Accuracy of wearable devices in predicting falls in older adults: a systematic review and meta-analysis

TL;DR

Wearable devices can predict falls in older adults with high specificity and moderate sensitivity, making them useful for early screening and risk assessment.

Contribution

This study provides the first meta-analysis on the accuracy of wearable devices in predicting falls among older adults.

Findings

Wearable devices showed a pooled sensitivity of 0.55 and specificity of 0.89 in predicting falls.

Machine learning models improved predictive accuracy, achieving an AUC of 0.90.

Factors like age structure and sensor placement affect the performance of wearable devices.

Abstract

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.

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.

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.

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.

https://www.crd.york.ac.uk/PROSPERO/view/CRD420251274570.

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