thesis for caffeine research paper

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• Published: 25 February 2021

The impact of daily caffeine intake on nighttime sleep in young adult men

• Janine Weibel 1 , 2 ,
• Yu-Shiuan Lin 1 , 2 , 3 ,
• Hans-Peter Landolt 4 , 5 ,
• Joshua Kistler 1 , 2 ,
• Sophia Rehm 6 ,
• Katharina M. Rentsch 6 ,
• Helen Slawik 7 ,
• Stefan Borgwardt 3 ,
• Christian Cajochen 1 , 2   na1 &
• Carolin F. Reichert 1 , 2   na1  

Scientific Reports volume  11 , Article number:  4668 ( 2021 ) Cite this article

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• Slow-wave sleep

Acute caffeine intake can delay sleep initiation and reduce sleep intensity, particularly when consumed in the evening. However, it is not clear whether these sleep disturbances disappear when caffeine is continuously consumed during daytime, which is common for most coffee drinkers. To address this question, we investigated the sleep of twenty male young habitual caffeine consumers during a double-blind, randomized, crossover study including three 10-day conditions: caffeine (3 × 150 mg caffeine daily), withdrawal (3 × 150 mg caffeine for 8 days, then switch to placebo), and placebo (3 × placebo daily). After 9 days of continuous treatment, electroencephalographically (EEG)-derived sleep structure and intensity were recorded during a scheduled 8-h nighttime sleep episode starting 8 (caffeine condition) and 15 h (withdrawal condition) after the last caffeine intake. Upon scheduled wake-up time, subjective sleep quality and caffeine withdrawal symptoms were assessed. Unexpectedly, neither polysomnography-derived total sleep time, sleep latency, sleep architecture nor subjective sleep quality differed among placebo, caffeine, and withdrawal conditions. Nevertheless, EEG power density in the sigma frequencies (12–16 Hz) during non-rapid eye movement sleep was reduced in both caffeine and withdrawal conditions when compared to placebo. These results indicate that daily caffeine intake in the morning and afternoon hours does not strongly impair nighttime sleep structure nor subjective sleep quality in healthy good sleepers who regularly consume caffeine. The reduced EEG power density in the sigma range might represent early signs of overnight withdrawal from the continuous presence of the stimulant during the day.

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Introduction.

Caffeine is the most popular psychoactive substance in the world 1 , consumed daily by around 80% of the population 2 . While caffeine is frequently used to counteract sleepiness and boost performance 3 , its consumption is commonly avoided in the evening 4 , 5 to prevent adverse consequences on nocturnal sleep 3 . The sleep disrupting effects of caffeine are mainly attributed to its influence on the homeostatic component of sleep-wake regulation. Sleep homeostasis describes the increase in sleep pressure during time awake and its dissipation during the following sleep episode 6 , which has been suggested to be related to rising and decreasing concentrations of adenosine 7 . Caffeine is an adenosine receptor antagonist, which blocks the A 1 and A 2A adenosine receptors in the central nervous system 1 . It may, thus, attenuate the increase in sleep pressure during wakefulness 8 and lead to delayed sleep initiation and more superficial sleep 9 .

The effects of caffeine intake on the quality and quantity of sleep depend on the timing of its consumption. More specifically, caffeine consumed in the evening hours prolongs sleep latency 10 , 11 , 12 , 13 , 14 , reduces total sleep time (TST) 10 , 11 , 12 , 14 , 15 , shortens deep sleep 10 , 12 , 13 , 14 , 15 , and decreases electroencephalographically (EEG)-derived slow-wave activity (SWA) 10 , while activity in the sigma range is increased 10 . However, evening caffeine intake only accounts for approximately 10–20% of the total daily caffeine intake in regular consumers 4 , 5 . It needs to be elucidated whether habitual caffeine intake restricted to the morning and afternoon hours similarly affects nighttime sleep.

Furthermore, not only the timing but also the frequency of preceding caffeine intake prior to sleep may be an important factor for the repercussions on sleep. The majority of the worldwide population consumes caffeine on a daily basis 2 , which can lead to tolerance development due to the recurrent supply of the psychostimulant 1 . In line with these results, the sleep-disrupting effects of continuous high-dose caffeine in the morning, afternoon, and evening (3 × 400 mg) intake vanished and only stage 4 sleep remained reduced after 1 week of caffeine intake 12 . However, whether more sensitive markers for sleep intensity such as spectral sleep EEG measures, adapt to the long-term exposure to the stimulant has to our best knowledge not yet been investigated.

