THE EFFECTS OF PROLONGED EXERCISE ON THE RUNNING ECONOMY OF TRAINED AND UNTRAINED INDIVIDUALS
Bibliographic record
Abstract
Previous research suggests that fatigue induced by long duration exercise may adversely influence running economy (RE). However, it is not fully understood how this fatigue affects the RE of individuals that are highly trained (HT) compared to relatively untrained (UT). PURPOSE To investigate the effects of a prolonged run at the ventilatory threshold (Tvent) on the RE of HT runners compared to UT individuals. METHODS Initially, 9 UT (VO2peak 51.3 ± 1.7 ml·kg−1·min−1) and 8 HT (VO2peak 71.3 ± 7.1 ml·kg−1·min−1) male subjects performed a continuous, incremental running protocol to determine Tvent. Measures of RE were obtained at 2.68 m·s−1 and at a velocity corresponding to Tvent. Subjects then completed a treadmill run at their Tvent velocity for a maximum of 60 minutes or until volitional fatigue. Immediately upon completion of the prolonged run, measures of RE were again determined at 2.68 m·s−1 and Tvent. RESULTS Initial test measures for RE were nonsignificant between the groups at 2.68 m·s−1 for VO2 in ml·kg−1·min−1 (HT 37.6 ± 2.7 vs. UT 37.4 ± 2.1; p = 0.813) and in L·min−1 (HT 2.56 ± 0.28 vs. UT 2.88 ± 0.50; p = 0.121). While the velocity at Tvent in m·s−1 was significantly different between groups (HT 3.77 ± 0.35 vs. UT 3.11 ± 0.13; p < 0.001) the RE at Tvent was nonsignificantly different for VO2 in ml·kg−1·km−1 (HT 233.9 ± 14.7 vs. UT 231.9 ± 12.5; p = 0.761). Following the prolonged run the HT runners were more economical at 2.68 m·s−1 than the UT group for VO2 in L·min−1 (2.47 ± 0.27 vs. 2.91 ± 0.40; p < 0.05). As well, there were significant differences in the duration of the prolonged run for the two groups (HT: 60·0min vs. UT: 43 ± 10 min; p < 0.05). CONCLUSION The RE of HT and UT individuals would seem to be comparable. However, following a prolonged run at Tvent, the economy of the UT group is adversely affected by fatigue suggesting that RE values are more stable in individuals that are HT compared to UT.
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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 | 0.000 | 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) | 0.001 | 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".