Which Educational Topics and Smartphone App Functions Are Prioritized by US Patients With Rheumatic and Musculoskeletal Diseases? A Mixed-Methods Study
Bibliographic record
Abstract
OBJECTIVE: We sought to identify (1) what types of information US adults with rheumatic and musculoskeletal diseases (RMD) perceive as most important to know about their disease, and (2) what functions they would use in an RMD-specific smartphone app. METHODS: Nominal groups with patients with RMD were conducted using online tools to generate a list of needed educational topics. Based on nominal group results, a survey with final educational items was administered online, along with questions about desired functions of a smartphone app for RMD and wearable use, to patients within a large community rheumatology practice-based research network and the PatientSpot registry. Chi-square tests and multivariate regression models were used to determine differences in priorities between groups of respondents with rheumatic inflammatory conditions (RICs) and osteoarthritis (OA), and possible associations. RESULTS: At least 80% of respondents considered finding a rheumatologist, understanding tests and medications, and quickly recognizing and communicating symptoms to doctors as extremely important educational topics. The highest-ranked topic for both RIC and OA groups was "knowing when the medication is not working." The app functions that most respondents considered useful were viewing laboratory results, recording symptoms to share with their rheumatology provider, and recording symptoms (eg, pain, fatigue) or disease flares for health tracking over time. Approximately one-third of respondents owned and regularly used a wearable activity tracker. CONCLUSION: People with RMD prioritized information about laboratory test results, medications, and disease and symptom monitoring, which can be used to create educational and digital tools that support patients during their disease journey.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".