A smart sock-based remote monitoring system to assess the progression of clinical disability and fatigue in people with Multiple Sclerosis.

Title: A smart sock-based remote monitoring system to assess the progression of clinical disability and fatigue in people with Multiple Sclerosis. Authors: Julie F. Stowell, DPT1,2, Victory A. Ladipo3, Sujay S. Galen2 and T. Bradley Willingham, PhD1,2 1 Virginia C. Crawford Research Institute, Shepherd Center. Atlanta, GA, USA. 2Georgia State University, Byrdine F. Lewis College of Nursing and Health Professions, Atlanta, GA, USA. 3Georgia Institute of Technology, Department of Biology. Atlanta, GA, USA. The purpose of this cross-sectional study was to determine whether gait metrics derived from a smart sock-based remote monitoring system can identify different disability levels in people with MS (PwMS) and detect changes in gait associated with motor fatigue during prolonged walking. Twenty participants (14F, 6M) were recruited by convenience sampling; mean age was 46.6±15.47 years and mean Patient Determined Disease Steps (PDDS; disability level) score was 4.2±1.74. Participants completed the timed 25-foot walk test (T25FTWT), timed up and go test (TUG), 6-minute walk test (6MWT) while wearing smart socks to measure gait metrics. Fatigue ratings were collected before, after, and during the walk tests using the Visual Analog Scale for Fatigue (VAS‐F). Participants also completed questionnaires related to walking, fatigue, and disability level. Gait metrics derived from smart socks were significantly correlated to disability level (Cadence r=-0.682-0.753, p≤0.001; speed r=-0.59-0.751, p≤0.008; gait cycle r=0.57-0.665, p≤0.011; step time r=0.533-0.661, p≤0.023; single support r=0.519-0.66, p≤0.023; double support r=0.513-0.564, p≤0.025; and stance time r=0.572-0.654, p≤0.010) across all walk tests. Gait metrics (cadence, gait cycle, step time, double support time, stance time; p≤0.05) significantly changed from minute 1 to 6 during the 6MWT. Self-reported fatigue increased 181.3±235.3% during the 6MWT; change from minute 1 to 6 is statistically significant (minute 1 x̄ =2.5±2.3, minute 6 x̄=5.5±3.2; p=0.000). Our results demonstrate the potential for wearable smart sock systems to assess the progression of clinical disability and fatigue in PwMS. Thus, this technology has the potential to enhance diagnostics, treatment, and research strategies for PwMS.