The Good Start Matters mHealth Parenting Program: Protocol for a Randomized Controlled Trial
Notice bibliographique
Résumé
BACKGROUND: The family plays a critical role in shaping children's health behaviors during early childhood. Family-based interventions are a cornerstone of childhood obesity prevention but often yield modest effects and have several limitations, including a focus on a single caregiver and insufficient attention to coparenting dynamics. Mobile health (mHealth) interventions that include multiple caregivers and target coparenting practices are rare, but have the potential to amplify parenting intervention effects, leading to stronger child health outcomes. OBJECTIVE: This protocol aims to describe a randomized controlled trial designed to evaluate the efficacy of the Good Start Matters mHealth Parenting Program in improving parenting and coparenting practices (primary outcomes) and child health behaviors (secondary outcomes) among 2.5- to 6-year-olds. METHODS: This randomized controlled trial will recruit 118 two-parent families (ie, families with 2 caregivers participating in the trial) from childcare centers across British Columbia, Canada. To promote inclusivity, one-parent families (ie, only one parent participating) will also be eligible but will not count toward the target sample size for recruitment purposes. Eligibility criteria include at least one parent having primary custody of a child aged 2.5-6 years, both parents being fluent in English, and each caregiver owning a smartphone device. In addition, children must not have severe limitations that prevent adherence to general nutritional and 24-hour Movement Guidelines, nor can they be undergoing weight management treatment. After baseline data collection, families will be randomized into either the intervention group, which will receive immediate access to the app, or a waitlist control group, which will gain access to the app after the follow-up assessment, approximately 2 months later. Baseline and follow-up assessments will collect data on food, physical activity, and media parenting practices, coparenting agreement, and child eating, active play, and screen time behaviors. To analyze the data while accounting for its nested structure, multilevel mixed-effects models, integrating intention-to-treat principles and imputation techniques when deemed necessary, will be used. Sensitivity dose-response analyses will assess the extent to which differential adherence/exposure to the intervention influences the study's outcomes. RESULTS: The trial was registered in March 2023. Recruitment took place from November 2023 to January 2025, with 121 two-parent families and 62 one-parent families recruited. Data collection took place between November 2023 and April 2025. Data cleaning and analyses started in May 2025. Results are expected to be published in 2026. CONCLUSIONS: This study will provide critical insights into the efficacy of mHealth interventions to improve parenting and coparenting practices while promoting healthier child behaviors during early childhood. The Good Start Matters mHealth Parenting Program has the potential to strengthen the foundation of family-centered interventions and set a new standard for systemic approaches to early childhood obesity prevention. TRIAL REGISTRATION: ClinicalTrials.gov NCT05802160; https://clinicaltrials.gov/study/NCT05802160. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/72642.
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 | 0,056 | 0,051 |
| Méta-épidémiologie (sens strict) | 0,008 | 0,006 |
| Méta-épidémiologie (sens large) | 0,015 | 0,007 |
| Bibliométrie | 0,004 | 0,005 |
| Études des sciences et des technologies | 0,006 | 0,005 |
| Communication savante | 0,008 | 0,006 |
| Science ouverte | 0,005 | 0,003 |
| Intégrité de la recherche | 0,010 | 0,014 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,151 | 0,024 |
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 ».