Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 68 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 →Sleep medicine reviews
Artificial intelligence in sleep medicine I: Diagnosis, treatment, care, and research
Artificial intelligence (AI) is transforming sleep medicine (SM) through improved diagnostics, therapeutics, and research. AI enhances diagnostic accuracy, treatment personalization, workflow efficiency. However, implementation requires careful validation and oversight. This review explores AI applications across the sleep disorders spectrum. AI in diagnostic SM covers applications including polysomnography and home sleep testing. We examine how AI detects early neurodegenerative changes in REM sleep behavior disorder, identify novel biomarkers, and optimize chronotherapeutic timing. Wearables complement these advances by continuously monitoring sleep patterns, movement and physiological signals in natural settings, generating rich datasets ideal for AI analysis. In therapeutics, machine learning models enhance sleep apnea phenotyping, enabling precise treatment selection, and improve CPAP adherence prediction. AI helps identify biomarkers to optimize the timing of chronotherapeutic interventions. In medical care, natural language processing facilitates unstructured clinical chart data extraction. Implementation challenges include data standardization, algorithmic bias, and the "generalization gap." We provide a structured framework for clinical implementation, emphasizing validation requirements and ethical considerations. Large datasets using routine clinical care, cohort studies, and wearable derived data create vast amounts of data. In return, AI tools and methodologies enhance and expand development of phenotypes and endotypes with the final goal of personalized and precision SM.
2 Aug 2026
Read appraisal →JMIR mHealth and uHealth
Wearable Devices for Monitoring and Management of Comorbid Obstructive Sleep Apnea and Hypertension: Scoping Review
BACKGROUND: As an established driver of hypertension, obstructive sleep apnea (OSA) generates a significant cardiovascular burden when both disorders are present. This intersection not only compromises nocturnal hemodynamics but also hampers long-term clinical management. Yet, current health care delivery rarely integrates the simultaneous and continuous tracking of these dual burdens. While wearable technology provides a noninvasive, pragmatic toolset for synchronized physiological monitoring, research remains largely siloed within single-disease frameworks. Consequently, clinical evidence supporting wearable applications specifically for comorbid populations remains sparse. OBJECTIVE: In this scoping review, we summarized the current applications of wearable technology for comorbid OSA and hypertension. The analysis primarily outlines device categories, monitored physiological indicators, prevalent clinical scenarios, and existing challenges. METHODS: The search for relevant literature spanned PubMed, Web of Science, Embase, and IEEE Xplore, covering the period from January 2015 to February 2026. Eligible studies included adults and used wearable or portable technologies to objectively track sleep- or respiratory-related indicators and cardiovascular/blood pressure metrics. Study selection, data charting, and evidence synthesis were conducted using a 2-reviewer process and descriptive and narrative approaches. RESULTS: Our initial search yielded 739 records. Following title and abstract screening, we reviewed 54 full texts, ultimately finalizing a cohort of 13 eligible studies. Published between 2015 and 2026 across 9 countries, these articles capture data from 5596 participants. Most were observational studies and device validation studies. Evaluated device types included wrist-worn devices, fingertip contact devices, patch and single-lead devices, and multiparameter portable monitoring systems. The clinical applications mainly focused on screening and risk stratification for comorbid OSA and hypertension, monitoring of abnormal nocturnal blood pressure and hemodynamic changes, and cardiovascular risk assessment and remote longitudinal management. However, research remains sparse, and different devices vary greatly in reference standards, diagnostic thresholds, and validation pathways; therefore, their clinical translational value still requires further validation. CONCLUSIONS: Wearable devices may complement traditional assessment by providing continuous nocturnal and longitudinal data. However, at present, they are more suitable as auxiliary monitoring and risk warning tools in comorbidity management and cannot yet replace standard sleep studies or standard blood pressure monitoring. Future research should further shift from feasibility validation to large-sample, prospective, multicenter clinical studies in comorbid populations.
1 Aug 2026
Read appraisal →Biomedical physics & engineering express
MELF: a multi-view ensemble learning framework for normative resting state EEG signal quality assessment
Normative resting-state electroencephalography (rsEEG), defined as standardized recordings from healthy populations, provides essential baselines for studying brain mechanisms and psychiatric disorders. However, its reliability critically depends on rigorous quality assessment, as artifacts or over-processing can distort subsequent analyses. Current approaches often rely on single-feature perspectives, which fail to capture the multidimensional nature of signal quality. We propose MELF, a quality assessment framework for normative rsEEG that systematically integrates three complementary feature domains: time domain (mobility, complexity), frequency domain (-band relative power, parallel log spectra index (PaLOSi)), and spatial domain (-band directed phase lag index, bad channel ratio, ICLabel-classified brain component proportion). A random forest-based ensemble classifier is constructed using dynamic weight allocation based on cross-validation accuracy (Acc). Experiments on four open rsEEG datasets (DSTR, TRCS, LEMON, and HBN) and expert-annotated clinical data demonstrate that: (1) MELF achieves up to 10% Acc improvement over single-view methods; (2) MELF outperforms traditional machine learning and deep learning baselines while reducing computational time, reaching 97.69% Acc (AUC = 0.9957), and maintains strong performance on an independent test set (92% Acc, AUC = 0.88); (3) the spectral view contributes the most, confirming the evaluative capability of the PaLOSi. MELF provides an interpretable, high-Acc, and robust automated framework for normative rsEEG quality assessment, with broad applications in neuroscience research and clinical quality control.
22 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 →Acta odontologica Scandinavica
Obstructive sleep apnea and periodontitis: a systematic review with considerations on CPAP therapy
OBJECTIVE: To evaluate the association between obstructive sleep apnoea (OSA) and periodontitis and to assess the available evidence on the effects of continuous positive airway pressure (CPAP) therapy on periodontal status. MATERIALS AND METHODS: This review was conducted in accordance with Preferred Reporting Items for Systematic reviews and Meta-Analyses 2020 guidelines. Comprehensive literature searches were performed in Cochrane, PubMed, Scopus, and Web of Science. Study selection and data extraction were carried out independently by two reviewers. A total of 20 original studies met the inclusion criteria. Methodological quality was assessed using the Joanna Briggs Institute critical appraisal tool. RESULTS: Fourteen of the 20 studies (70%) reported a significant association between OSA and periodontitis. However, only two studies fulfilled all methodological quality criteria, indicating an overall moderate to low quality of evidence. Data on the effects of CPAP therapy on periodontal outcomes were limited, and the available studies generally showed no significant impact. CONCLUSIONS: Current evidence supports an association between OSA and periodontitis, suggesting that OSA may contribute to periodontitis. Robust, well-designed studies with larger sample sizes and rigorous methodology are needed to clarify the nature of this relationship and to determine whether CPAP therapy influences periodontal health.
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 →Current psychiatry reports
Sleep Interventions and the Prevention of Pediatric Anxiety: A Systematic Review
PURPOSE OF REVIEW: Anxiety disorders are prevalent in children, adolescents, and young adults. As many youth either do not receive adequate intervention or do not achieve remission, prevention of anxiety disorders is a public health priority. Sleep problems are a modifiable risk factor that precedes anxiety. This systematic review includes recent pediatric sleep interventions and anxiety outcomes, and introduces anxiety sensitivity as an understudied mechanism. RECENT FINDINGS: In 21 intervention and experimental studies, sleep intervention reduced anxiety symptoms/disorders and the onset of anxiety disorders. Three additional recent studies examined anxiety sensitivity in the relationship between sleep problems and anxiety, supporting the inclusion of anxiety sensitivity in future research studies. Sleep health is a scalable, accessible target for reducing anxiety and is an important part of pediatric anxiety prevention. Sleep treatments can be brief and implemented in psychiatric practice. Future research studies should consider anxiety sensitivity as a potential mechanism.
14 July 2026
Read appraisal →Sleep
AI meets sleep surgery: assessing drug-induced sleep endoscopy interpretation with a large language model.
STUDY OBJECTIVES: To evaluate the performance and safety of a large language model in interpreting drug-induced sleep endoscopy (DISE) videos and providing treatment recommendations for obstructive sleep apnea, compared to expert human raters and the contemporaneous clinical report. METHODS: This prospective, blinded study included 16 adults undergoing drug-induced sleep endoscopy at a tertiary academic center. For each case, an anonymized procedural video, clinical vignette, and examination findings were independently reviewed by two sleep surgery experts, a senior otolaryngology resident, and a large language model. All the raters assessed the video quality, airway maneuvers, airway collapse at each anatomical site, and recommended therapy. Concordance with the clinical reference standard was evaluated using Cohen's kappa and intraclass correlation coefficients. Safety and reproducibility were assessed through subgroup and error-type analyses. RESULTS: Human raters demonstrated perfect or near-perfect agreement with the reference standard for the velum, oropharynx, tongue base, and jaw thrust response, and moderate agreement for epiglottic collapse. The large language model matched expert performance for all domains except the epiglottis, where moderate agreement was observed. Model-generated treatment recommendations were safe, consistent with accepted clinical practice, and highly reproducible between independent runs. No unsafe or discordant recommendations were identified. CONCLUSIONS: In this single-center study of 16 patients, a large language model accurately interpreted DISE videos and generated safe recommendations consistent with accepted clinical practice, approximating expert performance in most domains. Larger multicenter cohorts are needed to validate these findings and confirm generalizability. Statement of Significance This study demonstrates that artificial intelligence can interpret complex airway videos in sleep surgery and provide safe, expert-level treatment advice. By comparing the performance of a large language model with that of experienced clinicians, our findings suggest that advanced technology can help standardize decision-making in a highly subjective area of care. This work highlights the promise of artificial intelligence as an adjunct to clinical judgment, especially in settings where expert access is limited. However, important questions remain about the use of artificial intelligence in challenging cases and its integration into real-world practice. Future research should focus on larger, more diverse groups of patients and on ensuring ongoing oversight and safety.
14 July 2026
Read appraisal →JMIR formative research
Exploring Breast Cancer Survivors' Preferences for Text Messaging-Based Mobile Health Interventions Targeting Sleep and Physical Activity: Qualitative Study
BACKGROUND: Sleep disturbances and low physical activity are common among breast cancer (BC) survivors and are associated with increased morbidity and mortality. Given the increased access to technological devices and the growing popularity of SMS text messaging-based mobile health interventions, these tools have the potential to both address sleep disturbances and promote physical activity in a scalable and cost-effective way. To understand and make effective use of these tools, it is important to consider the preferences of BC survivors with sleep disturbances, including how SMS text messaging-based mobile health interventions could deliver interventions involving physical activity and sleep hygiene. OBJECTIVE: The objective of this study was to explore the perspectives and preferences of BC survivors regarding text messaging-based mobile interventions targeting sleep and physical activity. METHODS: Three focus groups (n=13 participants) and 3 individual interviews (n=3) were conducted from May 2020 to March 2021 with 16 BC survivors (mean age=59.3, SD 8.9 y) currently experiencing sleep disturbances. The interview questions focused on their experiences with poor sleep and preferences for text messaging-based mobile health interventions. Thematic analysis was applied to the deidentified transcriptions of audio recordings. RESULTS: Three themes were identified: (1) attitudes toward health interventions delivered through text messaging, (2) specific user needs, and (3) technology usage habits and preferences. Most participants reported a positive attitude toward the possibility of using technology to help improve their sleep and increase their physical activity. Most expressed a high level of acceptance toward some technologies, such as text messaging and mobile apps, but not others, such as voice interactions. In terms of desired features, reminders and accountability features, such as meeting physical activity goals, were mentioned most frequently. In addition, incorporating bedtime and relaxation exercise reminders was thought to be helpful. Regarding time and frequency, a daily reminder scheduled for 1 hour before bedtime was found to be acceptable. CONCLUSIONS: The insights have been used to guide the development of a messaging-based mobile health intervention for improving sleep and physical activity in BC survivors. Future research will focus on delivering an intervention addressing these health behaviors and assessing its acceptability and effectiveness.
11 July 2026
Read appraisal →Journal of medical systems
Automatic Sleep Staging Using Cardiorespiratory Signals: A Systematic Review of Methodologies and Performance
Cardiorespiratory-based methods offer promising alternatives to traditional PSG for longitudinal sleep monitoring, holding significant systemic medical value for scalable sleep health management. This systematic review synthesizes methodological frameworks and performance outcomes of automatic sleep staging using cardiorespiratory signals. Four databases were searched and a total of 35 studies published since 2010 were identified. The analysis revealed that cardiorespiratory signal-based sleep staging achieved a practically meaningful accuracy of 70%, with no significant performance differences observed among signal modalities (cardiac signals, cardiorespiratory signals, or cardiac/cardiorespiratory signals combined with other non-EEG modalities) or between modeling algorithms (traditional machine learning vs. deep learning). However, we identified significant methodological heterogeneity and several critical model failure modes that hinder clinical translation, including the widespread lack of external validation, consistently poor classification of the N1 sleep stage, and limited generalization across diverse patient populations. To realize the technology's potential, future research must establish consensus-driven methodological guidelines and rigorously validate algorithms on large, demographically and clinically diverse datasets. These advances are essential for integrating cardiorespiratory-based sleep staging into healthcare systems as a scalable tool for population-level screening, longitudinal monitoring, and tiered clinical decision support.
8 July 2026
Read appraisal →Sleep & breathing = Schlaf & Atmung
Which nocturnal hypoxemia parameters are associated with ambulatory blood pressure monitoring in adults with obstructive sleep apnea? A systematic review.
