Patient Perspectives on a Digital Mobile Health Application for RA
Bibliographic record
Abstract
BACKGROUND: Emerging evidence suggests that patients are increasingly willing to use digital mobile health applications for rheumatoid arthritis (RA apps). The development and diffusion of RA apps open the possibility of improved management of the disease and better physician-patient interactions. However, adoption rates among apps have been lower than hoped, and research shows that many available RA apps lack key features. There is little research exploring patient preferences for RA apps or patients' habits and preferences for app payment, which are likely key factors affecting adoption of this technology. This study seeks to understand characteristics of RA patients who have adopted RA apps, their preferences for app features, and their willingness to pay for, and experiences with app payment. METHODS: Data for this study come from a 33-question online survey of patients with RA in Canada and the United States (N=30). Information on demographics, diagnosis and management of RA, current use and desired features of RA apps, and prior experience with and willingness to pay for an app was collected. Descriptive statistics are reported, and bivariate analyses (chi-square, point-biserial correlation, and ANOVA) were performed to understand relationships between variables. RESULTS: Respondents showed a clear preference for certain app features, namely symptom tracking, scheduling appointments, and reminders. Physician recommendation for an app and patient tracking of symptoms with an app were significantly related to patient adoption of an RA app. Years since diagnosis with RA, physician recommendation for an RA app, and current use of a non-RA health tracking app were significantly related to patients' willingness to pay a subscription for an RA app. CONCLUSION: RA patients appear to prefer task support features in an RA app, notably symptom tracking, appointment scheduling, and reminders, over other features such as those related to dialogue support and social support. The choice of whether an RA app will be free or based on a subscription, pay-per-service, or one-time purchase model may also play a role in eventual adoption. Similarly, physician recommendation appears to influence patients' decision to use an RA app as well as their willingness to pay a subscription for an app.
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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 | 0.010 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".