Grocery Delivery to Support Healthy Weight Gain Among Pregnant Young Women With Low Income: Protocol for a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Excessive weight gain during pregnancy is associated with complications for both the mother and her infant including gestational diabetes, hypertensive disorders, operative delivery, and long-term obesity. A healthy diet during pregnancy promotes healthy gestational weight gain and determines fetal epigenetic programming in infants that impacts risk for future chronic disease. OBJECTIVE: This project will examine the impact of grocery delivery during pregnancy on the weight, diet, and health outcomes of young pregnant women and their infants. METHODS: A three-arm randomized controlled trial design will be performed. A total of 855 young pregnant women, aged 14-24 years, from across the state of Michigan will be enrolled and randomized equally into the three study arms. Participants in arm one (control) will receive usual care from the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC); arm two will receive WIC plus biweekly grocery delivery; and arm three will receive WIC plus biweekly grocery and unsweetened beverage delivery. Weight will be assessed weekly during pregnancy, and total pregnancy weight gain will be categorized as above, below, or within guidelines. Additionally, dietary intake will be assessed at three time points (baseline, second trimester, and third trimester), and pregnancy outcomes will be extracted from medical records. The appropriateness of pregnancy weight gain, diet quality, and occurrence of poor outcomes will be compared between groups using standard practices for multinomial regression and confounder adjustment. RESULTS: This study was funded in April 2021, data collection started in December 2021, and data collection is expected to be concluded in 2026. CONCLUSIONS: This study will test whether grocery delivery of healthy foods improves weight, diet, and pregnancy outcomes of young moms with low income. The findings will inform policies and practices that promote a healthy diet during pregnancy, which has multigenerational impacts on health. TRIAL REGISTRATION: ClinicalTrials.gov NCT05000645; https://clinicaltrials.gov/ct2/show/NCT05000645. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/40568.
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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 | 0.033 | 0.035 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.013 | 0.007 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.088 | 0.012 |
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".