Patient Engagement and Empowerment Using a mHealth Application for Management of Inflammatory Arthritis
Bibliographic record
Abstract
Objectives Despite the rise of mHealth applications for inflammatory arthritis, research on patient-reported outcomes related to usability and confidence in disease management is limited. While mHealth tools can improve self-management and patient-provider communication in chronic conditions, their impact on patient empowerment in arthritis remains underexplored. This study aims to address this gap by evaluating the “Arthritis+Patient” mHealth app, focusing on usability, self-management, and empowerment. Methods The “Arthritis+Patient” app, developed by Dr. Mulgund from Trillium Health Partners and available for free download, was evaluated through 2 cross-sectional surveys. The first survey, adapted from the Post-Study System Usability Questionnaire, evaluated user experience and was administered to 73 patients diagnosed with inflammatory arthritis by a rheumatologist between January 2019 and January 2024. A second survey on arthritis self-management was administered to 16 patients. We included patients from 3 community rheumatology clinics who consented to survey completion. Data was collected in office, either electronically or on paper, and descriptive statistics were used for analysis. There was some missing data, and denominators were provided accordingly. Results Among the 73 usability survey respondents, 81.9% (n = 59/72) agreed the app’s interface was user-friendly, and 83.5% (n = 61/73) found it easy to understand and navigate. Regarding health management, 69.8% (n = 51/73) found the app useful, and 62.5% (n = 45/72) reported increased confidence in managing their condition. Moreover, 70.8% (n = 51/72) intended to continue using the app, and 72.6% (n = 53/73) would recommend it. Of those who completed the self-management survey (n = 16), 86.7% (n = 13/15) found the educational material useful. App features for tracking medical history, and managing sleep and anxiety were valued by 92.3% (n = 12/13) and 69.2% (n = 9/13) of respondents, respectively. Additionally, 50% (n = 7/14) reported improved tracking of arthritis between appointments, and 28.6% (n = 4/14) felt more confident in decision-making. Most patients (75%, n = 9/12) noted feeling empowered through active symptom tracking and 50% (n = 7/14) used the app 1-2 times per week. Free-text feedback suggested adding trend tracking for symptoms. Conclusion The “Arthritis+Patient” app shows potential for enhancing patient engagement, empowerment, and self-management in inflammatory arthritis care. Usability and educational content were well-received, with users reporting increased confidence in disease management. Integrating mHealth apps into routine arthritis care could improve patient-centered outcomes and support quality improvement in health services.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".