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Does a caffeine mouth rinse help exercise and cognition?
Learn to train smart, run fast, and be strong with this endurance performance nerd alert from Thomas Solomon, PhD.
Revisiting the evidence on caffeine mouth rinse: effects on exercise and cognitive performance: a meta-analytic review
Deng et al. (2026) Journal of the International Society of Sports Nutrition (click here to open the original paper)
Are findings of this study useful for runners and coaches?
◦ The findings are useful, but only in a pretty narrow and practical way. If an athlete wants a low-fuss option when swallowing caffeine is inconvenient or rough on the gut, caffeine mouth rinse may offer a small performance nudge, mainly in aerobic endurance and mainly with short rinses. The force is strong with this one, just not that strong. Should coaches build a race plan around it? Maybe for some endurance settings, but not as the star of the show, because the effect is small and the evidence gets shakier once you move into cognition, women, trained athletes, or unusual dosing setups. I’d keep caffeine mouth rinses in the “worth testing in practice” drawer, not the “problem solved” drawer. There’s some promise here, but also a little worry that athletes will expect more than the data can honestly give.
What was my Rating of Perceived scientific Enjoyment (RPsE)?
9 out of 10 → I experienced high scientific enjoyment because the review searched 6 databases, clearly used protocol pre-registrationPreregistration is when a detailed description of a study plan is deposited in an open-access repository before collecting the study data. It promotes transparency and accountability, and boosts research integrity. Without preregistration, it is easier for scientists to change outcomes after seeing the data, selectively report “exciting” results, or run many analyses and only show the ones that work, which can introduce bias and weaken the trustworthiness of the findings., assessed study quality and publication biasPublication bias in meta-analysis occurs when studies with significant results are more likely to be published than those with non-significant findings, leading to distorted conclusions. This bias can inflate effect sizes and misrepresent the true effectiveness of interventions, making it crucial to identify and correct for it in research., and graded the certainty of evidenceCertainty of evidence tells us how confident we are that the published results accurately reflect the true effect. It’s based on factors like study design, risk of bias, consistency, directness, precision, and publication bias. High certainty means that the current evidence is so strong and consistent that future studies are unlikely to change conclusions. Whereas, low certainty means more doubt and less confidence, and that future studies could easily change current conclusions.. However, the underlying evidence was still messy, especially for cognition, and the reported statistical powerStatistical power is the probability that a statistical test will correctly detect a real effect if there is one: a true positive. (In jargon: power is the probability that a statistical test correctly rejects a false null hypothesis). Higher statistical power reduces the risk of a false negative (failing to detect a true effect; or a Type II error). Power is typically influenced by sample size, effect size, significance level, and variability in the data, with a common target being at least 80% (or 0.8). was low.
Remember: Don’t make any major changes to your training habits based on the findings of one study, especially if the study is small and/or provides a low quality of evidenceA low quality of evidence means that, in general, studies in this field have several limitations. This could be due to inconsistency in effects between studies, a large range of effect sizes between studies, and/or a high risk of bias (caused by inappropriate controls, a small number of studies, small numbers of participants, poor/absent randomization processes, missing data, inappropriate methods/statistics). When the quality of evidence is low, there is more doubt and less confidence in the overall effect of an intervention, and future studies could easily change overall conclusions. The best way to improve the quality of evidence is for scientists to conduct large, well-controlled, high-quality randomized controlled trials.. Do other trials on this topic confirm the findings of this study? If there is a meta-analysis, what is the effect sizeA standardised measure of the magnitude of an effect of an intervention. Unlike p-values, effect sizes show the size of the effect and how meaningful it might be. Common effect size measures include standardised mean difference (SMD), Cohen’s d, Hedges’ g, eta-squared, and correlation coefficients. and quality of evidenceCertainty of evidence tells us how confident we are that the results reflect the true effect. It’s based on factors like study design, risk of bias, consistency, directness, and precision. Low certainty means more doubt and less confidence, and that future studies could easily change the conclusions. High certainty means that the current evidence is so strong and consistent that future studies are unlikely to change conclusions.? Visit veohtu.com/caffeine-for-runners for a deep dive on this topic.