Importantly, not only caffeine per se, but also the state of acute abstinence to which regular consumers expose themselves every night, might affect sleep. This so-called overnight abstinence represents the start of a caffeine withdrawal phase 16 . Withdrawal symptoms such as increased tiredness 17 , longer sleep duration, and better sleep quality 18 can be observed at a subjective level starting roughly 12 h after last caffeine intake 17 . However, the influence of caffeine withdrawal on objective EEG-derived sleep variables were not systematically reported up to date and remain to be compared against a placebo-baseline.

Here we aimed at determining whether daily caffeine intake during morning and afternoon hours impairs nighttime sleep structure and sleep intensity after continuous daytime caffeine intake over 9 days. We hypothesized a reduced depth of sleep after caffeine intake, indexed in shortened slow-wave sleep (SWS) duration and a decrease in SWA compared to placebo. Moreover, we hypothesized that the abrupt cessation from the daily intake generates acute subjective withdrawal symptoms, and changes sleep structure and intensity compared to both the daily caffeine intake and the placebo-baseline.

Salivary caffeine levels

Caffeine levels significantly differed between each of the three conditions (main effect of condition: F 2,90.7  = 46.12, p  < 0.001) with the highest levels in the caffeine condition and the lowest in the placebo condition (post-hoc comparisons: p all  < 0.01). In addition, a significant interaction of the factors condition and time ( F 2,89.6  = 10.65, p  < 0.001) confirmed that caffeine levels were modulated by time with levels decreasing during nighttime sleep in the caffeine condition only (post-hoc comparison: p  < 0.001), see Fig.  1 .

Average caffeine levels collected prior to and after nighttime sleep (grey bar) in the placebo (black open circles), caffeine (blue filled circles), and withdrawal (red semi-filled circles) condition (mean values ± standard errors). The x-axis indicates the mean time of day of sample collection and color-coded asterisks represent significant ( p  < 0.05) post-hoc comparisons of the interaction effect condition × time.

Table 1 summarizes the statistical analyses of subjective sleep quality and objective sleep structure assessed during nighttime sleep. Analyses of subjective sleep quality assessed with the Leeds Sleep Evaluation Questionnaire (LSEQ) did not reveal significant differences among the three conditions in any of the four domains of sleep quality ( p all  > 0.05).

In line with these results, the analyses of the polysomnography (PSG) did not reveal significant differences in total sleep time (TST), sleep efficiency (SE), sleep latencies, or the relative amount of sleep stages among the three conditions ( p all  > 0.05).

In a next step, we analyzed all-night EEG power density in the range of 0.75–32 Hz over the central derivations recorded during non-rapid eye movement (NREM) sleep. In contrast to our assumptions, we did not find any significant differences among the three conditions in the lower frequency bins (0.75–13.25 Hz; p all  > 0.05). However, power density was significantly reduced compared to placebo in the sigma range during both withdrawal (frequency bins 13.5–17.25 Hz and 18–18.5 Hz; p all  < 0.05) and caffeine (frequency bins 13.5–16 Hz; p all  < 0.05).

In a second step, we were interested in the temporal dynamics of both SWA and sigma activity across the night assessed during NREM sleep. As depicted in Fig.  2 (top panel), SWA showed a typical temporal pattern with increased activity during the first NREM cycle followed by a steady decline across the night (main effect of time: F 39,613  = 26.28, p  < 0.001). However, differences among the three conditions did not reach significance (main effect of condition: F 2,178  = 1.33, p  = 0.27). Also, the interaction of condition and time was not significant ( F 78,1060  = 0.89, p  = 0.74).

Temporal dynamics of SWA (top) and sigma activity (bottom) during the first four sleep cycles in the placebo (black open circles), caffeine (blue filled circles), and the withdrawal (red semi-filled circles) condition (mean values). The x-axis indicates the mean time of day. While SWA (0.75–4.5 Hz) was not significantly affected by the treatment, sigma activity (12–16 Hz) showed reduced activity during both caffeine and withdrawal compared to the placebo condition ( p all  < 0.05). The inset in each right upper corner represents the mean values ± standard errors of the all-night SWA and sigma activity respectively during NREM sleep in the placebo, caffeine, and withdrawal condition. While all-night SWA (0.75–4.5 Hz) did not differ among the conditions, sigma activity (12–16 Hz) was lower in the caffeine and withdrawal condition compared to placebo ( p  < 0.05). All analyses are based on log-transformed data.

