The effect of maturation on adaptations to strength training and detraining in 11–15‐year‐olds
Bibliographic record
Abstract
To investigate how maturity status modifies the effects of strength training and detraining on performance, we subjected 33 young men to 8 weeks of strength training twice per week followed by 8 weeks without training. Changes in performance tests were analyzed in three maturity groups based on years from/to age of predicted peak height velocity (PHV): pre-PHV (-1.7 ± 0.4 years; n = 10), mid-PHV (-0.2 ± 0.4 years; n = 11), and post-PHV (1.0 ± 0.4 years; n = 12). Mean training effects on one repetition maximum strength (3.6-10.0%), maximum explosive power (11-20%), jump length (6.5-7.4%), and sprint times (-2.1% to -4.7%) ranged from small to large, with generally greater changes in mid- and post-PHV groups. Changes in force-velocity relationships reflected generally greater increases in strength at faster velocities. In the detraining period, the pre-PHV group showed greatest loss of strength and power, the post-PHV group showed some loss of sprint performance, but all groups maintained or improved jump length. Strength training was thus generally less effective before the growth spurt. Maintenance programs are needed for most aspects of explosive performance following strength training before the growth spurt and for sprint speed after the growth spurt.
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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 | 0.001 | 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".