Co-Design in the Development of a Mobile Health App for the Management of Knee Osteoarthritis by Patients and Physicians: Qualitative Study (Preprint)
Bibliographic record
Abstract
BACKGROUND Despite a doubling of osteoarthritis-targeted mobile health (mHealth) apps and high user interest and demand for health apps, their impact on patients, patient outcomes, and providers has not met expectations. Most health and medical apps fail to retain users longer than 90 days, and their potential for facilitating disease management, data sharing, and patient-provider communication is untapped. An important, recurrent criticism of app technology development is low user integration design. User integration ensures user needs, desires, functional requirements, and app aesthetics are responsive and reflect target user preferences. OBJECTIVE This study aims to describe the co-design process for developing a knee osteoarthritis minimum viable product (MVP) mHealth app with patients, family physicians, and researchers that facilitates guided, evidence-based self-management and patient-physician communication. METHODS Our qualitative co-design approach involved focus groups, prioritization activities, and a pre-post quality and satisfaction Kano survey. Study participants included family physicians, patient researchers and patients with knee osteoarthritis (including previous participants of related collaborative research), researchers, key stakeholders, and industry partners. The study setting was an academic health center in Southern Alberta. RESULTS Distinct differences exist between what patients, physicians, and researchers perceive are the most important, convenient, desirable, and actionable app functional requirements. Despite differences, study participants agreed that the MVP should be electronic, should track patient symptoms and activities, and include features customized for patient- and physician-identified factors and international guideline-based self-management strategies. Through the research process, participants negotiated consensus on their respective priority functional requirements. The highest priorities were a visual symptom graph, setting goals, exercise planning and daily tracking, and self-management strategies. The structured co-design with patients, physicians, and researchers established multiple collaborative processes, grounded in shared concepts, language, power, rationale, mutual learning, and respect for diversity and differing opinions. These shared team principles fostered an open and inclusive environment that allowed for effective conceptualization, negotiation, and group reflection, aided by the provision of tangible and ongoing support throughout the research process, which encouraged team members to question conventional thinking. Group-, subgroup-, and individual-level data helped the team reveal how and for whom perspectives about individual functional requirements changed or remained stable over the course of the study. This provided valuable insight into how and why consensus emerged, despite the presence of multiple and differing underlying rationales for functional requirement prioritization. CONCLUSIONS It is feasible to preserve the diversity of perspectives while negotiating a consensus on the core functional requirements of an mHealth prototype app for knee osteoarthritis management. Our study sample was purposely constructed to facilitate high co-design interactivity. This study revealed important differences between the patient, physician, and researcher preferences for functional requirements of an mHealth app that did not preclude the development of consensus.
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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 | 0.024 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".