Regular Long Runs and Higher Training Volumes Are Associated with Better Running Economy Durability in Performance Matched Well-Trained Male Runners
Bibliographic record
Abstract
INTRODUCTION: Running economy (RE) deteriorates during prolonged running (i.e., RE durability), although it is unknown if runners' training characteristics influence RE durability. Furthermore, the extent of the decrement in neuromuscular capabilities after running could also contribute to differences in RE durability. Therefore, this study aimed to compare RE durability during a 90-min run and the decrements in neuromuscular capabilities, between athletes who did or did not practice regular long runs, while pair-matched for performance status. METHODS: Two groups of 13 male runners were recruited as long (LDT; regular long runs ≥90 min) or short distance training runners (SDT; all runs <70 min) and matched for 10-km performance (39:10 vs 39:00 min:s; maximal oxygen uptake 56.6 vs 58.9 mL·kg -1 ·min -1 ). Participants completed preliminary assessments to determine lactate threshold and maximal oxygen uptake, and then on a separate occasion, a 90-min run at lactate threshold. Respiratory gases were collected every 15 min, and isometric squat peak force and countermovement jump were assessed before and after the run. RESULTS: Changes in RE occurred earlier and were larger for SDT than LDT, reaching +6.0% versus +3.1% at 90 min, respectively ( P < 0.001). Isometric squat force (-19.4% vs -12.2%; P = 0.002) and countermovement jump mean power (-6.6% vs +2.2%; P = 0.011) decreased more in SDT than LDT runners; however, these changes were not correlated with RE durability, whereas correlations were found between RE durability and the weekly longest run ( r = -0.67; P < 0.001) and training volume ( r = -0.48; P = 0.038). CONCLUSIONS: This study is the first to demonstrate that the presence of long runs and higher training volumes positively affects RE durability and decrements in neuromuscular capability in performance-matched runners. These results provide important insights into how training characteristics may help explain differences in durability, although intervention studies are needed to confirm these cross-sectional findings.
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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 | 0.001 |
| 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) | 0.003 | 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".