OBJECTIVE: Systemic arterial hypertension affects up to 50% of patients with obstructive sleep apnea (OSA) and represents one of the main modifiable cardiovascular risk factors. Hypoxemia is a recognized marker of OSA severity; however, it remains unclear which specific desaturation parameters during sleep, such as oxygen desaturation index (ODI), mean and minimum oxygen saturation (SpO2), percentage of sleep time with peripheral oxygen saturation < 90% (T90%) or hypoxic burden (HB) are most consistently associated with elevated blood pressure (BP). This systematic review aims to evaluate, in patients with OSA, the impact between nocturnal desaturation parameters and elevated BP as measured by ambulatory blood pressure monitoring (ABPM). METHODS: A literature search was performed in PubMed, Web of Science, Scopus and Embase databases, covering all records available up to June 2025. RESULTS: A total of 5,587 records were identified through database searches, and 24 studies met the inclusion criteria. The studies revealed heterogeneous results, with some showing significant associations between hypoxemia indices and hypertension, while others did not. Evidence suggests that ODI3% is commonly related to increased BP, whereas minimum SpO2, mean SpO2 and T90% yielded variable findings. HB was evaluated in only one study, suggesting a potential role in BP reduction after CPAP; however, the available evidence remains preliminary. CONCLUSION: ODI3% showed the most consistent association with increased BP across studies, whereas other hypoxemia indices yielded more variable results. Evidence regarding HB remains preliminary and warrants further investigation. These findings suggest that although ODI currently appears to be the most reliable marker, evidence from a single study indicates that composite indices such as HB may represent more informative predictors of BP outcomes and deserve further investigation in future studies.
5 July 2026
Read appraisal →Forensic science international
Is my smartwatch a valid witness? A systematic review and meta-analysis.
Wearable devices are being increasingly used not only in traditional research fields (e.g. clinical, health) but also in innovative ones such as digital forensics. Modern commercial smartwatches, equipped with sensors like accelerometers, gyroscopes, and photoplethysmographs, can record activity, heart rate, sleep, and other physiological parameters that may support forensic investigations, given that their data is valid. To perform a first step in studying such validity, this article presents a systematic review and meta-analysis summarizing the available studies validating data collected from smartwatches, focusing on their potential use in forensic investigations. The review examined studies evaluating smartwatches from four major brands, comparing their outputs, such as activity classification, step count, distance, heart rate, energy expenditure, and sleep metrics, against established gold-standard measures. We found that not all brands of smartwatches and not all outcomes were equally studied, with varying results in terms of accuracy, protocols, and reporting of results. Heart rate resulted the most studied (and most accurate) measure. Most studies validated smartwatches in healthy populations and performed validation in laboratory settings. Overall, while smartwatches show promise for enhancing digital forensic analyses, their current validation evidence is heterogeneous. The findings highlight the need for more extensive and standardized validation efforts, larger and more diverse study populations, and improved transparency from manufacturers. These conclusions are not only relevant for forensic applications but also extend to broader domains, such as clinical and health research, where the reliability of wearable-derived data is equally critical.
1 July 2026
Read appraisal →Langmuir : the ACS journal of surfaces and colloids
Wearable Sleep Detection System Based on Piezoelectric Signals and Convolutional Neural Network Analysis
Long-term and comfortable sleep monitoring is a core requirement for daily health management. However, existing sleep monitoring technologies fail to simultaneously achieve accurate sleep stage classification, wearing comfort, and direct monitoring of characteristic physiological signals. Here, we report a wearable sleep monitoring system (SMS) based on piezoelectric sensing and convolutional neural network (CNN) analysis. The system assesses sleep structure and quality by capturing characteristic eye movement signals and provides actionable recommendations for long-term sleep health management. The sensor employs a highly stereoregular polyacrylonitrile (PAN) composite fiber film modified with PDA@ZnO as the sensing unit, providing high sensitivity, excellent wearing comfort, and long-term durability. It conforms closely to the upper eyelid, enabling effective acquisition of direct physiological signals associated with sleep. Furthermore, we developed a CNN model for five-state sleep classification and validated it using original-recording-level 5-fold validation and a 1000-sample processed held-out test set, achieving an accuracy of 95.90% with a Cohen's Kappa coefficient of 0.949. The system efficiently presents sleep quality information through a simple and user-friendly human-computer interaction interface. The proposed system introduces a novel approach to convenient health monitoring and provides a feasible technical solution for accurate and long-term sleep health assessment in daily life.
1 July 2026
Read appraisal →BMJ supportive & palliative care
Efficacy and safety of acupuncture for breast cancer-related insomnia: a systematic review and meta-analysis
Breast cancer-related insomnia (BCRI) is a common and burdensome sequela. Acupuncture is frequently used, but robust evidence is limited, and previous systematic reviews included few trials yielding imprecise estimates. This updated systematic review and meta-analysis evaluated the efficacy and safety of acupuncture for BCRI. We systematically searched eight databases for randomized controlled trials (RCTs) up to July 1, 2025. RCTs comparing acupuncture with any control for BCRI were eligible. Methodological quality was assessed using the Cochrane Risk of Bias tool (RoB 2.0) and the modified Jadad scale. Meta-analyses employed a random-effects model. Subgroup analyses explored heterogeneity sources, and the GRADE framework rated evidence certainty. Twenty-seven RCTs (n=2,025) were included. Acupuncture significantly reduced Pittsburgh Sleep Quality Index (PSQI) scores (MD= -2.74, 95% CI [-3.92, -1.55], P<0.00001) and increased the overall efficacy rate (RR=1.51, 95% CI [1.27, 1.81], P<0.00001) compared to control, but did not significantly improve Insomnia Severity Index (ISI) scores. Subgroup analyses identified control intervention type and treatment frequency as significant sources of heterogeneity. Adverse events were infrequent and typically mild. GRADE assessments rated the certainty of evidence as low or very low, primarily due to risk of bias and substantial heterogeneity. Current evidence suggests acupuncture may improve sleep in patients with BCRI, with effects moderated by comparator type and treatment frequency. However, conclusions are limited by methodological weaknesses and heterogeneity. Future rigorous RCTs using validated sham controls, standardized protocols, and objective outcomes are needed to confirm efficacy and guide practice. PROSPERO Registration number (ID: CRD42024559828).
1 July 2026
Read appraisal →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
The effectiveness of non-pharmacological interventions on sleep quality in lung cancer patients: a systematic review and meta-analysis (2015-2025)
PURPOSE: Sleep disorders are highly prevalent among lung cancer patients and seriously affect their quality of life and mental health. This systematic review and meta-analysis aimed to assess the effects of non-pharmacological interventions on sleep quality, quality of life, and anxiety in lung cancer patients. METHODS: PubMed, Web of Science, Cochrane Library, CINAHL, Embase, CNKI, Wanfang Data, and VIP databases for studies published between 2015 and February 2025. The primary outcome was sleep quality, with quality of life and anxiety levels as secondary outcomes. We assessed the risk of bias using the Cochrane tool and synthesized data using meta-analysis. RESULTS: Twenty-four studies involving 2604 patients were included. Non-pharmacological interventions were compared with usual care, placebo, or standard oncology care. After excluding studies with inappropriate content and methods, 20 studies evaluated sleep quality, and 12 studies evaluated quality of life and anxiety levels, respectively. The quality of the evidence was low to moderate; the promotion results should be interpreted with caution, but they may have a positive effect on the sleep quality, quality of life, and anxiety levels of lung cancer patients. CONCLUSIONS: Diverse non-pharmacological interventions can improve sleep quality, quality of life, and anxiety in lung cancer patients. However, high-quality, rigorously designed randomized controlled trials are still needed to confirm these findings. Future research should focus on personalized interventions tailored to lung cancer patients' characteristics and integrate digital health interventions to better support clinical decision-making.
28 June 2026
Read appraisal →Pulmonology
Hypnodensity sleep metrics from fingertip photoplethysmography are better associated with daytime sleepiness and fatigue than traditional metrics from polysomnography
BACKGROUND: Traditional sleep metrics from polysomnography are often limited by the hypnogram, in which each 30-s epoch is classified into one of the five sleep stages. This oversimplifies sleep stage continuity and fragmented sleep, typical in sleep-disordered breathing (SDB). Deep learning offers more dynamic sleep modelling with a higher temporal resolution by providing probabilities for each sleep stage, known as hypnodensities. RESEARCH QUESTION AND METHODS: We investigated whether high-frequency (1/s) hypnodensities captured from nocturnal fingertip photoplethysmography (PPG) recordings better correlate with patient-reported outcomes in SDB than traditional metrics. RESULTS: Among 2,280 patients (65% male, mean age 62 years, mean apnoea-hypopnoea index (AHI) 23.5/h), we found that several PPG-derived hypnodensity metrics correlate with self-reported daytime fatigue and sleepiness. The level of correlation was better or similar compared to traditional hypnogram-based sleep metrics derived from manually scored polysomnography. Specifically, PPG-based hypnodensities for N1 and N3 sleep showed a stronger association (p < 0.005) with fatigue and sleepiness than manually scored N1 and N3 sleep. Multivariable logistic regression (adjusted for age, sex, body mass index, AHI, and total sleep time below 90% blood oxygen saturation) revealed significant associations (p < 0.01) between multiple PPG-based hypnodensity metrics and daytime sleepiness. CONCLUSION: These findings suggest that PPG-based hypnodensities improve the characterisation of sleep quality and better predict patient-reported outcomes in SDB patients. Future uses may include tracking sleep quality with fingertip pulse oximeters outside of sleep centres, potentially over multiple nights due to their simplicity.
14 June 2026
Read appraisal →Journal of robotic surgery
Transoral robotic surgery using the da Vinci single-port system: current evidence and clinical indications - a systematic review and meta-analysis
The DaVinci Single-Port (SP) transoral robotic surgery (TORS) platform was developed to overcome spatial constraints and instrument collisions encountered with multiport systems in head and neck surgery. We systematically reviewed and meta-analyzed perioperative performance, safety, functional recovery, and oncologic outcomes of SP TORS. Following PRISMA 2020, we searched PubMed/MEDLINE, Embase, Cochrane Library, Web of Science, and the first 25 pages of Google Scholar through January 1, 2026. Peer-reviewed English-language clinical and cadaveric studies evaluating SP TORS were included. Three reviewers screened studies and extracted data. Random-effects meta-analyses were performed where appropriate. Fourteen studies (2019-2025) including 479 patients met inclusion criteria. Patient-level evidence was concentrated in oncologic cohorts, with additional smaller clinical applications in obstructive sleep apnoea-related tongue-base surgery (37 patients), hypopharyngeal foreign body removal (1 patient), parapharyngeal space surgery (2 patients), and SP-assisted reconstruction (3 patients), as well as preclinical feasibility studies. No study reported intraoperative conversion to multiport or open surgery. Pooled estimates were console time 60.04 min (95% CI 29.56-90.51; I² = 99.1%), docking time 10.13 min (0.47-19.79; I² = 97.9%), operative time 57.72 min (37.23-78.21; I² = 0.0%), length of stay 10.63 days (2.43-18.82; I² = 99.8%), and blood loss 24.16 mL (- 6.95 to 55.27; I² = 86.8%). Bleeding was the most consistently reported surgical complication, with major bleeding requiring operative management reported in larger cohorts but remaining uncommon. Functional recovery suggested early oral intake, although timing was inconsistently reported. Margin status was predominantly R0 in several oncologic cohorts, although one matched cohort reported a lower R0 rate. SP TORS appears feasible across indications, but current evidence is limited by nonrandomized designs, inconsistent long-term reporting, and between-study heterogeneity.
14 June 2026
Read appraisal →Clinical oral investigations
Changes in the sagittal pharyngeal airway dimension following levelling and alignment in class II division 2 patients: a prospective clinical trial
OBJECTIVES: This study aimed to evaluate changes in the sagittal pharyngeal airway dimensions (SPAD) and the related mandibular position following maxillary levelling and alignment in late adolescent Class II division 2 (Class II/2) patients. MATERIALS AND METHODS: Thirty Class II/2 patients (mean age 14.07 ± 1.21 years) were included. Pre-treatment orthodontic records were acquired, including panoramic and lateral cephalometric x-rays (T0), photographs, and study models, followed by the onset of levelling and alignment in the upper arch. Lateral cephalometric x-rays and photographs were repeated six months later at the termination of this stage (T1), and measurements were performed in comparison to T0. SPAD was assessed at the nasopharyngeal, oropharyngeal, and laryngopharyngeal levels. Mandibular position was evaluated using the Ba-Ar distance, while sagittal and vertical skeletal relationships were assessed using SNA°, SNB°, ANB°, and FMA°. Upper incisor inclination was accounted for using the U1-FH° measurement. RESULTS: Alignment of the maxillary arch in Class II/2 patients elicited a significant SPAD increase at the nasopharyngeal and the oropharyngeal levels (p < 0.001), with a non-significant change evoked at the laryngopharyngeal level (p = 0.42). Antero-posteriorly, a significant increase in the SNAº, SNBº, together with a significant decrease in the ANBº angle have been documented at T1. A significant increase in the Ba-Ar has been noted at T1 in contrast to T0, in addition to a significant reduction in the vertical dimension (FMAº). Dentally, a significant increase in the U1-FHº has been documented following the implemented treatment stage (p < 0.001). CONCLUSIONS: Results of this study may suggest that maxillary levelling and alignment in Class II/2 patients during the decelerating growth phase is associated with augmentation of the nasopharyngeal and oropharyngeal airways. These changes coincide with the significant labial tipping of the maxillary incisors and subsequent functional mandibular advancement, which collectively improve the intermaxillary relationship and reduce the vertical skeletal dimension. While natural growth cannot be entirely excluded, the rapid change suggests that these improvements are closely linked to the functional mandibular response. CLINICAL RELEVANCE: Maxillary alignment in Class II division 2 cases may enhance upper airway patency by triggering a forward functional mandibular response, highlighting a potential link between orthodontic correction and upper airway patency. Name of the Registry: ClinicalTrials.gov Trial Registration Number: NCT06602518 Date of Registration: 19/09/2024 "Retrospectively registered". URL: https://clinicaltrials.gov/study/NCT06602518?cond=nct06602518&rank=1.
