Search PubMedSearch

PubMed · 42442508

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

Abstract

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.

Explore related subjects

Keep this discovery

BibTeXRIS

José Félix, Adriana Fernandes Vale, Catarina Sá, Rubim Santos, Elina Kontio, Ana Rita Pinheiro, Andreia S P Sousa. 2026-07-13. Use of wearable technologies for physical activity promotion in older adults: A systematic review.. https://doi.org/10.1016/j.arr.2026.103258

Cite the original work for its findings. Save a collection to share your selection of sources.

Discover connections

Connections use source metadata and explicit phrase matches, not verified experimental comparisons.

KEEP EXPLORING

Related citations

The application of artificial intelligence in healthcare practice: A mapping review of systematic reviews.

Artificial intelligence (AI) is rapidly transforming healthcare practice, with growing evidence supporting its use in diagnosis, prognosis, treatment planning, and operational decision-making. The proliferation of systematic reviews in recent years underscores the need for an updated synthesis of the literature to inform research, policy, and practice. We searched PubMed, Web of Science, Scopus, IEEE Xplore, and CINAHL for systematic reviews and meta-analyses published between 2019 and February 2026. Eligible reviews focused on AI applications in healthcare practice, were peer-reviewed, and written in English. A total of 368 reviews met the inclusion criteria. Publication volume increased steadily, peaking in 2025. AI research was concentrated in high-density domains, such as radiology, oncology, and critical care. Across reviews, diagnostic imaging, electronic health record (EHR) data, and biomarkers/laboratory results accounted for 68% of training data sources, though newer data types, such as wearable device and sensor data, emerged from 2022 onward. Diagnosis, prognosis, and treatment comprised over 80% of AI applications, with novel uses emerging in recent years, such as AI-assisted clinical documentation (e.g., ambient documentation tools) and patient education. Ethical concerns were reported in 78.5% of reviews, with privacy, model accuracy, data and algorithmic bias, and explainability as recurrent themes. The proportion of reviews reporting ethical concerns increased from 2021 to 2025. AI applications in healthcare are expanding in scope, diversifying in data sources, and evolving toward novel clinical and operational uses. The human-centered AI or augmented intelligence paradigm, integrating computational precision with clinical expertise, holds significant promise but will require parallel advances in governance, regulatory frameworks, and ethical oversight to ensure safe adoption.

Artificial Intelligence

The Effect of Slow Deep Breathing Relaxation Exercise on Pain and Anxiety Levels During and Post-Chest Tube Removal After CABG.

Chest tube removal after coronary artery bypass graft is frequently reported by patients as stressful and painful, highlighting the need for effective nonpharmacological interventions. Slow deep breathing relaxation exercises (SDBREs) may serve as a simple nursing strategy to reduce discomfort. In this study, we aimed to evaluate the effect of SDBRE on pain and anxiety during and after chest tube removal following coronary artery bypass grafting in Nablus hospitals. An experimental design was used with 80 patients recruited from 2 hospitals. Participants were randomly assigned to either an intervention group (n = 40) that practiced SDBRE or a control group (n = 40) that received standard care. Data were collected through a self-administered questionnaire, the Numeric Pain Scale, and the Visual Anxiety Scale. Data collection occurred from March to October 2024. The intervention group reported significantly lower pain scores during removal (M: 5.325 vs 7.125, P < .001) and after removal (P < .001). Anxiety scores were significantly lower both during and after removal (P < .001). Pain correlated with duration, with more complex operations and prolonged chest tube insertion linked to higher scores. SDBRE significantly reduced pain and anxiety during and after chest tube removal, supporting its integration into routine postoperative nursing care.

Humans

Predicting ACL injury risk in athletes: A systematic review of machine learning-based models.

BACKGROUND: Early ACL injury risk identification in athletes is essential. This systematic review examines machine learning (ML) models for predicting ACL injuries, evaluating their methodological quality, performance, and reliability. METHOD: A comprehensive electronic search was conducted across PubMed, Scopus, Web of Science, and IEEE Xplore databases, supplemented by Google Scholar for grey literature, covering articles published between January 1, 2015, and August 30, 2025. Eligible studies were appraised using the Prediction Model Study Risk of Bias Assessment Tool (PROBAST) for methodological quality and risk of bias, and the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) guidelines for quality of evidence. RESULTS: Ten studies were included. PROBAST showed eight studies had moderate risk of bias and two low risk. TRIPOD found only two studies met quality criteria. ML models included logistic regression (n&#xa0;=&#xa0;5), support vector machines (n&#xa0;=&#xa0;4), k-nearest neighbor (n&#xa0;=&#xa0;3), decision trees (n&#xa0;=&#xa0;3), random forests (n&#xa0;=&#xa0;5), neural networks (n&#xa0;=&#xa0;2), linear discriminant analysis (n&#xa0;=&#xa0;1), and pre-trained CNNs (n&#xa0;=&#xa0;1). AUC ranged from 0.63 to 0.98. Accuracy (reported in six studies) ranged from 26% to 95%; however, these values should be interpreted with caution due to the absence of confidence intervals, lack of class imbalance handling, and limited external validation across studies. Tree-based ensemble methods such as random forest achieved competitive accuracy (74-86%), while SVM, a non-ensemble classifier, reported accuracy ranging from 71% to 95%; however, the highest values were obtained in studies with notably small sample sizes (n&#xa0;=&#xa0;12 to n&#xa0;=&#xa0;39), raising concerns about overfitting and generalizability. CONCLUSION: Current ML algorithms show promise for identifying athletes at high ACL injury risk and detecting relevant risk factors. Although study quality was generally satisfactory, future research should prioritize external validation and model interpretability to support clinical translation.

Humans