospitalization and upon discharge. Pain levels were assessed using Visual Analog Scale (VAS) scores and Short-Form McGill Pain Questionnaire (SF-MPQ) at various time points. Secondary outcomes measured included opioid usage and nocturnal awakening. RESULTS: Memantine did not significantly lower pain levels compared to placebo, as measured by VAS (mean difference: 0.138, CI 95%: - 0.468-1.103, P = 0.418) and SF-MPQ (mean difference: 1.962, CI 95%: - 1.013-4.936, P = 0.190). Patients receiving memantine needed opioid analgesia (2.85 ± 0.67 times) significantly (P < 0.001) less frequently than the Placebo patients (4.14 ± 1.06 times). Nocturnal awakening did not differ significantly between the two groups (Memantine: 3.59 ± 2.15, Placebo: 2.33 ± 2.12, P = 0.067). CONCLUSION: The comparison of pain levels between oral memantine and placebo indicates that memantine does not significantly reduce pain in patients undergoing TKA. However, it significantly lowers the need for opioids postoperatively.
10 Apr 2026
Read appraisal →PloS one
The Relationships between physical activity, sedentary behaviour, sleep, and dementia: A systematic review and meta-analysis of cohort studies
OBJECTIVE: This study aimed to summarize the observational evidence from prospective cohort studies examining the associations of regular physical activity, sedentary behaviour, and sleep duration with incident dementia among community-dwelling adults aged 35 years and older. METHODS: Systematic literature searches (1946 to August 2025) of CINAHL, EMBASE, MEDLINE, PSYCINFO, and SPORTDISCUS were performed. Eligible studies included community-dwelling adults aged 35 + years with at least one year of follow-up and valid measures of movement behaviours and dementia outcomes. Studies were excluded if they included participants with baseline dementia, lacked risk estimates for all-cause dementia. Grey literature was excluded. Random effects meta-analysis generated pooled risk ratio (RR) and 95% confidence intervals (CI). Primary exposures were defined using national thresholds for physical activity, sedentary time, and sleep duration. Subgroup analyses were performed by age and follow-up duration. RESULTS: Forty-nine studies with physical activity (n = 2,855,529), 17 studies on sleep duration (n = 1,344,170), and three studies on sedentary duration (n = 295,809) were included. Regular physical activity significantly reduced the risk of incident dementia (pooled RR = 0.75, 95% CI = 0.68 to 0.82), though heterogeneity was substantial and partially explained by subgroup analyses. Prolonged sedentary behaviour (8 + hours/day sitting) increased dementia risk (RR = 1.27, 95% CI = 1.17 to 1.39) with low heterogeneity. Moreover, both short (<7 hours; RR = 1.18, 95% CI = 1.09 to 1.28) and long (>8 hours; RR = 1.28, 95% CI = 1.15 to 1.43) sleep were linked to higher dementia risk compared with 7-8 hours. Heterogeneity was moderate to substantial. CONCLUSION: Regular physical activity, less sedentary time and appropriate nightly sleep (7-8 h) may be associated with reduced risk of dementia and are potentially modifiable factors in the prevention or delay of dementia. Future studies with middle-aged adults and longer-term follow-up including changes in movement behaviours over time are needed to better understand the relationship between physical activity, sedentary behaviour, and sleep for dementia risk. PROSPERO REGISTRATION: CRD42021272054.
9 Apr 2026
Read appraisal →Brain : a journal of neurology
Leveraging machine learning to predict Parkinson's disease using pre-symptomatic proteomics data
In Parkinson's disease, non-motor symptoms precede the characteristic motor manifestations by up to 20 years. However, predicting the disease risk remains challenging. We applied machine learning to 2937 plasma proteins assayed in UK Biobank participants with Parkinson's disease to predict disease outcome up to 14 years before diagnosis. Of the 446 proteins found to be dysregulated in incident cases compared with controls, a subset of 23 predicted disease with an area under the curve (AUC) value of 0.78 in incident and 0.795 in prevalent cases. The utility of the identified proteomic signatures was validated in an independent cohort, based on 16 proteins shared across datasets, leading to a validation AUC of up to 0.76. Parkinson's disease-related pathways, including neuron death and amyloid-β clearance, were enriched up to 9 years before diagnosis. Furthermore, a co-expression network analysis revealed protein modules associated with disease risk and time-to-diagnosis. Our findings underscore the potential of applying machine learning to large-scale proteomics data from pre-symptomatic patients for early disease prediction.
9 Apr 2026
Read appraisal →JMIR mHealth and uHealth
Effectiveness of a Telecare-Based Intervention Program in Supporting Informal Caregivers of Community-Dwelling Older Adults in Reducing Stress Levels: Randomized Controlled Trial
BACKGROUND: Informal caregivers are essential in supporting community-dwelling older adults, especially as global populations age. However, caregiving responsibilities often result in high stress, depressive symptoms, and diminished quality of life. Traditional support services are fragmented, and many programs overlook caregivers' emotional and informational needs. Telecare-the remote delivery of health and social care via digital technologies-offers a promising approach to provide accessible, personalized, and timely support. OBJECTIVE: This study evaluated the effectiveness of a telecare-based intervention program, developed by a health-social partnership team, in improving psychological well-being and caregiving outcomes among informal caregivers of community-dwelling older adults. We hypothesized that caregivers in the intervention group would show greater improvements in stress, self-efficacy, depression, quality of life, and caregiving burden compared to those receiving usual care. METHODS: A single-blind randomized controlled trial was conducted from January to December 2023, with 75 informal caregivers providing care to older adults (aged ≥60 y) for at least 4 hours per week. Participants were randomized to an intervention group (n=38, 51%) or a control group (n=37, 49%). The 3-month intervention included (1) biweekly online nurse case management supported by a multidisciplinary health-social team, (2) personalized WhatsApp videos on caregiving skills, and (3) a password-protected caregiver website offering resources and peer discussion. The control group attended 6 in-person educational sessions at a community center. Outcomes such as stress, self-efficacy, depression, quality of life, and caregiving burden were measured at baseline and postintervention. RESULTS: Participants had a mean age of 65.5 (SD 9.617) years, and 81.3% (61/74) were female. Baseline characteristics were similar between groups. Compared to controls, the intervention group showed significantly higher self-efficacy (t=1.98; P=.04) and lower depression (t=-2.24; P=.03) at follow-up. Within-group improvements in the intervention group were also observed for stress, depression, self-efficacy, mental quality of life, and caregiving burden. No significant between-group differences were found for stress, quality of life, or caregiving burden. No significant within-group improvements were observed in the control group. CONCLUSIONS: This study provides novel evidence that a digitally delivered, nurse-led telecare program-integrating health and social services-can improve psychological outcomes among informal caregivers. These findings have practical implications for health systems seeking scalable, low-barrier solutions to support caregivers. Telecare models may be a valuable complement to traditional services, enhancing caregiver resilience and promoting aging in place while alleviating long-term strain on institutional care systems.
9 Apr 2026
Read appraisal →Genome research
spRefine denoises and imputes spatial transcriptomic data with a reference-free framework powered by genomic language model
The analysis of spatial transcriptomic data is hindered by high noise levels and missing gene measurements, challenges that are further compounded by the higher cost of spatial data compared to traditional single-cell data. To overcome this challenge, we introduce spRefine, a deep learning framework that leverages genomic language models to jointly denoise and impute spatial transcriptomic data. Our results demonstrate that spRefine yields more robust cell- and spot-level representations after denoising and imputation, substantially improving data integration. In addition, spRefine serves as a strong framework for model pretraining and the discovery of novel biological signals, as highlighted by multiple downstream applications across data sets of varying scales. Notably, spRefine enhances the accuracy of spatial aging clock estimations and uncovers new aging-related relationships associated with key biological processes, such as neuronal function loss, which offers new insights for analyzing aging effect with spatial transcriptomics.
