Monitoring of Disease Activity With a Smartphone App in Routine Clinical Care in Patients With Axial Spondyloarthritis
Bibliographic record
Abstract
Objective To investigate the performance of a health app with respect to usability, adherence, and equivalence of data in daily care of patients with axial spondyloarthritis (axSpA). Methods Consecutive patients with axSpA were asked to export patient-reported outcomes (PRO) electronically with the AxSpA Live App regularly every 2 weeks over a period of 6 months. The first clinical visit was followed by 2 further personal visits after 3 and 6 months. Patients completed paper-based PRO at every visit; they also completed the Mobile App Rating Scale and the System Usability Scale after 3 and 6 months. Results Of 103 patients with axSpA, 69 agreed to participate (67.0%): age 41.5 (11.3) years, 58.0% male, Bath Ankylosing Spondylitis Disease Activity Index (BASDAI) 4.3 (2.0), and 76.8% treated with biologic disease-modifying antirheumatic drugs. Patients’ adherence to regular app exports was 29.0% and 28.4% after 3 and 6 months, respectively. Significant predictors for good adherence were high disease activity (P = 0.02) and older age (P = 0.04). No systematic differences between digital and paper-based BASDAI scores were found (intraclass correlation coefficients 0.99 [95% CI 0.98-0.99]). Performance of the app was rated as good. Conclusion Collection of digital PROs by AxSpA Live App may be successfully used in patients with axSpA with high disease activity. Our study showed equivalence of digital data, but adherence to the app after 6 months was poor. Higher disease activity and older age resulted in increased adherence to the app. This suggests that the use of health apps like this should concentrate on more severely affected 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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".