The Development and Usability Testing of the “OA Coach” Mobile Application to Support Individuals with Osteoarthritis: A Feasibility Study (Preprint)
Bibliographic record
Abstract
Background: The OA Coach mobile app was developed to support individuals with knee osteoarthritis in self-managing their condition. The app aims to fill a current gap in the osteoarthritis mobile app field by combining key features such as symptom tracking, objective activity tracking, educational modules, and encouragement notifications underpinned by behavior change theory. Objective: The aim of this study was to describe the development of the OA Coach mobile app and assess its usability in a 6-week feasibility study. Methods: The app was designed in consultation with consumers, rheumatologists, physiotherapists, and osteoarthritis researchers. The app prototype contained four screens: (1) a home screen to track goals and activities, (2) a progress page, (3) a learning page with self-directed modules, and (4) an inbox for communication with the study team. For the feasibility study, 30 participants were recruited between March and April 2024 from a database of osteoarthritis trial participants or through the Osteoarthritis Chronic Care Program at Royal North Shore Hospital, Sydney, Australia. Participants were eligible if they were aged 45 years or older, had knee pain ≥4 on an 11-point numerical pain rating scale and knee stiffness lasting <30 minutes in duration, or stiffness >30 minutes and diagnosed with knee osteoarthritis through a health care provider or radiographs. Participants were provided access to the app and asked to interact with it daily for 6 weeks. Outcomes were assessed through online questionnaires or through mobile app data. The primary outcome was usability, assessed using the mHealth App Usability Questionnaire (MAUQ). Secondary outcomes included computer self-efficacy and osteoarthritis knowledge. The quantitative data were summarized descriptively. Qualitative feedback was collected through open-ended survey responses and discussed within the research team to improve the app. Results: A total of 30 participants completed the study. There was a 1:1 ratio of male to female participants, with an average age of 66.9 (SD 9.1) years and a mean pain level of 6.0 (IQR 5.0-6.8) on an 11-point numerical pain rating scale. Twenty-nine responses from the MAUQ were available for analysis. Most statements scored >5 out of 7 ("somewhat agree"), indicating that the app was easy to use. The mean satisfaction score for the app on the MAUQ was 4.7 (SD 2.0) out of 7. Qualitative feedback from participants indicated the need for clear instructions on how to use and navigate the app, improved structure and integration of the exercise program, and improved tailoring of osteoarthritis education and support. Conclusions: Overall, the OA Coach app was well accepted by participants. Based on participant feedback, the app will be revised to improve aspects of clarity, ease of use, and personalization. The updated app will be tested against other methods of care delivery in a randomized controlled trial.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.019 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".