Evaluation of Mobile Apps Targeted at Patients With Spondyloarthritis for Disease Monitoring: Systematic App Search
Bibliographic record
Abstract
BACKGROUND: There are many apps developed for patients with spondyloarthritis in the market, but their purpose and quality are not objectively evaluated. OBJECTIVE: The objective of this study was to identify and evaluate existing publicly available, high-quality apps that use validated measurement instruments for monitoring spondyloarthritis disease activity. METHODS: We conducted a review of apps available on the Apple App Store and the Google Play Store based on a combination of keywords and inclusion and exclusion criteria. Validated disease activity measurement instruments were identified. Data regarding app characteristics, including the presence of validated disease activity measurement, were extracted. The Mobile App Rating Scale (MARS) was used to review the apps for user experience. RESULTS: A total of 1253 apps were identified in the app stores, and 5 apps met the criteria and were further analyzed. Moreover, 2 apps (MySpA and Group for Research and Assessment of Psoriasis and Psoriatic Arthritis App) contained some of the validated disease activity monitoring instruments for specific spondyloarthritis subtypes. These 2 apps were also rated good on the MARS (with total mean scores ≥4 out of 5), whereas the other apps scored poorly in comparison. CONCLUSIONS: There are 2 high-quality spondyloarthritis disease activity monitoring apps publicly available, but they only target 2 spondyloarthritis subtypes-ankylosing spondylitis and psoriatic arthritis. There is a lack of high-quality apps that can measure disease activity for other spondyloarthritis subtypes, and no app that consolidates all validated disease activity instruments across subtypes was available.
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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 | 0.012 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.014 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".