Strength Training for Middle- and Long-Distance Performance: A Meta-Analysis
Bibliographic record
Abstract
PURPOSE: To assess the net effects of strength training on middle- and long-distance performance through a meta-analysis of the available literature. METHODS: Three databases were searched, from which 28 of 554 potential studies met all inclusion criteria. Standardized mean differences (SMDs) were calculated and weighted by the inverse of variance to calculate an overall effect and its 95% confidence interval (CI). Subgroup analyses were conducted to determine whether the strength-training intensity, duration, and frequency and population performance level, age, sex, and sport were outcomes that might influence the magnitude of the effect. RESULTS: The implementation of a strength-training mesocycle in running, cycling, cross-country skiing, and swimming was associated with moderate improvements in middle- and long-distance performance (net SMD [95%CI] = 0.52 [0.33-0.70]). These results were associated with improvements in the energy cost of locomotion (0.65 [0.32-0.98]), maximal force (0.99 [0.80-1.18]), and maximal power (0.50 [0.34-0.67]). Maximal-force training led to greater improvements than other intensities. Subgroup analyses also revealed that beneficial effects on performance were consistent irrespective of the athletes' level. CONCLUSION: Taken together, these results provide a framework that supports the implementation of strength training in addition to traditional sport-specific training to improve middle- and long-distance performance, mainly through improvements in the energy cost of locomotion, maximal power, and maximal strength.
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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 | 0.013 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.050 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".