8 Apr 2026
Read appraisal →Journal of the American Heart Association
Patient Care Pathways and Outcomes Following Newly Diagnosed Atrial Fibrillation
BACKGROUND: Care pathways for patients with atrial fibrillation (AF) are poorly understood and may influence the likelihood of guideline concordant oral anticoagulation or antiarrhythmic drug therapy and associated outcomes. METHODS: Adult patients in the Optum Clinformatics Database (2015-2023) with incident AF were identified. Care pathways, by treating clinician specialty (primary care, cardiology, electrophysiology) during each AF-related visit in the 1 year post diagnosis were examined. Cox regression models were used to assess associations between an electrophysiologist visit and AF-related treatments including antiarrhythmic drug therapy and oral anticoagulation and outcomes including AF-related hospitalizations, heart failure hospitalizations, and stroke. RESULTS: Of the 37 370 patients included, 7700 (20.6%) had a care pathway including an electrophysiologist visit. Older patients (70-79 and 80+ versus 18-49 years: hazard ratio [HR], 0.71 [95% CI, 0.64-0.78] and HR, 0.57 [95% CI, 0.51-0.63], respectively) and those with a higher CHA2DS2-VASc score (2-3 and ≥4 versus 0-1: HR, 0.79 [95% CI, 0.74-0.85] and HR, 0.76 [95% CI, 0.68-0.84], respectively) were less likely to have an electrophysiologist visit. Electrophysiologist visits were associated with a significant increase in rates of treatment with antiarrhythmic drug and oral anticoagulation. Patients aged ≥80 years with an electrophysiologist visit had significantly lower risks of stroke compared with those without an electrophysiologist visit (HR, 0.63 [95% CI, 0.42-0.95]). CONCLUSIONS: Certain demographic groups including older patients had lesser likelihood of electrophysiologist consultation, but guideline concordant care was more likely associated with electrophysiology care pathways. Electrophysiology care pathways were associated with improved outcomes especially among the groups with greatest stroke risk.
8 Apr 2026
Read appraisal →JBJS reviews
Treatment Outcomes for Distal Radius Fractures: A Meta-Analysis
BACKGROUND: Distal radius fractures (DRFs) are among the most prevalent in upper-extremity injuries, particularly in older adults and individuals with osteoporosis. The common treatment options include surgical interventions such as volar locking plate (VLP) fixation and percutaneous pinning, as well as conservative strategies like splinting and bracing. However, the comparative effectiveness of these modalities remains unclear. This study aims to compare the functional outcomes and complication rates associated with VLP, percutaneous pinning, bracing, and splinting in the treatment of DRFs. METHODS: A systematic search of PubMed, Google Scholar, and the Cochrane Library (September 2001-February 2025) identified randomized controlled trials, prospective cohort studies, and retrospective cohort studies comparing DRF treatments. Functional outcomes and complication rates (superficial infection, malunion, nonunion, delayed union) were analyzed. Data were analyzed using Review Manager Web with significance set at p ≤ 0.05. RESULTS: Twenty-seven studies involving 3,200 patients met inclusion criteria: VLP fixation (n = 896), percutaneous pinning (n = 666), splinting (n = 718), and bracing (n = 903). No significant differences were observed between VLP and splinting or between bracing and splinting for Disabilities of the Arm, Shoulder, and Hand (DASH); Patient-Rated Wrist Evaluation (PRWE); or grip strength. Compared with VLP, percutaneous pinning demonstrated better PRWE scores but a higher infection rate. Compared with bracing, percutaneous pinning showed significantly better DASH, PRWE, and grip strength outcomes, although with substantial heterogeneity. Compared with bracing, VLP fixation yielded significantly better DASH, PRWE, and grip strength outcomes and a lower malunion rate. CONCLUSION: All 4 treatments achieved acceptable outcomes, but with key trade-offs: Percutaneous Pinning had the best functional results but a higher infection risk. VLP provided better function and lower malunion rates than bracing. Splinting and bracing were effective conservative options, often yielding results similar to VLP, but were inferior in some specific measures. The findings suggest that a perfect anatomic repair is not always necessary for a good recovery. Ultimately, the choice of treatment must be individualized, weighing the functional benefits against the risks of complications, while considering patient health factors and preferences. LEVEL OF EVIDENCE: Therapeutic Level II. See Instructions for Authors for a complete description of levels of evidence.
8 Apr 2026
Read appraisal →JMIR research protocols
Enhancing Continuous Medication Safety Through e-Prescription and Clinical Decision Support Systems in Outpatient Practices and Pharmacies: Protocol for a Multiperspective Study (eRIKA Study)
BACKGROUND: Increased life expectancy is associated with increasing multimorbidity and polypharmacy, leading to a heightened risk of drug-drug interactions and adverse events, especially when multiple health care providers are involved. To address the urgent need for safer medication management in this population, tools such as medication plans (MP), electronic prescriptions (e-prescriptions), and clinical decision support systems (CDSS) offer valuable support. These instruments have the potential to enhance medication safety by providing physicians and pharmacists with a comprehensive overview of a patient's overall medication regimen and by assisting health care professionals in making informed prescribing decisions. OBJECTIVE: This study aims to improve medication therapy safety by combining e-prescriptions, the use of claims data, MPs, CDSS, and interprofessional communication. To comprehensively evaluate this complex intervention, a holistic multiphase study will be conducted, examining (1) the effectiveness of the intervention and (2) health-economic and (3) implementation-related aspects. METHODS: A multiphase study design is used. In the first phase, the intervention is implemented in selected outpatient practices (n=10) and pharmacies (n=10) in 2 regions in Germany as part of a cluster-randomized controlled trial to assess process-related outcomes. The primary outcome is the congruence between the MP and claims data. In phase 2, the intervention is scaled up in 3 regions and evaluated in a quasi-experimental study. The required sample size for the intervention group is 3528 patients, with a synthetic control group matched from existing claims data. The primary outcome is a combined end point of all-cause mortality and hospitalization within 3 months of an index prescription. Quantitative methods (descriptive, regression-based methods using claims data, calculation of the incremental cost-effectiveness ratio, and survey-based analyses of implementation-related aspects) and qualitative methods (interviews and focus groups to capture experiences of health care professionals and patients) are used. RESULTS: In phase 1, a total of 187 patients were recruited (74 in the intervention group and 113 in the control group) by June 2025. Phase 2 is currently ongoing, with data collection continuing through December 31, 2025. Final analyses are planned by March 2027. CONCLUSIONS: Medication safety in polypharmacy remains a critical challenge in Germany. This study provides multiperspective evidence supporting the nationwide implementation of the eRIKA (e-prescription as an element of interprofessional care pathways for continuous medication therapy management [eRezept als Element interprofessioneller Versorgungspfade für kontinuierliche AMTS]) intervention.
8 Apr 2026
Read appraisal →JMIR mHealth and uHealth
A Mobile App-Based Individualized Nonpharmacological Intervention for Behavioral and Psychological Symptoms in Dementia: Pilot Randomized Controlled Trial
BACKGROUND: Behavioral and psychological symptoms of dementia (BPSD) are common and negatively impact both individuals with dementia and their families. Although nonpharmacological interventions are recommended as the first-line treatments, their use in community settings is limited by access and caregiver resources. Existing approaches are often not individualized and rely on institutional or clinician-led delivery. OBJECTIVE: We developed a caregiver-initiated and individualized multimodal mobile app. The app delivers tailored nonpharmacological interventions-such as music therapy, exercise, and reminiscence therapy-based on each user's preferences and functional abilities. This study aimed to evaluate the effectiveness of this mobile app-based intervention in reducing BPSD in community-dwelling persons living with dementia. METHODS: This study used a single-blinded randomized controlled trial design. Participants were recruited from an outpatient clinic of a tertiary hospital, a dementia care center, and 5 home care service centers. A total of 36 dyads participated, each comprising a community-dwelling person living with dementia aged 60 years or older and their primary family caregiver. The dyads were randomly allocated to either the intervention or control group. The intervention group received a caregiver-initiated, multimodal, mobile app-based individualized intervention for 4 weeks, whereas the control group continued with usual care. The primary outcomes were overall BPSD, agitated behavior, and depression. The secondary outcomes were nighttime sleep efficiency and caregiver competency in managing BPSD. Assessments were conducted at baseline, immediately after the intervention, and at a 2-week follow-up. RESULTS: Of the 36 randomized dyads, 33 were included in the final analysis. Although the intervention group showed greater reductions in overall BPSD, agitated behavior, and depression after the intervention, no significant group-by-time interaction effects were observed in the total sample. In the subgroup analysis of participants with clinically significant baseline BPSD, a statistically significant improvement in overall BPSD was found in favor of the intervention group (β=-12.885, 95% CI -24.530 to -1.240; P=.03). No significant effects were observed for either nighttime sleep efficiency or competence in the management of BPSD. CONCLUSIONS: A mobile app-based individualized intervention may offer a flexible, caregiver-initiated approach to managing BPSD in home-care settings. While overall effects were limited, exploratory subgroup findings provided meaningful insights, indicating potential benefits for those with higher baseline symptom severity. The results highlight the need for further research on adaptive personalization and optimized intervention delivery to enhance the clinical effectiveness of digital dementia care.
