@Article{info:doi/10.2196/12153,
author="Yang, Yang
and Hirdes, John P
and Dubin, Joel A
and Lee, Joon",
title="Fall Risk Classification in Community-Dwelling Older Adults Using a Smart Wrist-Worn Device and the Resident Assessment Instrument-Home Care: Prospective Observational Study",
journal="JMIR Aging",
year="2019",
month="Jun",
day="07",
volume="2",
number="1",
pages="e12153",
keywords="falls; elderly; wearable devices; machine learning; interRAI",
abstract="Background:  Little 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). Objective:  This 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. Methods:  A prospective, observational study was conducted among 3 faller groups (G0, G1, G2+) based on the number of previous falls (0, 1, ≥2 falls) in a sample of older community-dwelling adults. Each participant was requested to wear a smart wristband for 7 consecutive days while carrying out day-to-day activities in their normal lives. The wearable and RAI-HC assessment data were analyzed and utilized to create fall risk classification models, with 3 supervised machine learning algorithms: logistic regression, decision tree, and random forest (RF). Results:  Of 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 {\{}G0+G1{\}} and G2+, RF achieved the best area under the receiver operating characteristic curve of 0.894 (overall accuracy of 89.2{\%}) based on wearable and RAI-HC data. Discrimination between G0 and {\{}G1+G2+{\}} did not result in better classification performance than that between {\{}G0+G1{\}} and G2+. Conclusions:  Both 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. ",
issn="2561-7605",
doi="10.2196/12153",
url="http://aging.jmir.org/2019/1/e12153/",
url="https://doi.org/10.2196/12153",
url="http://www.ncbi.nlm.nih.gov/pubmed/31518278"
}