Non-exercise Equations For Determining Change In Cardiorespiratory Fitness
Bibliographic record
Abstract
CRF is a strong and independent predictor of morbidity and all-cause mortality beyond traditional risk factors. However, CRF is not routinely measured in clinical settings where cost, time, training, and discomfort on behalf of the patient have all been cited as barriers to routine integration. An alternative to measuring CRF is to estimate it using a non-exercise CRF equation. It is currently unknown whether eCRF can be used to estimate change in mCRF following the adoption of regular exercise. PURPOSE: To determine whether change in estimated cardiorespiratory fitness (eCRF) is associated with change in measured CRF (mCRF) independent of exercise amount and intensity over 24 weeks. METHODS: Participants were 163 sedentary adults with abdominal obesity (waist circumference: mean 109.9 (SD; 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), iv) high-amount, high-intensity exercise (HAHI; n=31). mCRF was measured using a maximal treadmill test at baseline, 8, 16 and 24 weeks. eCRF was calculated using a published non-exercise equation with the following variables: sex, age, waist circumference, resting heart rate, 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 weeks was not different from mCRF change for control, LALI or HALI (P=.03). In HAHI, eCRF change was significantly greater than mCRF change at all time points (P<.001). Change in mCRF and eCRF at 24 weeks were separated into tertiles to determine whether there were systematic differences between the two measures. Tertile scores revealed that for LALI and HALI, eCRF change significantly overestimated the lowest mCRF tertile (P=.001) and underestimated the highest mCRF tertile (P=.003). For HAHI, eCRF change overestimated mCRF within both the lowest and middle tertile (P=.005). CONCLUSION: eCRF change was associated with mCRF change at 24 weeks 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".