Fit for comparison: controlling for cardiorespiratory fitness in exercise physiology studies of sex as a biological variable
Bibliographic record
Abstract
Abstract More studies in exercise physiology are investigating sex as a biological variable, but the potential confounding effect of cardiorespiratory fitness is often neglected. As maximal oxygen uptake () correlates with many physiological outcomes at rest and in response to exercise, differences in between male and female participants may hinder interpretation. Here we revisit decades‐old arguments that advocate for matching males and females for normalized to fat‐free mass (FFM) when investigating sex differences in the context of exercise. The rationale for using FFM to normalize , as opposed to total body mass, is that females, on average, have a greater proportion of body fat than males and body fat does not contribute to . Using a multistudy dataset of males (n = 54) and females (n = 54) matched for per FFM, we illustrate the different approaches to normalization and the effects of poor or incorrect matching on interpretation. Modern assessments of body composition allow for segregation of bone from total FFM and regional measures of body composition; however neither approach seems to be an improvement on whole‐body FFM as the normalization factor for . A group‐level difference of less than 5% for per unit FFM is a strong starting point for comparisons between males and females, but the allowable difference depends on the extent to which cardiorespiratory fitness influences the variable of interest and other competing study design decisions. Researchers should be encouraged to normalize to FFM for exercise physiology studies investigating sex differences. image
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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 | 0.118 | 0.269 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".