Disease-specific wearable sensor algorithms for profiling activity, gait, and balance in individuals with Charcot-Marie-Tooth disease type 1A

Background/AimsCharcot-Marie-Tooth Disease type 1A (CMT1A), the most common inherited peripheral neuropathy, is characterized by progressive sensory loss and weakness, which results in impaired mobility. Increased understanding of the genetics and pathophysiology of CMT1A has led to development of potential therapeutic agents, necessitating clinical trial readiness. Wearable sensors may provide useful outcome measures for future trials. MethodsIndividuals with CMT1A and unaffected controls were recruited for this 12-month study. Participants wore sensors for in-clinic assessments and at-home, from which activity, gait, and balance metrics were derived. Mann-Whitney U tests were used to analyze group differences for activity, gait, and balance parameters. Test-retest reliability of gait and balance parameters and correlations of these parameters with clinical outcome assessments (COAs) were examined. ResultsThirty individuals, 15 CMT1A, and 15 controls, participated. Gait and balance metrics demonstrated moderate to excellent reliability. CMT1A participants had longer step durations (p < .001), shorter step lengths (p = .03), slower gait speeds (p < .001), and greater postural sway (p < .001) than healthy controls. Moderate correlations were found between CMT-Functional Outcome Measure and step length (r = -0.59; p = .02), and gait speed (r = 0.64; p = .01); 11 out of 15 CMT1A participants demonstrated significant increases in stride duration between the first and last quarter of the 6-min walk test, suggesting fatigue. InterpretationIn this initial study, gait and balance metrics derived from wearable sensors were reliable and associated with COAs in individuals with CMT1A. Larger longitudinal studies are needed to confirm our findings and evaluate sensitivity and utility of these disease-specific algorithms for clinical trial use.