Effects of caffeine ingestion on dynamic visual acuity: a placebo-controlled, double-blind, balanced-crossover study in low caffeine consumers
Bibliographic record
Abstract
BACKGROUND: Acute caffeine ingestion has been associated with improvements in cognitive performance and visual functioning. The main objective of this study was to determine the effects of caffeine intake on dynamic visual acuity (DVA). METHODS: Twenty-one low caffeine consumers (22.5 ± 1.6 years) took part in this placebo-controlled, double-blind, and balanced crossover study. In two different days and following a random order, participants ingested either caffeine (4 mg/kg) or placebo, and DVA was measured after 60 min of ingesting the corresponding capsule. A recently developed and validated software (moV& test, V&mp Vision Suite, Waterloo, Canada) was used to assess DVA. RESULTS: We found a greater accuracy for both the horizontal and random motion paths of DVA after caffeine ingestion (p < 0.001 and p = 0.002, respectively). In regard to the speed of the response, our data revealed that caffeine intake was associated with a faster reaction time for horizontally (p = 0.012) but not for randomly (p = 0.846) moving targets. Also, participants reported higher levels of perceived activation after consuming caffeine in comparison to placebo (p < 0.001). CONCLUSIONS: Our data suggest that caffeine intake (i.e., a capsule containing 4 mg/kg) has an ergogenic effect on DVA, which may be of special relevance in real-word contexts that require to accurately and rapidly detect moving targets (e.g., sports, driving, or piloting).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".