ArticleCommunications medicine2024
At-home wearable-based monitoring predicts clinical measures and biological biomarkers of disease severity in Friedreich's Ataxia.
Article in Communications medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Prediction of Myasthenia Gravis Worsening: A Machine Learning Algorithm Using Wearables and Patient-Reported Measures.Annals of clinical and translational neurology · 2026Trial
- Advancing Neurorehabilitation and Recovery Through Human Movement Quantification via Wearable Sensing.Neurorehabilitation and neural repair · 2026Article
- Interactive multimodal disease risk evaluation using a self-service non-invasive device.Communications medicine · 2026Article
- Longitudinal wearable sensor data enhance precision of Long COVID detection.PLOS digital health · 2025Article
- Synthesis and Biological Profile of Omaveloxolone: The Cornerstone for Friedreich Ataxia Treatment.International journal of molecular sciences · 2025Review
- Video-Based Biomechanical Analysis Captures Disease-Specific Movement Signatures of Different Neuromuscular Diseases.NEJM AI · 2025Article
- The Development of a Wearable-Based System for Detecting Shaken Baby Syndrome Using Machine Learning Models.Sensors (Basel, Switzerland) · 2025Article
- Using wearable sensors and machine learning to assess upper limb function in Huntington's disease.Communications medicine · 2025Article
- Dietary and lifestyle interventions for the management of hereditary ataxias.Frontiers in nutrition · 2025Review
- Using Wearable Sensors and Machine Learning to Assess Upper Limb Function in Huntington's Disease.Research square · 2024Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundFriedreich 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.
methodsIn 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.
resultsThe 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.
conclusionsOur results establish the initial clinical validity of using wearable sensors in assessing disease severity and monitoring motor dysfunction in FRDA.
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