Dose-Response Effects of the Text4baby Mobile Health Program: Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Mobile health (mHealth) is growing rapidly, but more studies are needed on how to optimize programs, including optimal timing of messaging, dose of exposure, and value of interactive features. This study evaluates final outcomes of text4baby (a text message service for pregnant and postpartum women) from a randomized trial performed in a population of pregnant female soldiers and family members. OBJECTIVE: The study aims were to evaluate (1) treatment effects and (2) dose-response effects of text4baby on behavioral outcomes compared to control (no text4baby) condition. METHODS: The study was a randomized trial of text4baby at Madigan Army Medical Center. Female military health beneficiaries who met inclusion criteria were eligible for the study. Participants provided consent, completed a baseline questionnaire, and then were randomized to enroll in text4baby or not. They were followed up at 3 time points thereafter through delivery of their baby. Generalized estimating equation models were used to evaluate outcomes. We examined treatment effects and the effects of higher doses of text4baby messages on outcomes. RESULTS: We report descriptive statistics including dosage of text messages delivered. The main finding was a significant effect of high exposure to text4baby on self-reported alcohol consumption postpartum (OR 0.212, 95% CI 0.046-0.973, P=.046), as measured by the question "Since you found out about your pregnancy, have you consumed alcoholic beverages?" The text4baby participants also reported lower quantities of alcohol consumed postpartum. CONCLUSIONS: Studies of text4baby have helped to build the mHealth evidence base. The effects of text4baby offer lessons for future scalable mHealth programs and suggest the need to study dose-response effects of these interventions.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".