Relationship between muscle power, muscle volume and limb length in healthy male and female adolescents
Bibliographic record
Abstract
Objective Muscle power is essential for the activities of daily living. Muscle power production depends on numerous factors such as muscle size and length, muscle architecture and fiber type and varies with age during growth. The association between muscle power output during a jump and lower limb muscle volume and length in adolescents is largely unknown. This study determined the relationships between muscle power developed during a countermovement jump with lower limb muscle volume and length in adolescents aged between 16 and 19 years. Methods Forty healthy adolescent males (n = 20) and females (n = 20) aged 16 to 19 years underwent a counter-movement jump (CMJ) test. Muscle power (MP) during the jump was calculated using the Gomez-Bruton equation. Lower limb muscle volume (MV) and length were calculated in both sexes using anthropometric methods. Pearson correlation was used to assess the associations between variables. Independent-sample t-tests were used to compare anthropometric and muscle performance data between males and females. Cohen's d was used to determine the size of the differences. Results There were differences in all anthropometric variables between males and females (p < 0.001). CMJ height (d = 4.45; p = 0.001) and MP (d = 4.74; p = 0.001) were greater in males than in females (p < 0.001). These differences persisted when jump performance was normalized to the MV (d = 1.05; p = 0.01) and length (d = 4.07; p = 0.001). There were correlations between MV and MP for males and females, with a significant correlation between limb length and MP for males (r = 0.55; p = 0.002). Conclusions This study indicates that MV and length are associated with MP production during a CMJ in adolescents aged between 16 and 19 years, suggesting that these factors may be important determining factors for vertical jumping performance during adolescence. The sex-differences in jump performance variables persisted after normalization by MV and length, suggesting that MV and length did not entirely explain the sex difference in muscle power output during a CMJ.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.002 | 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".