The Design of a Randomized Clinical Trial to Evaluate a Pragmatic and Scalable eHealth Intervention for the Management of Gestational Weight Gain in Low-Income Women: Protocol for the SmartMoms in WIC Trial
Bibliographic record
Abstract
BACKGROUND: Less than one-third of women gain an appropriate amount of weight during pregnancy, which can influence the long-term health of both the mother and the child. Economically disadvantaged women are the most vulnerable to maternal obesity, excessive weight gain during pregnancy, and poor birth outcomes. Effective and scalable health care strategies to promote healthy weight gain during pregnancy specifically tailored for these women are lacking. OBJECTIVE: This paper presents the design and protocol of a biphasic, community-based eHealth trial, SmartMoms in WIC, to increase the adherence to healthy gestational weight gain (GWG) recommendations in low-income mothers receiving women, infant, and children (WIC) benefits. METHODS: Phase 1 of the trial included using feedback from WIC mothers and staff and participants from 2 community peer advisory groups to adapt an existing eHealth gestational weight management intervention to meet the needs of women receiving WIC benefits. The health curriculum, the format of delivery, and incentive strategies were adapted to be culturally relevant and at an appropriate level of health literacy. Phase 2 included a pragmatic randomized controlled trial across the 9 health care regions in Louisiana with the goal of enrolling 432 women. The SmartMoms in WIC intervention is an intensive 24-week behavioral intervention, which includes nutrition education and exercise strategies, and provides the technology to assist with weight management, delivered through a professionally produced website application. RESULTS: Phase 1 of this trial was completed in July 2019, and recruitment for phase 2 began immediately thereafter. All data are anticipated to be collected by Spring 2023. CONCLUSIONS: The SmartMoms in WIC curriculum was methodically developed using feedback from community-based peer advisory groups to create a culturally relevant, mobile behavioral intervention for mothers receiving WIC benefits. The randomized clinical trial is underway to test the effectiveness of a sustainable eHealth program on the incidence rates of appropriate GWG. SmartMoms in WIC may be able to offer an innovative, cost-effective, and scalable solution for GWG management in women served by WIC. TRIAL REGISTRATION: ClinicalTrials.gov NCT04028843; https://clinicaltrials.gov/ct2/show/NCT04028843. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/18211.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.056 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".