The Scale-Up of a Digital Health Intervention (Healthy Beginnings for HNEKids) Targeting the First 2000 Days: Protocol for a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Digital health interventions, delivered directly to parents' mobile phones, could transform the delivery of health care during the first 2000 days of a child's life. Healthy Beginnings for Hunter New England Kids (HB4HNEKids) is an innovative SMS text message-based model of care that provides age-and-stage relevant preventative health information to parents during the first 2000 days. While HB4HNEKids demonstrates promise for population-wide scale-up, the optimal method for achieving universal, cost-efficient, and equitable scale-up remains unclear. OBJECTIVE: This protocol outlines a randomized controlled trial that evaluates 2 models for scaling up HB4HNEKids: an "opt-in" clinician-initiated model (intervention) and an "opt-out" system-initiated model (control). The trial will assess program reach (the number and proportion of eligible parents receiving HB4HNEKids) and participant representativeness to identify the most effective, equitable, and efficient approach to scaling up care for families during the first 2000 days. METHODS: A randomized controlled trial will be conducted in 6 Child and Family Health Service (CFHS) sectors (39 CFHS units) within the Hunter New England region of New South Wales, Australia. The 6 CFHS sectors will be randomized in a 1:1 ratio to one of 2 arms, stratified by sector location and average number of births per annum. The intervention arm (a clinician-initiated opt-in model of care) will involve a series of implementation support strategies delivered to CFHS staff (ie, training, audit, and feedback) to support clinicians in connecting eligible families to the HB4HNEKids program. The control arm (a system-initiated opt-out model of care) will use existing health service data to identify participants meeting the predefined eligibility criteria to automatically initiate the commencement of HB4HNEKids messages to families (ie, will not require input from CFHS staff). The primary outcome will assess the reach and representativeness of participants receiving HB4HNEKids. Secondary outcomes will include child health behaviors (breastfeeding rates; age of introduction to solids; child fruit, vegetable, and discretionary food intakes; as well as child immunization rates) captured via parent survey, as well as the cost-effectiveness. RESULTS: This trial commenced in July 2024, and as of July 2025 enrolled 4212 participants. Data collection (via parent survey) commenced in January 2025 and is projected to end in July 2027. CONCLUSIONS: Currently, little is known about the most effective model of scaling up digital health interventions. This trial will generate novel evidence for informing the effective scale-up of evidence-based health promotion programs that aim to provide universal care, which are needed to maximize the potential population health gains. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Register ACTRN12624000655549p; https://tinyurl.com/4krc9za5. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/81390.
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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 | 0.073 | 0.065 |
| Meta-epidemiology (narrow) | 0.010 | 0.006 |
| Meta-epidemiology (broad) | 0.017 | 0.009 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.013 | 0.015 |
| Insufficient payload (model declined to judge) | 0.095 | 0.022 |
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".