Clinician Perspectives on Incorporating Physical Activity and Sleep Prescriptions Using eHealth for Youth With Comorbid Psychiatric Disorders: Qualitative Focus Group Study
Notice bibliographique
Résumé
BACKGROUND: Physical activity and sleep prescriptions are indicated for the treatment of psychiatric disorders among youth. However, there is limited clinical adoption of these practices. Exergaming (ie, games that require physical activity) is a feasible intervention to promote physical activity and sleep hygiene and is appealing to youth given their interest in video gaming. Integrating exergaming prescriptions into clinical mental health practices may offer an opportunity to expand access to these interventions, yet pragmatic considerations for adopting these programs are poorly understood. OBJECTIVE: This study aimed to gain feedback from practicing clinicians on adopting GamerFit, an app-based intervention that incorporates exergames, step and sleep tracking, and online coaching to promote physical activity and sleep, as a tool in treatment plans for youth aged 13 to 17 years with psychiatric disorders. METHODS: Mental health clinicians participated in 2 online focus groups. A semistructured interview collected information on perceptions of the importance of physical activity and sleep, considerations for using GamerFit with clients, and approaches for incorporating GamerFit into standard care. Qualitative analysis included a hierarchical thematic coding system of isolated quotes, with the structure, frequency, and interrelationships of the coded quotes used for analysis. RESULTS: All clinicians (8/8, 100%) endorsed physical activity and sleep prescriptions as important interventions, although they were not typically a focus of treatment. Clinicians reported varying levels of self-efficacy in encouraging physical activity goals (6/8, 75%) and, to a lesser extent, sleep hygiene (4/8, 50%). Most perceived eHealth approaches positively (7/8, 88%) and noted their appeal given the accessibility of this physical activity option via gaming (2/4, 50%). Clinicians were optimistic about the feasibility of using GamerFit; the exergame and health coaching aspects of GamerFit were perceived favorably (5/8, 62%). Clinicians desired to access app data in electronic health systems to incorporate in therapeutic sessions (4/8, 50%) and recommended using the app in residential settings with continued use at home (2/8, 25%). Clinicians expressed concern regarding the implementation of GamerFit with families with low technology literacy, noting that some patients would likely require parental assistance to help with reminders and technology use (1/8, 12%). Suggestions for improvement included a greater variety of exergames and features to increase adolescents' engagement (6/8, 75%). There was a considerable willingness to incorporate this technology into clinicians' clinical practices and a strong desire for insurance provisions to cover coaching and technological components (7/8, 88%). CONCLUSIONS: Clinicians perceived GamerFit as a feasible and acceptable clinical approach to physical activity and sleep prescriptions for youth with psychiatric disorders. The remote delivery of this intervention was perceived to be of interest to patients and provided helpful guidance for clinicians who were short on time to address many important topics within limited session time frames.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,022 | 0,031 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,007 | 0,004 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,002 | 0,005 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».