The effects of pre-exhaustion, exercise order, and rest intervals in a full-body resistance training intervention
Bibliographic record
Abstract
Pre-exhaustion (PreEx) training is advocated on the principle that immediately preceding a compound exercise with an isolation exercise can target stronger muscles to pre-exhaust them to obtain greater adaptations in strength and size. However, research considering PreEx training method is limited. The present study looked to examine the effects of a PreEx training programme. Thirty-nine trained participants (male = 9, female = 30) completed 12 weeks of resistance training in 1 of 3 groups: a group that performed PreEx training (n = 14), a group that performed the same exercise order with a rest interval between exercises (n = 17), and a control group (n = 8) that performed the same exercises in a different order (compound exercises prior to isolation). No significant between-group effects were found for strength in chest press, leg press, or pull-down exercises, or for body composition changes. Magnitude of change was examined for outcomes also using effect size (ES). ESs for strength changes were considered large for each group for every exercise (ranging 1.15 to 1.62). In conclusion, PreEx training offers no greater benefit to performing the same exercises with rest between them compared with exercises performed in an order that prioritises compound movements.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".