As illustrated in Fig.  2 (bottom panel), sigma activity was significantly reduced in both the caffeine and withdrawal conditions compared to placebo intake (main effect of condition: F 2,209  = 19.96, p  < 0.001; post-hoc comparisons: p  < 0.001) and the interaction of condition and time tended to be significant ( F 78,1049  = 1.25, p  = 0.08).

Taken together, we could not confirm our assumption of a caffeine-induced reduction of sleep depth, neither in terms of shorter SWS nor in terms of reduced SWA in the caffeine compared to the placebo condition. Based on the discrepancies between the present results and a previous study about the effects of chronic caffeine intake on sleep 12 , we thus explored whether differences in the individual levels of caffeine before sleep could explain the variance within SWS and SWA. However, no significant effects were observed when controlling for dependent observations within subjects ( p  > 0.05).

Subjective caffeine withdrawal symptoms

Analyses of the relative withdrawal symptoms yielded a significant main effect of condition ( F 2,20.2  = 11.30, p  < 0.01) indicating more withdrawal symptoms during the withdrawal compared to the caffeine condition (post-hoc comparison: p  < 0.01), depicted in Fig.  3 . This effect was modulated by time (interaction of condition × time: F 2,37.2  = 3.43, p  = 0.04), such that the increase in symptoms during the withdrawal compared to caffeine condition was particularly present during the last measurement ( p  < 0.01), i.e. 31 h after the last caffeine intake in the withdrawal condition.

Relative withdrawal symptoms in the caffeine and withdrawal condition (i.e. withdrawal score of the caffeine and withdrawal condition respectively minus the score of the placebo condition) assessed 35 min, 4 h, and 8 h after wake-up on day ten of treatment. Depicted are mean values and standard errors of the relative values (i.e. difference to placebo). Overall, volunteers reported more withdrawal symptoms in the withdrawal condition compared to the caffeine condition ( p  < 0.05). This difference was particularly present 8 h after wake-up during withdrawal compared to caffeine ( p  < 0.001).

The aim of the present study was to investigate the influence of daily daytime caffeine intake and its cessation on nighttime sleep in habitual caffeine consumers under strictly controlled laboratory conditions. Strikingly, caffeine consumption did not lead to clear-cut changes in nighttime sleep structure nor in subjective sleep quality when assessed 8 and 15 h after the last intake in the caffeine and withdrawal condition, respectively. The evolution of subjective withdrawal symptoms indicates that withdrawal becomes perceivable at earliest between 27–31 h after intake. However, compared to placebo, EEG power density was reduced in the sigma range during both caffeine and withdrawal conditions. We conclude that daily daytime intake of caffeine does not strongly influence nighttime sleep structure nor subjective sleep quality in healthy men when consumed in the morning, midday, and in the afternoon. In contrast to the reported increases in sigma activity after acute caffeine intake 10 , the observed changes in the sigma frequencies might point to early signs of caffeine withdrawal which occur due to overnight abstinence and presumably derive from preceding caffeine-induced changes in adenosine signaling.

To quantify the influence of caffeine on sleep, the stimulant is commonly administered close to the onset of a sleep episode 10 , 11 , 12 , 13 , 14 , for instance within 1 h prior to bedtime 10 , 11 , 13 , 14 . Taking into account that caffeine plasma levels peak within 30–75 min following caffeine ingestion 19 , consumption within 1 h prior to sleep allows the stimulant to exert its maximum effects at sleep commencement. Indeed, the sleep disrupting effects of caffeine are frequently reported to affect sleep initiation or the first half of the sleep episode 10 , 11 , 12 , 13 , 14 . Moreover, sleep intensity, which is usually strongest at the beginning of the night 20 , was particularly disrupted during the first sleep cycle, as indexed in reduced SWS and SWA 10 . However, caffeine intake in the evening, particularly after 9 pm is rare 5 , presumably to avoid impairment of subsequent sleep 3 . Up to date it remained fairly unclear whether caffeine intake in the morning and afternoon still bears the potential to disrupt nighttime sleep. While we observed a delay of 25 min in sleep episodes during caffeine intake prior to the laboratory part, PSG-derived data after 9 days of regular caffeine intake did not yield a significant change in sleep architecture. Thus, our data provide first evidence that daily daytime caffeine intake does not necessarily alter subsequent sleep structure and SWA when consumed > 8 h prior to sleep. Importantly, our findings do not preclude potential impairments of nighttime sleep after morning caffeine intake, if preceded by several days of abstinence from the stimulant 21 . It rather appears likely that the duration of preceding caffeine consumption drives the discrepancies between acute and chronic effects of caffeine on sleep.

