Smartphone-based gait assessment for multiple sclerosis

Introduction: 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 (R) app is a digital tool that uses sensors embedded in standard smartphones to measure various gait parameters. Objectives: To evaluate the use of Mon4t (R) technology in monitoring MS patients.Methods: 100 MS patients and age-matched healthy controls were evaluated using both a human rater and the Mon4t ClinicTM 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.Results: 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.Conclusion: The findings demonstrate the potential of digital gait assessment to augment traditional disease monitoring and support clinical decision making. The Mon4t (R) app provides a convenient and ecologically relevant tool for monitoring MS patients and detecting early changes in gait impairment.