7 June 2026
Read appraisal →JMIR research protocols
Digital Cognitive Behavioral Therapy for Insomnia Versus Sleep Hygiene Education for Prevention of Perinatal Depression: Protocol for a Randomized Clinical Trial
BACKGROUND: Insomnia during pregnancy is a modifiable risk factor for depression. Research in nonpregnant populations and preliminary findings in a pregnant population suggest that targeting insomnia with digital cognitive behavioral therapy for insomnia (CBT-I) may prevent depression. OBJECTIVE: This study aims to evaluate the efficacy of digital CBT-I for the prevention of depression through 12 months post partum compared to that of a credible, clinically relevant comparator intervention, sleep hygiene education (SHE). This project will also provide valuable information about how and for whom CBT-I prevents perinatal depression. METHODS: The Perinatal Research for Improving Sleep and Mental Health study is a fully remote, 2-arm parallel-group randomized controlled trial designed to test the superiority of digital CBT-I versus SHE for the prevention of perinatal depression. We aimed to randomize a target sample of at least 443 pregnant participants with insomnia disorder to digital CBT-I or SHE at a 1:1 ratio. Participants randomized to digital CBT-I received SleepioRx (Big Health, Inc), which uses sleep consolidation, stimulus control, cognitive therapy, relaxation techniques, and SHE in 6 interactive modules (5-15 min each). Participants randomized to SHE received information about sleep hygiene, the impact of sleep on performance, healthy sleep habits, lifestyle influences on sleep, and creating a sleep-friendly bedroom in 6 weekly digital handouts. Data are collected at baseline, 5 weeks after randomization, 10 weeks after randomization, 36 weeks' gestation, and 3, 6, 9, and 12 months post partum. The primary outcome is depression incidence, measured using the Structured Clinical Interview for DSM-5 (Diagnostic and Statistical Manual of Mental Disorders [Fifth Edition]) Disorders, Research Version. Other outcome measures include the Edinburgh Postnatal Depression Scale, Patient Health Questionnaire-9, Columbia Suicide Severity Rating Scale, Generalized Anxiety Disorder Scale, and a birth outcomes questionnaire. Mediator measures include the Insomnia Severity Index, Consensus Sleep Diary, Structured Clinical Interview for Sleep Disorders, Perseverative Thinking Questionnaire, Dysfunctional Beliefs about Sleep Scale, Pre-sleep Arousal Scale, and Difficulties in Emotion Regulation-Short Form. RESULTS: Data collection began in November 2022 and is projected to end in December 2026. CONCLUSIONS: If study hypotheses are supported, then this project will provide a new, scalable approach for preventing perinatal depression.
29 May 2026
Read appraisal →European respiratory review : an official journal of the European Respiratory Society
Implementation of long-term noninvasive positive airway pressure therapy in patients with COPD: a systematic review and meta-analysis of acceptance and adherence
BACKGROUND: Long-term noninvasive positive airway pressure (PAP) therapy is an effective, albeit complex, intervention for obstructive sleep apnoea (OSA) and/or chronic hypercapnic respiratory failure (CHRF) in patients with chronic obstructive pulmonary disease (COPD). PAP uptake and use is often reported in studies designed for other purposes. Systematic review methods were used to summarise acceptance of and adherence with PAP therapy. METHODS: A systematic search for studies that reported the proportion of patients that either declined or accepted PAP therapy and/or objective use of newly prescribed PAP therapy in patients with COPD was conducted according to a predefined protocol (PROSPERO CRD42021259262). Meta-analysis and meta-regression techniques were used to establish summary effect estimates and explore heterogeneity. RESULTS: On average, PAP therapy was declined or discontinued by 14% (95% CI 10-19) of participants, often within 6 weeks of initiation among studies reporting repeated measures. The pooled median adherence was 6.3 h·day-1 (95% CI 5.9-6.6). While meta-regression found higher acceptance (p=0.03) and shorter use per day (p<0.01) for the indication of OSA versus CHRF, this explained only some heterogeneity in the summary effect estimates. Summary of study-level variables associated with acceptance or adherence highlighted limitations in the available literature. CONCLUSIONS: An anticipated six out of seven patients with COPD newly prescribed PAP therapy will be successful using therapy; however, heterogeneity was substantial among studies, and only partly explained by indication for use. Prospective studies to explore barriers to use and detailed reporting of acceptance and adherence in studies on PAP therapy in patients with COPD are needed.
29 May 2026
Read appraisal →Journal of the International Society of Sports Nutrition
The effect of probiotic and synbiotic supplementation on sleep parameters in exercised population: a systematic review and synthesis without meta-analysis (SWiM) of randomized controlled trials
BACKGROUND: Sleep is crucial for recovery and optimal performance in athletes; however, poor sleep is common during periods of intensive training or competition. The microbiota-gut-brain axis suggests probiotics and synbiotics could modulate sleep, but evidence in exercised populations is limited. OBJECTIVE: To systematically review randomized controlled trials (RCTs) assessing the effects of probiotic and synbiotic supplementation on sleep in exercised populations, using a Synthesis Without Meta-analysis (SWiM) approach. METHODS: PubMed, Scopus, Web of Science, and ProQuest were searched up to September 2025. Eligible studies were RCTs including exercised individuals, testing probiotics or synbiotics, and reporting sleep outcomes. Data extraction, risk of bias assessment (RoB 2), and narrative synthesis followed SWiM guidelines. RESULTS: Six RCTs (n = 180) were included: four probiotic and two synbiotic interventions lasting 4-17 weeks. Populations included athletes from multiple sports across four continents. Nine of twelve primary sleep outcomes favored supplementation, with significant effects for probiotics (combined p < 0.01) and synbiotics (p < 0.001). Benefits were most consistent for subjective sleep quality and, in some cases, sleep latency. Secondary outcomes showed occasional reductions in stress, anxiety, and fatigue. CONCLUSION: Probiotic and synbiotic supplementation may improve sleep in exercised populations, especially perceived quality and latency. Evidence supports cautious, adjunctive use, but larger, standardized trials are needed to confirm effects.PROSPERO registration number: CRD420251151264.
26 May 2026
Read appraisal →Scientific reports
From sleep staging to spindle detection: a case study on end-to-end automated sleep analysis
Automation of sleep analysis, including both macrostructural (sleep stages) and microstructural (e.g., sleep spindles) elements, promises to enable large-scale sleep studies and to reduce variance due to inter-rater incongruencies. While individual steps, such as sleep staging and spindle detection, have been studied separately, the feasibility of automating multi-step sleep analysis remains unclear. In this case study, we evaluate whether a fully automated analysis using validated machine learning models for sleep staging (RobustSleepNet) and subsequent spindle detection (SUMOv2) can replicate findings from an expert-based study of bipolar disorder. The automated analysis qualitatively reproduced key findings from the expert-based study, including significant differences in fast spindle densities between bipolar patients and healthy controls, accomplishing in minutes what previously took months to complete manually. While the results of the automated analysis differed quantitatively from the expert-based study, possibly due to biases between expert raters or between raters and the models, the models individually performed at or above inter-rater agreement for both sleep staging and spindle detection. Our results demonstrate that fully automated approaches have the potential to facilitate large-scale sleep research. We are providing public access to the tools used in our automated analysis by sharing our code and introducing SomnoBot, a privacy-preserving sleep analysis platform.
25 May 2026
Read appraisal →Rheumatology international
Home-based postural exercise as an adjunct to duloxetine improves sleep quality, physical quality of life, and trunk mobility in fibromyalgia: a randomized controlled trial
In fibromyalgia syndrome (FMS), current guidelines recommend combining pharmacological treatments such as duloxetine with exercise interventions. This randomized controlled trial aimed to evaluate the effectiveness of a home-based postural exercise program as an adjunct to pharmacological treatment in individuals with FMS. Fifty-one patients diagnosed with FMS according to the 2016 American College of Rheumatology criteria were randomly assigned to an exercise group (EG, n = 26) or a control group (CG, n = 25). Both groups received duloxetine therapy, while the EG additionally performed a home-based postural exercise program three times per week for four weeks. Outcomes included pain intensity, pressure pain threshold (PPT), sleep quality, anxiety and depression, quality of life, and trunk range of motion (ROM). Significant improvements over time were observed in pain intensity, psychological symptoms, sleep quality, quality of life, and PPT in both groups (p < 0.05). Significant group × time interactions favored the EG for sleep quality assessed using the Pittsburgh Sleep Quality Index (F = 11.07, p = 0.002, η²=0.184), the physical component of the Short Form-12 (F = 4.31, p = 0.043, η²=0.081), trunk flexion (F = 20.85, p < 0.001, η²=0.299), trunk extension (F = 8.34, p = 0.006, η²=0.146), and global trunk ROM (F = 35.10, p < 0.001, η²=0.417). No significant group × time interactions were observed for pain intensity, anxiety, depression, mental quality of life, or PPT (p > 0.05). Adding a home-based postural exercise program to pharmacological treatment may provide short-term additional benefits in sleep quality, physical quality of life, and spinal mobility in individuals with FMS; however, these findings should be interpreted cautiously due to the short intervention duration and absence of follow-up assessment.
25 May 2026
Read appraisal →ACS sensors
Moisture-Gated Bio-Semiconductor Electronic Tattoos for Continuous and Imperceptible On-Skin Respiratory Monitoring
Continuous respiratory monitoring remains one of the most critical yet unmet needs in personal and clinical healthcare. To overcome the limitations of cumbersome systems and motion-prone wearables, we present a moisture-gated bio-semiconductor-based electronic tattoo (BSET) that enables imperceptible, skin-conformable, and wireless respiratory monitoring by leveraging the hydration-sensitive ionic-electronic conductivity of melanin and the skin-compatibility of silk. Fabricated from a silk fibroin-melanin nanofiber bio-composite, the BSET is ultrathin at 18 μm and highly breathable, achieving a water vapor transmission rate exceeding 3000 g·m-2·d-1. By detecting exhaled moisture directly on the philtrum, the sensor exhibits a rapid response time of 1 s and a recovery time between 2 and 10 s. A direct-spun nanofiber-based wiring strategy ensures robust integration, accommodating a 10 mm displacement under 10.4 MPa of stress without failure. Driven by a compact 3 g circuit with 20 mW of power consumption, the system supports continuous wireless data transmission for over 7.3 h. During vigorous exercise and sleep, the BSET reliably monitored respiratory dynamics, identifying 10-20 s apnea events and enabling multiparameter analysis of breathing frequency and exhalation intensity. This lightweight system establishes a scalable and clinically relevant solution for continuous respiratory surveillance.
23 May 2026
Read appraisal →JMIR formative research
Conceptualizing Acceptance and Knowledge as Process Variables in Internet-Delivered and Therapist-Supported Cognitive Behavioral Therapy and Acceptance and Commitment Therapy in Primary Care for Insomnia: Pilot Feasibility and Process-Oriented Randomized Controlled Trial
BACKGROUND: Internet-based interventions for insomnia show promise, but understanding the process variables, such as knowledge acquisition and psychological acceptance, is crucial for enhancing digital adherence and clinical effectiveness. OBJECTIVE: This study aimed to evaluate the feasibility, adherence, and preliminary clinical signals of 2 therapist-assisted interventions-internet-delivered cognitive behavioral therapy (iCBT) and internet-delivered acceptance and commitment therapy (iACT)-for insomnia in a primary care setting. METHODS: This was a pilot randomized controlled trial. Adults seeking help for insomnia (n=18) were recruited via primary care and randomized to either a 5-module iCBT or iACT program delivered via a secure digital platform with weekly therapist feedback. Blinding of participants and therapists was not possible due to the nature of the interventions. Primary outcomes included the Insomnia Severity Index; secondary outcomes included the 9-item Patient Health Questionnaire, 7-item Generalized Anxiety Disorder, and WHO Disability Assessment Schedule. A novel sleep knowledge test was used as a process variable. The data were analyzed using split-plot analyses of variance (intention-to-treat or last observation carried forward and complete case analysis) and nonparametric Friedman and Kruskal-Wallis tests. RESULTS: A total of 18 participants were randomized (iCBT: n=9; iACT: n=9). High attrition was observed, with only 33.3% (n=3) of iCBT and 55.6% (n=5) of iACT participants completing all modules. The iACT group demonstrated a significant within-group reduction in insomnia severity (P=.01, Friedman test), whereas iCBT results were nonsignificant (P=.10, Friedman test). No significant between-group differences were found for any clinical or process variables. Participants rated both treatments as credible (Credibility/Expectancy Questionnaire scores remained stable), though qualitative feedback indicated a need for more flexible, less burdensome content. CONCLUSIONS: This pilot study demonstrates that while internet-delivered insomnia treatments are feasible and credible in primary care, high attrition remains a significant barrier. Preliminary signals suggest that iACT may be a viable alternative to iCBT, potentially offering better adherence. Larger, fully powered pilot randomized controlled trials (estimated N=404) with refined recruitment and automated retention strategies are required to determine definitive comparative efficacy and the mediating role of sleep knowledge and acceptance.
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 →Proceedings of the National Academy of Sciences of the United States of America
Soft, skin-interfaced electronics enable cannula-free wireless monitoring of sleep respiration.
