Smartphone-based gait assessment for multiple sclerosis

• Published in: Multiple sclerosis and related disorders Vol. 82; p. 105394
• Main Authors: Regev, Keren, Eren, Noa, Yekutieli, Ziv, Karlinski, Keren, Massri, Ashraf, Vigiser, Ifat, Kolb, Hadar, Piura, Yoav, Karni, Arnon
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.02.2024
• Subjects: Digital monitoring; Disability Evaluation; Gait; Gait analysis; Humans; Multiple sclerosis; Multiple Sclerosis - complications; Multiple Sclerosis - diagnosis; Postural Balance; Smartphone; Time and Motion Studies; Multiple sclerosis; Digital monitoring; Smartphone; Gait analysis
• ISSN: 2211-0348; 2211-0356
• DOI: 10.1016/j.msard.2023.105394

•Evaluation of Mon4t® technology in monitoring multiple sclerosis patients.•Three motor tasks were tested: 3 m and 10 m timed up and go test and tandem walk.•There were significant gait differences between MS patients and healthy controls.•Combined features from 3 tasks differentiated patients without disability from HC.•Digital gait assessment with Mon4t® technology improves traditional monitoring. Multiple Sclerosis causes gait alteration, even in the early stages of the disease. Traditional methods to quantify gait impairment, such as performance-based measures, lab-based motion analyses, and self-report, have limited ecological relevance. The Mon4t® app is a digital tool that uses sensors embedded in standard smartphones to measure various gait parameters. To evaluate the use of Mon4t® technology in monitoring MS patients. 100 MS patients and age-matched healthy controls were evaluated using both a human rater and the Mon4t Clinic™ app. Three motor tasks were performed: 3m Timed up and go test (TUG), 10m TUG, and tandem walk. The digital markers were used to compare MS vs. HC, MS with EDSS=0 vs. HC, and MS with EDSS=0 vs. MS with EDSS>0. Within the MS EDSS>0 group, correlations between digital gait markers and the EDSS score were calculated. Significant differences were found between MS patients and HC in multiple gait parameters. When comparing MS patients with minimal disability (EDSS=0) and HC: On the 3m TUG task, MS patients took longer to complete the task (mean difference 0.167seconds, p =0.034), took more steps (mean difference 1.32 steps, p =0.003), and had a weaker ML step-to-step correlation (mean difference 0.1, p = 0.001). The combination of features from the three motor tasks allowed distinguishing a nondisabled MS patient from a HC with high confidence (AUC of 85.65 on the ROC). When comparing MS patients with minimal disability (EDSS=0) to those with higher disability (EDSS>0): On the tandem walk task, patients with EDSS>0 took significantly longer to complete 10 steps than those with EDSS=0 (mean difference 4.63 seconds, p < 0.001), showed greater ML sway (mean difference 0.2, p < 0.001), and had larger angular velocity in the SI axis on average (mean difference 2.31 degrees/sec, p = 0.01). A classification model achieved 81.79 ROC AUC. In the subgroup of patients with EDSS>0, gait features significantly correlated with EDSS score in all three tasks. The findings demonstrate the potential of digital gait assessment to augment traditional disease monitoring and support clinical decision making. The Mon4t® app provides a convenient and ecologically relevant tool for monitoring MS patients and detecting early changes in gait impairment.