Oxygen Uptake, Heart Rate, and Lactate Responses for Continuous Forward Running and Stop-and-Go Running With and Without Directional Changes
Bibliographic record
Abstract
Bekraoui, N, Boussaidi, L, Cazorla, G, and Léger, L. Oxygen uptake, heart rate, and lactate responses for continuous forward running and stop-and-go running with and without directional changes. J Strength Cond Res 34(3): 699-707, 2020-The purpose of this study is to measure and quantify the differences for the gross oxygen uptake (V[Combining Dot Above]O2), heart rate (HR), and blood lactate ([LA]) responses to various modes of in-line running. Twelve healthy participants did an indirect continuous multistage field test to determine the maximal aerobic speed (MAS) before randomly performing in separate sessions 1 of the 3 running protocols at 3 intensities (60, 70, and 80% MAS) (a) in-line continuous running (IR), (b) fractioned running (FR) with stop and go every 20 m, and (c) 20-m shuttle run (SR). V[Combining Dot Above]O2 varied between 34.2 ± 2.5 and 54.6 ± 5.4 ml·kg·min, HR between 141 ± 14 and 184 ± 9 b·min, and [LA] between 4.2 ± 0.8 and 6.6 ± 0.9 mmol·L. In-line continuous running V[Combining Dot Above]O2 was lower than those of SR and FR at all intensities (p < 0.05). However, SR V[Combining Dot Above]O2 were higher than those of FR at 80% MAS only. Heart rate was similar in SR and FR but lower in IR at all intensities (p < 0.05). No significant differences were recorded in [LA]. Because only V[Combining Dot Above]O2 was affected by the introduction of a 180° angle directional change (SR vs. FR) at 80% MAS, the use of HR to assess such activities at such intensities will result in underestimating workloads. This study offers new insights into V[Combining Dot Above]O2, HR, and [LA] associated with accelerations while running with and without directional changes at submaximal speeds.
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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) | 0.001 | 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) | 0.003 | 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".