Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 37 appraisals
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 →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 →IEEE transactions on pattern analysis and machine intelligence
50 Years of Automated Face Recognition
Over the past five decades, automated face recognition (FR) has progressed from handcrafted geometric and statistical approaches to advanced deep learning architectures that now approach, and in many cases exceed, human performance. This paper traces the historical and technological evolution of FR, encompassing early algorithmic paradigms through to contemporary neural systems trained on extensive real and synthetically generated datasets. We examine pivotal innovations that have driven this progression, including advances in dataset construction, loss function formulation, network architecture design, and feature fusion strategies. Furthermore, we analyze the relationship between data scale, diversity, and model generalization, highlighting how dataset expansion correlates with benchmark performance gains. Recent systems have achieved near-perfect large-scale identification accuracy, with the leading algorithm in the latest NIST FRTE 1:N benchmark reporting a False Negative Identification Rate (FNIR) of 0.15 percent at False Positive Identification Rate (FPIR) of 0.001 on a gallery of over 10 million identities. Larger galleries increase false positive rates and deployments at greater scales will see higher error rates. We delineate key open problems and emerging directions, including scalable training, multi-modal fusion, synthetic data, and interpretable recognition frameworks.
1 July 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 →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 →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
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 →Journal of medical Internet research
Acceptability of Technologies to Support Early Dementia Detection: Qualitative Study With the Boston University Alzheimer's Disease Center Cohort
BACKGROUND: Dementia is on the rise globally due to increasing life expectancies and population growth. Digital technologies may help detect early signs, enabling timely interventions to slow or reverse cognitive decline. However, to support the successful implementation of these digital technologies into health care settings, they must be acceptable to target users. Older adults and those with mild cognitive impairment (MCI) are at risk of developing dementia in later life and need to be able to use these technologies in order for this intervention to be approved and implemented in clinical practice. OBJECTIVE: This study explored the perspectives of older adults and those living with a clinical diagnosis of MCI on the acceptability of using various digital technologies that have the potential to support early dementia detection. METHODS: Participants were recruited from Boston University's Alzheimer's Disease Research Center. Participants selected at least 2 technologies from 9 different wearables and software to use for 2 weeks, at 3-month intervals, over a total duration of 2 years. A subgroup of self-selecting participants was interviewed after the first 2 weeks of use to gather initial perspectives regarding the acceptability of using the digital technologies. An inductive framework thematic analysis approach was used, assisted by NVivo (version 14.23.2; QSR International). RESULTS: In total, 13 individuals living with a clinical diagnosis of MCI and 11 adults aged 65 years and older were interviewed. Our analysis identified five key themes: (1) gamification, (2) wearability, (3) user guidance, (4) burden of use, and (5) usefulness. Gamified apps were generally liked, although users with little experience of digital games needed time to adjust. Wearables resembling everyday accessories (eg, watches) were preferred, but complaints about tight or uncomfortable straps were frequently reported. Clear instructions were critical to support correct use, but many participants would have liked more troubleshooting support when technical issues arose. The use of 5 or more devices led to a high burden, especially when devices had practicality issues such as not being waterproof. Devices offering personal feedback were perceived as useful to satisfy personal interests, though some questioned their usefulness within health care. Participants raised concerns about losing valued personal interactions with health care professionals and questioned how their existing health conditions and treatment for such conditions may affect the validity of the data collected by the devices. CONCLUSIONS: These findings can guide researchers in choosing appropriate devices and minimizing burden. Future work should explore the views of those experiencing digital exclusion to ensure equitable access to dementia-detection technologies.
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 →Science robotics
Embodied or virtually represented: Navigating the embodiment debate in human-robot interaction.
The validity of virtually represented robots in HRI experiments depends on when, where, and for whom it matters.
