Effect of Resistance Training Load on Metabolism During Exercise
Bibliographic record
Abstract
Abstract McCarthy, SF, Bornath, DPD, Murtaza, M, Ormond, SC, and Hazell, TJ. Effect of resistance training load on metabolism during exercise. J Strength Cond Res 38(12): 2029–2033, 2024—The effect of resistance training (RT) load on energy expenditure during exercise is unclear as most studies match low-load and high-load RT based on volume or total repetitions and matching volume can attenuate benefits of low-load protocols. This study explored the effect of whole-body low-load and high-load RT completed to volitional fatigue (not volume or repetition matched) on metabolism during exercise. Eleven resistance-trained adults (22 ± 2 years, 3 F) completed 3 experimental sessions: (a) no-exercise control (CTRL); (b) RT at 30% 1 repetition maximum (1RM; 30%); and (c) RT at 90% 1RM (90%) with oxygen consumption ( o 2 ) and heart rate measured continuously. The RT sessions consisted of 3 sets of back squats, bench press, straight-leg deadlift, military press, and bent-over rows to volitional fatigue completed sequentially with 90 seconds rest between sets and exercises. Changes were considered important if p < 0.100 with a greater than medium effect size. There were main effects of session for relative and absolute o 2 (L·min −1 ; p < 0.001, > 0.935), both 30 and 90% were greater than CTRL ( p < 0.001, d > 4.33) with no differences between RT protocols ( p > 0.999, d = 0.28). There was a main effect of session for total O 2 consumed (L; p < 0.001, > 0.901), both RT sessions were greater than CTRL ( p < 0.001, d > 3.08), and 30% was greater than 90% ( p = 0.002, d = 1.75). Taken together these data suggest that RT load does not affect metabolism during exercise when completing whole-body exercises to volitional fatigue, though lower loads may result in longer session duration generating a greater total amount of O 2 consumed simply because of the extended duration.
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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".