What type of study is this?
◦ This study is systematic reviewA systematic review answers a specific research question by systematically collating all known experimental evidence, which is collected according to pre-specified eligibility criteria. A systematic review helps inform decisions, guidelines, and policy. with meta-analysisA meta-analysis quantifies the overall effect size of a treatment by compiling effect sizes from all studies of that treatment..
What was the hypothesis or research question?
◦ The authors aimed to update the evidence on caffeine mouth rinse and quantify its effects on exercise and cognitive performance, while also testing whether factors like exercise type, feeding state, rinse duration, sex, and caffeine exposure changed the results.
How did the researchers test the hypothesis or answer the research question?
◦ The authors searched 6 databases and included 31 studies in healthy adults. The exercise studies included 384 participants, and the cognitive studies included 217 participants, with most exercise data coming from males, and no exercise study included only females. The review focused on placebo-controlled caffeine mouth-rinse trials and pooled 167 effect estimates across exercise and cognitive outcomes.
◦ The authors used a 3-level model to handle the fact that many studies reported more than 1 result. They also ran subgroup and sensitivity analysesA sensitivity analysis tests whether a study’s results stay the same when the researchers change some of their methods, assumptions, or data inputs. E.g., they might reanalyse the study without high risk of bias studies. Sensitivity analyses help show how robust the findings are and whether small changes could lead to different conclusions. to test whether the conclusions changed when the authors accounted for study differences, outliers, and assumptions built into the calculations.
What did the study find?
◦ For exercise performance, the pooled effect sizeA standardised measure of the magnitude of an effect of an intervention. Unlike p-values, effect sizes show the size of the effect and how meaningful it might be. Common effect size measures include standardised mean difference (SMD), Cohen’s d, Hedges’ g, eta-squared, and correlation coefficients. was trivial to small, but it did reach statistical significanceEvidence that a result is unlikely to be due to chance under a “no effect” model (or null hypothesis). Statistical significance is often judged by a p-value below 0.05 to flag that “something” is going on, but not how big or important that “something” is. One statistically significant result doesn’t mean proof; replication is needed. And, a statistically significant result doesn’t necessarily indicate clinical significance.. In plain English, caffeine mouth rinse seems to help a bit, not a lot. The effect looked most reliable in aerobic endurance tasks, in fed athletes, and with brief rinses of about 5 seconds. The certainty of evidenceCertainty of evidence tells us how confident we are that the published results accurately reflect the true effect. It’s based on factors like study design, risk of bias, consistency, directness, precision, and publication bias. High certainty means that the current evidence is so strong and consistent that future studies are unlikely to change conclusions. Whereas, low certainty means more doubt and less confidence, and that future studies could easily change current conclusions. for the overall exercise result was moderate, and the heterogeneity (variability in a meta-analysis)Heterogeneity shows how much the results in different studies in a meta-analysis vary from each other. It is measured as the percentage of variation (the I2 value). A rule of thumb: if I2 is roughly 25%, that indicates low heterogeneity (good), 50% is moderate, and 75% indicates high heterogeneity (bad). High heterogeneity means there’s more variability in effects between studies and, therefore, a less precise overall effect estimate. was low overall, which makes this part of the paper fairly reassuring. Still, the practical gain is probably modest rather than game-changing.
◦ For cognitive performance, the overall result was not consistently positive in the main analysis. After removing outliers, the results looked a bit better, especially for processing speed, but accuracy stayed variable. That means the cognitive side of the story is kinda interesting, but not settled. The certainty of evidence here was low to very low, heterogeneity was substantial, and the funnel plots suggested possible publication biasPublication bias in meta-analysis occurs when studies with significant results are more likely to be published than those with non-significant findings, leading to distorted conclusions. This bias can inflate effect sizes and misrepresent the true effectiveness of interventions, making it crucial to identify and correct for it in research., so confidence is limited.
