Effect of Pregnancy in 42 Elite to World-Class Runners on Training and Performance Outcomes
Bibliographic record
Abstract
PURPOSE: The International Olympic Committee expert group on pregnancy has identified a paucity of information regarding training and performance in truly elite athletes. Thus, the purpose of this study was to quantify elite runners' training volume throughout pregnancy and postpartum competition performance outcomes. METHODS: Forty-two elite (>50% competed at the World Championships/Olympic) middle-/long-distance runners' training before, during, and after pregnancy (quality/quantity/type) data (retrospective questionnaire) and competition data (published online) were collected. RESULTS: Running volume decreased significantly ( P < 0.01) from the first trimester (63 ± 34 km·wk -1 ) to the third trimester (30 ± 30 km·wk -1 ). Participants returned to activity/exercise at ~6 wk postpartum and to 80% of prepregnancy training volumes by 3 months. Participants who intended to return to equivalent performance levels postpregnancy, there was no statistical decrease in performance in the 1 to 3 yr postpregnancy compared with prepregnancy, and ~56% improved performances postpregnancy. CONCLUSIONS: This study features the largest cohort of elite runners training and competition outcomes assessed throughout pregnancy, with training volumes being approximately two to four times greater than current guidelines. For the first time, performance was directly assessed (due to the quantifiable nature of elite running), and study participants who intended to return to high-level competition did so at a statistically similar level of performance in the 1- to 3-yr period postpregnancy. Taken together, this article provides much needed insights into current training practices and performance of elite pregnant runners, which should help to inform future training guidelines as well as sport policy and sponsor expectations around return to training timelines and performance.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".