Low-load High-repetition Resistance Training Generates Similar Post-exercise Whole-body Metabolism Increases Compared To High-load Low-repetition Training
Bibliographic record
Abstract
The emergence of low-load high-repetition resistance training (RT, <50% of 1 RM) to failure as an alternative method of generating skeletal muscle hypertrophy has gained interest as an equally effective protocol to the traditional high-load low-repetition RT (>70% of 1 RM), however how these training protocols alter post-exercise whole-body metabolism as it relates to energy balance has yet to be examined. To fully understand the efficacy of these protocols for overall health, an exploration of factors affecting post-exercise metabolism such as excess post-exercise oxygen consumption (EPOC) and fat oxidation is warranted. PURPOSE: To investigate the effect of different RT loads performed to failure on post-exercise energy metabolism. METHODS: Ten recreationally active males (23 ± 2 y) with RT experience completed 3 experimental sessions: 1) RT workout at 30% 1RM; 2) RT workout at 90% 1RM; 3) no exercise control (CTRL) session. RT sessions consisted of back squat, bench press, straight-leg deadlift, military press, and bent-over rows were performed for 3 sets to failure with 90-s between each movement. Gas exchange measurements were obtained for 15 minutes at four time points: pre-exercise as well as 0, 1, and 2 h post-exercise. RESULTS: Total V̇O2 consumed post-exercise was increased (P < 0.010, d = 1.46) during the 30% session (42.9 ± 5.4 L) vs CTRL (35.9 ± 3.7 L) but not different vs the 90% session (40.3 ± 4.9; P = 0.545, d = 0.55). EPOC trended to be increased (P = 0.099, d = 0.54) in the 30% (7.0 ± 4.7 L O2) vs the 90% (4.4 ± 5.4) session. Fat oxidation was greater at 0 h post-exercise (P < 0.001, d > 2.40) following the 30% (0.19 ± 0.04 g·min-1) and 90% (0.17 ± 0.04) sessions vs CTRL (0.06 ± 0.23) and remained elevated through 1 h post-exercise following the 30% session (0.16 ± 0.04) vs 90% (0.08 ± 0.03) and CTRL (0.06 ± 0.02) sessions (P < 0.010, d > 1.00) as well as through 2 h post-exercise (P < 0.001, d = 2.00) following the 30% session (0.14 ± 0.04) vs 90% (0.085 ± 0.031) and CTRL (0.07 ± 0.02) sessions (P < 0.075, d > 1.67). CONCLUSION: A low-load high-repetition RT session produced similar perturbations to post-exercise whole-body energy metabolism compared to a traditional 90% 1RM session, suggesting either protocol is suitable in generating a negative energy balance however the 30% session may stimulate enhanced fat oxidation.
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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) | 0.005 | 0.001 |
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".