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PubMed2026

Use of wearable technologies for physical activity promotion in older adults: A systematic review.

This systematic review, conducted according to PRISMA guidelines and registered in PROSPERO (CRD420251055299), examined the use of wearable technologies for promoting physical activity (PA) in adults aged 60 years and older. Searches across five databases (PubMed, Scopus, Web of Science, CINAHL, Cochrane) identified 2438 records, of which only six randomized controlled trials published between 2021 and 2025 met inclusion criteria, with sample sizes ranging from 36 to 551 participants and mean ages between 65 and 79 years. Given the small number of included studies, findings should be interpreted as preliminary. The studies ranged from the standalone use of commercial trackers (Fitbit, Polar, ActiGraph) to multicomponent interventions combining wearables with physiotherapist feedback, telephone counseling, web-based platforms, or interactive cognitive-motor training. Wearables used alone, as in the REACT trial, produced small or non-significant PA effects. In contrast, interventions integrating devices with personalized feedback, professional support, or digital platforms, such as PROMOTE and TASMANIA, were associated with more consistent improvements in PA, physical function, and cognitive outcomes. Multicomponent programs, such as PEER and ICMT, reported broader benefits, including cognition, balance, and reductions in sedentary behavior, though these findings derive from individual trials and require replication. Risk of bias, assessed with the Cochrane Risk of Bias tool version 2 (RoB 2.0), was rated as "some concerns" for five studies and low for only one, mainly due to gaps in randomization reporting, missing data, and lack of preregistration. Tentatively, and based on a very limited evidence base, wearables may have greater impact when embedded within broader behavioral systems, incorporating feedback, coaching, or interactive components, rather than when used in isolation as passive monitoring tools. Adherence and psychosocial outcomes appeared related to comfort and perceived usefulness among older adults, though larger and more robust trials are needed to confirm these patterns.

Humans↗

PubMed2026

Development and Validation of Machine Learning Models for Predicting Early Cognitive Decline Using Home Sensor-Derived Behavioral Data: Sensors in-Home for Elder Wellbeing (SINEW) Cohort Study.

BACKGROUND: As the global population continues to age, the prevalence of geriatric conditions, including dementia and frailty, is also increasing. Early identification of individuals at an elevated risk of these conditions, such as those presenting with mild cognitive impairment (MCI) or prefrailty, can provide a critical window for prompt intervention aimed at preventing or reversing disease progression. To promote such early identification, there is a burgeoning interest in the use of digital sensor technology and predictive modeling. OBJECTIVE: This study aimed to use a continuous, home-based monitoring sensor system for older adults to distinguish those exhibiting normal aging from those with MCI, early dementia, prefrailty, or frailty, and to predict their transition from normal aging to one of these conditions. METHODS: This longitudinal cohort study will recruit 200 community-dwelling adults aged ≥65 years with normal cognition or MCI at baseline. A multi-sensor system will be installed in participants' homes, including passive infrared motion sensors, door contact sensors, bed sensors, medication box sensors, wearable activity bands, and Bluetooth proximity beacons. These devices will continuously capture spatiotemporal activity patterns, mobility indicators, sleep behaviors, and medication-taking routines. Annual assessments will include standardized cognitive tests (eg, Montreal Cognitive Assessment, Mini-Mental State Examination, Rey Auditory-Verbal Learning Test, digit span, Color Trails Test, semantic fluency, Stroop), frailty measures (modified Fried phenotype, gait speed, grip strength), mental health scales, sleep quality, and psychosocial indicators. Sensor-derived features-such as gait variability, activity regularity, sleep fragmentation, and medication adherence patterns-will be integrated with clinical data to develop supervised machine learning models. Planned approaches include logistic regression, random forests, gradient boosting, and deep learning. Model performance will be evaluated using cross-validation and independent test sets. Primary metrics will include area under the receiver operating characteristic curve, sensitivity, specificity, precision, recall, and F1-score. Models will be benchmarked against gold-standard clinical diagnoses and validated using temporal subsets of the dataset. RESULTS: Enrollment for this study started in November 2019 and will continue until March 2030. As of June 2025, we have enrolled 138 participants. Full data analysis has yet to begin. CONCLUSIONS: We aim to develop a reliable and effective sensor system for in-home use that will facilitate the early detection of cognitive and physical decline. In so doing, it will add to our current understanding of digital biomarkers. It is common for older adults to seek clinical intervention only when their cognitive impairment has already reached an advanced stage. The implementation of readily deployable sensor systems within community settings presents us with opportunities for prompt intervention, which holds the potential for delaying or reversing disease progression and allowing for a greater number of functional and meaningful years.

Humans↗

PubMed2026

Smart crutch tipsTM real-time feedback improves adherence to partial weight-bearing protocols following lower extremity fracture surgery: a pilot study.

