Loads and movement speed affect energy expenditure during circuit resistance exercise
Bibliographic record
Abstract
Circuit resistance training (CT) constitutes a high-intensity interval program commonly used to target weight loss; however, the loads and exercise patterns that maximize energy expenditure (EE) remain undetermined. We examined differences in EE among CT protocols using varying loads and contraction speeds in recreationally trained males and females. Seven males (age, 21.1 ± 0.5 years) and 8 females (age, 20.0 ± 0.9 years) performed 3 randomized CT protocols incorporating 3 circuits using heavy-load (80% 1-repetition maximum (1RM)) explosive (HLEC), heavy-load, controlled (2 s) (HLCC), and moderate-load (50% 1RM) explosive contractions (MLEC). Expired air was collected continuously before, during, and after exercise. Blood lactate was collected at rest, immediately postexercise, and 5 min postexercise. No significant differences were detected for resting EE; however, there was a significant difference among conditions during exercise (p = 0.034, ηp2 = 0.229). Post hoc analysis revealed that MLEC produced significantly higher EE than HLCC, but not HLEC (p = 0.023). There was a significant difference among conditions for rate of EE during exercise (p = 0.003, ηp2 = 0.361). Post hoc analysis revealed that HLEC produced a significantly higher EE rate than HLCC (p = 0.012) or MLEC (p = 0.001). A condition × sex interaction was seen for blood lactate changes (ηp2 = 0.249; p = 0.024). Females produced significantly greater change for MLEC than HLEC (p = 0.011), while males showed no significant differences. Our results favor CT using MLEC for a higher EE during a full workout; however, the rate of EE was highest when using HLEC.
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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".