Sleep-related breathing disorders are prevalent yet frequently underdiagnosed, in part due to limitations of conventional respiratory monitoring technologies. Standard nasal cannulas introduce airflow resistance, discomfort, and poor long-term adherence, constraining at-home and longitudinal assessment. Here, we report a soft, skin-interfaced nasal patch that enables cannula-free, wireless monitoring of respiratory activity during sleep. The device is constructed from ultrathin, elastomeric materials that conform to the nasal surface, coupling respiratory-induced tissue deformation to a strain-sensing element. The mechanics of the skin-device interface and the elastomeric response govern the sensitivity and linearity of signal transduction, enabling quantitative capture of breathing dynamics. An integrated wireless platform transmits deformation signals directly to mobile devices, eliminating the need for external tubing or tethered modules. Modular fabrication permits replacement of the strain sensor and skin-contact interface without compromising mechanical performance. Mechanical characterization under physiologically relevant deformation demonstrates high repeatability and low hysteresis, while in vivo studies confirm that the patch accurately reproduces respiratory waveforms and correlates closely with gold-standard nasal cannula measurements. By integrating soft materials mechanics, wearable strain sensing, and wireless electronics, this system provides a minimally obtrusive platform for continuous respiratory monitoring. The class of technologies presented in this work establishes design principles for skin-interfaced devices, in which elastomeric mechanics, strain transduction, and wireless integration combine to enable quantitative, unobtrusive physiological monitoring in clinical and home environments.
22 May 2026
Read appraisal →ACS applied materials & interfaces
Breathable Bioadhesive Electronic Skin for Precise Multimodal Electrophysiological Monitoring with AI-Assisted Physiological-State Prediction
Accurate and stable monitoring of electrophysiological (EP) signals is essential for wearable healthcare applications. However, conventional commercial electrodes suffer from poor conformal contact, limited breathability, and weak adhesion under sweaty skin and human motion condition, leading to unstable skin-electrode interface and low-quality signal acquisition. Moreover, single-modality EP signal measurements are insufficient for reliable physiological and health evaluation. Herein, we develop a multimodal electrophysiological signal monitoring electrode (MESME) featuring a fiber membrane combining thermoplastic polyurethane (TPU) and poly(acrylic acid) functionalized with N-hydroxysuccinimide ester (PAA-NHS), seamlessly integrated with a liquid metal (LM) serpentine conductive path. Compared with commercial electrodes (gel-based wet electrode), this MESME exhibits high mechanical flexibility and conformability, superior breathability, robust bioadhesion, stable electrical performance under perspiration, and motion conditions and lower interfacial impedance, enabling high-fidelity multimodal signal acquisition. Leveraging this capability, the multimodal signals generated by MESME can be further integrated with deep learning for drowsiness prediction, achieving a validation accuracy of 97.14%. This platform offers a practical strategy for multimodal electrophysiological monitoring and supports future development of data-driven health assessment models.
21 May 2026
Read appraisal →JMIR formative research
Detection of Posttraumatic Stress Disorder With Rest-Activity Data: Machine Learning Approach Using Wearable and Self-Report Data
BACKGROUND: Growing evidence suggests that disruptions in rest-activity rhythms may serve as relevant markers of posttraumatic stress disorder (PTSD). Despite the emergence of machine learning methods applied to actigraphy and self-report data, few studies have used these approaches to identify individuals with clinically diagnosed PTSD. Prior work has focused on predicting probable PTSD based on self-report measures, yet discrepancies exist between clinical diagnoses and probable PTSD derived from self-reports. OBJECTIVE: This study explored whether wrist actigraphy and sleep logs could be used to accurately predict clinician-rated PTSD diagnosis and probable diagnosis of PTSD based on established self-report cutoffs (PTSD Checklist for Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition [PCL-5] ≥31 and ≥38) among trauma-exposed service members and veterans. We also explored which features were most strongly predictive of each outcome and whether models were able to predict PTSD diagnosis even when accounting for other mental health disorders. METHODS: Wrist actigraphy data and daily sleep logs were collected over 1 week from trauma-exposed male service members and veterans (N=36; mean age 41, SD 5.3 y). Candidate features were identified using univariate feature selection. Extreme gradient boosting models were trained using leave-one-subject-out cross-validation to predict the diagnosis of PTSD and probable diagnosis of PTSD based on 2 self-report cutoffs (PCL-5≥31 and ≥38). Performance metrics were then calculated at the person level. Linear regression was used to assess the discriminant validity of model-predicted scores and each PTSD outcome specifically, relative to other mental health diagnoses. RESULTS: Machine learning models predicting PTSD diagnosis and probable PTSD based on the PCL-5≥31 threshold demonstrated satisfactory performance in this sample. The diagnosis model achieved an area under the curve (AUC) of 0.83 (95% CI 0.61-1.00), with high accuracy (88%) and specificity (96%) and moderate sensitivity (63%). The PCL-5≥31 model yielded comparable performance (AUC=0.84, 95% CI 0.71-0.98) with balanced sensitivity (73%) and specificity (82%). For both models, a combination of subjective and objective features was the most impactful. These models were able to predict PTSD even when accounting for non-PTSD mental health diagnoses, as model-predicted scores were significantly associated with 2 outcomes: clinician-rated PTSD (B=0.19; P=.002) and probable PTSD based on a PCL-5≥31 cutoff (B=0.24; P=.003). In contrast, the model predicting probable PTSD based on the PCL-5≥38 threshold performed poorly (AUC=0.47, 95% CI 0.24-0.69), with a nonsignificant relationship between model-predicted scores and the outcome (B<0.01; P=.89). CONCLUSIONS: Both subjective and objective rest-activity features may improve the prediction of PTSD. Further research is needed to validate these findings and explore the use of integrating wearable sensor data and subjective information to support PTSD assessment.
20 May 2026
Read appraisal →Science advances
Wireless, skin-interfaced multimodal sensing system for continuous psychophysiological monitoring-A wearable polygraph device
Accurate, continuous monitoring of psychophysiological states is central to understanding stress and autonomic dysfunction across diverse medical contexts. Current approaches such as polygraphy and polysomnography rely on cumbersome, wired sensors that limit real-world utility and burden patients, particularly vulnerable populations such as infants. Here, we introduce a wireless, skin-interfaced multimodal sensing system capable of simultaneously recording cardiac, respiratory, electrodermal, and thermal signals in a time-synchronized manner. Leveraging compact and soft designs, the technology enables unobtrusive monitoring across controlled, clinical, and naturalistic settings. Validation studies performed in parallel with gold standard systems demonstrate high fidelity in quantifying stress responses during polygraph interviews, cognitive load tasks, and cold pressor tests. In pediatric sleep studies, the data reliably identify arousals, hypopnea, and apnea while revealing disease-specific autonomic signatures in infants with Down syndrome. Real-world deployment during emergency simulation training shows that multimodal stress signatures correlate inversely with performance, underscoring translational value in medical education. Machine learning analyses across all studies confirm that multimodal features outperform single-signal approaches in detecting stress and clinical events with high sensitivity and specificity. Collectively, these findings establish the technology as a next-generation wearable platform that bridges engineering innovation and clinical practice, offering mechanistic insight and diagnostic potential in stress medicine, sleep medicine, and beyond.
18 May 2026
Read appraisal →Thorax
Clinical impact of customised positive airway pressure (PAP) therapy interfaces (3DPiPPIn): a single site randomised controlled trial
INTRODUCTION: Positive airway pressure (PAP) therapy is the recognised treatment for sleep disordered breathing (SDB), delivered via a tight-fitting face mask (interface). Conventional interfaces do not consider facial geometries, often resulting in poor fit and ineffective therapy. Three-dimensional (3D) printing of customised interfaces may improve comfort and outcomes. OBJECTIVES: To evaluate the clinical impact of customised versus conventional oronasal interfaces in adults with obstrcutive sleep apnoea (OSA). The primary outcome was residual Apnoea Hypopnea Index (AHI) at 6 months; secondary outcomes included interface leak, therapy concordance and patient reported symptoms. METHODS: A randomised controlled trial with 160 adults naïve to PAP therapy and diagnosed with SDB (AHI ≥15 events/hour). Randomisation was minimised by age and ethnicity. Structured light facial scans (POP2, Revopoint, China) were used to produce 3D printed moulds (Fuse 30+, Formlabs, Massachusetts, USA) for silicone injected oronasal customised interface cushions.AHI was compared using quantile regression to account for the skewed distribution of the AHI data. Secondary outcomes were compared using logistic, quantile and linear regressions. RESULTS: 160 participants were recruited (intervention: 82, control: 78). Customised interfaces were associated with a 1.5 (events/hour) increase in AHI (p=0.059), higher interface leak (difference in medians 30.0 L/min, 95% CI 7.36 to 40.14, p<0.0001) and lower compliance (difference in compliance 0.78, 95% CI 0.05 to 1.54, p=0.04) at 6 months. CONCLUSIONS: This trial did not demonstrate customised oronasal interfaces were superior to conventional interfaces. Future research should focus on addressing design and manufacturing limitations before any potential advantages can be evaluated. TRIAL REGISTRATION NUMBER: ISRCTN74082423.
16 May 2026
Read appraisal →European respiratory review : an official journal of the European Respiratory Society
Normal polysomnography parameters in healthy children: a systematic review and meta-analysis
INTRODUCTION: There is a lack of normative values for polysomnographic parameters in childhood. This study presents a comprehensive meta-analysis of paediatric polysomnography parameters scored using recent criteria to establish normative values adjusted for age and sex. METHODOLOGY: A systematic search was conducted in Web of Science and Scopus for studies that performed overnight polysomnography in healthy children and reported polysomnographic parameters scored using the American Academy of Sleep Medicine criteria (2007 or 2012). Children with previously diagnosed health conditions were excluded. Estimates for sleep, arousals and cardiorespiratory parameters were pooled using a random-effects meta-analysis and the influence of sex and age was assessed using meta-regression. RESULTS: Of 3612 articles, 66 studies were eligible, resulting in a sample size of 2947 healthy children. Pooled estimates and 95% confidence intervals for 34 polysomnographic parameters were established. Meta-regression revealed age-related decrease in total sleep time (TST), sleep period time, TST in stage N3 and REM, number of sleep cycles, total arousals, total leg movements, periodic leg movement index, central apnoea index, apnoea-hypopnoea index (AHI), mean peripheral oxygen saturation (S pO2 ), mean heart rate in sleep and REM, while the number of awakenings, TST in stage N2, number of stage shifts, and S pO2 nadir increased with age. Sleep latency was the only parameter influenced by sex (change of -1.27 min per 10% increase in the proportion of males). CONCLUSION: This meta-analysis provides normative polysomnographic data for the paediatric population that can be used as control values in both clinical and research context.
15 May 2026
Read appraisal →Sleep
Effects of a guided digital intervention on sleep and mental health outcomes in university students - a randomized controlled trial
STUDY OBJECTIVES: To evaluate the effectiveness of a guided digital intervention targeting sleep and circadian rhythms in university students. METHODS: We randomized 195 university students with self-reported insomnia (Insomnia Severity Index ≥10) from nine Dutch Universities to receive either the 5-week guided digital cognitive-behavioral therapy for insomnia "i-Sleep & BioClock", or digital sleep psychoeducation and sleep monitoring. Online assessments were at baseline, 3 (mid-treatment), 6 (post-treatment), and 18 (follow-up) weeks. The primary outcome was insomnia severity at 6 weeks. Secondary outcomes were symptoms of depression and anxiety; functioning; quality of life (QoL); academic performance; and sleep and light exposure diary outcomes. RESULTS: From Nov 1st 2023 until Sept 5th 2024, we randomly assigned 96 students (age 23.8 ± 3.8, 75.4% female) to the intervention and 99 to the control condition, stratified by university and gender. Fifty-one percent completed the post-treatment assessment, and 30% the follow-up. 31% were intervention completers. Linear mixed models showed no significant time x treatment interaction for insomnia severity ("i-Sleep & BioClock": baseline ISI M = 16.8, SD = 4.0; post-treatment ISI M = 10.2, SD = 3.9; Digital psychoeducation: baseline ISI M = 16.5, SD = 3.6; post-treatment ISI M = 11.1, SD = 4.2). The difference in change between groups was -1.17 points (95% CI -2.72 to 0.38), p=.14, Cohen's d = -0.31. Most secondary outcomes showed no significant time x treatment effects. CONCLUSIONS: "i-Sleep & BioClock" was not more effective than online psychoeducation on most outcomes, possibly due to active elements present in both conditions or low adherence. Future research should identify factors for early disengagement and investigate which intervention components drive improvements in sleep. CLINICAL TRIAL: Effectiveness of a Guided Self-help Intervention for Improving Sleep in University Students, https://clinicaltrials.gov/study/NCT06023693, prospectively registered at ClinicalTrials.gov on August 3rd, 2023.
14 May 2026
Read appraisal →Sleep
Testing affect regulation theories of dreaming
STUDY OBJECTIVES: Dreaming is theorized to regulate waking affect via fear extinction learning, similar to exposure therapy. We tested whether fear in dreams was associated with lower negative affect (NA) and higher positive affect (PA) on subsequent mornings and whether these effects were moderated by emotion regulation strategies or mixed dream emotion states. Additionally, we examined whether individuals with higher average dream fear showed more adaptive emotion regulation. METHODS: We employed a natural language processing tool and Bayesian multilevel modeling using online daily survey data in a community sample (n = 536; 85.6% women; Mage = 39.3 ± 17.3; days = 4715). RESULTS: Frightening dreams were associated with more negative affect the following morning (+7% NA, pd = 100%), which was amplified for individuals with more adaptive emotion regulation (+3% NA, pd = 98.69). Dreams concurrently high in fear and joy were related to 20% higher odds of zero NA the next morning (pd = 97.82%). Higher average dream joy was related to 9% higher morning PA (pd = 98.79%). Individuals with higher average dream fear showed greater adaptive emotion regulation (β = 0.18, pd = 99.93%). CONCLUSIONS: Findings supported affect continuity from dream to wake, yet affect regulation at the individual level. Frightening dreams were related to worse morning affect, with a more pronounced effect among high-regulation individuals. High-regulation individuals tended to experience more fearful dreams. Mixed dream emotions were related to mornings without NA, and dream joy was related to higher morning PA. Our findings suggest adaptive, though dynamic, effects of dream emotions on psychological functioning.