8 Apr 2026
Read appraisal →Journal of the American Heart Association
Blood Pressure Parameters and Risk of Cognitive Decline and Dementia in Type 2 Diabetes: Results From the ADVANCE Trial
BACKGROUND: Blood pressure (BP) variability and cumulative BP load are significantly associated with cardiovascular disease risk beyond mean systolic BP, but less is known regarding their associations with cognitive decline/dementia and whether these associations differ by cognitive function at baseline or by sex. The aims of this study were to determine associations of different BP parameters with cognitive decline/dementia in patients with type 2 diabetes and explore differences by mild cognitive impairment at baseline and sex. METHODS: Using data from the ADVANCE (Action in Diabetes and Vascular Disease: Preterax and Diamicron Modified Release Controlled Evaluation) study, BP parameters were calculated from an 18-month exposure window comprising measurements at 3, 4, 6, 12, and 18 months, after randomization. Logistic regression was used to estimate the odds ratio (OR) per SD higher and 95% CI for the associations of BP parameters with the composite outcome of cognitive decline (≥3 points from baseline on the Mini-Mental State Examination) or clinical diagnosis of dementia. RESULTS: Of the 11 140 ADVANCE participants, 9586 patients had 5 complete BP measurements within the 18-month exposure window. After a mean follow-up of 3.5 years, 1674 (17.5%) participants were diagnosed with cognitive decline and/or dementia. Overall, variability and baseline pulse pressure (PP), but not BP load, were associated with higher odds of cognitive decline/dementia (OR: variability in systolic BP, 1.11 [95% CI, 1.05-1.17]; diastolic BP, 1.11 [95% CI, 1.05-1.17]; PP, 1.05 [95% CI, 1.00-1.11]; mean arterial pressure, 1.13 [95% CI, 1.07-1.19]; and baseline PP, 1.19 [95% CI, 1.13-1.25]). There were no differences by mild cognitive impairment at baseline or sex. CONCLUSIONS: Higher BP variability and baseline PP, but not mean BP or BP load, were associated with higher odds of cognitive decline/dementia in patients with type 2 diabetes. BP variability and PP may be important therapeutic markers for the preservation of brain health. REGISTRATION: URL: https://clinicaltrials.gov; Unique Identifier: NCT00145925.
8 Apr 2026
Read appraisal →Journal of the American Heart Association
Impact of Atrial Fibrillation Pattern on Edoxaban Antithrombotic Therapy in Patients With Atrial Fibrillation and Stable Coronary Artery Disease: A Secondary Analysis of the EPIC-CAD Randomized Clinical Trial
BACKGROUND: Paroxysmal atrial fibrillation (AF) is associated with a risk of stroke or systemic embolism similar to nonparoxysmal (persistent or permanent) AF. However, whether the optimal antithrombotic strategy in patients with AF and stable coronary artery disease differs by AF pattern remains uncertain. METHODS: This prespecified substudy of the EPIC-CAD (Edoxaban Antithrombotic Therapy for Atrial Fibrillation and Stable Coronary Artery Disease) trial compared edoxaban monotherapy with dual antithrombotic therapy (edoxaban plus a single antiplatelet agent) in patients with AF and stable coronary artery disease. Stratified analyses were performed by AF pattern. The primary outcome was net adverse clinical events, defined as a composite of all-cause death, myocardial infarction, stroke, systemic embolism, unplanned urgent revascularization, major bleeding, or clinically relevant nonmajor bleeding within 12 months. RESULTS: Among 1040 patients, 575 (55.3%) had paroxysmal and 465 (44.7%) had nonparoxysmal AF. The 12-month primary outcome rate was similar between paroxysmal and nonparoxysmal AF (10.3% versus 12.2%; adjusted hazard ratio [HR], 1.15 [95% CI, 0.79-1.68]). Edoxaban monotherapy was associated with a significantly lower risk of the primary outcome than dual antithrombotic therapy in both paroxysmal AF (4.6% versus 16.2%; adjusted HR, 0.23 [95% CI, 0.12-0.42]) and nonparoxysmal AF (9.3% versus 15.0%; adjusted HR, 0.56 [95% CI, 0.32-0.87]). No significant interaction was observed between antithrombotic strategy and AF pattern (P for interaction=0.10). CONCLUSIONS: In patients with AF and stable coronary artery disease, edoxaban monotherapy significantly reduced net adverse clinical events at 12 months compared with dual antithrombotic therapy, irrespective of AF pattern. REGISTRATION: URL: https://www.ClinicalTrials.gov; Unique identifier: NCT03718559.
8 Apr 2026
Read appraisal →BMJ open
Adverse childhood experiences and cognitive outcomes among older adults in low-income and middle-income countries: a systematic review
OBJECTIVES: While adverse childhood experiences (ACEs) have been consistently linked to poorer cognitive outcomes in later life, far less is known about ACEs' contribution to dementia and cognitive ageing risk in low-income and middle-income countries (LMICs), despite their growing and disproportionate share of global Alzheimer's disease and related dementias (ADRD) burden. This study aimed to systematically review existing evidence on the association between individual ACEs and cognitive outcomes among adults aged 40 years and older in LMIC settings. DESIGN: Systematic review and narrative synthesis. DATA SOURCE: We searched Medline, Embase, PsycINFO and CINAHL from the inception of each database to January 2026. ELIGIBILITY CRITERIA: Observational studies examining the association between individual ACEs and cognitive outcomes among adults aged 40 and older in LMICs. DATA EXTRACTION AND SYNTHESIS: Two reviewers independently screened each record, assessed risk of bias using the Joanna Briggs Institute critical appraisal tool and extracted data. Results were illustrated using descriptive forest plots and a narrative synthesis. RESULTS: Our systematic review included 14 studies, primarily from upper-middle-income countries, that assessed the relationship between individual ACEs and cognitive impairment (n=4) and function (n=10). 10 studies reported significant associations between ACEs and poorer cognitive function and increased risk of cognitive impairment. No study assessed the association between ACEs and ADRD. Parental death, neglect and mental health issues in the family showed consistent associations with cognitive outcomes, whereas experiencing hunger and poor health in childhood showed inconsistent associations. CONCLUSIONS: Our review corroborated evidence from high-income countries that exposure to ACEs is associated with a long-term risk of poorer cognitive outcomes among older populations in LMICs. However, the available literature remains limited, with most studies originating from upper-middle-income countries, few examining cognitive impairment and none assessing ADRD. These findings underscore substantial gaps in knowledge and highlight the need for research across diverse LMIC settings to clarify the role of broader psychosocial and contextual childhood experiences in shaping cognitive ageing and dementia risk. PROSPERO REGISTRATION NUMBER: CRD42024501816.
8 Apr 2026
Read appraisal →PloS one
Appreciating the complexity of frailty and user context in digital health intervention design: A qualitative study with personas
Frailty is a complex and dynamic process that is becoming increasingly common as the population ages. Early intervention has been shown to reverse or slow progression, but requires support from increasingly limited healthcare resources. Digital health interventions could potentially assist, but first it is vital to understand older adults' experiences and perspectives. This study describes feedback from older adults with symptoms of pre or early frailty on the design concept of a digital device intended to function as an adjunct to physiotherapy and occupational therapy by increasing frequency and quality of safe movement at home. Objectives were to better understand experiences of frailty, and to identify potential influencing factors on adoption and adherence. In-person audio-recorded qualitative interviews were conducted with 17 older adults and 5 carers recruited from community settings. Reflexive thematic analysis generated two themes: 'autonomy and prompts: importance of personalisation'; and 'digital interfaces and accountability: importance of usability and purpose'. In addition, three personas were generated from the data, each describing different needs and preferences for such a device. Situating the results of analysis in the context of current literature, a set of Guiding Principles was created to support future design development in this area. These highlight important factors for consideration when designing for this population, and provide key design feature suggestions and implementation strategies to address these factors including: ensuring the device has clear purpose; is adaptable to the diversity of needs within this clinical population; any form of feedback is safe, relevant, useful and rewarding to multiple users; and technical support is accessible and ongoing. It is recommended that the target population, and clinical purpose be more clearly defined within the healthcare context to encourage personal rehabilitation through enhancing the therapeutic alliance rather than focussing on individual behaviour change of diverse older adults.
