Effects of Nordic Hamstring Exercise Set Configuration on Eccentric Hamstring Strength Changes in Youth Female Athletes
Bibliographic record
Abstract
ABSTRACT: Bounias, T, Henry, G, Goswami, R, Moran, J, Behm, DG, and Drury, B. Effects of Nordic hamstring exercise set configuration on eccentric hamstring strength changes in youth female athletes. J Strength Cond Res 39(8): 829-836, 2025-Anterior cruciate ligament injury poses a significant risk in youth female athletes. The Nordic hamstring exercise (NHE) can improve eccentric hamstring strength (EHS), a key factor related to anterior cruciate ligament injury risk, yet limited research exists in this population. This study compared 2 NHE set configurations-traditional sets (TS) and rest redistribution (RR)-on EHS in youth female athletes. Subjects (age: 13.93 ± 1.58 years; body mass: 48.95 kg ± 11.15 kg; percentage of predicted adult height: 96.50% ± 4.30%) were randomly assigned to TS ( n = 17) or RR ( n = 18) groups. Both groups performed a 6-week NHE program, increasing weekly volume from 6 to 18 repetitions. Pretests and post-tests assessed EHS changes using the NordBord and isokinetic testing at 60°·s -1 and 180°·s -1 , assessing peak torque (PT), angle of peak torque (°PT), and torque at 20°, 40°, 60°, and 80° of knee flexion. Both TS and RR significantly increased NordBord EHS ( g = 0.34-0.98). Isokinetic data at 60°·s -1 and 180°·s -1 revealed significant small increases in PT and torque at 40°, 60°, and 80° ( g = 0.22-0.46). Yet, no changes were observed in torque at 20° ( g = 0.01-0.23) and the °PT increased ( g = 0.33-0.83). No between-group differences were observed for any measure. These findings suggest that TS or RR set configurations can effectively enhance EHS in youth female athletes. However, to target EHS at longer muscle lengths, practitioners should include additional exercises beyond the NHE.
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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 | 0.000 |
| 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) | 0.002 | 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".