Nonexercise Equations for Determining Change in Cardiorespiratory Fitness
Bibliographic record
Abstract
PURPOSE: This study aimed to determine whether change in estimated cardiorespiratory fitness (eCRF) is associated with change in measured cardiorespiratory fitness (mCRF) independent of exercise amount and intensity over 24 wk. METHODS: Participants were 163 sedentary adults with abdominal obesity (mean ± SD waist circumference, 109.9 ± 11.5 cm) randomly assigned to (i) no-exercise control (n = 42); (ii) low-amount, low-intensity exercise (LALI; n = 39); (iii) high-amount, low-intensity exercise (HALI; n = 51); and (iv) high-amount, high-intensity exercise (HAHI; n = 31). mCRF was measured using a maximal treadmill test at baseline, 8, 16, and 24 wk. eCRF was calculated using a published nonexercise equation with the following variables: sex, age, waist circumference, resting heart rate, and self-selected physical activity. RESULTS: Participants attended 115 of 120 exercise sessions prescribed (96.0% ± 4.0% adherence). eCRF change from baseline to 8, 16, and 24 wk was not different from mCRF change for control, LALI, or HALI (P = 0.03). In HAHI, eCRF change was significantly greater than mCRF change at all time points (P < 0.001). Further analysis revealed that change in eCRF systematically overestimated and underestimated small and large changes in mCRF, respectively, in all groups (P < 0.001). CONCLUSIONS: eCRF change was associated with mCRF change at 24 wk independent of exercise amount but not intensity. Systematic variation between eCRF and mCRF highlights a possible limitation when using eCRF to follow change in mCRF, specifically that eCRF does not capture the individual variability of the mCRF response.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".