Efficacy of a Waist-Mounted Sensor in Predicting Prospective Falls Among Older People Residing in Community Dwellings: A Prospective Cohort Study

Falls pose a significant health risk for older people, necessitating accurate predictive tools for fall prevention. This study evaluated the sensitivity of a wearable waist-belt sensor, the Booguu Aspire system, in predicting prospective fall incidents among 37 community-dwelling older people in Hong Kong. A prospective cohort design was employed, involving two analytical groups: the overall cohort and a subset with cognitive performance data available, measured using the Montreal Cognitive Assessment (MoCA). Participants were categorized into moderate- or high-risk groups for falls using the sensor and further assessed with physical function tests, including the Single Leg Stand Test (SLST), 6 Meter Walk Test (6MWT), and Five Times Sit to Stand Test (5STS). Fall incidents were monitored for 12 months through quarterly follow-up phone calls. Statistical analyses showed no significant differences in physical performance between high- and moderate-risk groups and no significant correlations between sensor-based fall risk ratings and physical function test outcomes. The SLST, 6MWT, 5STS, and MoCA tests classified sensor-determined fall risk ratings with accuracies of 51.4%, 64.9%, 59.5%, and 50%. The sensor showed low sensitivity, with 13.51% true positives for fallers and a 20% sensitivity for high-risk individuals. ROC analysis yielded an Area Under the Curve of 0.688. Our findings indicate that the wearable waist-belt Sensor System may not be a sensitive tool in predicting prospective fall incidents. The algorithm for fall risk classification in the wearable sensor merits further exploration.