7 Apr 2026
Read appraisal →PloS one
Randomized trial of transcutaneous tibial nerve stimulation to treat overactive bladder in older women
OBJECTIVES: To evaluate the effects of transcutaneous tibial nerve stimulation (TTNS) associated with behavior therapy (BT) compared to BT alone in the treatment of Overactive Bladder Syndrome (OAB) in older women. STUDY DESIGN: Randomized controlled clinical trial in two groups, G1 that received BT (n = 19) and G2 with the addition of TTNS (n = 19). MAIN OUTCOME MEASURES: The variables analyzed were impact on quality of live (QOL) and degree of discomfort of the symptoms of OAB by International Consultation on Incontinence Questionnaire Overactive Bladder (ICIQ-OAB) and voiding habit by a 3-day voiding diary (VD). The assessments were conducted at the beginning and the end of the treatment. RESULTS: Both groups showed a significant reduction in the impact of QOL by ICIQ-OAB. The G1 (BT) reduced the discomfort of nocturia symptoms and urgency urinary incontinence, while in G2 (BT+TTNS), it just did not present a reduction in the discomfort of urinary frequency by ICIQ-OAB. In the VD, despite G1 presenting an episode reduction of urgency urinary incontinence and nocturia, it showed no significant difference. In the G2, the same variables had significant reduction. CONCLUSION: Behavioral therapy reduced OAB symptoms and discomfort in older women, but its combination with TTNS led to greater improvements, particularly in urgency and nocturia. TTNS was safe, well tolerated, and enhanced quality of life, although further studies with larger samples and longer follow-up are warranted. No adverse events or complications were observed, supporting the safety and tolerability of TTNS in older women with OAB.
7 Apr 2026
Read appraisal →JMIR aging
AI-Enhanced Automatic Life Story Structuring for Reminiscence Therapy in Older Adults: Technical Feasibility Study
BACKGROUND: Storytelling interventions have demonstrated substantial potential in improving emotional well-being, cognitive function, and quality of life for older adults. However, its effectiveness is often limited by the challenges of processing disorganized and redundant life stories, which impose substantial cognitive demands on caregivers. Although storytelling interventions are a well-established therapeutic approach, current practices depend heavily on manual narrative organization, restricting both the scalability and consistency of treatment delivery. Prior research has primarily focused on validating the clinical outcomes of storytelling interventions, with insufficient attention given to technological solutions that could enhance narrative processing while preserving therapeutic integrity. Digital approaches to life story structuring remain underexplored, despite their potential to amplify storytelling benefits by reducing cognitive load and improving recall accuracy. OBJECTIVE: This study aims to design an event timeline generation algorithm to optimize the prior work of the Story Mosaic system. The optimized system enables (1) the automatic extraction of event elements from life narratives, (2) the automatic organization of fragmented life stories into structured timelines, and (3) the preservation of clinically relevant contextual details during compression. The goal is to reduce manual intervention costs while increasing treatment efficacy through artificial intelligence-driven narrative structuring. METHODS: We have designed a novel method, CARE event timeline (CARE-ET), which combines a temporal attention mechanism with graph-based event relationship modeling. Furthermore, we used the CARE-ET algorithm to optimize existing story collage systems. The system uses multifeature extraction technology to capture event clues from oral histories, prioritizes the 6 elements of events through a hierarchical attention mechanism, and uses adaptive compression algorithms to reduce redundancy while maintaining narrative continuity. To verify the effectiveness of the CARE-ET method, this paper adopts a multidimensional evaluation framework, which encompasses event summary assessment, timeline quality evaluation, and usability testing of the optimized system. RESULTS: The proposed CARE-ET algorithm outperforms the baseline in both narrative flow and temporal accuracy. The Story Mosaic system, optimized by the CARE-ET algorithm, underwent usability evaluation by 10 caregivers recruited for this study. Based on standardized assessment metrics, the system received an A rating for usability. The comprehensive experimental results demonstrate that the CARE-ET method can effectively structure fragmented narratives from older adults, enhancing the usability of the Story Mosaic system. CONCLUSIONS: The proposed method enables the structured extraction of representative event summaries, transforming disorganized life stories into an event timeline for caregiver-supported older adult well-being interventions. Future research should investigate longitudinal effects on cognitive preservation and explore integration with existing dementia care protocols. This work establishes a critical foundation for intelligent assistive technologies in geriatric mental health interventions.
7 Apr 2026
Read appraisal →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
User experiences of digital health interventions among older adults with cancer: a systematic review of qualitative data
PURPOSE: The aim of this review was to synthesize and analyze the results of several qualitative studies to understand the perceptions of older adults with cancer using digital health interventions to manage cancer in a digital context. METHODS: Six databases were systematically queried to identify qualitative studies of digital health interventions in older cancer populations, published from the inception of the databases until December 10, 2024. Studies that met the inclusion and exclusion criteria were selected, and the quality of the literature was assessed according to established guidelines. The results were synthesized using a thematic synthesis approach. RESULTS: A total of 12 papers were included. Three analytic themes emerged, (1) positive experiences, (2) barriers to use, (3) user expectations. The first theme was with three descriptive themes, including "improved self-management," "safe and accessible support," and "better access to health information"; the second theme with three descriptive themes, including "inadequate content design," "technical difficulties," and negative user perceptions and behaviors"; the third theme with two descriptive themes: "enhanced functionality" and "optimized user experience." CONCLUSIONS: This study found that older adults with cancer recognize the benefits of digital health Interventions in improving their health. However, their experiences are influenced by various factors. Patients expressed diverse needs regarding the functionality, information support, and social support aspects of digital health interventions.
6 Apr 2026
Read appraisal →European journal of cardiovascular nursing
Impact of home-based cardiac rehabilitation in people with heart failure with elevated depressive and anxiety symptoms: a secondary analysis of the REACH-HF randomized trials
AIMS: This study examined whether elevated baseline depression and anxiety moderated the effects of the REACH-HF home-based cardiac rehabilitation (CR) programme on patient outcomes in individuals with heart failure. METHODS AND RESULTS: Individual patient data from 263 patients with HF from the REACH-HF HFrEF and HFpEF trials were analysed. At baseline, 62 (24%) had elevated depression [Hospital Anxiety and Depression Scale (HADS) D > 7], and 86 (33%) had elevated anxiety (HADS-A > 7). Patients were randomized to home-based CR plus usual care or usual care alone. Outcomes at 6-12 months included depression and anxiety (HADS), health-related quality of life (HRQoL) [Minnesota Living with Heart Failure Questionnaire (MLHFQ), HeartQoL], and accelerometer-measured physical activity (PA). Linear regression models, adjusting for baseline values, stratification variables, and confounders, included interaction terms (baseline depression/anxiety ×intervention group) to assess moderation effects. Those with elevated depression showed greater benefits from CR effects in depressive symptoms and HRQoL, with significant interactions for HADS-D (P = 0.004), MLHFQ (P = 0.007), HeartQoL (P = 0.001). No effects were found for PA. In participants with elevated anxiety, significant interactions were observed for HADS-D (P = 0.024), HeartQoL physical (P = 0.04), inactivity (P = 0.02), and light PA (P = 0.04). No effects were found for anxiety symptoms or HRQoL measures. CONCLUSION: People with HF participating in REACH-HF with elevated depression and anxiety experienced greater improvements in their mental health and HRQoL than individuals without elevated symptoms. Diagnostic clinical interviews in future studies would allow for stronger conclusions regarding the impact of home-based CR on patients with confirmed depression or anxiety.