14 May 2026
Read appraisal →JMIR medical informatics
Resource Use Patterns in US Telehealth Services: Machine Learning and Clustering Analysis Across 4 Specialties
BACKGROUND: The expansion of telehealth services, particularly during the COVID-19 pandemic, has transformed health care delivery in the United States. Telehealth promises greater access and resource efficiency by reducing wait times and appointment lengths, especially in specialties like psychiatry, behavioral health, bariatrics, and sleep medicine. However, disparities exist in adoption based on demographics, geography, and socioeconomic status, raising concerns about equitable access and optimal resource use. OBJECTIVE: This study aims to evaluate how telehealth impacts health care resource use across 4 specialties by examining 2 key metrics: patient-to-provider ratios and appointment durations. It seeks to understand how factors such as patient demographics, facility characteristics, and social determinants influence telehealth adoption and efficiency using a national dataset spanning from 2018 to 2023. METHODS: We analyzed a deidentified dataset from Epic Cosmos, covering outpatient visits across 48 US states (2018-2023). After data preprocessing and feature engineering, we applied 3 machine learning (ML) models (random forest, extreme gradient boosting, and deep neural networks) to predict resource use. Using the model performing the best, feature importance was assessed using Shapley Additive Explanations values. We then used k-means clustering to group facilities into clusters per specialty. Comparative analyses were conducted to evaluate differences in use among clusters, during and after the pandemic. RESULTS: Telehealth use peaked in 2020 and has remained above prepandemic levels since then. In 2018-2023, telehealth adoption reached 36.9% (4,543,021/12,311,710) in psychiatry, 23.9% (5,321,099/22,264,013) in behavioral health, 21.2% (924,333/4,360,061) in bariatrics, and 16.8% (851,803/5,070,256) in sleep medicine. Telehealth visits were consistently shorter than office visits (mean reduction 12.24 minutes; SD 3.33 minutes; P=.18), while patient-to-provider ratios varied significantly across specialties. Among ML models, extreme gradient boosting regression achieved the best performance (patient-to-provider ratios: R2=0.96-0.99; appointment durations: R2=0.61-0.69). Shapley Additive Explanations analysis identified visit type, telehealth use, facility size, rurality, and Social Vulnerability Index household vulnerability as the strongest predictors. Comparative analyses showed significant differences across clusters (all P<.05). CONCLUSIONS: Telehealth has become a sustainable component of health care, enhancing access and efficiency across both rural and urban areas. However, its impact varies across specialties and regions, highlighting the need for targeted strategies such as staffing support for vulnerable populations, infrastructure investments in rural facilities, and reimbursement models that reflect telehealth's resource use. This study provides robust evidence from ML and clustering analyses, demonstrating how telehealth shapes resource use and offering actionable insights for equitable and sustainable integration.
12 May 2026
Read appraisal →JMIR medical informatics
Machine Learning-Based Multidimensional Oximetry for Obstructive Sleep Apnea Screening: Development and External Validation
BACKGROUND: Obstructive sleep apnea (OSA) affects nearly one billion people globally and poses a substantial public health threat. Effective and accessible methods for OSA risk identification are urgently needed. OBJECTIVE: This study aims to develop and externally validate a machine learning model derived from multi-parameter pulse oximetry (SpO2) for OSA screening, and to evaluate its performance, interpretability, and robustness across sex and age subgroups. METHODS: Of 4156 screened participants, 2195 underwent polysomnography (internal cohort) and 446 received home sleep apnea testing (external cohort). Eight SpO2-derived parameters, including oxygen desaturation index (ODI), hypoxic burden (HB), and ST90 (percentage of sleep time with SpO2 < 90%), were used to construct models. Six machine learning algorithms were trained, with F1-score as the primary metric and area under the curve as the secondary metric. Model interpretability was assessed using Shapley additive explanations and intrinsic feature importance scores. RESULTS: Nonlinear parameter-risk relationships were observed between oximetry indices and OSA probability. The 4-parameter ODI-HB-MinSpO2-ST90 model achieved optimal performance (F1-score = 0.9516, area under the curve = 0.9879), surpassing all single-parameter models. Shapley additive explanations analysis identified ODI, HB, and MinSpO2 as key predictors. The ODI-HB-MinSpO2-MeanSpO2 configuration demonstrated superior performance in female and younger subgroups, whereas the ODI-HB-MinSpO2-ST90 model remained optimal for male and older participants. Categorical boosting outperformed other algorithms across multiple metrics and remained robust in both subgroup and external validation analyses. CONCLUSIONS: The multi-parameter oximetry model based on the categorical boosting algorithm provides a simple and accurate tool for OSA screening. Sex- and age-stratified strategies can further enhance its clinical applicability.
12 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 →Journal of neural engineering
Uncovering relationships in multi-channel EEG data using principal Hessian directions and Ricci flow
Objective.The high-dimensional nature of multi-channel EEG data poses major challenges for downstream classification. Uncovering connectivity relationships and network structure between EEG channels can improve high-dimensional EEG signal recovery by identifying redundant inputs and guiding more targeted feature selection. In this study, we aim to demonstrate a systematic framework for extracting useful interchannel structure in EEG data.Approach.We present two complementary methods for inferring network structure and connectivity in EEG data: (1) a new supervised algorithm for inferring classification-relevant community structure based on principal Hessian directions (pHds), and (2) a discrete Ricci flow-based unsupervised community detection algorithm. We demonstrate these systematic methods on high-dimensional real-world EEG datasets involving classifying imagined digits versus non-digits and detecting emotional valence.Main results.We show that our pHd and Ricci flow methods-when combined-can detect interchannel relationships that meaningfully hold on unseen test classification data. Moreover, we demonstrate that this interchannel structure extracted via pHd and Ricci flow can enable subsequent improvements in downstream EEG signal classification.Significance.Our combined pHd-Ricci flow method expands the existing EEG preprocessing toolkit by offering a systematic framework for extracting meaningful network structure from high-dimensional EEG data. By facilitating more targeted and effective feature selection, our method has the potential to improve EEG signal recovery in real-world applications.
11 May 2026
Read appraisal →European heart journal
Cardiovascular benefit of continuous positive airway pressure according to high-risk obstructive sleep apnoea: a multi-trial analysis
BACKGROUND AND AIMS: Randomized trials of continuous positive airway pressure (CPAP) treatment for obstructive sleep apnoea (OSA) in patients with cardiovascular disease have not detected reduced risk of major adverse cardiovascular and cerebrovascular events (MACCEs). This study tested whether the cardiovascular benefit of CPAP occurs preferentially in high-risk OSA, characterized by greater OSA-related heart rate acceleration or hypoxaemia. METHODS: In a post hoc analysis of pooled Randomized Intervention with Continuous Positive Airway Pressure in Coronary Artery Disease and Obstructive Sleep Apnoea, Impact of Continuous Positive Airway Pressure on Patients with Acute Coronary Syndrome and Nonsleepy Obstructive Sleep Apnoea, and Sleep Apnoea Cardiovascular Endpoints Study randomized trials; outcomes were stratified by high-risk OSA status, defined by heart rate response following OSA respiratory events >9.4 b.p.m. (third tertile) or oxygen desaturation area under baseline (hypoxic burden) > 87.1% min/h (third tertile). Cox mixed models quantified the CPAP treatment effect on MACCE (including cardiovascular mortality, myocardial infarction, and stroke) within high-risk OSA and the difference vs low-risk status (primary test). Secondary analyses examined participants without excessive sleepiness (Epworth <11 points) or without increased blood pressure (systolic/diastolic <140/90 mmHg). RESULTS: In 3549 participants, 16.6% and 16.3% reached the MACCE endpoint with CPAP (n = 1778) and usual care (n = 1771), respectively. The CPAP treatment effect was greater in participants with vs without high-risk OSA [interaction hazard ratio (iHR) .69, 95% confidence interval (CI) .50-.95, Pinteraction = .024; Nhigh-risk = 1832]. The differential effect was stronger in those without excessive sleepiness (iHR .59, 95% CI .41-.84; Nhigh-risk = 1509), or without increased blood pressure (iHR .54, 95% CI .36-.81; Nhigh-risk = 1244). Continuous positive airway pressure benefits in high-risk OSA were observed alongside harm in low-risk OSA. CONCLUSIONS: Continuous positive airway pressure preferentially improves cardiovascular outcomes in high-risk OSA, while harm in low-risk OSA may counteract this effect. These findings provide a pathway to identify patients likely to benefit.
11 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 →The Cochrane database of systematic reviews
Cognitive behavioural therapeutics for insomnia symptoms in the perimenopause through to the early postmenopausal period
This is a protocol for a Cochrane Review (intervention). The objectives are as follows: Primary Objective i) To assess the effects of cognitive behavioural therapy for insomnia (CBT-I) and cognitive behavioural therapy for menopausal symptoms (CBT-M) on sleep disturbances in women in perimenopause through to the early postmenopausal period. Secondary Objectives i) To assess the effects of CBT-I and CBT-M on vasomotor symptoms in women in perimenopause through to the early postmenopausal period. ii) To assess the impact of socioeconomic status on the effectiveness of CBT-I and CBT-M, in order to determine the interventions' impact on health equality.
28 Apr 2026
Read appraisal →Brain topography
Effects of Sleep Deprivation and Neuromodulatory Interventions on Olfactory-Related Brain Function
Sleep deprivation (SD) has been shown to impair sensory functions, including olfactory processing, which may be related to changes in brain activity. This study aims to investigate the effects of SD, short-term sleep recovery (RS), and neuromodulation interventions on the functional activity of olfactory-related brain regions. We hypothesize that these interventions could restore some of the neural activity disrupted by SD, particularly in areas associated with olfactory processing. In this study, 90 male participants first underwent a baseline sleep period of at least 8 h, followed by 36 h of sleep deprivation (SD36h). The participants were randomly assigned to four groups: control group (CON, n = 30, age 20.8 ± 1.6 years), magnetic stimulation group (MS, n = 30, age 21.3 ± 1.6 years), electrical stimulation group (ES, n = 17, age 20.3 ± 0.7 years), and combined stimulation group (CS, n = 13, age 20.8 ± 1.0 years). After SD36h, participants underwent an 8-hour restorative sleep period, before and after completing the 8-hour RS period, additionally received 30 min of MS(MS was applied over the occipital region using a rhythmic gamma magnetic-field device), ES(ES was delivered as tDCS targeting the left DLPFC, anode at F3, cathode at FP1, 2 mA), and CS. All participants underwent three resting-state functional MRI (fMRI) scans: at baseline, after sleep deprivation, and after intervention. This experiment analyzed the Amplitude of Low-Frequency Fluctuations (ALFF) and functional connectivity (FC) in olfactory-related regions. ALFF and FC were analyzed using a group (CON/MS/ES/CS) × time (baseline/SD36h/RS) repeated-measures ANOVA with FDR correction, followed by FDR-corrected post-hoc tests for significant main effects. No group × time interaction survived FDR correction for ALFF or FC. For ALFF, a main group effect was observed in Hippocampus_L and Olfactory_R, with the ES (and CS in Olfactory_R) showing lower ALFF than CON and/or MS. For time effects, ALFF in Hippocampus_L was reduced at SD36h and remained lower after RS relative to baseline, while Olfactory_L ALFF was lower after RS than baseline. For FC, main group effects were found in Hippocampus_L and Fusiform_R, with ES and CS showing higher FC than CON and MS. A main time effect cluster with a peak in Cingulum_Mid_R showed reduced FC after SD36h and increased FC after RS, returning to a level not significantly different from baseline. SD disrupts the ALFF and FC in olfactory-related brain regions, while RS can partially restore these functions. Short-term neuromodulation interventions, such as electrical stimulation and combined stimulation, may improve network-level connectivity but are insufficient to fully restore local neural activity.
26 Apr 2026
Read appraisal →Sleep & breathing = Schlaf & Atmung
Health-related quality of life in patients with obstructive sleep apnea - a systematic review update assessing the quality of measurement properties of patient-reported outcome measures
PURPOSE: The purpose of this systematic review update was to identify existing health-related quality of life (HRQOL) patient-reported outcome measures (PROMs) for obstructive sleep apnea (OSA). Building upon a 2016 systematic review, which recommended four PROMs with the highest evidence, this investigation was warranted to synthesize new evidence and evaluate new PROMs that have emerged since then via the Consensus based Standards for the Selection of Health Measurement Instruments (COSMIN). METHODS: We searched Medline via PubMed, Embase and the Cochrane Library for HRQOL PROM development and validation studies published in English or German. Studies focusing on cross-cultural validation, those in which PROMs were assessed as secondary outcomes, and studies limited to specific demographic subgroups or single HRQOL domains were excluded. For risk of bias and quality of measurement properties (MP) assessment, we used the COSMIN methodology to facilitate comparability with the previous review, as well as the GRADE approach for evidence level evaluation. RESULTS: We identified two new PROMs, the Patient-Reported Apnea Questionnaire (PRAQ) and the Symptoms, Tiredness, Alertness, Mood and Psychosocial instrument (STAMP), which were validated in two studies and a single study, respectively. Overall, the PRAQ displays an moderate risk of bias with excellent quality of MP and high evidence for content validity in particular, whereas the STAMP shows a higher risk of bias and low quality of MP. CONCLUSION: The PRAQ has demonstrated the strongest evidence among PROMs since 2016. In total, including the previous review, five PROMs are currently recommended for assessing HRQOL in individuals with OSA. REVIEW REGISTRATION: The review was registered with PROSPERO under the registration number CRD42024602020.
