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Near-falls, defined as events in which individuals momentarily lose their balance but avoid falling, are strong predictors of subsequent falls. Wearable technologies have the potential to accurately detect near-falls in both laboratory and real-world settings, providing opportunities for early intervention in geriatric nursing practice.
This study has a two-fold aim: (1) to appraise and synthesize current evidence on wearable sensor technologies for near-fall detection, and (2) to discuss their potential applications for monitoring near-fall risk and implementing prevention strategies in older adults.
This is a systematic review. Articles were searched in five electronic databases (PubMed/MEDLINE, Embase, CINAHL, Web of Science, and IEEE Xplore) that explored wearable sensors for near-fall detection. The review adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.
A total of 18 studies, mostly experimental or observational, were included. Inertial Measurement Units (IMUs) were the most commonly used wearable technology, and the most frequently captured biomarker was linear acceleration. Lower-body placements (feet, ankles, and lower back) demonstrated superior performance in detecting near-falls. Single-sensor systems achieved sensitivities of 80%–98%, whereas multi-sensor configurations achieved 100% sensitivity, 99% specificity, and 100% accuracy.
Integrating wearable technologies for near-fall detection into geriatric nursing practice may enhance early identification of older adults at high risk for falls and enable timely, personalized interventions. Future research should validate these technologies in real-world settings and assess their acceptability among nurses, caregivers, and older adults.
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