5 Apr 2026
Read appraisal →Medicine
Adverse events associated with the use of nivolumab in the middle-aged and older population (>45 years): A pharmacovigilance analysis based on the FAERS database.
The clinical use of nivolumab has significantly improved the prognosis for survival in patients with a wide range of advanced malignancies. Given the increased incidence of tumors, there is a need to gain insight into the true extent of adverse events (AEs) associated with the nivolumab drug in the middle-aged and elderly population. This pharmacovigilance study was based on an analysis of reports from the US Food and Drug Administration's adverse event reporting system for the period January 1, 2014 to December 31, 2024, and compared AEs of the drug nivolumab in the middle-aged and elderly populations using proportional reporting ratio, reporting odds ratio, BCPN, and multi-item gamma Poisson shrinker methods. The analyses showed the presence of AEs in a variety of systems, mainly involving several systems such as endocrine disorders, nervous system disorders, skin and subcutaneous tissue disorders. The immune-related AEs listed in the package insert were validated by us and their clinical significance warrants careful consideration to further guide clinical dosing, adjust therapeutic decision-making, and develop age-adapted dosing algorithms for population pharmacokinetic development in order to minimize potential risks to patients. With the exception of reports of an unspecified nature, a greater proportion of the sample was male (65.7%) than female (32.5%), indicating significant variations. The highest percentage of reports was observed in patients aged between 65 and 85 years and in patients with a body mass index ranging from 50 to 100 kg/m2. The primary indication was non-small cell lung cancer. The top 3 AE signals reported with nivolumab were malignant neoplasm progression, death, and pyrexia, with the majority of AE cases occurring within the first month following nivolumab initiation.
5 Apr 2026
Read appraisal →PloS one
Real-world safety of aliskiren in primary hypertension: A cross-database study
Hypertension is one of the main causes of cardiovascular diseases worldwide, affecting over one billion people. Although aliskiren offers a valuable option for inhibiting the renin-angiotensin system, its safety profile in the real world remains insufficiently explored, especially for rare or under-recognized adverse events (AEs), which have not been fully clarified. Therefore, leveraging large-scale post-marketing surveillance data is crucial for identifying rare AEs and guiding safer clinical practice. This study aims to elucidate pharmacovigilance signals associated with aliskiren (an antihypertensive drug) by systematically analyzing the characteristics of adverse events (AEs) from the U.S. Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) database and WHO-VigiAccess database, which provides a reliable scientific basis for clinical practice and regulatory decision-making. We conducted a retrospective quantitative analysis of aliskiren-related AE reports from the aforementioned two databases, employing the Proportional Reporting Ratio (PRR), Reporting Odds Ratio (ROR), Bayesian Confidence Propagation Neural Network (BCPNN), and Multi-item Gamma Poisson Shrinker (MGPS) algorithms for signal detection. The results indicate that there were 5,596 and 5,549 aliskiren-related reports in the FAERS and WHO-VigiAccess databases, respectively. The median duration of these AEs during the observation period was 62 days, with an interquartile range (IQR) of 7-282 days. In both databases, signals for aliskiren were distributed across 28 System Organ Classes (SOCs), among which investigations, cardiac disorders, renal and urinary disorders, vascular disorders, and metabolism and nutrition disorders exhibited significant signals based on specific criteria applied across the four algorithms. A total of 607 preferred terms (PTs) with significant disproportionality signals were detected using the four algorithms, including potential AEs not previously well-documented, such as palpitations, myalgia, proteinuria, muscular weakness, pulmonary edema, and pollakiuria. This study not only confirms the known adverse reactions of aliskiren but also uncovers new potential risks, highlighting the importance of strengthening drug safety monitoring to enhance therapeutic efficacy and reduce the risk of adverse reactions. It provides valuable safety insights for physicians considering the use of aliskiren in the management of primary hypertension.
5 Apr 2026
Read appraisal →Medicine
Prediction model for frailty risk in ischemic stroke patients: Application and validation of support vector machines and nomograms
This study aimed to develop a prediction model based on nomograms and support vector machines (SVM) to assess frailty risk in ischemic stroke patients. Clinical information of ischemic stroke patients admitted to our hospital from January 2023 to December 2024 was retrospectively collected. First, independent risk factors associated with frailty in ischemic stroke patients were identified through univariate and multivariate logistic regression analyses. Subsequently, a nomogram was constructed based on regression analysis results and validated using 10-fold cross-validation. Data were divided into training (70%) and validation (30%) sets. A SVM predictive model was constructed on the training set. The predictive performance of both models was compared using receiver operating characteristic curves. This study included 867 ischemic stroke patients, among whom 296 (34.14%) were classified as frail. The entire cohort was randomly divided into a training set (n = 607) and a validation set (n = 260) at a 7:3 ratio. Logistic regression analysis identified hypertension, impaired self-care ability, physical inactivity, reduced prognosis nutrition index, and depressive status as independent risk factors for frailty in ischemic stroke patients (P < .05). The nomogram demonstrated an area under the receiver operating characteristic curve (AUC) of 0.814 for frailty prediction, while the SVM model achieved an AUC of 0.842, indicating superior predictive capability. Further comparison revealed that while the nomogram offers greater intuitiveness and operational convenience in clinical practice, the SVM model demonstrated superior predictive accuracy. Hypertension, impaired self-care ability, physical inactivity, reduced prognosis nutrition index, and depressive states are independent risk factors for frailty in ischemic stroke patients. Both predictive models (nomogram and SVM) demonstrated high predictive accuracy; however, the SVM model outperformed the nomogram in predictive capability, while the latter retains unique advantages due to its simplicity and clinical applicability.
5 Apr 2026
Read appraisal →Biomedical physics & engineering express
Detecting early signs of patient deterioration at home using wearable sensors: a personalized anomaly detection approach
In the rapidly emerging field of transferring healthcare from hospitals to patients' homes, it is essential that signs of remote deterioration at home should be detected early to timely upscale care when needed. As patient deterioration is a rare event, datasets have insufficient event data for supervised machine learning. Personalized anomaly detection (AD) may be a good alternative. Therefore, this study aimed to obtain insight into the use of personalized AD models in detecting early signs of patient deterioration at home using wearable sensor data. To address this, Isolation Forest and Local Outlier Factor models were applied to detect signs of deterioration in terms of mortality or unplanned readmission in the 24 preceding hours in two datasets: one of heterogeneous patients, the other of postoperative patients. A pipeline was developed for continuously updating personalized AD models over time and applying them to detection windows twice per day. Results were compared with the Remote Early Warning Score. Isolation Forest (AUROC: 0.69) and Local Outlier Factor (AUROC: 0.67) models were able to find some early signs of deterioration in heterogeneous patients (n = 113). However, in postoperative patients (n = 193), Isolation Forest (AUROC: 0.41) and Local Outlier Factor (AUROC 0.44) performed badly. The Remote Early Warning Score was able to find some early signs of deterioration for both groups (AUROC: 0.63-0.76). Based on these findings, three requirements were formulated that should be fulfilled for a potentially successful application of personalized AD. First, the training set should be normal and representative of non-deteriorating patients. Second, signs of deterioration should exhibit abnormal characteristics. Third, non-deteriorating patients should not exhibit abnormal characteristics.
