@article{ref1,
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",
author="Yang, Yang and Hirdes, John P. and Dubin, Joel A. and Lee, Joon",
volume="2",
number="1",
pages="e12153-e12153",
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). <br><br>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. <br><br>METHODS:  A prospective, observational study was conducted among 3 faller groups (G<sub>0</sub>, G<sub>1</sub>, G<sub>2+</sub>) 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). <br><br>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 {G<sub>0</sub>+G<sub>1</sub>} and G<sub>2+</sub>, 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 G<sub>0</sub> and {G<sub>1</sub>+G<sub>2+</sub>} did not result in better classification performance than that between {G<sub>0</sub>+G<sub>1</sub>} and G<sub>2+</sub>. <br><br>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.<br><br>©Yang Yang, John P Hirdes, Joel A Dubin, Joon Lee. Originally published in JMIR Aging (http://aging.jmir.org), 07.06.2019.<p /> <p>Language: en</p>",
language="en",
issn="2561-7605",
doi="10.2196/12153",
url="http://dx.doi.org/10.2196/12153"
}