A Smartphone App to Promote Healthy Weight Gain, Diet, and Physical Activity During Pregnancy (HealthyMoms): Protocol for a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Excessive gestational weight gain is common and associated with adverse outcomes both in the short and long term. Although traditional lifestyle-based interventions have shown to mitigate excess gestational weight gain, little is known about whether mobile Health (mHealth) apps can promote healthy weight gain, diet, and physical activity during pregnancy. OBJECTIVE: The primary aim of the HealthyMoms trial is to determine the effectiveness of a smartphone app (HealthyMoms) for mitigating excess gestational weight gain during pregnancy. Secondary aims are to determine the effectiveness of the app on dietary habits, physical activity, body fatness, and glycemia during pregnancy. METHODS: HealthyMoms is a two-arm randomized controlled trial. Women are being recruited at routine visits at the maternity clinics in Linköping, Norrköping and Motala, Sweden. Women are randomized to the control or intervention group (n=150 per group). All women will receive standard care, and women in the intervention group will also receive the HealthyMoms smartphone app. RESULTS: Recruitment of participants to the trial was initiated in October 2017, and 190 women have so far completed the baseline measurement. The baseline measures are estimated to be finalized in December 2019, and the follow-up measures are estimated to be completed in June 2020. CONCLUSIONS: This project will evaluate a novel smartphone app intervention integrated with existing maternity health care. If successful, it has great potential to be implemented nationally in order to promote healthy weight gain and health behaviors during pregnancy. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/13011.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".