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 Hon...

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Main Authors: Ka-Ming Lai, Kenneth N. K. Fong
Format: Article
Language:English
Published: MDPI AG 2025-04-01
Series:Sensors
Subjects:
Online Access:https://www.mdpi.com/1424-8220/25/8/2516
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author Ka-Ming Lai
Kenneth N. K. Fong
author_facet Ka-Ming Lai
Kenneth N. K. Fong
author_sort Ka-Ming Lai
collection DOAJ
description 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.
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spelling doaj-art-f8071e9c12a04c6cb7cfa46b878b32c22025-08-20T02:18:10ZengMDPI AGSensors1424-82202025-04-01258251610.3390/s25082516Efficacy of a Waist-Mounted Sensor in Predicting Prospective Falls Among Older People Residing in Community Dwellings: A Prospective Cohort StudyKa-Ming Lai0Kenneth N. K. Fong1Hong Kong Red Cross, Hong Kong SAR, ChinaDepartment of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong SAR, ChinaFalls 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.https://www.mdpi.com/1424-8220/25/8/2516wearable sensorsfall risk predictionolder peoplegerontechnologyfall preventiondigital health solutions
spellingShingle Ka-Ming Lai
Kenneth N. K. Fong
Efficacy of a Waist-Mounted Sensor in Predicting Prospective Falls Among Older People Residing in Community Dwellings: A Prospective Cohort Study
Sensors
wearable sensors
fall risk prediction
older people
gerontechnology
fall prevention
digital health solutions
title Efficacy of a Waist-Mounted Sensor in Predicting Prospective Falls Among Older People Residing in Community Dwellings: A Prospective Cohort Study
title_full Efficacy of a Waist-Mounted Sensor in Predicting Prospective Falls Among Older People Residing in Community Dwellings: A Prospective Cohort Study
title_fullStr Efficacy of a Waist-Mounted Sensor in Predicting Prospective Falls Among Older People Residing in Community Dwellings: A Prospective Cohort Study
title_full_unstemmed Efficacy of a Waist-Mounted Sensor in Predicting Prospective Falls Among Older People Residing in Community Dwellings: A Prospective Cohort Study
title_short Efficacy of a Waist-Mounted Sensor in Predicting Prospective Falls Among Older People Residing in Community Dwellings: A Prospective Cohort Study
title_sort efficacy of a waist mounted sensor in predicting prospective falls among older people residing in community dwellings a prospective cohort study
topic wearable sensors
fall risk prediction
older people
gerontechnology
fall prevention
digital health solutions
url https://www.mdpi.com/1424-8220/25/8/2516
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AT kennethnkfong efficacyofawaistmountedsensorinpredictingprospectivefallsamongolderpeopleresidingincommunitydwellingsaprospectivecohortstudy