Effect of Pre-Exercise Caffeine Intake on Endurance Performance and Core Temperature Regulation During Exercise in the Heat: A Systematic Review with Meta-Analysis
Bibliographic record
Abstract
Abstract Background Heat is associated with physiological strain and endurance performance (EP) impairments. Studies have investigated the impact of caffeine intake upon EP and core temperature (C T ) in the heat, but results are conflicting. There is a need to systematically determine the impact of pre-exercise caffeine intake in the heat. Objective Use a meta-analytical approach to determine the effect of pre-exercise caffeine intake on EP and C T in the heat. Design Systematic review with meta-analysis. Data sources Four databases and cross-referencing. Data analysis Weighted mean effect summaries using random-effects models for EP and C T , as well as meta regressions with robust standard errors to explore confounders. Study selection Placebo-controlled, randomized studies in adults (≥ 18 yrs old) with caffeine intake at least 30 min before endurance exercise ≥ 30 min, performed in ambient conditions ≥ 27°C. Results Respectively 6 and 12 studies examined caffeine’s impact on EP and C T , representing 52 and 205 endurance-trained individuals. On average, 6 mg/kg body mass of caffeine were taken 1 h before exercises of ∼ 70 min conducted at 34°C and 47% relative humidity. Caffeine supplementation improved EP by 2.0 ± 0.7% (95% CI: 0.6 to 3.5%) and increased the rate of change in C T by 0.10 ± 0.04°C/h (95% CI: 0.03 to 0.16°C/h), compared with the ingestion of a placebo. Conclusion Caffeine ingestion of 6 mg/kg body mass ∼ 1 h before an exercise in the heat provides a worthwhile improvement in EP of 2%, while trivially increasing the rate of change in C T by 0.10°C/h.
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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 | 0.010 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.038 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".