The Good Start Matters mHealth Parenting Program: Protocol for a Randomized Controlled Trial
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.056 | 0.051 |
| Meta-epidemiology (narrow) | 0.008 | 0.006 |
| Meta-epidemiology (broad) | 0.015 | 0.007 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.151 | 0.024 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".