At-home wearable-based monitoring predicts clinical measures and biological biomarkers of disease severity in Friedreich’s Ataxia

Background Friedreich ataxia (FRDA) results in progressive impairment in gait, upper extremity coordination, and speech. Currently, these symptoms are assessed through expert examination at clinical visits. Such in-clinic assessments are time-consuming, subjective, of limited sensitivity, and provide only a limited perspective of the daily disability of patients. Methods In this study, we recruited 39 FRDA patients and remotely monitored their physical activity and upper extremity function using a set of wearable sensors for 7 consecutive days. We compared the sensor-derived metrics of lower and upper extremity function as measured during activities of daily living with FRDA clinical measures (e.g., mFARS and FA-ADL) and biological biomarkers of disease severity (guanine-adenine-adenine (GAA) and frataxin (FXN) levels), using Spearman correlation analyses. Results The results show significant correlations with moderate to high effect sizes between multiple sensor-derived metrics and the FRDA clinical and biological outcomes. In addition, we develop multiple machine learning-based models to predict disease severity in FRDA using demographic, biological, and sensor-derived metrics. When sensor-derived metrics are included, the model performance enhances 1.5-fold and 2-fold in terms of explained variance, R², for predicting FRDA clinical measures and biological biomarkers of disease severity, respectively. Conclusions Our results establish the initial clinical validity of using wearable sensors in assessing disease severity and monitoring motor dysfunction in FRDA. Plain language summary Friedreich ataxia (FRDA) is a condition that impairs movement and coordination. Current clinical assessments are subjective, highlighting the need for better ways to monitor disease severity. By using wearable devices to track symptoms in everyday life, we can gain better insights into how patients function outside the clinical environment, offering a more comprehensive understanding of the disease’s impact. In this study, 39 patients were observed using wearable sensors for a week to track their physical activity and arm movements. The data collected was compared with traditional clinical tests and biological markers of the disease. The findings demonstrate that wearable sensors can accurately predict disease severity, offering continuous real-world monitoring that could enhance patient care and treatment outcomes. Mishra, Nunes et al. monitor the physical activity and upper