A Critical Evaluation of Current Methods for Exercise Prescription in Women and Men
Bibliographic record
Abstract
Common methods to prescribe exercise intensity are based on fixed percentages of maximum rate of oxygen uptake (V˙O2max), peak work rate (WRpeak), maximal HR (HRmax). However, it is unknown how these methods compare to the current models to partition the exercise intensity spectrum. PURPOSE: Thus, the aim of this study was to compare contemporary gold-standard approaches for exercise prescription based on fixed percentages of maximum values to the well-established, but underutilized, "domain" schema of exercise intensity. METHODS: One hundred individuals participated in the study (women, 46; men, 54). A cardiopulmonary ramp-incremental test was performed to assess V˙O2max, WRpeak, HRmax, and the lactate threshold (LT), and submaximal constant-work rate trials of 30-min duration to determine the maximal lactate steady-state (MLSS). The LT and MLSS were used to partition the intensity spectrum for each individual in three domains of intensity: moderate, heavy, and severe. RESULTS: V˙O2max in women and men was 3.06 ± 0.41 L·min and 4.10 ± 0.56 L·min, respectively. Lactate threshold and MLSS occurred at a greater %V˙O2max and %HRmax in women compared with men (P < 0.05). The large ranges in both sexes at which LT and MLSS occurred on the basis of %V˙O2max (LT, 45%-74%; MLSS, 69%-96%), %WRpeak (LT, 23%-57%; MLSS, 44%-71%), and %HRmax (LT, 60%-90%; MLSS, 75%-97%) elicited large variability in the number of individuals distributed in each domain at the fixed-percentages examined. CONCLUSIONS: Contemporary gold-standard methods for exercise prescription based on fixed-percentages of maximum values conform poorly to exercise intensity domains and thus do not adequately control the metabolic stimulus.
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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 | 0.113 | 0.199 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".