Preliminary findings of emotion regulation in 12‐month‐old infants of mothers enrolled in a randomized controlled trial assessing a nutrition + exercise intervention
Bibliographic record
Abstract
Abstract Improved offspring emotion regulation (ER) has been associated with maternal intake of single nutrients or exercise during pregnancy but has not been examined in randomized trials. We investigated the impact of a maternal nutrition + exercise intervention during pregnancy on offspring ER at 12 months of age. Mothers in the Be Healthy In Pregnancy randomized controlled trial were randomly assigned to an individualized nutrition + exercise intervention plus usual care (UC) or UC alone (control group). A multimethod assessment of infant ER using parasympathetic nervous system function (high frequency heart rate variability [HF‐HRV] and root mean square of successive differences [RMSSD]) as well as maternal reports of infant temperament (Infant Behavior Questionnaire‐ Revised short form) was completed with a subsample of infants of enrolled mothers (intervention = 9, control = 8). The trial was registered at www.clinicaltrials.gov (NCT01689961). We observed greater HF‐HRV (M = 4.63, SD = 0.50, p = .04, ƞ2p = .25) and RMSSD (M = 24.25, SD = 6.15, p = .04, ƞ2p = .25) in infants of mothers in the intervention versus control group. Intervention group infants also had higher maternally rated surgency/extraversion (M = 5.54, SD = 0.38, p = .00, ƞ2p = .65) and regulation/orienting (M = 5.46, SD = 0.52, p = .02, ƞ2p = .81), and lower negative affectivity (M = 2.70, SD = 0.91, p = .03, ƞ2p = .52). These preliminary results suggest that pregnancy nutrition + exercise interventions could improve infant ER but these findings require replication in larger, more diverse samples.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".