The Effects of Load, Crank Position, and Sex on the Biomechanics and Performance during an Upper Body Wingate Anaerobic Test
Bibliographic record
Abstract
INTRODUCTION: The upper body Wingate Anaerobic Test (WAnT) is a 30-s maximal effort sprint against a set load (percentage of body mass). However, there is no consensus on the optimal load and no differential values for males and females, even when there are well-studied anatomical and physiological differences in muscle mass for the upper body. Our goal was to describe the effects of load, sex, and crank position on the kinetics, kinematics, and performance of the upper body WAnT. METHODS: Eighteen participants (9 females) performed three WAnTs at 3%, 4%, and 5% of body mass. Arm crank forces, 2D kinematics, and performance variables were recorded during each WAnT. RESULTS: Our results showed an increase of ~49% effective force, ~36% peak power, ~5° neck flexion, and ~30° shoulder flexion from 3% to 5% load ( P < 0.05). Mean power and anaerobic capacity decreased by 15%, with no changes in fatigue index ( P < 0.05). The positions of higher force efficiency were at 12 and 6 o'clock. The least force efficiency occurred at 3 o'clock ( P < 0.05). Sex differences showed that males produced 97% more effective force and 109% greater mean power than females, with 11.7% more force efficiency ( P < 0.001). Males had 16° more head/neck flexion than females, and females had greater elbow joint variability with 17° more wrist extension at higher loads. Males cycled ~32% faster at 3% versus 5% WAnT load with a 65% higher angular velocity than females. Grip strength, maximal voluntary isometric contraction, mass, and height positively correlated with peak and mean power ( P < 0.001). CONCLUSIONS: In conclusion, load, sex, and crank position have a significant impact on performance of the WAnT. These factors should be considered when developing and implementing an upper body WAnT.
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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) | 0.003 | 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".