Development and Feasibility Assessment of a Multimodal Digital Health Technology for Remote Monitoring of Symptoms in Myasthenia Gravis.

Abstract

Introduction: Myasthenia gravis (MG) is a chronic autoimmune neuromuscular disease. Patients with MG are typically evaluated by neuromuscular experts through in-person neurologic examinations. These assessments are time-consuming, require significant disease expertise, and capture only a snapshot of disease.

Methods: Given this need, we developed a multimodal digital health technology (DHT) called BioDigit MG, for monitoring MG symptoms and objectively measuring disease severity. BioDigit MG includes tablet-guided speech and video-based assessments, electronic patient-reported outcomes relevant to MG, and a wearable sensor to measure physical activity and posture during activities of daily living.

Results: We assessed the feasibility and acceptability of BioDigit MG by conducting a clinical study with 20 participants with MG who used the DHT. During the study, a total of 219 speech tasks and 119 videos were collected by the DHT, achieving 100% reliability in data collection and transfer. To evaluate technology acceptance and usability, we conducted face-to-face interviews with the 20 MG patients and 5 expert clinicians. Participants found the DHT highly effective, easy to use, and well-suited to their needs. Efficient and reliable data transfer capabilities of BioDigit MG ensured that patient-generated data were promptly and securely delivered to healthcare providers.

Conclusion: These feasibility findings demonstrate that BioDigit MG is capable of reliable multimodal data collection and is acceptable to both patients and clinicians, supporting its potential for use in future larger scale validation studies.