Zapping the brain to enhance sport performance? An umbrella review of the effect of transcranial direct current stimulation on physical performance
Bibliographic record
Abstract
Concepts such as "neurodoping" have contributed to an expansion in the area of transcranial direct current stimulation (tDCS) and its impact over physical performance in recent years. This umbrella review examines meta-analyses to evaluate tDCS's impact on exercise performance in healthy individuals. We identified 9 meta-analyses that met our inclusion criteria, encompassing 50 crossover studies and 683 participants. Like previous meta-analyses, we found a small but significant effect across individual studies (gz = 0.28, 95%CI [0.18, 0.39]). However, we also found clear evidence of publication bias, low power and substantial variability in methodological decisions. The average effect became non-significant after accounting for publication bias (grm = 0.10, 95%CrI [−0.04, 0.20], BF10 = 0.99), and a specification curve analysis showed that the final effect could range from g = −0.23 to g = 0.33, depending on decisions such as the formula used for estimating the effect size and multiple additional analytic steps. Overall, our findings suggest that current evidence does not conclusively support acute tDCS as an exercise performance enhancer.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".