Does Delivery of a Nutrition and Exercise Intervention Simultaneously or Sequentially Prevent Excessive Gestational Weight Gain? The NELIP Trial
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effectiveness of sequential versus simultaneous introduction of nutrition and exercise behavior intervention strategies at preventing early or late excessive gestational weight gain (EGWG). METHODS: Parallel-group randomized trial at a single center (London, Canada) included 84 healthy pregnant individuals (mean age: 32.4 ± 3.4 yr; prepregnancy body mass index: 26.0 ± 5.1 kg·m -2 ) randomly allocated at 12-18 wk gestational age (GA; baseline) to either NE (nutrition and exercise delivered simultaneously; n = 25), N + E (nutrition first and exercise added at 25 wk GA; n = 29) or E + N (exercise first and nutrition added at 25 wk GA; n = 30). Early weight gain was analyzed weekly from baseline up to 25 wk GA (midpoint) and later from midpoint to 36 wk GA. RESULTS: From baseline to 25 wk, no differences were found for the amount of EGWG (NE: 1.6 ± 1.4 kg, N + E: 1.9 ± 1.7 kg, E + N: 1.3 ± 1.3 kg; P = 0.62) or for the number of those who gained excessively ( P = 0.38). However, from midpoint to final assessment, N + E gained more excessive weight (2.9 ± 2.3 kg; NE 2.5 ± 1.7 kg; E + N 1.6 ± 1.3 kg; P = 0.002, respectively) with more participants ( n = 21; P = 0.03) gaining excessively than NE ( n = 11) and E + N ( n = 12). CONCLUSIONS: Delivering the components of a nutrition and exercise intervention sequentially or simultaneously equally influences early EGWG. However, after 25 wk GA, introducing nutrition sequentially into an exercise program (E + N) or the continuation of combined nutrition and exercise (NE), mitigated EGWG compared with introducing exercise sequentially to a nutrition program (N + E). Sequencing of components may be an important factor to consider for intervention success, specifically by introducing an exercise component first followed by nutrition led to superior overall program adherence, with the least amount of EGWG.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".