PURPOSE: To evaluate the effectiveness of Smart Crutch Tips™ in promoting adherence to prescribed early partial weight-bearing (PWB) protocols and to assess patient experience with real-time weight-bearing feedback during home rehabilitation. METHODS: Twenty patients with lower extremity fractures prescribed partial weight-bearing (PWB) were randomized into two groups utilizing real-time feedback crutch tips (RFC). The Intervention group (n = 10) used Smart Crutch Tips™ (ComeBack Mobility Inc., Kiev, Ukraine), which provided real-time feedback on weight-bearing compliance. The Control group (n = 10) also used crutches equipped with Smart Crutch Tips™, but notifications were disabled, allowing passive data collection without patient feedback. Weight-bearing compliance was defined as the percentage of steps within ± 10% of the prescribed target. Secondary outcomes included patient satisfaction and device usability assessed using the System Usability Scale (SUS) throughout the home rehabilitation period. RESULTS: The Intervention group demonstrated significantly greater PWB compliance compared with the Control group (73.6% vs. 21.1%, p < 0.01). The Intervention group reported a mean SUS score of 84.25, indicating excellent usability. No device-related complications or adverse events were observed. Patient satisfaction, assessed through video interviews, was high, with participants in the Intervention group expressing a strong willingness to recommend the device to others with similar injuries and rating it 10 out of 10. CONCLUSION: Smart Crutch Tips™ significantly improved adherence to prescribed partial weight-bearing protocols following lower extremity fracture surgery. The RFC device demonstrated high usability, excellent patient satisfaction, and no device-related complications during home rehabilitation throughout the study period.

Humans↗

PubMed2026

Polygenic score for sleep duration in relation to the risk of Alzheimer's disease: results from the UK biobank.

Studies have suggested that sleep duration may be associated with Alzheimer's disease risk; however, findings based on self-reported sleep duration are likely to be influenced by reverse causation and residual confounding bias. We derived weights for genetic variants associated with wearable-derived sleep duration using the LDpred2-auto method in 77,770 white British participants from the UK Biobank, following the generation of new genome-wide association summary statistics. We then used these weights to generate polygenic scores (PGSs) for the remaining 264,746 white British participants for the association analysis, independent of the sample used to develop PGS weights. We assessed the association of fifths between genetically predicted sleep duration and the risk of Alzheimer's disease (1,451 cases/264,746 individuals over a median 12.5 years of follow-up). The PGS explained approximately 2% of the variation in device-measured sleep duration. Compared with individuals in the middle fifth of PGSs, those in the highest fifth (indicating approximately 15 min/day longer sleep) had a lower risk of Alzheimer's disease (hazard ratio (HR) = 0.79[95%CI, 0.67-0.94]). Our results indicate that genetic predisposition to relatively long sleep duration is associated with a lower Alzheimer's disease risk.

Alzheimer’s disease↗

PubMed2026

Reliability, Device Agreement and Validity of Load-Velocity Profiles: A Systematic Review with Meta-analysis.

BACKGROUND: For a valid one-repetition maximum (1RM) prediction via load-velocity (LV) relationships, high reliability and accuracy must be assumed. OBJECTIVE: Since individual study results indicate ambivalent prediction, this systematic review and meta-analysis was designed to provide a updated and comprehensive overview, extending knowledge about the validity and reliability of commercially available velocity sensors in Part I and the validity and reliability of velocity-based 1RM prediction models in Part II. METHODS: A systematic literature search was conducted in PubMed/MEDLINE, Web of Science, and Scopus. Validity and/or reliability studies or velocity-based 1RM prediction evaluations were included. Methodological quality was assessed using adapted COSMIN. The analysis was performed for intraclass correlation coefficient (ICC), Lin's concordance correlation coefficient (CCC), and Pearson's correlation coefficient (r). The review was preregistered in PROSPERO (CRD42025634595). RESULTS: Sixty-three studies were included for sensor validity and reliability and 38 for 1RM prediction models. Part I: Velocity sensors demonstrated good-to-excellent pooled validity and device agreement (ICC = 0.91-0.92 [0.83-0.97]; k = 55 and 439, respectively); intra- and inter-day reliability were classified as good to excellent with ICC = 0.90-0.91 [0.85-0.95] (k = 228 and 608, respectively), with sensor technology moderating the results. However, substantial heterogeneity and wide ranges of study-level estimates indicated considerable variability across moderators, linear position transducer (LPT) generally showing more consistent performance than inertial measurement units (IMU). Part II: Velocity-based 1RM prediction showed ICCs = 0.90 [0.83-0.94] (k = 124) and ICC = 0.91 [0.72-0.98] (k = 9); for reliability and validity, respectively. DISCUSSION: Commercial velocity sensors generally provide high relative validity and reliability. Results varied depending on exercise complexity, intensity, sensor technology, and modeling approach. While velocity-based 1RM prediction demonstrated high average validity, large heterogeneity in lower body exercises significantly biased the results. Furthermore, the dearth of measurement error and agreement analyses prohibits final conclusions. CONCLUSION: Therefore, velocity-based monitoring and 1RM prediction require cautious interpretation, as sensor- and exercise-specific evidence remains limited.

Load–velocity relationship↗