@article{ref1,
title="Evaluation of unsupervised 30-second chair stand test performance assessed by wearable sensors to predict fall status in multiple sclerosis",
journal="Gait and posture",
year="2022",
author="Tulipani, Lindsey J. and Meyer, Brett and Allen, Dakota and Solomon, Andrew J. and McGinnis, Ryan S.",
volume="94",
number="",
pages="19-25",
abstract="BACKGROUND: One in two people with multiple sclerosis (PwMS) will fall in a three-month period. Predicting which patients will fall remains a challenge for clinicians. Standardized functional assessments provide insight into balance deficits and fall risk but their use has been limited to supervised visits. RESEARCH QUESTION: The study aim was to characterize unsupervised 30-second chair stand test (30CST) performance using accelerometer-derived metrics and assess its ability to classify fall status in PwMS compared to supervised 30CST. <br><br>METHODS: Thirty-seven PwMS (21 fallers) performed instrumented supervised and unsupervised 30CSTs with a single wearable sensor on the thigh. In unsupervised conditions, participants performed bi-hourly 30CSTs and rated their balance confidence and fatigue over 48-hours. ROC analysis was used to classify fall status for 30CST performance. <br><br>RESULTS: Non-fallers (p = 0.02) but not fallers (p = 0.23) differed in their average unsupervised 30CST performance (repetitions) compared to their supervised performance. The unsupervised maximum number of 30CST repetitions performed optimized ROC classification AUC (0.79), accuracy (78.4%) and specificity (90.0%) for fall status with an optimal cutoff of 17 repetitions. SIGNIFICANCE: Brief durations of instrumented unsupervised monitoring as an adjunct to routine clinical assessments could improve the ability for predicting fall risk and fluctuations in functional mobility in PwMS.<p /> <p>Language: en</p>",
language="en",
issn="0966-6362",
doi="10.1016/j.gaitpost.2022.02.016",
url="http://dx.doi.org/10.1016/j.gaitpost.2022.02.016"
}