24 Apr 2026
Read appraisal →Medicine
Drug-associated insomnia: A pharmacovigilance study based on FDA adverse event reporting system.
Insomnia has become an increasingly serious public health issue with complex causes, among which medications act as a significant factor. This study aims to systematically detect and evaluate drug adverse event signals associated with insomnia risk using the Food and Drug Administration Adverse Event Reporting System (FAERS) database. The analysis utilized data from the FAERS database covering January 2004 to December 2024. Disproportionality analysis was conducted using 4 algorithms: reporting odds ratio, proportional reporting ratio, information component (IC), and empirical Bayes geometric mean. Potential risk signals were deemed significant only when all 4 algorithms simultaneously met their thresholds. Subgroup analyses were further performed, stratified by age and sex, to assess the robustness of signals across different populations. From 2004 to 2024, there were 179,697 adverse event reports of insomnia in FAERS in which one or more medications were designated as the primary suspect. The top 30 medications with the strongest signal strength were predominantly nervous system medicines (18 types, 60%), followed by respiratory system medicines (3 types, 10%), and genitourinary system and sex hormones (3 types, 10%). The top 3 medications with the highest reporting frequency were mefloquine, viloxazine, and flibanserin. Subgroup analyses revealed distinct drug signal profiles across age groups and genders, with pediatric cases dominated by nervous system and anti-infective agents, adults and the elderly showing additional endocrine or hormonal signals, and sex specific signals such as finasteride in males and flibanserin in females. This pharmacovigilance study identifies insomnia risk signals across multiple drug classes, underscoring the need for clinical vigilance regarding drug-related sleep disturbances. Further prospective research is required to confirm these associations.
20 Apr 2026
Read appraisal →Pharmaceutical biology
Valeriana species and insomnia: multi-organ mechanisms and translational perspectives.
CONTEXT: Insomnia is a common sleep disorder that substantially impairs quality of life. Conventional treatments, including cognitive behavioral therapy and pharmacological agents, are often limited by side effects and insufficient long-term safety data. Valeriana species, such as Valeriana officinalis L. and Valeriana jatamansi Jones, have been traditionally used for their sedative and neuroprotective properties. OBJECTIVE: This review evaluates the therapeutic potential of Valeriana species for insomnia, emphasizing their pharmacological mechanisms, multi-organ effects, and evidence from preclinical and clinical studies. METHODS: A systematic literature search was performed in databases including PubMed and Web of Science. Studies addressing Valeriana-induced sleep regulation, neurotransmitter modulation, oxidative stress reduction, and neural, hepatic, and gastrointestinal interactions were included to assess efficacy and safety. RESULTS: Valeriana species modulate neurotransmission, mitigate oxidative stress, and influence hepatic and gastrointestinal functions. Preclinical studies support their sedative and neuroprotective effects, while clinical outcomes vary with formulation, dosage, and individual response. Overall, safety profiles are favorable, though inconsistencies remain. CONCLUSION: Valeriana species represent a promising multi-target strategy for insomnia, integrating neural and peripheral pathways. Future research should clarify the mechanisms underlying multi-organ effects, optimize formulations, and conduct rigorous clinical trials to establish standardized, effective, and safe interventions.
19 Apr 2026
Read appraisal →Sleep
Optimizing automated sleep stage scoring of 5-s mini-epochs: a transfer learning study
STUDY OBJECTIVES: Conventional sleep staging relies on 30-s epochs, potentially concealing transient sleep stage intrusion and reducing precision. Building on our previous study of mini-epochs, we investigated whether U-Sleep, an existing automatic deep learning-based sleep staging model with high performance in epochs, could be optimized to similar performance level in 5-s mini-epoch scoring, thereby enabling more detailed sleep characterization. METHODS: We created a dataset of 48 000 human-scored 5-s mini-epochs from 100 polysomnographies. We compared mini-epochs to human-scored epochs before U-Sleep was optimized using transfer learning and evaluated on a test set. Model performance was assessed using F1-scores, confusion matrices, stage distributions and transition rates comparing scorings of the original U-Sleep before, and the optimized U-Sleep after transfer learning to human-scored mini-epochs. RESULTS: Compared to human-scored epochs, human-scored mini-epochs captured significantly more transitions (1.70/min vs. 0.21/min, p < .001), and significantly more wake (8.4 per cent vs. 5.4 per cent), N1 (7.2 per cent vs. 5.4 per cent), and N2 (51.8 per cent vs. 40.9 per cent), less N3 (15.4 per cent vs. 25.2 per cent), and REM sleep (16.7 per cent vs. 23.0 per cent) (all p < .001). Optimizing U-Sleep improved its performance significantly from F1 = 0.74 to F1 = 0.81 (p < .05) and gave increased transition rates in the test set (original U-Sleep: 1.06/min, optimized U-Sleep: 1.34/min, human-scored mini-epochs: 1.70/min). Stage distributions did not differ between optimized U-Sleep's scorings and human-scored mini-epochs. CONCLUSION: After optimization, U-Sleep performance in mini-epochs matched the high performance levels previously reported in both human and automated 30-s epoch scoring. This demonstrates the feasibility of precise, automated high-resolution sleep staging. Future work should include external validation and application to full-night recordings. Statement of Significance Conventional 30-s epochs limit temporal resolution in sleep staging and may conceal transient intrusions of wake or sleep stages. However, no validated methods are available for high-resolution scoring. In this study, we trained and validated the state-of-the-art deep learning model U-Sleep for accurate automatic 5-s mini-epoch scoring using a large dataset of human-scored mini-epochs. The optimized model achieved a high performance, matching levels from previously reported automatic and human epoch scoring. Compared to epoch scoring, mini-epochs captured significantly more stage transitions, supporting their ability to uncover sleep dynamics that are otherwise lost. Our findings show the potential of high-resolution sleep staging for more detailed characterization of sleep architecture and demonstrate the feasibility of precise, automatic mini-epoch scoring.
19 Apr 2026
Read appraisal →Neuron
Spatial and single-cell transcriptomic atlas of human suprachiasmatic nucleus
The suprachiasmatic nucleus (SCN) is considered the master pacemaker of the circadian clock in mammals, but our current knowledge of the SCN is mostly based on rodent studies. Here, we report a comprehensive molecular and cellular atlas for the adult human SCN by spatial transcriptomics, single-nucleus RNA sequencing, and deep-learning-based histological analysis. We identified seven human SCN neuron subtypes with specific transcriptomes and spatial distributions. Comparison of humans, mice, and non-human primates revealed the conserved functional segregation within the SCN regulated by LIM homeobox 1 (LHX1) and RAR-related orphan receptor B (RORB). Furthermore, our results suggested that the human SCN has undergone marked reorganization of its neuropeptide signaling network. Finally, integrative analysis of human SCN transcriptomes and genome-wide association studies (GWASs) identified arginine vasopressin (AVP)/neuromedin S (NMS) subtype as the potential neuronal correlate for morningness chronotype. Thus, our spatial and single-cell transcriptomic atlas of the human SCN provided a basis for the understanding of neural and molecular mechanisms of the human circadian clock.
18 Apr 2026
Read appraisal →Sleep
Improving sailor's amount and efficiency of sleep in two-section watchkeeping schedules: A cluster randomized trial study
STUDY OBJECTIVES: The present study aimed at comparing sailors' amount and efficiency of sleep in two two-section watchkeeping schedules, i.e. the traditional 6/6 and the 7-5-5-7. Intention for the latter schedule was to align the major sleep episodes with biologically driven nocturnal sleep patterns. METHODS: Using a parallel crossover design with cluster randomized trials, forty-eight crew members from two vessels of the German Navy participated in a field trial with two 1-week underway periods varying the watchkeeping schedule in randomized order. Sleep minutes and sleep efficiency were assessed on the participant level using wrist-worn actigraphs and analyzed for sleep episodes occurring during the day (i.e. between 10:00 am and 10:00 pm) and during the night (i.e. between 10:00 pm and 10:00 am). RESULTS: Nighttime sleep duration was 14 min longer and with 10% higher sleep efficiency in the 7-5-5-7 schedule compared to the 6/6. In contrast, daytime sleep duration was 40 min longer in the 6/6 with no statistically significant differences in sleep efficiency between the two watchkeeping schedules. CONCLUSIONS: Our results suggest that the amount and efficiency of nighttime sleep can be enhanced with the 7-5-5-7. Hence, even in shift schedules with restricted periods for rest, recuperation can be improved by more closely aligning watchkeeping schedules to the innate human diurnal circadian cycles. Statement of Significance The size of ships and the associated limitations on the size of their crews make two-section watchkeeping schedules common in civil and military maritime settings. Such watchkeeping schedules-in which the 24-h day must be shared between two watches-limit time available for rest and recuperation. The present study shows that even in this context, slight modifications in two-section watchkeeping schedules lead to more and better nighttime sleep because the circadian fluctuations of sleep and performance are considered. Our research findings have high practical significance for all industries with shift workers and demonstrate how the negative effects of watchkeeping schedules can be reduced if they are adapted to better align with innate human circadian rhythms.
17 Apr 2026
Read appraisal →Sleep
Mamba-based deep learning approach for sleep staging on a wireless multimodal wearable system without electroencephalography
STUDY OBJECTIVES: We investigate a Mamba-based deep learning approach for sleep staging on signals from ANNE One (Sibel Health, Chicago, IL), a non-intrusive dual-module wireless wearable system measuring chest electrocardiography, triaxial accelerometry, chest temperature, and finger photoplethysmography and finger temperature. METHODS: We obtained wearable sensor recordings from 357 adults undergoing concurrent polysomnography at a tertiary care sleep lab. Each polysomnography recording was manually scored, and these annotations served as ground truth labels for training and evaluation of our models. Polysomnography and wearable sensor data were automatically aligned using their electrocardiography channels with manual confirmation by visual inspection. We trained a Mamba-based recurrent neural network architecture on these recordings. Ensembling of model variants with similar architectures was performed. RESULTS: After ensembling, the model attains a 3-class (wake, non-rapid eye movement sleep, rapid eye movement sleep) balanced accuracy of 84.02 per cent, F1 score of 84.23 per cent, Cohen's κ of 72.89 per cent, and a Matthews correlation coefficient (MCC) score of 73.00 per cent; a 4-class (wake, light NREM [N1/N2], deep NREM [N3], REM) balanced accuracy of 75.30 per cent, F1 score of 74.10 per cent, Cohen's κ of 61.51 per cent, and MCC score of 61.95 per cent; a 5-class (wake, N1, N2, N3, REM) balanced accuracy of 65.11 per cent, F1 score of 66.15 per cent, Cohen's κ of 53.23 per cent, MCC score of 54.38 per cent. CONCLUSIONS: Our Mamba-based deep learning model can successfully infer major sleep stages from the ANNE One, a wearable system without electroencephalography, and can be applied to data from adults attending a tertiary care sleep clinic.
17 Apr 2026
Read appraisal →Sleep
Fetal sleep: a cross-species review of physiology, measurement, and classification
STUDY OBJECTIVES: Fetal sleep is a vital yet underexplored aspect of prenatal neurodevelopment. Its cyclic organization reflects the maturation of central neural circuits, and disturbances in these patterns may offer some of the earliest detectable signs of neurological compromise. This is the first review to integrate more than seven decades of research into a unified, cross-species synthesis of fetal sleep. We examine: (1) Physiology and Ontogeny-comparing human fetuses with animal models; and (2) Methodological Evolution-transitioning from invasive neurophysiology to non-invasive monitoring and deep learning frameworks. METHODS: A structured narrative synthesis was guided by a systematic literature search across four databases (PubMed, Scopus, IEEE Xplore, and Google Scholar). From 2925 identified records, 169 studies involving fetal sleep-related physiology, sleep-state classification, or signal-based monitoring were included in this review. RESULTS: Across the 169 studies, fetal sleep states become clearly observable as the brain matures. In fetal sheep and baboons, organized cycling between active and quiet sleep emerges at approximately 80%-90% gestation. In humans, this differentiation occurs later, around 95% gestation, with full maturation reached near term. Despite extensive animal research, no unified, clinically validated framework exists for defining fetal sleep states, limiting translation into routine obstetric practice. CONCLUSIONS: By integrating evidence across species, methodologies, and clinical contexts, this review provides the scientific foundation for developing objective, multimodal, and non-invasive fetal sleep monitoring technologies-tools that may ultimately support earlier detection of neurological compromise and guide timely prenatal intervention.
