ArticleJMIR aging2019
Fall Risk Classification in Community-Dwelling Older Adults Using a Smart Wrist-Worn Device and the Resident Assessment Instrument-Home Care: Prospective Observational Study.
Article in JMIR aging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Performance and Characteristics of Wearable Sensor Systems Discriminating and Classifying Older Adults According to Fall Risk: A Systematic Review.Sensors (Basel, Switzerland) · 2021Pooled it
- Digital Phenotyping in Health Research: Scoping Review of Methods, Gaps, and Opportunities.Journal of medical Internet research · 2026Article
- Sleep Quality and Falls Risk in the ELSA Study: A Cross-Sectional and Prospective Cohort Study.Brain and behavior · 2026Article
- Leveraging Digital Health Technologies to Assess Older Adults' Frailty and Nutritional Status: Two Cross-Sectional Studies.JMIR aging · 2026Observational
- A Scoping Review of Interventions Using an interRAI Information System to Guide Care Management and Assess Intervention Efficacy in Older Adults.Healthcare management forum · 2026Article
- Article
- Wearable Technologies for Health Promotion and Disease Prevention in Older Adults: Systematic Scoping Review and Evidence Map.Journal of medical Internet research · 2025Article
- Clinical Sensor-Based Fall Risk Assessment at an Orthopedic Clinic: A Case Study of the Staff's Views on Utility and Effectiveness.Sensors (Basel, Switzerland) · 2023Review
- Fall classification, incidence and circumstances in patients undergoing total knee replacement.Scientific reports · 2022Article
- Article
- Identifying falls remotely in people with multiple sclerosis.Journal of neurology · 2022Article
- The Impact of Wearable Technologies in Health Research: Scoping Review.JMIR mHealth and uHealth · 2022Article
- Application Scenarios for Artificial Intelligence in Nursing Care: Rapid Review.Journal of medical Internet research · 2021Review
- Prediction of fall risk among community-dwelling older adults using a wearable system.Scientific reports · 2021Article
- Analysis of Older Adults in Spanish Care Facilities, Risk of Falling and Daily Activity Using Xiaomi Mi Band 2.Sensors (Basel, Switzerland) · 2021Article
- Wearables and Deep Learning Classify Fall Risk From Gait in Multiple Sclerosis.IEEE journal of biomedical and health informatics · 2021Article
- Machine Learning and the Future of Cardiovascular Care: JACC State-of-the-Art Review.Journal of the American College of Cardiology · 2021Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundLittle is known about whether off-the-shelf wearable sensor data can contribute to fall risk classification or complement clinical assessment tools such as the Resident Assessment Instrument-Home Care (RAI-HC).
objectiveThis study aimed to (1) investigate the similarities and differences in physical activity (PA), heart rate, and night sleep in a sample of community-dwelling older adults with varying fall histories using a smart wrist-worn device and (2) create and evaluate fall risk classification models based on (i) wearable data, (ii) the RAI-HC, and (iii) the combination of wearable and RAI-HC data.
methodsA prospective, observational study was conducted among 3 faller groups (G
resultsOf 40 participants aged 65 to 93 years, 16 (40%) had no previous falls, whereas 8 (20%) and 16 (40%) had experienced 1 and multiple (≥2) falls, respectively. Level of PA as measured by average daily steps was significantly different between groups (P=.04). In the 3 faller group classification, RF achieved the best accuracy of 83.8% using both wearable and RAI-HC data, which is 13.5% higher than that of using the RAI-HC data only and 18.9% higher than that of using wearable data exclusively. In discriminating between {G
conclusionsBoth wearable data and the RAI-HC assessment can contribute to fall risk classification. All the classification models revealed that RAI-HC outperforms wearable data, and the best performance was achieved with the combination of 2 datasets. Future studies in fall risk assessment should consider using wearable technologies to supplement resident assessment instruments.
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