Evaluation of unsupervised 30-second chair stand test performance assessed by wearable sensors to predict fall status in multiple sclerosis
Open accessAbstract
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 (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. 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.
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The instrumented 30-s chair stand test was shown to predict risk of falls in people with multiple sclerosis.
“The instrumented 30-s chair stand test was shown to predict risk of falls in pwMS (25).”
From The instrumented single leg stance test detects early balance impairment in people with multiple sclerosis (2023) · cites this paper as [25]
The Results (ROC Analysis and Falls Classification; page unavailable) show that the instrumented 30-second chair stand test classified fall status in people with multiple sclerosis. The paper discusses its potential to screen for fall risk, but it classified existing faller status rather than demonstrating prediction of future falls. (LLM, conf=0.84)
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- Authors
- Tulipani, L. J., Meyer, B., Allen, D., Solomon, A. J., & McGinnis, R. S.
- Affiliations
- University of Vermont (US)
- Corresponding author
- McGinnis, Ryan S.
- Venue
- Gait & Posture
- Publisher
- Elsevier BV
- Published
- 2022-05
- Volume
- 94
- Pages
- 19-25
- ISSN
- 0966-6362 (ISSN-L)
- PMC
- PMC9086135
- OpenAlex
- W4212977478
- Cited by
- 32
- References
- 39
- Citations by year
- 2026: 4 · 2025: 9 · 2024: 6 · 2023: 10 · 2022: 3
- FWCI
- 11.34
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- Gait & Posture (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute of Biomedical Imaging and Bioengineering (R21EB027852)
- Topics
- Health Sciences › Health Professions › Physical Therapy, Sports Therapy and Rehabilitation › Balance, Gait, and Falls Prevention
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Health Sciences › Medicine › Pathology and Forensic Medicine › Multiple Sclerosis Research Studies
- Keywords
- multiple sclerosis, fall risk prediction, 30-second chair stand test, wearable sensors, balance deficits, functional mobility, balance confidence, fatigue
- MeSH terms
- Wearable Electronic Devices, Postural Balance, Fatigue, Humans, Multiple Sclerosis
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