The Effect of Neurofeedback on the Reaction Time and Cognitive Performance of Athletes: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Our study evaluated the effect of training with neurofeedback (NFB) in improving athletes' reaction time and decision-making. A computerized search in PubMed, PsycINFO, Scielo, Web of Science, EMBASE, Scopus, BVS, and Cochrane databases was performed to identify studies published from 2011 to June 2021. The protocol was registered in PROSPERO. The quality of studies that was peer-reviewed and included was assessed using the Review Manager tool, Cochrane Risk of Bias, and design and reporting quality according to the CRED-nf checklist. Standard mean differences and 95% confidence intervals (CIs) were calculated and combined using a random-effects model. A total of 07 randomized controlled trials (RCTs) (173 athletes) met the inclusion criteria. Significant effects of NFB in the experimental group in relation to reaction time were found, indicating an improvement in sports performance [standardized mean difference (SMD) = −1.08; 95% CI = (−1.90, −0.25), p = 0.0009] and cognitive performance vs. decision-making with moderate effect [SMD = 1.12; 95% CI = (−0.40, 1.85), p = 0.0001]. However, the control group had a very small effect on cognitive performance [SMD = 0.19; 95% CI = (−0.20, 0.59), p = 0.086]. NFB could improve athletes' reaction time and decision-making, effectively increasing their performance in the sports field. Future studies should focus on standardized protocols for NFB training. Systematic Review Registration http://www.crd.york.ac.uk/PROSPERO/ , identifier: CRD42021258387.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".