Comparative Assessment of Diet Quality and Adherence to a Structured Nutrition and Exercise Intervention Compared with Usual Care in Pregnancy in a Randomized Trial
Bibliographic record
Abstract
In trials testing the efficacy of diet and exercise modifications during pregnancy on health outcomes, assessment of participant adherence to interventions of diet and exercise is rarely reported, with few standard methods existing to measure adherence. We aimed to assess the maternal diet quality and create an algorithm to evaluate adherence to an intervention of high protein/dairy nutrition and walking exercise from early pregnancy to birth. In Be Healthy in Pregnancy randomized trial (NCT01693510), diet quality was measured using scores from an adapted PrimeScreen food frequency questionnaire, nutrient intake assessed by 3-day diet records, and physical activity using accelerometry at 14–17 (early), 26–28 (middle), and 36–38 (late) weeks’ gestation. A novel adherence score was derived by combining data for compliance with prescribed protein and energy intakes and daily step counts in the intervention group. Between-group diet quality scores and changes in adherence scores in the intervention group across pregnancy were analyzed using generalized estimating equations adjusted for prepregnancy body mass index and study site. Diet scores were similar for intervention (n = 55) and control (n = 56) groups at baseline but only the intervention group significantly improved and maintained their scores from early to middle (18.7 ± 7.6 vs. 22.9 ± 6.1; P < 0.001) and late (22.5 ± 6.9; P < 0.008) pregnancy. Protein intake was significantly (P < 0.001) higher but energy intakes were similar in the intervention group compared with those in the control group. Adherence scores for the intervention increased significantly (P < 0.01) from early (1.52 ± 0.70) to midpregnancy (1.89 ± 0.82) but declined from midpregnancy to late (1.55 ± 0.78; P < 0.0005) pregnancy primarily owing to lower step counts. Adherence to an intervention may decline toward the end of pregnancy, particularly in maintaining physical activity. Creation of adherence scores is a feasible approach to measure combined intervention compliance for diet and physical activity and may increase transparency in interpreting results of randomized trials in pregnancy. This trial was registered at clinicaltrials.gov as NCT01689961 (https://clinicaltrials.gov/ct2/show/NCT01689961?cond=NCT01689961&rank=1; registered on 21 September 2012).
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".