Assessment of gait and balance impairment in people with spinocerebellar ataxia using wearable sensors

• Published in: Neurological sciences Vol. 43; no. 4; pp. 2589 - 2599
• Main Authors: Zhou, He, Nguyen, Hung, Enriquez, Ana, Morsy, Louie, Curtis, Michael, Piser, Timothy, Kenney, Christopher, Stephen, Christopher D., Gupta, Anoopum S., Schmahmann, Jeremy D., Vaziri, Ashkan
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.04.2022; Springer Nature B.V
• Subjects: Adult; Aged; Ankle; Ataxia; Balance; Cerebellar Ataxia; Clinical trials; Gait; Gait - physiology; Humans; Medicine; Medicine & Public Health; Middle Aged; Neurology; Neuroradiology; Neurosciences; Neurosurgery; Original Article; Patients; Pelvis; Postural Balance - physiology; Psychiatry; Regression analysis; Sensors; Spinocerebellar Ataxias - complications; Spinocerebellar Ataxias - diagnosis; Wearable computers; Wearable Electronic Devices; BARS2; Gait; Spinocerebellar ataxia; Wearables; SARA; Balance; Movement disorders
• ISSN: 1590-1874; 1590-3478
• DOI: 10.1007/s10072-021-05657-6

Objective To explore the use of wearable sensors for objective measurement of motor impairment in spinocerebellar ataxia (SCA) patients during clinical assessments of gait and balance. Methods In total, 14 patients with genetically confirmed SCA (mean age 61.6 ± 8.6 years) and 4 healthy controls (mean age 49.0 ± 16.4 years) were recruited through the Massachusetts General Hospital (MGH) Ataxia Center. Participants donned seven inertial sensors while performing two independent trials of gait and balance assessments from the Scale for the Assessment and Rating of Ataxia (SARA) and Brief Ataxia Rating Scale (BARS2). Univariate analysis was used to identify sensor-derived metrics from wearable sensors that discriminate motor function between the SCA and control groups. Multivariate linear regression models were used to estimate the subjective in-person SARA/BARS2 ratings. Spearman correlation coefficients were used to evaluate the performance of the model. Results Stride length variability, stride duration, cadence, stance phase, pelvis sway, and turn duration were different between SCA and controls ( p  < 0.05). Similarly, sway and sway velocity of the ankle, hip, and center of mass differentiated SCA and controls ( p  < 0.05). Using these features, linear regression models showed moderate-to-strong correlation with clinical scores from the in-person rater during SARA assessments of gait ( r  = 0.73, p  = 0.003) and stance ( r  = 0.90, p  < 0.001) and the BARS2 gait assessment ( r  = 0.74, p  = 0.003). Conclusion This study demonstrates that sensor-derived metrics can potentially be used to estimate the level of motor impairment in patient with SCA quickly and objectively. Thus, digital biomarkers from wearable sensors have the potential to be an integral tool for SCA clinical trials and care.