Evaluation of unsupervised 30-second chair stand test performance assessed by wearable sensors to predict fall status in multiple sclerosis

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. Methods: Thirty-seven PwMS (21fallers) 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. 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.

Automated summary

This study characterized the performance of unsupervised 30-second chair stand test (30CST) using accelerometer-derived metrics and assessed its ability to classify fall status in people with multiple sclerosis (PwMS) compared to supervised 30CST. The results showed that non-fallers had different unsupervised 30CST performance compared to supervised 30CST, and the maximum number of 30CST repetitions in unsupervised conditions had a good ability to classify fall status.