Acute metabolic responses following different resistance exercise protocols
Bibliographic record
Abstract
Resistance exercise (RE) can be an excellent modality for glycemic control. Studies have demonstrated that a single RE session can reduce glycemia in subjects with or without diabetes. Little is known about the dose-response effect of RE on glycemic control. This study aimed to investigate the acute metabolic responses after different RE protocols. Eighty-nine males were separated into six groups that completed RE protocols: 2 sets of 18 repetitions (2 × 18 at 50% of 1-repetition maximum (1RM); n = 19); 3 sets of 12 repetitions (3 × 12 at 70% of 1RM; n = 14); 4 sets of 9 repetitions (4 × 9 at 80% of 1RM; n = 13); 6 sets of 6 repetitions (6 × 6 at 90% of 1RM; n = 19); circuit (2 × 18 at 50% of 1RM; n = 12); and a control session (n = 12). The exercise sequence consisted of 8 exercises. An oral glucose tolerance test was conducted with metabolic measurements immediately after each RE protocol and every 15 min until 120 min of recovery. All groups exhibited significantly lower values (p < 0.05) in the glucose area under the curve (AUC) when compared with control over a 120 min monitoring period. The 6 × 6 group showed a significantly lower glucose AUC versus the 3 × 12 and 4 × 9 groups (p = 0.004; p = 0.001, respectively). As for blood lactate, the control and 6 × 6 groups exhibited lower AUC values versus all other groups (p < 0.05), and AUC for glucose and lactate concentration showed a negative and significant correlation (r = -0.46; p < 0.0001). It appears that a combination of 9-12 repetitions per set and 3-4 sets per muscle group might be optimal for acute postprandial glucose control.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".