Development of the Happy Hands Self-Management App for People with Hand Osteoarthritis: Feasibility Study
Bibliographic record
Abstract
BACKGROUND: Patient education, hand exercises, and the use of assistive devices are recommended as first-line treatments for individuals with hand osteoarthritis (OA). However, the quality of care services for this patient group is suboptimal in primary care. OBJECTIVE: The overarching goal was to develop and evaluate feasibility of an app-based self-management intervention for people with hand OA. This feasibility study aims to assess self-reported usability and satisfaction, change in outcomes and quality-of-care, exercise adherence and patients' experiences using the app. METHODS: The development and feasibility testing followed the first 2 phases of the Medical Research Council framework for the development and evaluation of complex interventions and were conducted in close collaboration with patient research partners (PRPs). A 3-month pre-post mixed methods design was used to evaluate feasibility. Men and women over 40 years of age diagnosed with painful, symptomatic hand OA were recruited. Usability was assessed using the System Usability Scale (0-100), while satisfaction, usefulness, pain, and stiffness were evaluated using a numeric rating scale (NRS score from 0 to 10). The activity performance of the hand was measured using the Measure of Activity Performance of the Hand (MAP-Hand) (1-4), grip strength was assessed with a Jamar dynamometer (kg), and self-reported quality of care was evaluated using the Osteoarthritis Quality Indicator questionnaire (0-100). Participants were deemed adherent if they completed at least 2 exercise sessions per week for a minimum of 8 weeks. Focus groups were conducted to explore participants' experiences using the app. Changes were analyzed using a paired sample t test (mean change and 95% CI), with the significance level set at P<.05. RESULTS: The first version of the Happy Hands app was developed based on the needs and requirements of the PRPs, evidence-based treatment recommendations, and the experiences of individuals living with hand OA. The app was designed to guide participants through a series of informational videos, exercise videos, questionnaires, quizzes, and customized feedback over a 3-month period. The feasibility study included 71 participants (mean age 64 years, SD 8; n=61, 86%, women), of whom 57 (80%) completed the assessment after 3 months. Usability (mean 91.5 points, SD 9.2 points), usefulness (median 8, IQR 7-10), and satisfaction (median 8, IQR 7-10) were high. Significant improvements were observed in self-reported quality of care (36.4 points, 95% CI 29.7-43.1, P<.001), grip strength (right: 2.9 kg, 95% CI 1.7-4.1; left: 3.2 kg, 95% CI 1.9-4.6, P<.001), activity performance (0.18 points, 95% CI 0.11-0.25, P<.001), pain (1.7 points, 95% CI 1.2-2.2, P<.001), and stiffness (1.9 points, 95% CI 1.3-2.4, P=.001) after 3 months. Of the 71 participants, 53 (75%) were adherent to the exercise program. The focus groups supported these results and led to the implementation of several enhancements in the second version of the app. CONCLUSIONS: The app-based self-management intervention was deemed highly usable and useful by patients. The results further indicated that the intervention may improve quality of care, grip strength, activity performance, pain, and stiffness. However, definitive conclusions need to be confirmed in a powered randomized controlled trial. TRIAL REGISTRATION: NCT05150171.
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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 | 0.010 |
| 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 | 0.002 |
| 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".