The effects of strength training and endurance training order on running economy and performance
Bibliographic record
Abstract
This study examined the acute effect of strength and endurance training sequence on running economy (RE) at 70% and 90% ventilatory threshold (VT) and on running time to exhaustion (TTE) at 110% VT the following day. Fourteen trained and moderately trained male runners performed strength training prior to running sessions (SR) and running prior to strength training sessions (RS) with each mode of training session separated by 6 h. RE tests were conducted at baseline (Base-RE) and the day following each sequence to examine cost of running (CR), TTE, and lower extremity kinematics. Maximal isometric knee extensor torque was measured prior to and following each training session and the RE tests. Results showed that CR at 70% and 90% VT for SR-RE (0.76 ± 0.10 and 0.77 ± 0.07 mL·kg(-0.75)·m(-1)) was significantly greater than Base-RE (0.72 ± 0.10 and 0.70 ± 0.11 mL·kg(-0.75)·m(-1)) and RS-RE (0.73 ± 0.09 and 0.72 ± 0.09 mL·kg(-0.75)·m(-1)) (P < 0.05). TTE was significantly less for SR-RE (237.8 ± 67.4 s) and RS-RE (275.3 ± 68.0 s) compared with Base-RE (335.4 ± 92.1 s) (P < 0.01). The torque during the SR sequence was significantly reduced for every time point following the strength training session (P < 0.05). However, no significant differences were found in torque following the running session (P > 0.05), although it was significantly reduced following the strength training session (P < 0.05) during the RS sequence. These findings show that running performance is impaired to a greater degree the day following the SR sequence compared with the RS sequence.
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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) | 0.002 | 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".