Physiological effects of caffeine, epigallocatechin-3-gallate, and exercise in overweight and obese women
Bibliographic record
Abstract
The aim of this study was to evaluate the combined effects of a 10-week exercise program with ingestion of caffeine and epigallocatechin-3-gallate (EGCG) on body composition, cardiovascular fitness, and strength in overweight and obese women. In a double-blind, placebo-controlled approach, overweight and obese women (n = 27) were randomly assigned to treatment groups with exercise (an active-supplementing group with exercise (EX-Act) and a placebo group with exercise (EX-PL)) or without exercise (an active-supplementing group without exercise (NEX-Act) and a placebo group without exercise (NEX-PL)). All participants consumed 1 drink per day for 10 weeks; EX-Act and EX-PL participated in a concurrent endurance and resistance training program. Changes in body composition were assessed using a 4-compartment model. Changes in muscle mass (MM) were evaluated using a DXA-derived appendicular lean-soft tissue equation. There was a significant time × treatment interaction for MM (p = 0.026) and total cholesterol (TC) (p = 0.047), and a significant time × training interaction for peak oxygen consumption (p = 0.046) and upper-body and lower-body strength (p < 0.05). Significant differences between the EX groups and NEX groups for percentage change in MM and peak oxygen consumption, and upper-body and lower-body strength, were revealed. Clinical markers for hepatic and renal function revealed no adverse effects. TC significantly decreased for the active-supplementing groups (EX-Act, NEX-Act). The current study suggests that implementing a caffeine-EGCG-containing drink prior to exercise may improve MM, fitness, and lipid profiles in overweight women.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| 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".