17 Apr 2026
Read appraisal →Sleep
Augmenting management of obstructive sleep apnea with consumer wearable devices to increase use of positive airway pressure therapy: a pilot randomized trial
STUDY OBJECTIVES: This pilot randomized trial measured the effects of a consumer-grade wearable-augmented program, which provides nightly pulse oximetry data to patients in a weekly report, on positive airway pressure (PAP) use in adults with obstructive sleep apnea (OSA) recruited from an outpatient sleep center. METHODS: This single-blind, parallel, two-group waitlist-controlled, single-site randomized trial recruited individuals with OSA with insufficient PAP use from a Department of Veterans Affairs medical center and randomized them to immediate participation in the consumer wearable-augmented self-monitoring program versus waitlist. The primary outcome was the change in modem-measured PAP use (mean hours or minutes per night over a 7-day period) from baseline (T1) to T3 assessment, which was 35 days following randomization (7 days post-intervention for the immediate group and prior to intervention for the waitlist group). RESULTS: We randomized 50 participants (mean age 47.9 years [SD 11.4]; 88% male). The change in PAP use was +1.33 hours (+79.8 minutes) for the immediate group and -0.17 hours (-10.2 minutes) for the waitlist control group, with a between-group difference of 1.50 hours (90 minutes) (p = .0097). The change in the number of days PAP was used for 4 hours or more over a 7-day period was +1.32 days for the immediate and 0.12 days for the waitlist control group, with a between-group difference of 1.20 (p = .0401). CONCLUSIONS: This pilot trial demonstrated that the consumer-grade wearable-augmented program is a promising intervention for increasing PAP use. A future full-scale trial that follows participants for a longer period is needed. Statement of Significance This pilot trial tested a novel use of the pulse oximetry feature available on some consumer-grade wearable devices to increase use of positive airway pressure (PAP) devices among adults with obstructive sleep apnea (OSA) and insufficient PAP use. The widespread and growing use of consumer-grade wearable devices offers an opportunity to leverage the features on these devices to tackle a common, problematic issue among individuals with OSA-insufficient use of PAP therapy. The randomized controlled design provides support for future research involving these devices.
17 Apr 2026
Read appraisal →Sleep
Multimodal cardiovascular risk profiling using self-supervised learning of polysomnography
STUDY OBJECTIVES: Polysomnography (PSG) provides a comprehensive assessment of brain, cardiac, and respiratory activity during sleep. While it is widely used for diagnosing sleep disorders, its potential to assess future health risks has not been fully explored. This study aimed to develop and evaluate an interpretable framework to identify physiological patterns in PSG data linked to cardiovascular disease (CVD) outcomes, without relying on manual annotations (e.g. sleep stages). METHODS: We developed a self-supervised deep learning model that extracts meaningful patterns from multi-modal signals (electroencephalography [EEG], electrocardiography [ECG], and respiratory signals [Resp]). The model was trained on data from 4398 participants. Projection scores were derived by contrasting embeddings from individuals with and without CVD outcomes. External validation was conducted in an independent cohort with 1093 participants. The source code is available on https://github.com/miraclehetech/sleep-ssl. RESULTS: The projection scores revealed distinct and clinically meaningful patterns across modalities. ECG-derived features were predictive of both prevalent and incident cardiac conditions, particularly CVD mortality. EEG-derived features were predictive of incident hypertension and CVD mortality. Resp added complementary predictive value. Combining projection scores with the Framingham Risk Score consistently improved prediction, with area under the curve values ranging from 0.607 to 0.965 in the Sleep Heart Health Study test cohort and 0.710 to 0.807 across four of five outcomes in the external validation cohort, demonstrating robust cross-cohort generalizability. CONCLUSIONS: Our findings demonstrate that the proposed framework can generate individualized CVD risk scores directly from PSG data. The resulting projection scores have the potential to be integrated into clinical practice, enhancing risk assessment and supporting personalized care. Statement of Significance This study is among the first to apply a self-supervised framework for cardiovascular risk profiling using polysomnography (PSG) data. By transforming electroencephalography, electrocardiography, and Resp into interpretable projection scores, we identified physiological markers predictive of multiple cardiovascular outcomes. These risk profiles, combined with traditional risk scores, significantly improved prediction across both internal and external cohorts. Our findings highlight the untapped potential of PSG signals beyond sleep staging, providing an interpretable, scalable, and clinically actionable approach for personalized cardiovascular risk stratification.
17 Apr 2026
Read appraisal →Physiological measurement
Towards real-time sleep stage classification: a deep learning approach leveraging PPG and ECG
Objective.This work aims to enable adaptive consumer sleep technologies (CSTs) for sleep intervention by developing a deep learning model for sleep stage classification using wearable sensor data.Approach.We propose an end-to-end deep learning approach leveraging Photoplethysmography (PPG) signals, commonly available in CSTs. Model performance is improved by pretraining with electrocardiography (ECG) from the large-scale multi-ethnic study of atherosclerosis dataset datasets. Training and evaluation are conducted with the Dataset for Real-time sleep stage EstimAtion using Multisensor wearable Technology and an additional dataset comprising of 13 participants (aged 22-71 years) without prior known sleep disorders. The dataset contains combined synchronized polysomnography (PSG) and Empatica E4 wearable data, annotated with American Academy of Sleep Medicine (AASM) sleep stages.Main results.The proposed method demonstrates sleep stage classification from minimally processed PPG signals for real-time intervention. While ECG-trained models are not directly transferable to PPG, fine-tuning significantly improves performance, achieving up to a 29% increase in multi-stage classification accuracy.Conclusion.Pretraining with ECG and fine-tuning with PPG increases sleep stage classification for end-to-end deep learning models, exceeding previous efforts particularly in 3-stage sleep classification.Significance.This work contributes to sleep health by developing a sleep stage classification model for minimally processed PPG sensor data and takes a step further towards making adaptive CSTs feasible for use with wearable sensors.
16 Apr 2026
Read appraisal →Open heart
Chronotype-aligned exercise timing in middle-aged adults at cardiometabolic risk: a randomised controlled trial
OBJECTIVE: To investigate whether aligning exercise timing with chronotype enhances cardiometabolic and sleep-related benefits in sedentary adults with cardiovascular risk factors. METHODS: In this 12-week randomised controlled trial conducted in Lahore, Pakistan (January-June 2025), 150 sedentary adults (aged 40-60) with at least one cardiovascular risk factor were recruited and categorised as morning-type or evening-type using the Morningness-Eveningness Questionnaire and validated by 48 hour core body temperature monitoring. Participants were randomised into a chronotype-aligned exercise (CAE) group exercising at their preferred time, or a chronotype-misaligned exercise (CME) group exercising at their non-preferred time. Moderate-intensity aerobic training (5 sessions/week, 40 min/session) was supervised. Primary outcomes included systolic and diastolic blood pressure (BP) and heart rate variability (RMSSD); secondary outcomes included peak oxygen consumption (VO₂ peak), low density lipoprotein (LDL), fasting glucose and sleep quality (PSQI), assessed pre- and post-intervention. RESULTS: Of 150 randomised participants, 134 completed the study (CAE: n=64; CME: n=70). CAE led to significantly greater improvements in systolic BP (-10.8 vs -5.5 mm Hg, p=0.002), diastolic BP, RMSSD, VO₂ peak, LDL, fasting glucose and PSQI scores compared with CME. Repeated measures analysis of variance (ANOVA) revealed significant group×time interactions across all outcomes (eg, systolic BP: η²=0.095, p=0.005). The reduction in systolic BP in the CAE group was substantial and significantly greater than in the CME group. CONCLUSION: Aligning exercise timing with individual chronotype significantly enhances cardiometabolic and sleep-related outcomes in at-risk adults. Chronotype-based exercise prescriptions may offer a cost-effective, personalised approach to improving cardiovascular health.
16 Apr 2026
Read appraisal →Journal of affective disorders
The effects of Omega-3 supplementation on stress, anxiety, depression, sleep quality, and everyday memory in individuals with psychological distress: A randomized, double-blind, placebo-controlled trial
BACKGROUND/OBJECTIVES: Psychological stress, anxiety, depression, memory impairment, and poor sleep quality are prevalent issues that affect individuals' quality of life and overall health. Omega-3 polyunsaturated fatty acids (PUFAs) have been suggested to have mood-regulating effects. However, there is lack of data on the effect of omega-3 supplementation on depression, stress, anxiety, everyday memory, and sleep quality on Saudi population. Therefore, this study aimed to evaluate the impact of omega-3 supplementation on stress, anxiety, depression, everyday memory, and sleep quality in individuals with severe psychological distress. METHODS: A randomized, double-blind, placebo-controlled trial was conducted involving 64 participants with high levels of stress, anxiety, depression, memory issues and with poor quality of sleep with 32 allocated to the intervention group and 32 to the control group. The participants received either omega-3 supplementation (500 mg EPA + 250 mg DHA) or a placebo daily for three months. Pre- and post-intervention assessments were conducted using validated scales, including the Perceived Stress Scale (PSS), Generalized Anxiety Disorder-7 (GAD-7), the Patient Health Questionnaire-9 (PHQ-9), the Pittsburgh Sleep Quality Index (PSQI), the Epworth Sleepiness Scale (ESS), and the Everyday Memory Questionnaire (EMQ). RESULTS: Significant improvements were observed in the intervention group regarding stress, anxiety, depression, sleep quality, and memory outcomes (p < 0.001 for PSS, GAD-7, PHQ-9, PSQI, and EMQ). Between-group comparisons showed statistically significant reductions in post-intervention scores for the intervention group versus the control group. Regression analysis revealed strong predictive relationships between pre- and post-scores, particularly for stress and depression. CONCLUSIONS: Omega-3 supplementation resulted in notable psychological and cognitive improvements, suggesting its potential as a safe and effective adjunct for managing stress, mood disorders, and sleep disturbances among participants with psychological distress. Further research should explore how individual responses are influenced by baseline inflammation and omega-3 status. CLINICALTRIALS: gov: NCT07157241.
16 Apr 2026
Read appraisal →Computers in biology and medicine
Forecasting-based biomedical time-series data synthesis for open data and robust AI
The limited data availability due to strict privacy regulations and significant resource demands severely constrains biomedical time-series AI development, which creates a critical gap between data requirements and accessibility. Synthetic data generation presents a promising solution by producing artificial datasets that maintain the statistical properties of real biomedical time-series data without compromising patient confidentiality. While GANs, VAEs, and diffusion models capture global data distributions, forecasting models offer inductive biases tailored for sequential dynamics. We propose a framework for synthetic biomedical time-series data generation based on recent forecasting models that accurately replicates complex electrophysiological signals such as EEG and EMG with high fidelity. These synthetic datasets can be freely shared for open AI development and consistently improve downstream model performance. Numerical results on sleep-stage classification show up to a 3.71% performance gain with augmentation and a 91.00% synthetic-only accuracy that surpasses the real-data-only baseline.
16 Apr 2026
Read appraisal →Neurology
Wearable Technology and Its Role in Neurologic Care: Emerging Issues in Neurology
Consumer wearable devices are commonly used by patients and consumers for several reasons with increasing application as new technologies are developed. Use of these devices is an emerging issue in Neurology because of increased adoption and the additional data reported to providers by patients. Understanding of possible functions, limitations, and effect on patients of non-US Food and Drug Administration (FDA)-cleared wearable technology to inform neurologic care is needed. A common theme in people with neurologic conditions regarding consumer wearables and associated tracking applications is that there is significant promise in these tools, but adherence (days per use/per week), continued engagement (attrition), and unintended consequences such as heightened anxiety remain important issues. Further understanding and validation of these devices is needed within the field of Neurology before full use and confidence can be achieved. Below, we provide examples of non-FDA-cleared wearable devices used in Neurology in the areas of epilepsy, headache, cardiac monitoring, and sleep.
14 Apr 2026
Read appraisal →Journal of prosthodontic research
Short- and long-term effects of mandibular advancement device therapy for obstructive sleep apnea on temporomandibular disorders: A systematic review and meta-analysis
PURPOSE: This systematic review and meta-analysis investigated the effects of mandibular advancement device (MAD) therapy on temporomandibular disorders (TMDs) in patients with obstructive sleep apnea (OSA). STUDY SELECTION: We included 14 clinical studies assessing the signs and symptoms of TMDs in patients with OSA treated with MAD therapy. The analysis was divided into short-term (<6 months) and long-term (≥1 year) follow-up phases. Odds ratios (OR) and 95% confidence intervals (CI) were calculated for each study during follow-up. The certainty of the evidence was assessed using the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) methodology, and meta-analysis was conducted using Review Manager version 5.4. RESULTS: Across the 14 studies included, the average follow-up period was 18.4 months, and the overall certainty of the evidence was rated as very low. Four studies used the Research Diagnostic Criteria for Temporomandibular Disorders (RDC/TMD), six used the Helkimo Anamnestic Index, and four employed questionnaires. In short-term follow-up, a significant increase in pain-related TMDs was observed, including pain in the temporomandibular joint (TMJ) or masticatory muscles (pain-related TMDs: OR:4.49; 95%CI: 1.46 to 13.81, TMJ or masticatory muscles pain: OR:2.90; 95%CI: 1.26 to 6.71). Conversely, during long-term follow-up, the odds of pain in the TMJ or masticatory muscles were significantly reduced (OR:0.21; 95%CI, 0.05 0.89)Conclusions: MAD therapy for OSA did not cause long-term adverse effects on the TMJ or masticatory muscles. However, its short-term use may result in pain-related TMDs, highlighting the need to monitor and manage these symptoms during this period.
13 Apr 2026
Read appraisal →ACS applied materials & interfaces
NeuroTex: Functionalized Hybrid Polymer-Metal Textile for Soft, Robust, and Dry Auricular Neural Interfaces
Wearable bioelectronics would benefit from dry, easy-to-apply electrodes for sensing physiological signals. Bioelectronic interfaces must maintain high conductivity, low electrode-skin impedance, and reliable coupling between the electrode and skin under sweat, motion, and prolonged use. Metallic textiles offer good conductivity and stable connections but are hydrophobic, prone to cracking and oxidation, and dependent on conductive gels to achieve the low impedance required for low-amplitude electrophysiological signals. Here, we introduce NeuroTex, a commercially available silver nanowire textile that is PEDOT:PSS-functionalized to improve the electrode-skin-electronics interface. The PEDOT:PSS coating reduced the area-normalized resistance of the textile by almost 50%, suppressed silver oxidation in phosphate-buffered saline, and lowered the contact impedance by more than 85%. Mechanical evaluations under torsion, stretching, bending, and low-force fatigue confirmed the preserved fiber integrity and intact electronic connectivity. Furthermore, the soft and hydrophilic properties of the coating enhanced the conformal skin interfacing. Integrated into an in-ear monitoring platform, NeuroTex enabled multimodal biosignal recordings of in-ear electroencephalography (ieEEG), electro-oculography (ieEOG), electromyography (ieEMG), and electrocardiography (ieECG). Reliable acquisition of the technically demanding ieEEG signal was achieved, demonstrating alpha-modulation responses comparable to those of gel-based electrodes. ieEEG recordings recorded over several hours captured distinct sleep patterns to further confirm stable performance during natural sleep. These results establish NeuroTex as a gel-free, mechanically resilient, and electrochemically stable interface suitable for wearable physiological monitoring.
