A Promising Food-Coaching Intervention Program to Achieve Optimal Gestational Weight Gain in Overweight and Obese Pregnant Women: Pilot Randomized Controlled Trial of a Smartphone App
Bibliographic record
Abstract
BACKGROUND: Traditional dietary recommendations for achieving optimal gestational weight gain are ineffective for pregnant women due to the lack of real-time communication and tedious consultation processes. OBJECTIVE: In this pilot study, we aimed to determine the feasibility of a novel food-coaching smartphone app for controlling gestational weight gain and macronutrient intake among overweight and obese pregnant women. METHODS: We designed a randomized controlled trial and recruited 30 overweight and obese pregnant women (1:1 ratio) during 18-20 weeks of gestation and followed them up after 4 and 8 weeks, respectively. Both groups received standard pregnancy dietary orientation at recruitment, while the intervention group received 8 weeks of real-time food coaching via a smartphone app. This food-coaching smartphone app (Glycoleap, Holmusk, Singapore) aimed to improve care and outcomes for people with diabetes. Pregnant women using this app were able to upload food images (eg, a picture of a meal, a drink, or a dessert) and received real-time and detailed food-coaching comments and guidance provided by professional dietitians during the day (8 AM to 8 PM). We recorded detailed characteristics during recruitment and examined anthropometry at all visits. We compared the mean differences of the 8-week gestational weight gain and macronutrient intake between the two groups. RESULTS: Upon study completion, three subjects dropped out from the intervention, and one gave birth prematurely in the control group. The acceptance rate of the smartphone app was 90%. More participants achieved optimal gestational weight gain per week in the intervention group (8/12, 67%) than in the control group (5/14, 36%). After the 8-week intervention, women in the intervention group appeared to have lower gestational weight gain (mean difference=-0.08 kg; 95% CI -1.80 to 1.63) and cholesterol intake (mean difference=-31.73 mg; 95% CI -102.91 to 39.45) than those in the control group. CONCLUSIONS: Our findings showed that this food-coaching smartphone app is feasible and favorable for weight gain control and cholesterol intake control among overweight and obese pregnant women. Although our results were not significant (perhaps, attributed to the small sample size), it provided proof of concept for the feasibility of applying such technology in future randomized controlled trials with a larger sample size, an earlier intervention onset, and a longer follow-up for overweight and obese pregnant women.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".