Evaluation of unsupervised 30-second chair stand test performance assessed by wearable sensors to predict fall status in multiple sclerosis |
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| Affiliation: | 1. M-Sense Research Group, Department of Electrical and Biomedical Engineering, University of Vermont, Burlington, VT, United States;2. Department of Neurological Sciences, University of Vermont, Burlington, VT, United States;1. Movement Analysis Laboratory, IRCCS Istituto Ortopedico Rizzoli, Bologna, Italy;2. Department of Mechanical Engineering/Centre for Therapeutic Innovation, University of Bath, Bath, UK;3. II Clinical Department, IRCCS Istituto Ortopedico Rizzoli, Bologna, Italy;4. Nursing, Technical and Rehabilitation Assistance Service, IRCCS Istituto Ortopedico Rizzoli, Bologna, Italy;1. Department of Physical Medicine & Rehabilitation, Mayo Clinic, Rochester, MN, USA;2. Program in Physical Therapy, Mayo Clinic School of Health Sciences, Mayo Clinic College of Medicine and Science, Rochester, MN, USA;1. Department of Rehabilitation and Movement Science, University of Vermont, Burlington, USA;2. Department of Electrical and Biomedical Engineering, University of Vermont, Burlington, VT, USA;1. Arizona State University, College of Health Solutions, USA;2. University of Kansas Medical Center, Department of Physical Therapy and Rehabilitation Science, USA;3. Phoenix VA Veterans Affairs Medical Center, USA |
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| Abstract: | BackgroundOne 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 questionThe 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.MethodsThirty-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.ResultsNon-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.SignificanceBrief 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. |
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| Keywords: | Wearable Accelerometer Chair stand test Multiple sclerosis Falls |
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