App- and Wearable-Based Remote Monitoring for Patients With Myasthenia Gravis and Its Specialists: Feasibility and Usability Study
Bibliographic record
Abstract
Background: Myasthenia gravis (MG) is rare, chronic autoimmune disorder of the neuromuscular junction that requires specialized care and often lifelong treatment, facing challenges due to its rarity and the limited availability of specialists. Telemedical solutions in specialized centers hold considerable promise in bridging this gap by increasing access to this care to a broader patient population in a timely manner. However, there is no research regarding interventional remote care solutions in the field of MG to date. Objective: This study aimed to assess the feasibility and usability among patients with MG and specialists of a telemedicine platform, tailored to patients with MG and designed to facilitate remote monitoring, treated in a specialized center. Methods: The telemedicine platform consisted of an app for patients and a web-based portal for physicians. Over a period of 3 months, 30 patients continuously monitored their vital parameters through external devices, including a digital spirometer and a wearable (activity tracker). Furthermore, patients completed 7 different patient-reported outcome measures (PROMs) through the app at predefined intervals. Specialists could review this monitoring data and adjust therapy as necessary. In addition, communication between patients and physicians was facilitated through a chat module. Feasibility was evaluated by total adherence rates for completing PROMs within the app, alongside the collection of spirometry and wearable data. Furthermore, user satisfaction was assessed among both patients with MG and physicians at the end of study. Results: Total adherence rates ranged from 74.3% (1830/2464) to 97.9% (327/334) across all data types, with the highest adherence observed for PROMs (1139/1179, 96.6%), followed by spirometry (293/334, 87.7%) and wearables (1830/2261, 80.9%). Notably, patients wore the wearable longer than required by protocol and conducted a higher number of spirometry measurements during the study than required per protocol (median 20 h/d [IQR 15-24] vs 14 h/d and median 49 [IQR 15-59] measurements vs 11 measurements, respectively). Technical issues and discomfort with wearables were factors affecting lower adherence in some patients. The System Usability Scale yielded a median score of 85 indicating "excellent usability." In addition, results from a more detailed user evaluation questionnaire showed high levels of user satisfaction among both patients and health care professionals across diverse categories, including their experience of the care program, communication, and evaluation of the program. Conclusions: Remote monitoring of patients with MG through the telemedical platform demonstrated good feasibility and acceptability, as evidenced by above-average adherence rates and user satisfaction for both patients and physicians. The majority of patients wanted to continue using the app. These findings highlight the potential for user-friendly digital tools to enhance care for patients with MG, although addressing technical challenges and ensuring patient comfort with wearables are essential for optimal implementation. Further research involving larger cohorts and longer study duration is warranted to validate these findings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".