Physiological Indices During Continuous And Sinusoidal Running Exercise In Football Players
Bibliographic record
Abstract
It is well established that intermittent exercises are very specific for performance in field and combat sports. However, few studies have examined the effect of sinusoidal oscillation in exercise intensity could maintain or ameliorates energetic coast. The aim of this work was to investigate if the variation of exercise allures (constant speed (CT-sp) vs. sinusoidal speed (SIN-sp) on physiological responses during submaximal exercise. Ten male footballers (182.6 ± 6.2 cm and 79.6 ± 6.4 kg) were volunteered to participate to this study. After measuring maximal aerobic velocity (MAV) and corresponding maximal oxygen uptake (VO2max) during the University of Montreal incremental test', subjects performed, in a randomized order, six test sessions of 10 min at different intensities (65, 75 and 85% VMA) in either CT-sp (a constant distance of 12.5 m between cones) or SIN-sp with an amplitude of 3 km.h-1 (alternating distances of 9.85 m and 15.15 m in each speed). Heart rate (HR), blood lactate concentration [La] and oxygen uptake (VO2) were determined during each test session. Results showed that HR, [La] and VO2 were higher during SIN-sp than CT-sp in the different exercise intensities. In addition, multiple linear regression was performed as below: Y = a *X1+ b * X2+ C with X2 corresponding to exercise allure (EA) (Vcte vs Vsin) as independent variable (taking the value of 0 or 1 ) to study the relationships: VO2 / VO2max = a * HR / HR max + b * EA + C, [La] / [Lamax] = a * HR / HR max + b * TE + C and [La] / [Lamax ] VO2 = a * / b * VO2max + TE + C. The statistical analysis shows that only the VO2/ VO2max and HR / HRmax were significant (P <0.001). The results of this study raise the question of the effectiveness of sinusoidal training allure on cardiorespiratory and metabolic parameters in football players.
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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".