Caffeine consumption patterns, motivations, and adverse effects among Brazilian esports players: a cross-sectional study
Bibliographic record
Abstract
Background Electronic sports (esports) are a growing global phenomenon engaging millions of competitive players worldwide. Caffeine is a widely used compound for individuals seeking cognitive enhancement. However, evidence on consumption patterns, motivations, and safety in esports remains limited. We aimed to describe daily caffeine intake among Brazilian esports players and examine associations with competitive level, gaming habits, and adverse effects.Methods Cross-sectional study of 181 Brazilian esports players. A 64-item questionnaire captured demographics, gaming habits, and caffeine intake from all dietary sources. We compared amateurs vs semi-professional/professional players and performance-motivated vs other motivations, and examined dose-response using intake categories (≤100, 101–300, 301–600, >600 mg/day) and correlations for continuous variables.Results Median 168 mg/day (IQR 52–402; mean 280 ± 316); coffee was the main source (72.2% of total), and 55.8% consumed energy drinks, contributing 14.0% of intake. Overall, 25.7% exceeded 400 mg/day (46/179); intake did not differ between competitive levels (Amateur 172 vs Semi-Pro/Pro 121 mg/day; p = 0.387). No correlation with gaming hours (ρ = 0.068; p = 0.369). Under the primary positivity rule (≥“occasional”), adverse effects were common among respondents with symptom frequency data: any adverse effect 76.5%, insomnia 45.2%, tachycardia 29.1%, stomach pain 45.5%, tremors 23.7%. Linear trend tests across dose categories were not significant (any 0.822; insomnia 0.530; tachycardia 0.905; stomach pain 0.409; tremors 0.877), and per-category effect sizes were small (r-trend ≈ 0.01–0.08; OR per +1 category ≈ 0.89–1.16). Comparing >300 vs ≤300 mg/day for any adverse effect yielded OR 1.38 (95% CI 0.56–3.45). Performance-motivated players (12.6%) consumed more (+89 mg/day; p < 0.001). Using caffeine to combat fatigue (56.0%) was associated with higher insomnia (OR 2.50; 95% CI 1.37–4.55; q = 0.004). Notably, insomnia was common (45.2%), underscoring applied relevance.Conclusions Brazilian esports players show moderate caffeine intake, mainly from coffee. Adverse effects were common, although linear dose-response across intake categories was not observed; the observed fatigue-caffeine cycle highlights the need for practical guidance on timing and source awareness, alongside sleep-hygiene strategies, to support sustainable performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".