Development of the Happy Hands Self-Management App for People with Hand Osteoarthritis: Feasibility Study
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,008 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».