Exploring the Use of Digital Technology to Support Health Behavior Change in Young People Under the Care of Complications of Excess Weight (CCEW) Clinics: Qualitative Patient-Centered Design Study
Notice bibliographique
Résumé
BACKGROUND: Specialist multidisciplinary clinics have been established to provide care for the burgeoning number of young people presenting with comorbidities related to severe obesity in childhood. Digital technology, an integral component of most young people's lives, may enable clinics to offer accessible, ongoing support between appointments to the patients, thereby increasing the likelihood of successful health behavior change. However, while short-term engagement with technology-based behavior change interventions is good, engagement tends to decrease over time, limiting their overall impact. Little is known about the views of young people living with obesity on the role of digital technology as an adjunct to current traditional care pathways. OBJECTIVE: This study aims to explore the views of adolescent patients and their families on whether digital technology should be used by obesity services to support health behavior change. METHODS: Participants included patients aged between 10 and 16 years from an obesity clinic, along with their adult family members. Four focus groups and co-design workshops, facilitated by a cross-disciplinary team of clinicians, academics, and technology innovators, explored young people's health priorities, identified the barriers to and facilitators of health behavior change, and co-designed ways in which technology could be used to support them in overcoming these barriers to achieving their health goals. Data were analyzed using inductive content analysis, with findings integrated with key co-design workshop outputs. RESULTS: . The mean socioeconomic decile was 4.3 (SD 2; range 1-8). Participants did not mention weight as an important aspect of their health. Instead, mental health, sleep, and peer support were identified as the domains where patients felt they would most benefit from additional support. Addressing these aspects of health was viewed as foundational to all other aspects of health, with poor mental health, sleep, and social support reducing young people's ability to engage in the process of health behavior change. Participants reported that technology could help provide this support as an adjunct to in-person support. Participants expressed a preference for technologies able to individually tailor content to the young person's needs, including relatable peer-produced content. The need for support for both the young people and their family members was highlighted, along with the need to integrate in-person strategies to maintain engagement with any technological offering. CONCLUSIONS: There is clear potential for digital technology to support the holistic health priorities of young people receiving specialist care for the comorbidities of excess weight. This study's findings will serve as a foundation for developing innovative approaches to the use of technology to support this high-need population.
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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 | 0,011 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,004 | 0,003 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 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 ».