PP115 A Mobile Health App To Improve Knee Osteoarthritis Self-Management
Bibliographic record
Abstract
Introduction: A gap exists between the evidence for reducing risk of knee osteoarthritis (KOA) progression and its application in patients’ daily lives. We aimed to bridge this gap by identifying patient and family physician (FP) self-management priorities to conceptualize and develop a mobile-health application (m-health app). Our co-design approach combined priorities and concerns solicited from patients and FPs with evidence on risk of progression to design and develop a KOA self-management tool. Methods: Parallel qualitative research of patient and FP perspectives was conducted to inform the co-design process. Researchers from the Enhancing Alberta Primary Care Research Networks (EnACT) evaluated the mental models of FPs using cognitive task analysis through structured interviews with four FPs. Using grounded theory methods, patient researchers from the Patient and Community Engagement Research (PaCER) program interviewed five patients to explore their perspectives about needs and interactions within primary care. In three co-design sessions relevant stakeholders (four patients, five FPs, and thirteen researchers) participated to: (i) identify user needs with regard to KOA self-management; and (ii) conceptualize and determine design priorities and functionalities of an m-health app using a modified nominal group process. Results: Priority measures for symptoms, activities, and quality of life from the user perspective were determined in the first two sessions. The third co-design session with our industry partner resulted in finalization of priorities through interactive patient and FP feedback. The top three features were: (i) a symptoms graph and summary; (ii) information and strategies; and (iii) setting goals. These features were used to inform the development of a minimum viable product. Conclusions: The novel use of co-design created directive dialog around the needs of patients, highlighting the contrasting views that exist between patients and FPs and emphasizing how exploring these differences might lead to strong design options for patient-oriented m-health apps. Characterizing these disjunctions has important implications for operationalizing patient-centered health care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".