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 →Physiological measurement
Continuous blood pressure monitoring via hemodynamic parameter and pulse transit time derived from capacitive sensing pads
Objective.Continuous blood pressure (BP) monitoring is crucial for detecting nocturnal hypertension and acute hemodynamic changes. Conventional cuff-based methods disrupt sleep and miss transient BP fluctuations from sleep-related events or instability. Cuffless methods, such as pulse transit time (PTT), offer potential but often struggle to reliably track acute BP fluctuations due to complex and nonlinear hemodynamics.Approach.We developed a capacitive sensing pad system incorporating PTT with additional hemodynamic features for unobtrusive, continuous BP monitoring in supine subjects. The pad contains ultra-sensitive single-electrode capacitive (SEC) sensors made from carbon nanotube composites. In passive contact, SEC sensor on the back of the chest captures intrathoracic blood volume changes through deep tissue permittivity and ballistocardiography, while another under the leg detects arterial pulse-induced tissue vibrations. PTT is derived from the temporal delay between chest and leg signals. A neural network model incorporates PTT and intrathoracic blood volume features to improve BP estimation.Main results.In human trials (N= 30) with arm-cuff reference, the system showed strong correlation (r⩾ 0.94), with a mean error (ME) ⩽ 0.1 mmHg and standard deviation (SD) ⩽ 5.6 mmHg. In a subset (N= 8) with continuous finger-cuff reference, it maintained strong correlation (r⩾ 0.89), with a ME ⩽ 0.2 mmHg and SD ⩽ 7.8 mmHg.Significance.These results suggest the possibility of bed-based unobtrusive and continuous BP monitoring in the supine position.
9 Apr 2026
Read appraisal →Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine
Subjective and objective measures of sleep-related function from the Cardiovascular Endpoints For Obstructive Sleep Apnea with Twelfth Cranial Nerve Stimulation (CARDIOSA-12) trial : Sleep-related functional outcomes in CARDIOSA-12.
PURPOSE: The effect of therapeutic versus partially therapeutic levels of hypoglossal nerve stimulation (HGNS) therapy on cognitive measures and patient-reported outcomes (PROs) was evaluated in patients with moderate-severe obstructive sleep apnea (OSA). METHODS: In a 10-week, double-blind, randomized crossover therapy trial, subjects optimized on HGNS therapy underwent 4 weeks each of active and control (partially therapeutic) HGNS therapy. Cognitive measures (psychomotor vigilance test [PVT], digit symbol substitution test [DSST]) and sleep-related PROs were assessed after each 4-week treatment period. RESULTS: Sixty subjects were randomized and completed the primary study protocol, including PROs. A subset of randomized subjects (n = 43) underwent cognitive testing. This subset was, on average, older (65.7 ± 10.2 years), overweight/obese (body mass index [BMI] 29.7 ± 4.6 kg/m2), and had severe OSA at baseline (apnea-hypopnea index [AHI] 34.2 ± 15.0 events/h). There were no differences in cognitive performance measures between active and control (partially therapeutic) HGNS conditions in all subjects; in the per-protocol analysis, however, improvement in DSST reaction time with active HGNS was demonstrated (mean [95% CI] change on active therapy = - 153.6 [- 285.3, - 22.0] ms; p = 0.025). Significant improvements in all PROs (Epworth Sleepiness Scale, Insomnia Severity Index, Functional Outcomes of Sleep Questionnaire, and Snoring Visual Analog Scale) were observed in the active HGNS condition. CONCLUSIONS: In this secondary analysis of the CARDIOSA-12 randomized crossover trial of subjects using HGNS, subjective PROs, but not objective cognitive measures, were improved with active HGNS compared to control (partially therapeutic) HGNS therapy. These findings warrant additional investigation examining the relationship between subjective and objective neurocognitive outcomes in OSA. The effect of hypoglossal nerve stimulation (HGNS), a novel and promising therapy for obstructive sleep apnea (OSA), on neurocognitive deficits remains underexplored. This study evaluated whether HGNS therapy improves cognitive performance using the psychomotor vigilance test (PVT) and digit symbol substitution test (DSST), along with patient-reported outcomes (PROs) related to sleepiness, snoring, insomnia, and sleep-related function. In the modified intention-to-treat analysis, no significant differences in PVT or DSST outcomes were observed between active and control (partially therapeutic) HGNS therapy; however, in a subset of participants with ≥ 50% reduction in AHI with active HGNS, improvement in DSST reaction time was noted. All PROs significantly improved with active HGNS, suggesting benefits in subjective measures without substantial changes in objective cognitive measures.
9 Apr 2026
Read appraisal →Physiological measurement
NeuroSleep: neuromorphic event-driven single-channel EEG sleep staging for edge-efficient sensing
Objective. Reliable, continuous neural sensing on wearable edge platforms is fundamental to long-term health monitoring; however, for electroencephalography (EEG)-based sleep monitoring, dense high-frequency processing is often computationally prohibitive under tight energy budgets.Approach. To address this bottleneck, this paper proposes NeuroSleep, an integrated event-driven sensing and inference system for energy-efficient sleep staging. NeuroSleep first converts raw EEG into complementary multi-scale bipolar event streams using Residual Adaptive Multi-Scale Delta Modulation, enabling an explicit fidelity-sparsity trade-off at the sensing front end. Furthermore, NeuroSleep adopts a hierarchical inference architecture that comprises an Event-based Adaptive Multi-scale Response module for local feature extraction, a Local Temporal-Attention Module for context aggregation, and an epoch-leaky integrate-and-fire module to capture long-term state persistence.Main results. Experimental results using subject-independent 5-fold cross-validation on the sleep-EDF expanded sleep-cassette subset (78 subjects, 153 overnight recordings) with single-channel EEG demonstrate that NeuroSleep achieves a mean accuracy of 74.2% with only 0.932 M parameters while reducing sparsity-adjusted effective operations by approximately 53.6% relative to dense processing. Compared to the representative dense Transformer baseline, NeuroSleep improves accuracy by 7.5% with a 45.8% reduction in computational load.Significance. By coupling neuromorphic event encoding with state-aware context modeling, NeuroSleep offers a deployment-oriented framework for single-channel sleep staging that reduces redundant high-rate processing and improves energy scalability for wearable and edge platforms.
8 Apr 2026
Read appraisal →Psychological medicine
Sleep diary-derived parameters and sleep measures mediate the efficacy of transdiagnostic sleep and circadian intervention for depression.
The present study aimed to explore sleep diary-derived parameters and sleep measures as mediators of the effects of the Transdiagnostic Intervention for Sleep and Circadian Dysfunction (TSC) on psychological outcomes. A secondary analysis of a two-arm randomized controlled trial of a group-based TSC for major depressive disorder was conducted. The participants included 152 adults (mean age = 34.0; 79.6% female) who were randomized into either the TSC or care-as-usual group. Mediation analysis indicated that reduction in insomnia symptom severity (standardized indirect effects: -0.06 to -0.17), sleep disturbance (-0.04 to -0.22), and sleep-related impairment (-0.04 to -0.17) was significantly mediated by sleep diary-derived sleep parameters. The treatment effects on depressive symptoms (standardized indirect effects: -0.05 to -0.10), anxiety symptoms (-0.04 to -0.07), fatigue (-0.05 to -0.09), functional impairment (-0.06 to -0.09), and quality of life (0.04 to 0.08) were sequentially mediated by sleep parameters and insomnia symptom severity. However, the severity of insomnia symptoms alone (magnitudes of standardized indirect effects: 0.09-0.17) but not sleep parameters alone (0.00-0.07) mediated the treatment effects on psychological outcomes, indicating that sleep parameters need to influence subjective sleep measures to sequentially affect psychological outcomes. These results underscore the critical roles of subjective sleep measures in clinical improvements within a sleep-targeted intervention.
8 Apr 2026
Read appraisal →The European respiratory journal
Impact of positive airway pressure therapy on pulmonary arterial pressures in obstructive sleep apnoea or obesity hypoventilation syndrome: a systematic review and meta-analysis
BACKGROUND: There is limited and conflicting evidence on the impact of obstructive sleep apnoea (OSA) and its treatment with positive airway pressure (PAP) therapy on pulmonary hypertension (PH). Our research question was: does treatment of OSA and obesity hypoventilation syndrome (OHS) with PAP therapy improve pulmonary arterial pressures? METHODS: A systematic review and random effects meta-analysis was performed (PROSPERO: CRD42024595312). PubMed, Embase, Scopus, Web of Science, EBSCO, Cochrane Central Register of Controlled Trials (CENTRAL) and ClinicalTrials.gov were searched using the PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) guidelines. Studies reporting pulmonary arterial pressure in patients with OSA and OHS pre- and post-PAP therapy were included. The primary outcome was post-PAP therapy change in pulmonary arterial pressures, with separate analyses on pulmonary arterial systolic pressure and mean pulmonary arterial pressure. Sensitivity analysis was performed to assess the impact of PAP therapy in patients with only OSA or OHS. RESULTS: 23 studies (20 observational and three randomised controlled trials) with 733 patients were included (19 with OSA (n=518) and four with OHS and concomitant OSA (n=215)). Most patients had severe OSA. PAP therapy was associated with a mean reduction in pulmonary arterial pressure of 5.96 (95% CI 3.20-8.73) mmHg; p<0.0001. In patients with baseline PH (n=233), the reduction was 11.41 (95% CI 8.66-14.15) mmHg; p<0.00001. The beneficial effect of PAP therapy was similar in OSA and OHS. There was no sex difference in the impact of PAP therapy. Heterogeneity was high across studies (I2>85%), reflecting differences in study design, patient populations and methodologies. CONCLUSIONS: PAP therapy reduces pulmonary arterial pressures in patients with mostly severe OSA or OHS, with a more pronounced effect in those with baseline PH. As such, our findings may not apply to patients with milder degrees of OSA. While reductions in pulmonary arterial pressures were observed in all groups, the low certainty necessitates cautious application of these findings in clinical practice.
5 Apr 2026
Read appraisal →Journal of medical Internet research
The Role of Digital Biomarkers in Physiological Signal-Based Depression Assessment: Systematic Review and Meta-Analysis
BACKGROUND: Digital biomarkers are increasingly being used to support depression assessment by providing objective, continuous, and real-time physiological and behavioral data. However, most existing studies have focused on individual biomarkers, such as sleep or cardiac parameters, while integrative evaluations that capture the multidimensional nature of depression remain limited. OBJECTIVE: This systematic review evaluated digital biomarkers for depression and synthesized evidence on differences between individuals with depression and controls. METHODS: Eligible studies included observational or interventional studies examining digital biomarkers for depression with validated outcome measures. We searched major international and Korean databases, including MEDLINE, PsycINFO, CINAHL, IEEE Xplore, Web of Science, Cochrane Library, KISS, RISS, KMbase, and KoreaMed, from inception to December 28, 2025. Risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool and the Scottish Intercollegiate Guidelines Network checklist. Meta-analyses were conducted using random-effects models with the Hartung-Knapp-Sidik-Jonkman method, and other outcomes were narratively summarized. RESULTS: The search yielded 39,617 records, of which 132 studies involving 57,852 participants met the inclusion criteria. These studies encompassed various digital biomarkers, including sleep, physical activity, cardiac measures, smartphone-derived data, speech, GPS data, and circadian rhythms. A meta-analysis of 22 studies (6947 participants) revealed that individuals with depression had significantly longer sleep onset latency (5 studies; n=292; +4.75 min, 95% CI 2.46-7.04; P=.005; 95% prediction interval [PI] 0.01-10.27) and time in bed (3 studies; n=236; +31.81 min, 95% CI 18.22-45.39; P=.01; 95% PI 2.28-55.16). Physical activity counts were also significantly lower (5 studies; n=462; standardized mean difference -0.71, 95% CI -1.33 to -0.09; P=.03; 95% PI -2.18 to 0.71). Although individuals with depression showed a lower sleep efficiency, higher mean heart rate, and lower SD of normal-to-normal intervals, these differences were not statistically significant. Other digital markers yielded inconsistent results. Overall, these findings indicate that no single digital biomarker sufficiently captures depression-related changes. Instead, the results support the superiority of personalized, multimodal approaches. However, the generalizability of these findings is limited by the lack of standardized data collection protocols and high clinical heterogeneity across studies, as reflected in wide PIs. CONCLUSIONS: Certain digital biomarkers, particularly sleep onset latency and physical activity counts, showed consistent average differences between the depression and control groups. However, wide PIs indicate substantial variability across settings, suggesting that no single marker is sufficient for reliable detection. This study advances the field by providing a comprehensive meta-analysis of multidimensional digital biomarkers, establishing a quantitative foundation for objective depression screening and monitoring. These findings support the use of personalized, multimodal digital phenotyping approaches and highlight the need for standardized, clinically interpretable frameworks for real-world depression monitoring.
4 Apr 2026
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