Optimizing Gestational Weight Gain With the Eating4Two Smartphone App: Protocol for a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Approximately 50% of women gain excessive weight in pregnancy. Optimizing gestational weight gain is important for the short- and long-term health of the childbearing woman and her baby. Despite this, there is no recommendation for routine weighing in pregnancy, and weight is a topic that many maternity care providers avoid. Resource-intensive interventions have mainly targeted overweight and obese women with variable results. Few studies have examined the way that socioeconomic status might influence the effectiveness or acceptability of an intervention to participants. Given the scale of the problem of maternal weight gain, maternity services will be unlikely to sustain resource intensive interventions; therefore, innovative strategies are required to assist women to manage weight gain in pregnancy. OBJECTIVE: The primary aim of the trial was to examine the effectiveness of the Eating4Two smartphone app in assisting women of all body mass index categories to optimize gestational weight gain. Secondary aims include comparing childbirth outcomes and satisfaction with antenatal care and examining the way that relative advantage and disadvantage might influence engagement with and acceptability of the intervention. METHODS: This randomized controlled trial will randomize 1330 women to control or intervention groups in 3 regions of different socioeconomic status. Women will be recruited from clinical and social media sites. The intervention group will be provided with access to the Eating4Two mobile phone app which provides nutrition and dietary information specifically tailored for pregnancy, advice on food serving sizes, and a graph that illustrates women's weight change in relation to the range recommended by the Institute of Medicine. Women will be encouraged to use the app to prompt conversations with their maternity care providers about weight gain in pregnancy. The control group will receive routine antenatal care. RESULTS: Recruitment has commenced though the recruitment rate is slower than expected. Additional funds are required to employ research assistants and promote the study in an advertising campaign. CONCLUSION: Feasibility testing highlighted the inadequacy of the original recruitment strategy and the need to provide the app in both major platforms (Android and iOS). Smartphone technologies may offer an effective alternative to resource intensive strategies for assisting women to optimize weight gain in pregnancy. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry ACTRN12617000169347; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=371470 (Archived by WebCite at http://www.webcitation.org /6zDvgw5bo). REGISTERED REPORT IDENTIFIER: RR1-10.2196/9920.
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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 | 0.049 | 0.045 |
| Meta-epidemiology (narrow) | 0.007 | 0.004 |
| Meta-epidemiology (broad) | 0.014 | 0.008 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.125 | 0.019 |
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".