Examining the effect of L-theanine on sleep: a systematic review of dietary supplementation trials
Bibliographic record
Abstract
Objective: Sleep problems are a common complaint among adults worldwide, however, the use of prescription and over-the-counter products may not always be an appropriate or desirable solution. L-theanine is a naturally occurring, non-protein amino acid that can be found in the leaves of the tea plant Camellia sinensis. Previous studies have reported that consumption of L-theanine can help to aid relaxation, without causing sedation or adversely impacting cognitive function. Building on these calming effects and results from recent pre-clinical studies, the aim of this review was to systematically appraise the scientific literature to establish whether dietary supplementation with L-theanine can also help to support sleep in humans.Methods: Electronic database searches of Ovid MEDLINE, PsycINFO, Embase, CENTRAL and Google Scholar were conducted from inception to 3rd February 2025. Retrieved articles were independently reviewed by three authors.Results: Thirteen eligible trials (n = 550) that examined the effect of L-theanine (50–900 mg/day) as a standalone intervention on sleep-related outcome measures were identified. This included two single-arm, open-label trials and eleven randomised controlled trials.Discussion: Based on the current evidence, supplementation with 200–450 mg/day of L-theanine appears to be a safe and effective way to support healthy sleep in adults. Among the included trials, beneficial effects were reported on both objective and participant-reported outcomes, including measures linked to sleep latency, maintenance and efficiency, perceived sleep satisfaction and feelings of refreshment and recovery on waking. Further high-quality trials using objective measures, into the mechanisms underlying these effects, and among those with clinical insomnia would provide further useful insights.
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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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".