Multi-modal Digital Health Technology for Remote Monitoring of Disease Activity in Prodromal Huntington’s Disease and Huntington’s Disease
Ram Kinker Mishra1, Ashkan Vaziri1, Daniel Claassen2, Jamie Adams3
1Biosensics LLC, 2Vanderbilt University Medical Center, 3University of Rochester Medical Center
Objective:
To develop and validate a multi-modal digital health technology for remote monitoring of disease activity in Huntington’s disease (HD).
Background:
HD is a neurodegenerative disorder characterized by progressive motor, cognitive, and behavioral symptoms. The principal means of measuring motor impairment in Huntington disease (HD) is the Unified Huntington’s Disease Rating Scale total motor score. Although valuable, the scale is subjective and categorical and requires significant training to administer correctly. Digital health technologies (DHTs) offer a promising approach to capture frequent objective data that can improve future clinical trials and care for HD.
Design/Methods:
We developed a multi-modal DHT solution for monitoring motor, speech and cognitive symptoms in prodromal HD and HD. The solution combines wearable sensors for monitoring physical activity, posture and hand function during activities of daily living, with tablet-based digital speech, cognitive and fine motor control assessments.
Results:
In our pilot study, sensor and speech data were collected from 18 individuals with HD, 7 with prodromal HD, and 11 healthy controls. Both speech- and sensor-derived digital features showed significant group-level differences. Machine learning–based models were developed to distinguish HD, prodromal HD, and control participants based on these digital measures.
Conclusions:
This study highlights the potential of DHTs to enhance clinical care and research in Huntington’s disease by providing more sensitive measures of disease severity, enabling differentiation across disease stages, and facilitating prediction of disease progression.
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