Effect of a Health System–Sponsored Mobile App on Perinatal Health Behaviors: Retrospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: Pregnancy mobile apps are becoming increasingly popular, with parents-to-be seeking information related to their pregnancy and their baby through mobile technology. This increase raises the need for prenatal apps with evidence-based content that is personalized and reliable. Previous studies have looked at whether prenatal apps impact health and behavior outcomes among pregnant and postpartum individuals; however, research has been limited. OBJECTIVE: The primary objective of this study is to assess whether the use of a health system-sponsored mobile app-Circle by Providence-aimed at providing personalized and reliable health information on pregnancy, postpartum recovery, and infant care is associated with improved health outcomes and increased healthy behaviors and knowledge among users. METHODS: This observational study compared app users and app nonusers using a self-reported survey and electronic medical records. The study took place over 18 months and was conducted at Providence St. Joseph Health in Portland, Oregon. The sample included patients who received prenatal care at one of seven Providence clinics and had a live birth at a Providence hospital. Recruitment occurred on a rolling basis and only those who completed the survey were included. Survey respondents were separated into app users and app nonusers, and survey responses and clinical outcomes were compared across groups using univariate and adjusted multivariate logistic regression. RESULTS: A total of 567 participants were enrolled in the study-167 in the app user group and 400 in the nonuser group. We found statistically significant differences between the two groups for certain behavior outcomes: subjects who used the app had 75% greater odds of breastfeeding beyond 6 months postpartum (P=.012), were less likely to miss prenatal appointments (P=.046), and were 50% more likely to exercise 3 or more times a week during pregnancy (P=.04). There were no differences in nutritional measures, including whether they took prenatal vitamins, ate 5 fruits or vegetables a day, or drank caffeine. We found no differences in many of the infant care outcomes; however, there was an increase in awareness of "purple crying." Finally, there were no significant differences in measured clinical health outcomes, including cesarean births, length of hospital stays (in minutes), low birth weight infants, preterm births, small-for-gestational-age births, large-for-gestational-age births, and neonatal intensive care unit stays. CONCLUSIONS: The use of the Circle app, which provides access to personalized and evidence-based health information, was associated with an increase in certain healthy behaviors and health knowledge, although there was no impact on clinical health outcomes. More research is needed to determine the impact of mobile prenatal apps on healthy pregnancies, clinical health outcomes, and infant care.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".