THE RELATIONSHIP BETWEEN EVOKED MUSCLE PROPERTIES, FATIGUE, STRENGTH, AND POWER IN MAXIMAL EFFORT SQUAT JUMPS
Bibliographic record
Abstract
Electrically evoked muscle twitch properties may non-invasively provide information regarding the functional properties of muscle. The relationship between evoked properties and neuromuscular function in dynamic movement is unclear. PURPOSE To examine the association between evoked properties and indices of strength, power and fatigue. METHODS 6 females and 5 males, age 25±5 y (mean±SD), were placed in a knee extensor myograph from which evoked characteristics (peak force, rate of force development, half relaxation time and time to peak force) of the quadriceps femoris were measured using supramaximal electrical stimulation. Participants also performed a 5s maximum isometric voluntary contraction and a 30s isometric knee extension fatigue test. Subsequently, maximal effort squat jumps were performed at 0 to 70% of 1RM, from which the load-power relationship was determined. Relationships between evoked characteristics and strength and power indices are reported as Pearosn correlation coefficients. RESULTS Significant correlations were found between the evoked contractile characteristics and power, strength, and fatigue indices, ranging from 0.60 (95% confidence limits 0.00–0.88) to 0.80 (0.38 to 0.95). In general, faster contractile properties were associated with higher strength and power and lighter relative loads at peak power, yet greater fatigability. Stepwise regression showed that twitch force and rate of force development together were better predictors of peak power (R2=64%) than any single contractile characteristic, as were the combination of half relaxation time and rate of force development in predicting the fatigue index (R2=76%). CONCLUSION Evoked muscle properties moderately predict voluntary strength and power, and neuromuscular fatigue. The determination of evoked properties may assist in understanding individual differences in power and fatigue.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".