Remote Monitoring of Motor Function in Friedreich Ataxia


Clinical Trials

Poster Number: S78


Ashkan Vaziri, PhD, Biosensics LLC, Ram kinker Mishra, PhD, BioSensics LLC, Newton, MA, USA, Adonay Sastre Nunes, PhD, BioSensics LLC, Newton, MA, USA, Victoria Profeta, BS, Children's Hospital of Philadelphia, Philadelphia, PA, USA, McKenzie Wells, MS, Children's Hospital of Philadelphia, Philadelphia, PA, USA, David Lynch, MD, PhD, Children's Hospital of Philadelphia

Objective: To develop and validate a robust wearable-based solution for tracking motor function in Friedreich ataxia (FA).

Background: FA is an autosomal recessive neurodegenerative disorder that gradually impairs coordination, balance, and mobility. Traditional clinical assessments of FA are subjective and may encounter challenges such as ceiling effects, daily fluctuations, and limited sensitivity to changes. Wearable sensors present a viable solution, as they enable continuous monitoring of physical activity, mobility and upper limb health in real-world settings, thereby offering valuable insights derived from daily living experiences.

Design/Methods: Individuals diagnosed with FA wore a PAMSys™ pendant and a PAMSys ULM™ wrist sensor (on their dominant hand) for a week to measure daily physical activity, posture and hand goal-directed movements. conventional FA outcome assessments, such as modified Friedreich’s Ataxia Rating Scale (mFARS), 9-hole peg tests, and activity of daily living (ADL) scales, were administered. Feature selection was carried out through Spearman correlation analysis, and machine learning model performance was assessed using a leave-one-out cross validation technique.

Results: The study involved 40 participants diagnosed with FA (average age of 26.7 ± 1.5 years, including 19 females). We observed significant correlations between physical activity outcomes and clinical assessments, including total mFARS, mFARS Section E, mFARS Section B, and ADL scores, showing a moderate to high effect size. Machine learning models successfully accounted for the variance in the clinical scores, explaining between 21% and 51% of the variance within the test dataset.

Conclusions: Our results provide a data-driven, objective, and remote monitoring approach to measure and track FA. Wearable technology allows for remote monitoring, reducing the need for frequent in-person clinical assessments. Patients can be monitored in their natural environments, leading to greater convenience and potentially lowering healthcare costs.