Impact of data averaging strategies on VO<sub>2max</sub> assessment: Mathematical modeling and reliability
Bibliographic record
Abstract
Background No consensus exists on how to average data to optimize O2max assessment. Although the O2max value is reduced with larger averaging blocks, no mathematical procedure is available to account for the effect of the length of the averaging block on O2max. Aims To determine the effect that the number of breaths or seconds included in the averaging block has on the O2max value and its reproducibility and to develop correction equations to standardize O2max values obtained with different averaging strategies. Methods Eighty‐four subjects performed duplicate incremental tests to exhaustion (IE) in the cycle ergometer and/or treadmill using two metabolic carts (Vyntus and Vmax N29). Rolling breath averages and fixed time averages were calculated from breath‐by‐breath data from 6 to 60 breaths or seconds. Results O2max decayed from 6 to 60 breath averages by 10% in low fit ( O2max < 40 mL kg−1 min−1) and 6.7% in trained subjects. The O2max averaged from a similar number of breaths or seconds was highly concordant (CCC > 0.97). There was a linear‐log relationship between the number of breaths or seconds in the averaging block and O2max (R2 > 0.99, P < 0.001), and specific equations were developed to standardize O2max values to a fixed number of breaths or seconds. Reproducibility was higher in trained than low‐fit subjects and not influenced by the averaging strategy, exercise mode, maximal respiratory rate, or IE protocol. Conclusions The O2max decreases following a linear‐log function with the number of breaths or seconds included in the averaging block and can be corrected with specific equations as those developed here.
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 | 0.040 | 0.153 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".