Evaluation of a web-based self-monitoring application (MyRA) to empower people with rheumatoid arthritis in daily life
Bibliographic record
Abstract
OBJECTIVES: To evaluate MyRA, a web-based self-monitoring application for RA, on patient empowerment, usability and perceived usefulness. METHODS: MyRA was co-developed with patients and used at their own discretion during a 4-month prospective study with patient questionnaires at T0 (baseline), T1 (2 months) and T2 (4 months). The primary outcome was patient empowerment (Patient Activation Measure-13; 0-100). Secondary outcomes included frequency of use, usability (System Usability Scale; 0-100) and perceived usefulness (study specific questions). Descriptive statistics and repeated measures ANOVA were applied with post-hoc subgroup analysis based on frequency of use [subgroup A (infrequent users): 1-7 times; subgroup B (frequent users): ≥8 times]. RESULTS: Among 548 registered patients [90.1% female, mean age 51.8 (s.d. 11.9) years, mean disease duration 10.2 (s.d. 10.1) years], 54 patients never used the application (9.9%), 405 patients were infrequent users (73.9%) and 89 patients were frequent users (16.2%). In the total user group, no statistical difference was found for patient empowerment after 4 months (T0: 55.8, T2: 54.4, P = 0.09). However, subgroup B showed a statistically significant, though not clinically meaningful, decrease (T0: 56.2, T2 53.6, P = 0.04). Subgroup B reported higher usability scores compared with subgroup A (75.9 vs 62.9, P < 0.001) and was more outspoken in perceived usefulness. CONCLUSION: Despite major patient involvement throughout development, self-monitoring via MyRA did not increase patient empowerment. The study had a considerable decline in application engagement over time, with only a small subgroup of frequent users. These users showed more positive attitudes regarding usability and perceived usefulness of MyRA.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".