Energy Drinks and Their Adverse Health Effects: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
CONTEXT: Energy drinks are the fastest growing product in the beverage industry. However, there is concern regarding potential for adverse effects with use. OBJECTIVE: To evaluate the reported adverse effects of energy drink consumption. DATA SOURCES: The electronic databases MEDLINE, EMBASE, and PubMed were searched for relevant studies from inception to November 2019, and pertinent data were abstracted. STUDY SELECTION: Only clinical studies reporting adverse events after energy drink consumption were included. STUDY DESIGN: Systematic review. LEVEL OF EVIDENCE: Level 4. DATA EXTRACTION: Data regarding sample size characteristics, energy drink characteristics, comparators, and all adverse events were extracted in duplicate and recorded. RESULTS: A total of 32 studies and 96,549 individuals were included. Frequently reported adverse events in the pediatric population were insomnia (35.4%), stress (35.4%), and depressive mood (23.1%). Frequently reported adverse events in the adult population were insomnia (24.7%), jitteriness/restlessness/shaking hands (29.8%), and gastrointestinal upset (21.6%). Alcohol mixed with energy drinks significantly reduced the likelihood of sedation effects but increased the likelihood of stimulatory effects. Energy drink consumption significantly increased the odds of insomnia (OR, 5.02; 95% CI, 1.72-14.63) and jitteriness/activeness (OR, 3.52; 95% CI, 1.28-9.67) compared with the control group. CONCLUSION: The authors recommend that individuals avoid frequent energy drink consumption (5-7 energy drinks/week) and avoid co-consumption with alcohol; increased regulatory standards should be placed in the sale of energy drinks, particularly with regard to the pediatric population.
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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 | 0.012 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.031 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".