VO2peak Prediction and Exercise Prescription for Pregnant Women
Bibliographic record
Abstract
PURPOSE: The present study was designed to develop and validate a prediction equation for peak oxygen consumption VO2peak) using a progressive treadmill test and to refine the current target HR exercise guidelines for pregnancy (PARmed-X for Pregnancy). METHODS: One hundred fifty-six women between 16 and 22 wk of gestation performed the test to volitional fatigue (peak exercise test). Data from every fourth subject were used to form the cross-validation group. The women were separated into two age groups; 20-29 (N = 60) and 30-39 (N = 96) yr of age and then further separated into fit (VO2peak at the top 25th percentile), unfit (VO2peak at the bottom 25th percentile), and active (between these two ranges). HR and VO2peak values were used in the regression equation to predict target HR ranges at 60 and 80% VO2peak. RESULTS: The prediction equation (R2 = 0.72, R2adjusted = 0.71 and SEE = 2.7) was compared with cross validation (N = 39; P = 0.78). Fit women had a VO2peak > or = 27.2 mL.kg(-1).min(-1) and > or = 26.1 mL.kg.min for ages 20-29 and 30-39 yr, respectively, representing the top 25th percentile. Unfit women had a VO2peak of < or = 21.0 mL.kg(-1).min(-1) and < or = 19.6 mL.kg(-1).min(-1), respectively, representing the bottom 25th percentile. HR/VO2peak regression lines for each fitness level were used to generate the target HR zones in each age group. CONCLUSION: This is the first study to provide a validated prediction equation of VO2peak for pregnant women using a progressive treadmill exercise test. The defined target HR zones based on age and the appropriate fitness levels can be used for exercise prescription in healthy pregnant women.
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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 | 0.008 |
| 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 | 0.000 |
| 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".