SynthesisFrontiers in public health2026
Accuracy of wearable devices in predicting falls in older adults: a systematic review and meta-analysis.
Synthesis in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Graph Topology-Guided Multi-Task Learning for Fall Detection.Sensors (Basel, Switzerland) · 2026Article
- Validation of an Instrumental Device to Estimate the Risk of Falls and Frailty in Older People.Sensors (Basel, Switzerland) · 2026Observational
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Wearable devices enable the continuous collection of kinematic information, such as gait and postural control, in real-life environments, offering potential for the early identification and stratified management of fall risk in older adults. However, quantitative integrated evidence regarding their overall accuracy in predicting future falls is lacking. This systematic review and meta-analysis aims to evaluate the accuracy of wearable devices in predicting falls among older adults and to explore the potential influence of key study characteristics on predictive performance. Methods: A systematic search was conducted in PubMed, Embase, Web of Science, and the Cochrane Library from database inception to October 9, 2025. Using a bivariate random-effects model, we pooled sensitivity and specificity, calculated likelihood ratios, and fitted a summary receiver operating characteristic (SROC) curve to determine the area under the curve (AUC). Subgroup analysis and meta-regression explored potential sources of heterogeneity. The risk of bias was assessed with the PROBAST tool. Results: A total of 20 studies were included. The pooled sensitivity was 0.55 (95% CI: 0.42-0.67), specificity was 0.89 (95% CI: 0.84-0.93), positive likelihood ratio was 5.2, negative likelihood ratio was 0.50, and diagnostic odds ratio was 10.39. The area under the summary receiver operating characteristic (SROC) curve was 0.85 (95% CI: 0.81-0.88). Subgroup and regression analyses indicated that studies employing machine learning modeling demonstrated superior overall discriminative ability (AUC = 0.90). Predictive performance may be influenced by factors such as population age structure, sample size, and sensor placement location. Conclusion: Wearable devices exhibit good discriminative ability for predicting future falls in older adults, characterized overall by high specificity and moderate sensitivity. They are more suitable as tools for early screening and risk stratification in community and institutional settings, thereby supporting decision-making regarding intervention priorities. Systematic review registration: https://www.crd.york.ac.uk/PROSPERO/view/CRD420251274570.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.