Safety of Heavy Resistance Exercise During Pregnancy and Postpartum: A Controlled, Prospective Cohort Study
Bibliographic record
Abstract
Objective : To address the knowledge gap on the influence of heavy free-weight resistance exercise on the health of the pregnant athlete and infant. Design : This is a prospective, controlled cohort study with two groups of pregnant women; those engaging in mainly heavy free-weight resistance exercise (REX, n = 33) and those regularly physically active (REF, n = 20). Data originate from eight questionnaires answered prospectively throughout pregnancy and 12 months postpartum. Variables cover exercise routines, pregnancy symptoms and complications, fetal growth, delivery, and mother and infant’s health. Results : Bayesian logistic regression with random and fixed effects identified group membership as a significant predictor of proteinuria, with REX having higher exposure (posterior inclusion probability [PIP] = 1.00; β 1 = 197.07, 95% credible interval [CrI] [163.05, 231.08]). Further, Bayesian logistic regression with random and fixed effects confirmed a higher risk of urinary incontinence in the REF group during pregnancy (PIP = 0.98; β 1 = −1.17, 95% CrI [−1.89, −0.46]). Gas incontinence was also found more common in the REF group during pregnancy (PIP = 1.00; β 1 = −7.62, 95% CrI [−8.68, −6.57]) and postpartum (PIP = 0.91; β 1 = −49.39, 95% CrI [−53.57, −45.43]). There were no significant differences in the postpartum period for fecal and urinary incontinence, injuries, infant health outcomes (all PIP < 0.01), or need for assisted delivery (PIP = 0.33). Conclusion : Female athletes experienced in heavy resistance exercise, who continue training during pregnancy and postpartum, do not face increased pregnancy complications. These findings provide evidence supporting safety of such exercise. Yet we highlight the need for a confirmatory study under full clinical supervision.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".