A Mobile App With Optical Imaging for the Self-Management of Hand Rheumatoid Arthritis: Pilot Study (Preprint)
Bibliographic record
Abstract
BACKGROUND Patient outcomes are improved and the burden to the health care system is reduced when individuals are active self-managers of their own health. There is a need for technology that facilitates self-management of rheumatoid arthritis (RA) and can reduce the number of patient visits, promptly identify treatment needs, and reduce the costs associated with poor RA management. A mobile app named LiveWith Arthritis (eTreatMD, Vancouver, BC) has been developed that allows patients with RA to use their mobile device to regularly collect self-management data and to take objective measurements of the impact of RA on their finger joints using optical imaging technology. OBJECTIVE The objectives of this pilot study were to (1) gather preliminary data as to whether a mobile app with hand optical imaging capabilities improves self-management behaviors (self-efficacy in managing symptoms and patient activation), (2) determine if app use shows promise in improving health outcomes (Pain, Health Assessment Questionnaire-II [HAQ-II]), and (3) determine barriers to using the mobile app in adults with RA. METHODS This pilot study used a mixed-methods design. The quantitative portion was a traditional 2-group experimental design, and the qualitative portion was a follow-up telephone interview for intervention participants who did not complete the study. Measures of self-management included the Patient-Reported Outcomes Measurement Information System (PROMIS) self-efficacy in managing symptoms (P-SEMS) and Patient Activation Measure (PAM). Health outcomes included pain by Visual Analog Scale and disability by HAQ-II. RESULTS The final sample consisted of 21 intervention participants and 15 controls. There was a statistically significant improvement in P-SEMS and promising trends for improvement in PAM, HAQ-II, and pain scores for participants who used the app. Of the intervention participants who did not complete the study, 12 completed the qualitative interview on barriers to use. Qualitative content analysis revealed 3 themes for barriers to using the app, including (1) frustration with technology, (2) RA made the app difficult to use, and (3) satisfaction with current self-management system. CONCLUSIONS The LiveWith Arthritis app shows promise for improving self-management behaviors and health outcomes in adults with RA. Future study with a larger sample size is required to confirm findings. Initial app experience is important for adoption and continual use of the app. Individuals with significant disability to the hand would benefit from voice-activated app features. Participants who already have a system of managing their RA may not feel compelled to switch methods, even when a novel optical imaging feature is 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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".