To validate the use of wearable sensors in monitoring fatigue and disease symptoms in myasthenia gravis (MG).

MG is a chronic autoimmune disease characterized by fluctuating muscle weakness and fatigue. Traditional clinical assessments can be too infrequent to capture the day-to-day variability of MG symptoms and susceptible to variability in operator and participant performance that can impact their sensitivity and clinical meaningfulness. Wearable sensors can provide a more precise, frequent, and quantitative tool for assessing disease symptoms in MG and other neurological disorders.

Twenty individuals diagnosed with MG (mean age 59.2 ± 16.2 years) were monitored at home for seven consecutive days using the PAMSys pendant sensor, which tracks physical activity and posture. A correlation analysis between sensor-derived measures and standard validated questionnaires and clinical assessments, including MG activities of daily living profile (MG-ADL), MG composite scale (MGC), and Neuro-QOL Fatigue was performed.

19 of the 20 subjects achieved 100% compliance, wearing the sensor continuously around the clock (24 hours a day, 7 days a week). The results demonstrated significant correlations between physical activity metrics and clinical outcomes, highlighting the potential of wearable sensors to provide continuous, objective, and real-world data on disease manifestations. Stepwise linear regression analysis identified total standing time and total walking bouts as significantly correlated with patient-reported outcomes and clinical scores, including MG-ADL and QOL.

Our study establishes the feasibility and initial clinical validity of using wearable sensors for monitoring disease severity and fatigue in MG.