Development of an Application for Self-Monitoring to Empower Patients With Rheumatoid Arthritis (MyRA)
Bibliographic record
Abstract
OBJECTIVE: To develop a web-based self-monitoring tool including motivational elements to improve empowerment of patients with rheumatoid arthritis (RA). METHODS: Following a design-thinking approach, the development included 3 iterative, cocreative phases involving different stakeholders. In the empathize and define phase, 2 focus groups gave insight into patients' wishes and needs regarding self-monitoring with an application. During the ideation phase, 2 cocreation sessions were organized to establish the content of the application and consider motivational elements. For the prototyping and testing phase, usability was assessed through both formative (heuristics evaluation) and summative (system usability scale [SUS] 0-100; ≥ 68 was considered good to excellent) evaluations. RESULTS: The focus group meetings resulted in a shortlist of what to monitor (physical function, quality of life, pain, fatigue, mental well-being, and social participation) and preferences on how to monitor (single-item questions, 0-10 scale, use as needed). The cocreation sessions revealed preference for empathetic dialogues with an avatar for self-monitoring. Setting goals, adding notes, sharing results, and receiving tips could further increase motivation for use. Initial experiences regarding heuristics of the tool were generally positive and confirmed by a mean SUS score of 84.4 (SD 11.6). Points for improvement included simplifying login procedures, adding notifications, and adjusting the avatar's tone of voice. CONCLUSION: A web-based self-monitoring application (MyRA) was developed, with an avatar that asks patients through dialogues to score 6 domains, with graphical displays, diary functionality, and practical tips. Further studies are needed to confirm its usability and effectiveness in empowering patients.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".