The Effectiveness of a Web-Based Application for a Balanced Diet and Healthy Weight Among Indonesian Pregnant Women: Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Pregnant women have self-declared that they have difficulties in estimating nutrient intakes. The Nutrition Information System for Indonesian Pregnant Women (SISFORNUTRIMIL) application was created as a dietary assessment and calorie-counting tool to guide pregnant women to eat the right portion sizes for each meal. OBJECTIVE: The study aimed to examine the effectiveness of the SISFORNUTRIMIL application in helping users achieve a balanced diet and healthy maternal weight gain in comparison to nonusers in Indonesia. METHODS: First-pregnancy women in the second trimester aged 19-30 years (N=112) participated in the randomized controlled trial. Recruited women who were eligible and consented to participate in the study were allocated into the intervention group, or application user (n=56), and the control group, or application nonuser (n=56). The intervention recommended that pregnant women consume at least 5 food groups and calculate a recommended average portion size for 12 weeks. Both groups were self-monitored and recorded their intake in food records for 3 days every week. The dietary diversity consumed, macro- and micronutrient intake, and maternal weight gain were assessed pre- and postintervention. Data were collected three times during the intervention. Diversity food consumption was measured by the Minimum Dietary Diversity for Women of reproductive age. Furthermore, the Indonesian recommended dietary allowances were used to refer to and validate appropriate energy and nutrient amounts. Independent sample t test was used to compare differences between the intervention and control groups. RESULTS: The mean dietary diversity score for the application user group (7.79, SD 1.20) was significantly greater than for the application nonuser group (7.02, SD 1.39; adjusted mean difference 0.77, 95% CI 0.28-1.25; d=0.28; P=.005). Macro- and micronutrient intake was significantly more in accordance with the dietary recommendations for the user group compared to the control group, including an energy daily intake of 156.88 kcal (95% CI 114.52-199.23; d=-1.39; P=.002), 102.43 g of carbohydrates (95% CI -125.2 to -79.60; d=-1.68; P=.02), 14.33 g of protein (95% CI 11.40-17.25; d=1.86; P<.001), and 10.96 g of fat (95% CI -13.71 to -8.20; d=-1.49; P<.001). Furthermore, there was a significantly higher intake of daily vitamins and minerals in the intervention group than in the control group. Other results showed that maternal weight gain in the intervention group was in accordance with the parameters of healthy weight gain. CONCLUSIONS: Recording food intake using the application was significantly effective in improving the dietary diversity consumed, improving adequate energy and nutrient intake, and producing healthy maternal weight during pregnancy. TRIAL REGISTRATION: ISRCTN Registry ISRCTN42690828; https://www.isrctn.com/ISRCTN42690828.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".