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 →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 →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 →Scandinavian journal of primary health care
Responsible digital transformation in rural primary healthcare services: critical factors from a Norwegian case study
OBJECTIVE: To explore the critical factors that shape the potential for co-creating and implementing a virtual department for home-based healthcare in a rural municipality. METHODS: A qualitative case study was conducted in a rural municipality in Northern Norway. Data were collected through semi-structured interviews, focus groups, and co-creation workshops involving healthcare leaders, frontline staff, service users and relatives, and representative from the pensioners' federation. Thematic analysis was applied to identify organisational, contextual, and cultural factors influencing digital transformation. RESULTS: While the co-creation of virtual department for home-based healthcare showed potential for piloting, readiness for full implementation was limited. Six critical factors were identified: structural readiness, digital competence, technology in practice, cultural change, autonomy and care, and motivation and dedication. These factors highlight the importance of organisational culture, workforce capacity, and stakeholder engagement in shaping readiness for responsible digital transformation. CONCLUSION: Digital transformation holds potential for addressing healthcare challenges in rural municipalities, but success depends on more than technological efficacy. Effective coordination between bottom-up and top-down approaches is required to ensure responsible and sustainable transformation of primary healthcare services.
25 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 →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 →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 →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 →Journal of cross-cultural gerontology
Mapping the Use of Information and Communication Technologies in Health Interventions for Older Adults: A Rapid Review
This rapid review mapped how information and communication technologies (ICTs), particularly digital health applications, are being used within healthcare services for adults aged 55 years and older. Following the Joanna Briggs Institute methodology and guided by the Population-Concept-Context framework, studies published between 2016 and 2024 were identified and reported in accordance with the PRISMA extension for scoping reviews, based on a previously published protocol registered on the Open Science Framework. Nineteen studies were included after critical appraisal. The findings indicate that ICT use is most frequently associated with improved accessibility and coordination of healthcare services, support for health professionals' training and care management practices, and positive health-related outcomes for older adults, particularly in the domains of mental health, social participation, and self-care. However, the evidence remains heterogeneous and often limited in its reporting of design and implementation processes, highlighting the need for further context-sensitive research that addresses ethical, equity, and implementation considerations in the use of digital health technologies for older populations.
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 →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 →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 →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 →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 →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 →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 →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 →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 →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 →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 →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 →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 →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 →BMC Geriatr
Smart aging: integrating AI into elderly healthcare
Artificial Intelligence (AI) is transforming geriatric healthcare by improving early disease detection, optimizing patient management, and enhancing clinical decision-making. AI-driven tools, including machine learning (ML) algorithms, predictive analytics, and assistive robotics, are increasingly utilized to address challenges associated with aging, such as frailty, multimorbidity, polypharmacy, and fall prevention. These technologies can facilitate personalized medicine, improve diagnostic accuracy, and support healthcare administration by streamlining workflows and resource allocation. Despite its promising applications, the integration of AI in geriatric care presents several challenges, including issues related to data privacy, algorithmic bias, ethical considerations, and the digital literacy of older adults. Ensuring the inclusivity of AI models by incorporating diverse and representative datasets is essential to avoid disparities in healthcare delivery. Additionally, the role of AI must be carefully regulated to complement, rather than replace, human clinical expertise. This paper provides a comprehensive overview of AI applications in geriatric medicine, discussing its benefits, limitations, and future directions. By leveraging AI responsibly, healthcare systems can improve patient outcomes, reduce hospital readmissions, and promote aging in place. However, addressing existing challenges through interdisciplinary collaboration, policy development, and continued research is crucial to fully realizing AI's potential in elderly care.
1 Dec 2025
Read appraisal →Gerontologist
The AI-aging-enterprise: a political economy of aging and artificial intelligence
The current discourse on artificial intelligence (AI) in gerontology remains mostly on an interventionist level and focused on solving problems faced by individuals, leaving the wider social conditions that shape the relationship between aging and AI out of view. The considerable accumulation of power, particularly for technology development companies, in the development and implementation of AI, however, calls for a deeper and more complex analysis of the relationships between AI, older adults and the "aging enterprise." Building on the classical political economy of aging, and expanding it with concepts from material gerontology, we propose a "political economy of aging and AI" as a conceptual tool to analyze the relationships between (older) individuals, social and political structures, and (technological) materialities. We exemplify how such a political economy perspective enables a more complex consideration of the current discourse on (a) AI solutions, (b) AI ethics, and (c) AI policies. We close by summarizing our understanding of a political economy of aging and AI that centers materialities as the critical-gerontological focus of analysis and by outlining calls for action toward strengthening the power of older adults in the emerging AI-aging-enterprise.
1 Nov 2025
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