A Smart-sock Based Remote Monitoring System to Assess Progression of Clinical Disability and Fatigue in People with Multiple Sclerosis

Research Objectives: To determine whether gait metrics derived from a smart-sock based remote monitoring system can distinguish people with MS (PwMS) with different disability levels and detect changes in gait associated with motor fatigue during prolonged walking.

Design: A cross-sectional study was conducted to assess the relationships between smart-sock derived gait metrics, clinical measures of disability, and fatigue ratings in PwMS during a single visit.

Setting: All study procedures were conducted at the Shepherd Center MS Institute, a comprehensive outpatient MS rehabilitation center.

Participants: 20 participants (14F, 6M) were recruited by convenience sampling from attendees of the MS Institute. The mean age was 46.6±15.47 years and the mean Patient Determined Disease Steps (PDDS; disability level) score was 4.2±1.74.

Interventions: Participants completed the timed 25-foot walk test (T25FTWT), timed up and go test (TUG), 6-minute walk test (6MWT) while wearing smart-sock sensors to measure gait. Fatigue ratings were collected before, after, and during the walk tests using the Visual Analog Scale for Fatigue (VAS‐F). In addition, participants also completed the Multiple Sclerosis Walking Scale (MSWS), Modified Fatigue Impact Scale (MFIS), and patient determined disease steps (PDDS) questionnaires.

Main Outcome Measures: Spatiotemporal gait metrics (cadence, gait cycle, step time, single and double support time, step length, swing time, and stance time) were quantified using a wearable, smart-sock based remote monitoring system.

Results: Gait metrics derived from smart-socks were significantly correlated to disability level (Cadence r2=-0.682-0.753, p0.001; speed r2=-0.59-0.751, p0.008; gait cycle r2=0.57-0.665, p0.011; step time r2=0.533-0.661, p0.023; single support r2=0.519-0.66, p0.023; double support r2=0.513-0.564, p0.025; and stance time r2=0.572-0.654, p0.010) across all walk tests. Cadence differed significantly between lower (PDDS 0-4) and greater disability participants (PDDS 5-6, p< 0.05) across all walking tests. Self-reported state fatigue increased 181.3±235.3% during the 6MWT; change from minute 1 to minute 6 is statistically significant (minute 1 x̄ =2.5±2.3, minute 6 x̄=5.5±3.2; p=0.000).

Conclusions: Our results demonstrate the potential for wearable smart-sock systems to assess the progression of clinical disability and fatigue in PwMS. By offering an objective outcome measure to remotely evaluate changes in disability in real-world settings, smart-sock technology has the potential to enhance diagnostic, treatment, and research strategies for PwMS. Furthermore, the capacity of smart-sock systems to detect changes in gait associated with motor fatigue during prolonged walking may provide opportunities to develop real-time feedback systems that help manage fatigue and sustained walking in the community.

Author(s) Disclosures: n/a