◦ The authors concluded that caffeine mouth rinse is a practical, ingestion-free strategy that can produce small, context-dependent performance benefits, especially for aerobic endurance, but better standardised trials are still needed.
What were the strengths of the study?
◦ The paper did a lot right. The authors searched 6 databases, pre-registered the protocolPreregistration is when a detailed description of a study plan is deposited in an open-access repository before collecting the study data. It promotes transparency and accountability, and boosts research integrity. Without preregistration, it is easier for scientists to change outcomes after seeing the data, selectively report “exciting” results, or run many analyses and only show the ones that work, which can introduce bias and weaken the trustworthiness of the findings., assessed study quality with the Cochrane risk of bias (RoB2) toolThe Cochrane Risk of Bias 2 (RoB 2) tool is a standardised instrument developed by Cochrane for assessing the risk of bias in randomised controlled trials (RCTs). It is widely used to evaluate the internal validity of results from studies in a systematic review by examining bias arising from the randomisation process, deviations from intended interventions, missing outcome data, the measurement of the outcome, and the selection of the reported result., checked for publication biasPublication bias in meta-analysis occurs when studies with significant results are more likely to be published than those with non-significant findings, leading to distorted conclusions. This bias can inflate effect sizes and misrepresent the true effectiveness of interventions, making it crucial to identify and correct for it in research., ran several sensitivity analysesA sensitivity analysis tests whether a study’s results stay the same when the researchers change some of their methods, assumptions, or data inputs. E.g., they might reanalyse the study without high risk of bias studies. Sensitivity analyses help show how robust the findings are and whether small changes could lead to different conclusions., and graded the certainty of evidenceCertainty of evidence tells us how confident we are that the published results accurately reflect the true effect. It’s based on factors like study design, risk of bias, consistency, directness, precision, and publication bias. High certainty means that the current evidence is so strong and consistent that future studies are unlikely to change conclusions. Whereas, low certainty means more doubt and less confidence, and that future studies could easily change current conclusions.. They also used a more advanced model that reduced the risk of double-counting multiple outcomes from the same study. That is the sort of nerdy housekeeping that makes a review more trustworthy.
What were the limitations of the study?
◦ The main weakness is that the included trials were all over the place in protocol details, participant types, and outcome measures. Female data were sparse, trained groups were often small, dose reporting was sometimes incomplete, and the longer rinse-duration estimates rested on very few studies. Cognitive outcomes were especially shaky because there was a low-to-very-low quality of evidenceA low quality of evidence means that, in general, studies in this field have several limitations. This could be due to inconsistency in effects between studies, a large range of effect sizes between studies, and/or a high risk of bias (caused by inappropriate controls, a small number of studies, small numbers of participants, poor/absent randomisation processes, missing data, inappropriate methods/statistics). When the quality of evidence is low, there is more doubt and less confidence in the overall effect of an intervention, and future studies could easily change overall conclusions. The best way to improve the quality of evidence is for scientists to conduct large, well-controlled, high-quality randomised controlled trials., possible publication biasPublication bias in meta-analysis occurs when studies with significant results are more likely to be published than those with non-significant findings, leading to distorted conclusions. This bias can inflate effect sizes and misrepresent the true effectiveness of interventions, making it crucial to identify and correct for it in research., and poor statistical powerStatistical power is the probability that a statistical test will correctly detect a true positive, i.e., detect a real effect if there is one (and correctly reject an incorrect null hypothesis). Higher statistical power reduces the risk of a false negative (failing to detect a true effect; or a Type II error). Power is typically influenced by sample size, effect size, significance level, and variability in the data, with a common target being at least 80% (or 0.8).. That trims the confidence quite a bit.
Who funded the study, and were there any conflicts of interestA conflict of interest happens when a person or group has a personal, financial, or professional interest that could influence their judgment. It does not always mean they did something wrong. But it can create bias or make others question whether the decision or result is fully fair and trustworthy.?
◦ The paper states that it received no external funding, and the authors declared no conflicts of interest.
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