5 Apr 2026
Read appraisal →Annals of medicine
An explainable online frailty prediction model for community-dwelling older adults based on machine learning algorithms: a cross-sectional study based on retrospective health data
BACKGROUND: Frailty is a significant health concern associated with diminished physiological reserves and increased healthcare burdens. Currently, effective models for predicting frailty risk are lacking. This study utilizes machine learning-based models to early identify community-dwelling older adults, enhancing risk assessment accuracy and guiding targeted interventions to slow frailty progression. METHODS: A cross-sectional analysis of data from 1,156 older adults across 31 community health centers in Nanjing, conducted between January and October 2024, was performed. Independent predictors of frailty were identified using univariate analysis and the least absolute shrinkage and selection operator. The dataset was divided into 70% training and 30% testing subsets. Six machine learning (ML) models were developed and their performances compared. The SHapley Additive exPlanations (SHAP) method was applied to interpret the models, and a web-based risk calculator was created. RESULTS: Our dataset showed that 22.3% of older adults were frail. Significant predictors of frailty were identified as age, education, medicine, vegetable, cognitive status, number of diseases, hemoglobin, total cholesterol, and neutrophil-to-lymphocyte ratio. Among the six ML models, Categorical Boosting (CatBoost) exhibited the highest performance, attaining an AUROC of 0.886 in the training set and 0.831 in the testing set. CONCLUSIONS: The developed CatBoost model and web calculator can be employed by general practitioners to proactively identify high-risk community-dwelling older adults, thereby enabling timely interventions to mitigate the progression of frailty. The tool's simplicity and replicability effectively facilitate the promotion and management of frailty prevention within the community. An effective model was developed to predict frailty risk in community-dwelling older adults.The integration of biomarkers and self-reported data enhances frailty prediction and optimizes risk-based intervention strategies.The CatBoost model demonstrates superior predictive performance compared to other models.The web-based tool integrates a frailty prediction model for proactive risk management.
5 Apr 2026
Read appraisal →Blood
Belantamab mafodotin, lenalidomide, and dexamethasone for intermediate-fit and frail patients with newly diagnosed myeloma.
The phase 1/2 BelaRd (belantamab mafodotin [belamaf], lenalidomide, and dexamethasone) study evaluated the efficacy and safety of belamaf combined with lenalidomide and dexamethasone in unfit and frail transplant-ineligible patients with newly diagnosed multiple myeloma (NDMM). Part 1 (n = 36) established a recommended belamaf phase 2 dose (RP2D) of 1.9 mg/kg every 8 weeks (median follow-up, 39.3 months). In part 2, 30 patients were randomized 1:1 in group A (n = 15), where belamaf dosing was guided by ophthalmologist-assessed ocular adverse events (OAEs), whereas in group B (n = 15), belamaf dosing was based on hematologist-led vision-related anamnestic (VRA) tool and ophthalmologist-assessed grade ≥3 OAEs. Among the RP2D patients (n = 42), overall response rate was 97.6%, median progression-free survival (PFS)/overall survival have not been reached yet, and the 18-month PFS and time to progression rates were 83.0% and 97.2%, respectively. Ocular toxicities were similar between assessments by hematologists and ophthalmologists, and no ophthalmologist withholding of a hematologist-led dosing occurred. Less than 1% of patients stopped driving/reading because of OAEs. Median time to belamaf reinfusion was 13 weeks. Overall, BelaRd is an effective regimen for transplant-ineligible patients with NDMM and warrants a phase 3 study in this setting. OAEs' impact on quality of life appears limited, and implementation of the hematologist-led VRA tool may eventually reduce the necessity for ophthalmologist assessments. This trial was registered at www.clinicaltrials.gov as #NCT04808037.
5 Apr 2026
Read appraisal →JMIR aging
Digital Resources and Social Connectedness Among Ethnic Minority Older Adults: Systematic Review and Meta-Analysis
BACKGROUND: Being socially connected is essential for health and well-being. Nonetheless, many older adults face social isolation, especially in ethnically diverse societies. Digital technologies offer a pragmatic approach to addressing problems with social connectedness; however, a consolidated understanding of their association with social connectedness among ethnic minority older adults remains unaddressed. OBJECTIVE: We conducted a systematic review and meta-analysis to investigate the prevalence and utility of digital resources in promoting social connectedness among ethnic minority older adults. METHODS: This systematic review and meta-analysis was guided by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Studies were retrieved from 4 electronic databases. A random-effects meta-analysis was used to estimate the pooled prevalence along with its 95% CI. RESULTS: Twelve studies were included. The research approaches used by the included studies were as follows: quantitative (n=5), qualitative (n=5), and mixed methods (n=2). Overall, the estimated prevalence of internet use, which was defined as engaging in online activities through digital devices, was 81.8%. Commonly used digital devices were computers (66.7%), smartphones (63.2%), and tablets (29.3%). Sociodemographic, economic, and health-related factors influenced the adoption and use of digital resources among ethnic minority older adults. These older adults relied on digital resources to build meaningful social connections through social participation, maintaining cultural and religious ties, promoting transnational social networks, and fostering their potential for social connectedness. However, digital resources were not always useful for social connectedness and adversely affected in-person contact. CONCLUSIONS: Digital resources can promote social connectedness among ethnic minority older adults by enabling them to maintain their cultural and social values. Cities and regions aiming to promote the social connectedness and well-being of ethnic minority older adults should consider intersectional factors that affect access to digital resources and the sustainable adoption of digital resources.
4 Apr 2026
Read appraisal →BMJ open
Utilisation of brain MRI for the diagnosis of dementia in low and middle-income countries: systematic review and meta-analysis
OBJECTIVES: To synthesise the prevalence and patterns of dementia-relevant structural brain MRI abnormalities in adults with suspected or confirmed dementia in low- and middle-income countries (LMICs), and to summarise MRI protocols and the incremental diagnostic contribution of MRI beyond cognitive screening. DESIGN: Systematic review and meta-analysis. DATA SOURCES: PubMed, EMBASE, Web of Science and PsycINFO (January 1990-27 January 2025), plus reference list screening and targeted manual searches. ELIGIBILITY CRITERIA: Observational or diagnostic-accuracy studies from World Bank-defined LMICs including adults (≥50 years) with suspected or confirmed dementia who underwent brain MRI as part of diagnostic evaluation. DATA EXTRACTION AND SYNTHESIS: Two reviewers independently screened, extracted data and assessed risk of bias using ROBINS-I. Random-effects models pooled prevalence of dementia-relevant MRI abnormalities; diagnostic-accuracy outcomes were synthesised narratively due to heterogeneous reference standards and incomplete reporting. RESULTS: 39 LMIC studies were included; 23 studies (2513 participants) contributed to the meta-analysis. Dementia-relevant MRI abnormalities (defined as ≥1 clinically relevant structural abnormality per study definition) were present in 1248/2513 participants. The pooled prevalence of dementia-relevant MRI abnormalities was 58% (95% CI 43% to 72%), with substantial heterogeneity (I²=95%) and a wide prediction interval (8-96%), indicating marked between-study variability; this estimate should be interpreted as a descriptive summary of study-level proportions rather than a precise population parameter. CONCLUSIONS: Brain MRI frequently demonstrates dementia-relevant pathology in LMIC clinical cohorts, usually with mixed neurodegenerative-vascular patterns. Structured visual ratings may add aetiologic specificity beyond cognitive screening, but pooled estimates should be interpreted as summaries of heterogeneous study-level findings rather than precise population parameters, given high heterogeneity and risk of bias. PROSPERO REGISTRATION NUMBER: CRD42024510241.
4 Apr 2026
Read appraisal →Respiratory medicine
A review of artificial intelligence-based research on chronic obstructive pulmonary disease.
In recent years, with the rapid development of artificial intelligence (AI), Chronic Obstructive Pulmonary Disease (COPD), one of the world's three major chronic diseases, has achieved remarkable progress in diagnosis, grading, and prognosis, which is of great significance for promoting the clinical transformation of respiratory diseases. To deeply explore the application of AI in the diagnosis and management of COPD, this paper reviews recent studies based on machine learning and deep learning, covering screening and diagnosis, disease grading and assessment, disease management and monitoring, and treatment. First, the technical basis of COPD-related research is analyzed from five perspectives: traditional research methods, supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. Then, the commonly used datasets and model evaluation metrics are summarized. Finally, the application scenarios of AI in COPD research are elaborated, focusing on three aspects: early screening and diagnosis, disease monitoring and risk prediction, and disease classification and risk stratification. This paper summarizes the main research hotspots at home and abroad over the past five years with representative models, and analyzes and compares the advantages and limitations of each type of model in specific COPD tasks through comparative experiments. The study also outlines prospects for the future development of this field, aiming to provide theoretical references and insights for subsequent research.
