Exploring User Visions for Modeling mHealth Apps Toward Supporting Patient-Parent-Clinician Collaboration and Shared Decision-making When Treating Adolescent Knee Pain in General Practice: Workshop Study
Notice bibliographique
Résumé
BACKGROUND: Long-standing knee pain is one of the most common reasons for adolescents (aged 10-19 years) to consult general practice. Generally, 1 in 2 adolescents will continue to experience pain after 2 years, but exercises and self-management education can improve the prognosis. However, adherence to exercises and self-management education interventions remains poor. Mobile health (mHealth) apps have the potential for supporting adolescents' self-management, enhancing treatment adherence, and fostering patient-centered approaches. However, it remains unclear how mHealth apps should be designed to act as tools for supporting individual and collaborative management of adolescents' knee pain in a general practice setting. OBJECTIVE: The aim of the study was to extract design principles for designing mHealth core features, which were both sufficiently robust to support adolescents' everyday management of their knee pain and sufficiently flexible to act as enablers for enhancing patient-parent collaboration and shared decision-making. METHODS: Overall, 3 future workshops were conducted with young adults with chronic knee pain since adolescence, parents, and general practitioners (GPs). Each workshop followed similar procedures, using case vignettes and design cards to stimulate discussions, shared construction of knowledge and elicit visions for mHealth designs. Young adults and parents were recruited via social media posts targeting individuals in Northern Jutland. GPs were recruited via email and cold calling. Data were transcribed and analyzed thematically using NVivo (QSR International) coding software. Extracted themes were synthesized in a matrix to map tensions in the collaborative space and inform a conceptual model for designing mHealth core-features to support individual and collaborative management of knee pain. RESULTS: Overall, 38% (9/24) young adults with chronic knee pain since adolescence, 25% (6/24) parents, and 38% (9/24) GPs participated in the workshops. Data analysis revealed how adolescents, parents, and clinicians took on different roles within the collaborative space, with different tasks, challenges, and information needs. In total, 5 themes were identified: adolescents as explorers of pain and social rules; parents as supporters, advocates and enforcers of boundaries; and GPs as guides, gatekeepers, and navigators or systemic constraints described participants' roles; collaborative barriers and tensions referred to the contextual elements; and visions for an mHealth app identified beneficial core features. The synthesis informed a conceptual model, outlining 3 principles for consolidating mHealth core features as enablers for supporting role negotiation, limiting collaborative tensions, and facilitating shared decision-making. CONCLUSIONS: An mHealth app for treating adolescents with knee pain should be designed to accommodate multiple users, enable them to shift between individual management decision-making, take charge, and engage in role negotiation to inform shared decision-making. We identified 3 silver-bullet principles for consolidating mHealth core features as enablers for negotiation by supporting patient-GP collaboration, supporting transitions, and cultivating the parent-GP alliance.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, pas un consensus.
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