Effects of a Transtheoretical Model–Based mHealth Intervention on Transition Readiness in Adolescents With Epilepsy: Quasi-Experimental Study
Notice bibliographique
Résumé
BACKGROUND: Enhancing self-management and transition readiness in adolescents with epilepsy is essential for successful transition to adult care. The combination of the transtheoretical model (TTM) and mobile health (mHealth) management provides a framework for reducing intervention costs while personalizing care. OBJECTIVE: This quasi-experimental study evaluates the feasibility of TTM-based mHealth management for improving transition services in adolescents with epilepsy. METHODS: A total of 98 adolescent patients with epilepsy aged 12-18 years were recruited. Using a nonrandomized design based on treatment locations, they were allocated into either the intervention group (n=49) or the control group (n=49). The intervention group received a TTM-based mHealth management program, which included phase-specific group sessions led by a multidisciplinary team and conducted via Tencent Meeting every 2 weeks or monthly (biweekly for the precontemplation, contemplation, and preparation, and monthly for the action and maintenance). The sessions involved lectures, discussions, and a mini-program that provided disease management support, motivational strategies, and digital reminders tailored to each stage. The control group received conventional remote extended care, consisting of biweekly group lectures and discussions for all patients and their families via Tencent Meeting, supplemented by regular health education materials delivered through a WeChat group. Telephone follow-ups were conducted at the third and sixth months. The total intervention duration was 6 months for both groups. Outcomes were assessed after 6 months using the self-management stage, Self-Management and Transition to Adulthood with Rx=Treatment questionnaire, and a self-developed program acceptability questionnaire. RESULTS: Postintervention, the intervention group demonstrated significantly better self-management behavior stages compared with controls. At the end of 6 months of intervention, the majority of participants in the intervention group reached the action stage (16/49, 32.65%) and maintenance stage (14/49, 28.57%), whereas most controls remained in precontemplation (12/49, 24.49%) and contemplation stages (13/49, 26.53%). Both groups showed significant improvements from baseline in medication management, health care participation, disease knowledge, doctor-patient communication, and transition readiness total scores at 6-month follow-up (all P<.05). Notably, the intervention group achieved additional incremental benefits versus controls (medication management: 3.81, 95% CI 1.26-6.36; health care engagement: 2.77, 95% CI 0.52-5.02; disease knowledge: 1.30, 95% CI 0.28-2.31; provider communication: 3.42, 95% CI 1.62-5.22; transition readiness: 11.30, 95% CI 5.70-6.89; effect sizes [Cohen d] ranged from 0.527 to 0.864, indicating moderate-to-large clinical effects). The overall satisfaction scores were 4.43 (SD 0.50) for patients and 4.16 (SD 0.82) for health care providers. CONCLUSIONS: The TTM-based mHealth management program may effectively improve self-management behavior changes and enhance readiness for transition among adolescents with epilepsy, thereby facilitating a smooth transition to adult health care. The program demonstrated high acceptability, providing a reference for establishing clinical transition service protocols. However, this study was a single-center, quasi-experimental trial with a small sample size and short intervention duration. The findings need to be confirmed by larger-scale randomized controlled trials to verify efficacy.
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 | 0,007 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 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 ».