4 Apr 2026
Read appraisal →Regional anesthesia and pain medicine
Deciphering risk factors for severe postherpetic neuralgia in patients with herpes zoster: an interpretable machine learning approach
INTRODUCTION: Postherpetic neuralgia (PHN) is a common complication of herpes zoster (HZ). This study aimed to use a large real-world electronic medical records database to determine the optimal machine learning model for predicting the progression to severe PHN and to identify the associated risk factors. METHODS: We analyzed the electronic medical records of 23,326 patients diagnosed with HZ from January 2010 to June 2020. PHN was defined as pain persisting for ≥90 days post-HZ, based on diagnostic and prescription codes. Five machine learning algorithms were compared with select the optimal predictive model and a subsequent risk factor analysis was conducted. RESULTS: Of the 23,326 patients reviewed, 8,878 met the eligibility criteria for the HZ cohort. Among these, 801 patients (9.0%) progressed to severe PHN. Among the various machine learning approaches, XGBoost-an approach that combines multiple decision trees to improve predictive accuracy-performed the best in predicting outcomes (F1 score, 0.351; accuracy, 0.900; area under the receiver operating characteristic curve, 0.787). Using this model, we revealed eight major risk factors: older age, female sex, history of shingles and cancer, use of immunosuppressants and antidepressants, intensive initial pain, and the neutrophil-to-lymphocyte ratio. When patients were categorized into low-risk and high-risk groups based on the predictive model, PHN was seven times more likely to occur in the high-risk group (p<0.001). CONCLUSIONS: Leveraging machine learning analysis, this study identifies an optimal model for predicting severe PHN and highlights key associated risk factors. This model will enable the establishment of more proactive treatments for high-risk patients, potentially mitigating the progression to severe PHN.
4 Apr 2026
Read appraisal →JMIR research protocols
Development and Validation of Machine Learning Models for Predicting Early Cognitive Decline Using Home Sensor-Derived Behavioral Data: Sensors in-Home for Elder Wellbeing (SINEW) Cohort Study
BACKGROUND: As the global population continues to age, the prevalence of geriatric conditions, including dementia and frailty, is also increasing. Early identification of individuals at an elevated risk of these conditions, such as those presenting with mild cognitive impairment (MCI) or prefrailty, can provide a critical window for prompt intervention aimed at preventing or reversing disease progression. To promote such early identification, there is a burgeoning interest in the use of digital sensor technology and predictive modeling. OBJECTIVE: This study aimed to use a continuous, home-based monitoring sensor system for older adults to distinguish those exhibiting normal aging from those with MCI, early dementia, prefrailty, or frailty, and to predict their transition from normal aging to one of these conditions. METHODS: This longitudinal cohort study will recruit 200 community-dwelling adults aged ≥65 years with normal cognition or MCI at baseline. A multi-sensor system will be installed in participants' homes, including passive infrared motion sensors, door contact sensors, bed sensors, medication box sensors, wearable activity bands, and Bluetooth proximity beacons. These devices will continuously capture spatiotemporal activity patterns, mobility indicators, sleep behaviors, and medication-taking routines. Annual assessments will include standardized cognitive tests (eg, Montreal Cognitive Assessment, Mini-Mental State Examination, Rey Auditory-Verbal Learning Test, digit span, Color Trails Test, semantic fluency, Stroop), frailty measures (modified Fried phenotype, gait speed, grip strength), mental health scales, sleep quality, and psychosocial indicators. Sensor-derived features-such as gait variability, activity regularity, sleep fragmentation, and medication adherence patterns-will be integrated with clinical data to develop supervised machine learning models. Planned approaches include logistic regression, random forests, gradient boosting, and deep learning. Model performance will be evaluated using cross-validation and independent test sets. Primary metrics will include area under the receiver operating characteristic curve, sensitivity, specificity, precision, recall, and F1-score. Models will be benchmarked against gold-standard clinical diagnoses and validated using temporal subsets of the dataset. RESULTS: Enrollment for this study started in November 2019 and will continue until March 2030. As of June 2025, we have enrolled 138 participants. Full data analysis has yet to begin. CONCLUSIONS: We aim to develop a reliable and effective sensor system for in-home use that will facilitate the early detection of cognitive and physical decline. In so doing, it will add to our current understanding of digital biomarkers. It is common for older adults to seek clinical intervention only when their cognitive impairment has already reached an advanced stage. The implementation of readily deployable sensor systems within community settings presents us with opportunities for prompt intervention, which holds the potential for delaying or reversing disease progression and allowing for a greater number of functional and meaningful years.
4 Apr 2026
Read appraisal →Journal of medical Internet research
Predictive Value of Machine Learning for Poststroke Mortality Risk: Systematic Review and Meta-Analysis
BACKGROUND: People with stroke face a high mortality risk, and an accurate prediction model is essential to the guidance of clinical decision-making in this population. Recently, with growing attention paid to machine learning (ML) in stroke care, some researchers have investigated the effectiveness of ML in predicting the mortality risk in stroke. However, systematic evidence is still lacking for its effectiveness. OBJECTIVE: This systematic review aims to evaluate the value of ML in predicting the stroke mortality risk. The findings are expected to offer an evidence-based basis for developing and assessing clinical risk prediction tools. METHODS: A search was made in Cochrane Library, PubMed, Embase, and Web of Science up to June 23, 2025, and studies that reported a complete performance of ML in predicting stroke mortality were included. Studies with only risk factors analyzed were excluded. The risk of bias of the included studies was assessed using PROBAST (Prediction model Risk of Bias Assessment Tool). Pooled risk ratios with 95% CIs and prediction intervals (PIs) were derived using the Hartung-Knapp-Sidik-Jonkman method under a random-effects model. Subgroup analyses were also conducted by model type, stroke type, patient source, and treatment background. Moreover, a metaregression was conducted on the C-index for out-of-hospital mortality at different time points to explore the influence of time factors on the model's predictive performance. RESULTS: Sixty-eight studies were included (23 predicting in-hospital mortality and 45 predicting out-of-hospital mortality), describing the development of 75 prediction models and 43 external validations. The follow-up period was 1 month to 15 years. For predicting in-hospital mortality, the external validation set had a pooled C-index of 0.727 (95% CI 0.677-0.781, 95% PI 0.521-1.000), with sensitivity and specificity of 0.64 (95% CI 0.57-0.70) and 0.74 (95% CI 0.70-0.77), respectively. For predicting out-of-hospital mortality, the pooled C-index was 0.847 (95% CI 0.808-0.887, 95% PI 0.750-0.956) in the external validation set, with sensitivity and specificity of 0.71 (95% CI 0.55-0.82) and 0.76 (95% CI 0.74-0.78), respectively. Comparatively, the overall pooled C-indexes were 0.788 (95% CI 0.766-0.810, 95% PI 0.621-0.999) and 0.812 (95% CI 0.798-0.826, 95% PI 0.693-0.952), respectively. The metaregression revealed a gradual decline in the predictive performance of the overall model and logistic regression model alone, whereas a random forest model maintained sustained performance. Age, National Institutes of Health Stroke Scale score, and stroke-related complications were the most frequently used variables for modeling. CONCLUSIONS: This is the first meta-analysis to demonstrate that ML-based prediction of stroke mortality is feasible. The performance of ML supports its role as an auxiliary tool for identifying high-risk populations, thereby optimizing clinical monitoring and resource allocation. However, due to substantial heterogeneity and a relatively high risk of bias in available studies, caution is warranted in real-world application. The effectiveness of ML may vary across settings, and external validation is recommended before broader implementation. TRIAL REGISTRATION: PROSPERO CRD420251086321; https://www.crd.york.ac.uk/PROSPERO/view/CRD420251086321.
4 Apr 2026
Read appraisal →GeroScience
Integrative machine learning approach to risk prediction for dementia and Alzheimer's disease
Dementia, particularly Alzheimer's disease (AD), presents a growing global health challenge characterized by cognitive decline, behavioral changes, and loss of independence. With increasing life expectancy, early diagnosis and improved clinical strategies are urgently needed. This study developed and evaluated machine learning (ML) models to predict AD risk using UK Biobank data, integrating health, genetic, and lifestyle factors. The cohort included 2878 AD cases and 72,366 controls. Among several algorithms, CatBoost performed best (ROC-AUC = 0.773), especially in females. Inputs included ICD-10 codes from 5 years pre-diagnosis, ApoE-ε4 genotype, and large collection of modifiable risk factors. Despite fewer cases, the risk predictive models for vascular dementia (VaD) outperformed the unique AD models. ApoE-ε4 was the most predictive genetic marker, while other common variants had limited utility. Key non-genetic predictors included comorbidities (e.g., diabetes, hypertension), education, physical activity, and diet. These findings highlight the value of integrating diverse data sources for dementia risk prediction and emphasize the role of sex-specific modeling and modifiable factors in early, personalized intervention strategies.
