Changes in running economy, respiratory exchange ratio and VO2max in runners following a 10-day altitude training camp
Bibliographic record
Abstract
International Journal of Exercise Science 10(4): 629-639, 2017. Running economy (RE) and VO2max are important predictors of endurance performance for elite and semi-elite endurance athletes, with RE being an appropriate predictor in a homogenous running population. Altitude training has been observed to change RE (mL.kg-1.min-1), and VO2max due to alterations resulting from acclimatization. This study tracked changes in RE and VO2max before and after a 10-day altitude training camp at 1828 meters. VO2max, RE expressed calorically, and respiratory exchange ratio (RER), were measured below anaerobic threshold (AT) to observe differences between pre-and post-altitude training. Eight varsity cross-country runners between the ages of 18 and 22 years performed an incremental treadmill test, pre- and post-10-day altitude training. Paired samples t-tests were used to statistically analyze the data. Average RE (VO2 mL.kg-1.min-1)improved following altitude intervention (M= 56.44 ± 4.28) compared to pre-altitude training (61.30 ± 7.56). These differences were statistically significant t(7)= 2.71, p =.014. RE expressed as kcals.kg-1.km-1 improved following altitude training (16.73 ± 2.96) compared to (18.44 ± 4.04) pre-altitude training and was statistically significant t(7) =3.08, p = .008. RER taken during the last minute of steady-state was higher (0.97, ± .019) post-altitude training, compared to (0.90 ± .043) pre-altitude. These differences were statistically significant t(7) -3.62, p =.008. VO2max (mL.kg-1.min-1) was lower in 6 out of 8 participants (63.91, ± 8.65) post-altitude compared to (69.90, ± 10.80) pre-altitude and was statistically significant t(7) = 2.33, p =.026. The observed improvements in RE may be beneficial for endurance athletes competing and/or training at moderate altitudes near 1828 meters.
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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".