Comparison of Measures of Maximal and Submaximal Fitness in Response to Exercise
Bibliographic record
Abstract
INTRODUCTION: Adoption of physical activity (PA) consistent with current guidelines does not improve maximal cardiorespiratory fitness (mCRF; V˙O2peak) beyond the error of measurement for approximately 30% of adults. Whether PA improves measures of exercise tolerance at submaximal levels (submaximal cardiorespiratory fitness [sCRF]) independent of change in mCRF is unknown. Here we assessed the relationship between exercise-induced changes in mCRF and sCRF. METHODS: Twenty-five physically inactive men 30-60 yrs old (mean ± SD = 44.3 ± 9.1 yr) completed 4 wk of supervised exercise consisting of 30 min of exercise, five times per week at 65% mCRF. mCRF was assessed using a maximal treadmill test. sCRF was measured as follows: 1) exercise tolerance, the distance traveled during a 12-min time trial on a treadmill, and 2) change in heart rate (HR) at submaximal work rates during the maximal treadmill test. Daily PA was measured by accelerometry at baseline and 4 wk. RESULTS: mCRF (P = 0.009) and both measures of sCRF (P < 0.001) improved at 4 wk. No change in measures of daily PA was observed at 4-wk compared with baseline (P > 0.05). No association was observed between exercise-induced change in mCRF and change in either measure of sCRF (P > 0.05) after exercise training. In the group of participants who did not improve mCRF beyond the measurement error (n = 13, or 52%), we observed a significant improvement in both measures of sCRF (P < 0.001). Among these 13 individuals, all improved in at least one measure of sCRF. CONCLUSION: Exercise-induced improvements in mCRF were not associated with improvements in either measure of sCRF. Improvements in submaximal measures of cardiorespiratory fitness are observed in the absence of change in mCRF. Measures of sCRF capture peripheral adaptations to exercise not captured by measures of mCRF alone.
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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 | 0.001 |
| 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) | 0.002 | 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".