2 Apr 2026
Read appraisal →J Med Internet Res
Use of Health and Welfare Technology in Palliative Care: State-of-the-Art Review
BACKGROUND: As more individuals live longer with complex conditions, the need for effective palliative care (PC) grows. It has been stated that access to PC should be integrated early and delivered in a timely manner to patients with life-threatening illnesses. Health and welfare technologies (HWTs) offer tools to enhance care delivery, particularly in home and rural settings. Although there is a profound lack of evidence regarding the impact when used in PC, it is necessary to critically assess the current state of knowledge regarding impacts and consequences of technologies, ensuring that their integration considers broader implications for patients, caregivers, and health care systems in PC. OBJECTIVE: This review explores health and welfare technology used in PC, aiming to inform practice and improve care quality. METHODS: This state-of-the-art review included empirical studies describing the use of HWT in PC for adult patients. We used a thematic synthesis approach to compare studies and provide a synthesis of the key points. RESULTS: Based on the inclusion criteria, 94 studies were included. PC is both a clinical specialty and an overall approach to care that focuses on improving quality of life and relieving suffering for patients and families facing serious illness, based on needs and not prognosis. HWT shows potential to increase access and continuity of care, for symptom management to support patients to remain at home and prevent frequent emergency visits. It can have the potential to build and remain relationships between patients, their families, and the health care team, as well as for interprofessional collaboration and support. However, there are challenges to overcome that might affect the quality of care when using technology. CONCLUSIONS: HWT shows potential as a complement to usual PC. Our findings point toward the importance of caution in choosing when to use HWT in PC, and for which patients.
1 Mar 2026
Read appraisal →Ann Thorac Surg
The Society of Thoracic Surgeons Intermacs 2025 Annual Report: Focus on Outcomes in Older Adults
The 16th Annual Report from the Society of Thoracic Surgeons (STS) Interagency Registry for Mechanically Assisted Circulatory Support (Intermacs) includes 28,029 continuous-flow left ventricular assist devices (CF LVADs) implanted from 2015 to 2024. Survival remains excellent with the fully magnetically levitated CF LVAD (1-year survival: 85.9%, 5-year survival: 60.2%, 7-year survival: 51.8%). Patients also experience a low incidence of adverse events, including freedom from gastrointestinal bleeding (73.5%), device malfunction (81.8%), and stroke (87.1%) at 5 years, reinforcing the effectiveness of contemporary device platforms. However, despite these advancements, national durable LVAD implant volumes have remained stagnant. A focus on the subgroup of patients aged ≥65 years demonstrates excellent outcomes with respect to survival (1-year survival: 80.6%, 5-year survival: 51.1%), quality of life measures, and adverse events. Survival outcomes in this cohort are robust across a wide spectrum of clinical scenarios, including variation in acuity of illness, temporary mechanical circulatory support use, and psychosocial risk factors, historically associated with worse outcomes. Several clinical inferences may be drawn from these data. First, the increasingly elusive clinical equipoise between heart transplant listing vs a strategy of durable LVAD support merits an individualized and value-based approach to therapy in transplant-eligible older adults. Second, the prospect of 5-year survival exceeding 50%-even in septuagenarians-is a call to action to revise patient eligibility criteria, close the implementation gap, and realize the full potential of this life-saving therapy.
1 Jan 2026
Read appraisal →Artif Cells Nanomed Biotechnol
Unveiling the ageing-related genes in diagnosing osteoarthritis with metabolic syndrome by integrated bioinformatics analysis and machine learning
Ageing significantly contributes to osteoarthritis (OA) and metabolic syndrome (MetS) pathogenesis, yet the underlying mechanisms remain unknown. This study aimed to identify ageing-related biomarkers in OA patients with MetS. OA and MetS datasets and ageing-related genes (ARGs) were retrieved from public databases. The limma package was used to identify differentially expressed genes (DEGs), and weighted gene coexpression network analysis (WGCNA) screened gene modules, and machine learning algorithms, such as random forest (RF), support vector machine (SVM), generalised linear model (GLM), and extreme gradient boosting (XGB), were employed. The nomogram and receiver operating characteristic (ROC) curve assess the diagnostic value, and CIBERSORT analysed immune cell infiltration. We identified 20 intersecting genes among DEGs of OA, key module genes of MetS, and ARGs. By comparing the accuracy of the four machine learning models for disease prediction, the SVM model, which includes CEBPB, PTEN, ARPC1B, PIK3R1, and CDC42, was selected. These hub ARGs not only demonstrated strong diagnostic values based on nomogram data but also exhibited a significant correlation with immune cell infiltration. Building on these findings, we have identified five hub ARGs that are associated with immune cell infiltration and have constructed a nomogram aimed at early diagnosing OA patients with MetS.
1 Dec 2025
Read appraisal →Int J Surg
Multi-omic profiling reveals age-specific blood biomarkers and aging-driven B cell remodeling in osteoarthritis
BACKGROUND: Osteoarthritis (OA) pathogenesis involves age-related immune dysregulation, yet non-invasive diagnostic tools and mechanistic insights remain limited. METHODS: We integrated transcriptomic profiling of four OA-affected joint tissues with machine learning to identify potential peripheral blood biomarkers. Weighted gene co-expression network analysis was employed to explore gene modules and pathways associated with OA. Single-cell RNA sequencing was performed on 217 983 joint cells to delineate B cell differentiation trajectories. Flow cytometry was used to validate age-associated B cell imbalances in peripheral blood. RESULTS: We identified five peripheral blood biomarkers: MAPK1, MAP3K8, ING1, LDLR, and NUP153 in distinguishing OA patients from controls (the area under the curve [AUC] = 0.966). Importantly, these markers exhibited age-specific expression profiles; ING1, NUP153, and MAP3K8 were elevated, while MAPK1 was reduced in elderly compared to younger OA patients. A refined predictive model based on these age-specific markers demonstrated superior performance specifically for elderly Knee OA (KOA, AUC: 0.8 vs. 0.7 for younger KOA). These biomarkers correlated with immune cell infiltration and inflammatory cytokines. In osteoarthritic joint tissues, B cells predominantly originated from subchondral bone and synovium. Single-cell analysis identified age-specific B cell differentiation patterns, with elderly KOA patients enriched in an activated B cell cluster (C1). Furthermore, B cells from elderly KOA patients showed altered energy metabolism and increased proportions in peripheral blood, and functionally promoted chondrocyte damage. CONCLUSION: Our findings establish a novel blood-based diagnostic framework for OA and uncover aging-driven B cell remodeling as a key contributor to elderly OA pathogenesis. These findings offer non-invasive diagnostics and immunomodulatory targets for age-specific OA therapy.
1 Nov 2025
Read appraisal →Nat Hum Behav
Machine learning-assisted optimization of dietary intervention against dementia risk
A healthy diet has been associated with a reduced risk of dementia. Here we devised a Machine learning-assisted Optimizing Dietary intERvention against demeNtia risk (MODERN) diet based on data from 185,012 UK Biobank participants, 1,987 of whom developed all-cause dementia over 10 years. We first identified 25 food groups associated with dementia in a food-wide association analysis. Second, we ranked their importance using machine learning and prioritized eight groups (for example, green leafy vegetables, berries and citrus fruits). Finally, we established and externally validated a MODERN score (0-7), which showed stronger associations with lower risk of dementia-related outcomes (hazard ratio comparing highest versus lowest tertiles: 0.64, 95% CI: 0.43-0.93) than the a priori-defined MIND diet (0.75, 0.61-0.92). Across 63 health-related outcomes, the MODERN diet showed particularly significant associations with mental/behavioural disorders. Multimodal neuroimaging, metabolomics, inflammation and proteomics analyses revealed potential pathways and further support the potential of MODERN diet for dementia prevention.
1 Nov 2025
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