The Effects of Utilizing the Valsalva Acceleration Technique on Speed and Power Performance
Bibliographic record
Abstract
There is a growing trend among trainers and coaches to instruct their athletes to perform the Valsalva acceleration technique (VAT) during sprinting and jumping actions to improve performance. However, to our knowledge there is no literature that has correlated the physiological responses to the Valsalva maneuver to increased speed and power performance. PURPOSE: To evaluate the effects of performing the Valsalva maneuver on speed and power performance. METHODS: 11 untrained male and female subjects (ages 21–33 years) completed two 40 yard (36.6m) sprints, two Wingate tests, and two vertical jump tests (one trial using the VAT and one control (C) trial). The trials for all tests were assigned in random order and subjects had at least an hour rest between trials. During the VAT trials subjects performed a valsalva maneuver for 3 seconds prior to performing each test. A second VAT was also done at the 15s mark of the Wingate test. Measurements of time to completion and 10m acceleration were analyzed for the 40 yard sprint. Jump height and flight time were observed for the vertical jump test, while peak power achieved and power produced at the 15 second mark were examined during the Wingate test. RESULTS: The mean 40 yard sprint time while subjects performed the VAT was 5.76 seconds while the mean control time was 5.77s (P >0.05). Similarly, the subjects displayed almost equivocal 10m acceleration values in both conditions (4.47m/s2 VAT and 4.44 m/s2 control). During the Wingate test in the VAT condition, subjects averaged a peak power of 10.78 W/kg versus a mean of 11.04W/kg in the control condition (P >0.05). There was a significant difference (P >0.05) in power production from 15s to 20s during the Wingate test (VAT= 8.00 W/kg, control= 8.36 W/kg). There was no difference in flight time or jump height between the C or VAT conditions. CONCLUSION: The data suggests that the Valsalva acceleration technique does not improve speed and power test parameters in untrained subjects.
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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 | 0.002 |
| 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".