Effects of caffeine on heart rate and QT variability during sleep
Bibliographic record
Abstract
Administration of caffeine in the evening produces poor sleep. Patients with insomnia have characteristic electrocardiogram (ECG) changes, including increased heart rate (HR), increased sympathetic activity, and decreased parasympathetic activity. Fifteen young adult normal subjects slept in the laboratory for several nights prior to randomization into a caffeine protocol where subjects received caffeine 400 mg 30 min prior to one night of sleep and placebo randomly prior to another night. ECG was sampled at a rate of 500 Hz and recorded onto a PC. Data samples of 256-s periods of the ECG trace were taken from wake (before sleep), stage II, and REM for placebo and caffeine conditions. A peak detection algorithm was used to identify the R-R intervals (in milliseconds) from the ECG. A common QT variability algorithm was used to find the QT interval for each beat using the time-stretch model. The powers for HR and QT series were integrated in the bands of LF (low frequency: 0.04-0.15 Hz) and HF (high frequency: 0.15-0.5 Hz) bands. There was a significant caffeine by sleep stage interaction for LF/HF ratios (F = 4.0; df = 2, 18; P = .04). LF/HF ratios were significantly higher during REM following caffeine administration. There was also a significant caffeine by sleep stage interaction for QTvi (QT variability normalized for mean QT interval divided by HR variability normalized for mean HR; F = 5.6; df = 2, 12; P = .02). QTvi was also significantly higher during REM following caffeine administration. The higher LF/HF ratios and QTvi during REM are most likely due to the sympathetic effects of caffeine. These findings suggest that excessive caffeine intake may result in adverse cardiovascular events in vulnerable subjects.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".