Chronic caffeine intake induces some tolerance development in both physiological measures such as cortisol 22 , blood pressure 23 , heart rate 24 , and also subjective measures such as alertness 18 . Over time, the stimulatory effects of the substance vanish potentially due to changes in adenosine levels 25 and/or adenosine receptors 26 , 27 , 28 . Accordingly, a 1-week treatment of caffeine reduced the sleep disrupting effects, even under conditions of high evening dosages 12 . Thus, the available evidence and the absence of clear-cut changes in the present study point to adaptive processes in sleep initiation, sleep structure, and subjective sleep quality due to the long-term exposure to the stimulant.

However, chronic caffeine consumption bears the risk of withdrawal symptoms when abruptly ceased. These symptoms have been reported to occur as early as 6 h but with peak intensity being reached within 20–51 h after last caffeine intake 17 . While 25 h of caffeine abstinence might not affect nighttime sleep structure 12 , 32 h of abstinence improved subjective sleep quality 18 . Thus, scheduling the start of the sleep episode to 15 h after the last caffeine intake, as in our withdrawal condition, was probably too early to detect changes in sleep structure or subjective sleep quality. In line with this assumption, volunteers subjectively indicated withdrawal symptoms 31 h after caffeine abstinence in the withdrawal condition compared to caffeine. Thus, our findings support the notion that the alterations in sleep structure and subjective sleep quality induced by caffeine abstinence potentially develop at a later stage (> 27 h) of caffeine withdrawal.

Most strikingly and unexpectedly, a reduction in NREM sigma activity during both the withdrawal and caffeine conditions was observed, a phenomenon which is commonly reported under conditions of enhanced sleep pressure 29 , 30 , 31 , 32 . Thus, it seems at first glance in contrast to the reported increases in this frequency range 10 , 21 and the well-known alerting effects after acute caffeine intake 18 . However, during conditions of chronic caffeine intake, mice showed a deeper sleep compared to placebo 33 . Moreover, repeated caffeine intake enhances the sensitivity of adenosine binding 34 presumably due to upregulated adenosine receptors 26 , 27 , 28 or changes in the functions of adenosine receptor heteromers 35 . These neuronal alterations in the adenosinergic system might drive the commonly observed changes in the homeostatic sleep-wake regulation such as increased sleepiness when caffeine intake is suddenly ceased 17 . As reported previously, we also observed in the present study higher subjective sleepiness following caffeine withdrawal when compared to the placebo and caffeine conditions 36 . Thus, the reduction in sigma activity might reflect adenosinergic changes which already emerge 8 and 15 h after the last caffeine intake in the caffeine and withdrawal condition, respectively. This reduction might reflect withdrawal symptoms which chronic consumers reverse daily by the first caffeine dose. Given the high prevalence of daily caffeine consumers in the society, these findings stress the importance to carefully control for prior caffeine intake when assessing sleep in order to exclude potential confounding by induced withdrawal symptoms which are only detectable in the microstructure of sleep.

Our study has some limitations which must be taken into careful consideration when interpreting the present findings. First, age moderates the effects of caffeine on sleep 11 , 14 . Thus, the present results cannot be generalized to other age groups such as to middle-aged consumers which are more vulnerable to the caffeine-induced effects on sleep 11 , 14 . Second, only a limited number of participants were studied. However, a well-controlled study design was employed and power calculation on the basis of an earlier study 12 indicated a sufficient sample size. Third, we do not have any information about the participants’ genetic polymorphisms which have been shown to modulate the metabolism of caffeine 37 . In addition, a genetic variation of the ADORA2A genotype has been linked with caffeine sensitivity to the effects on sleep 38 . Thus, carriers of this genetic variance are more likely to curtail caffeine consumption and are consequently excluded from the present study leading to a selection bias. However, the focus of the present study was to investigate habitual caffeine consumers as they represent the majority of the worldwide population 2 . Fourth, to reduce variance in the data incurred by the influence of the menstrual cycle on sleep 39 and the interaction between caffeine metabolism and the use of oral contraceptives 40 , 41 , only male volunteers were included which clearly reduces the generalizability of the findings.

In conclusion, we report evidence that daily daytime intake of caffeine and its cessation has no strong effect on sleep structure or subjective sleep quality. However, the quantitative EEG analyses revealed reduced activity in the sigma range during both caffeine and withdrawal. These subtle alterations point to early signs of caffeine withdrawal in the homeostatic aspect of sleep-wake regulation which are already present as early as 8 h after the last caffeine intake. Thus, habitual caffeine consumers constantly expose themselves to a continuous change between presence and absence of the stimulant. Around the clock, their organisms dynamically adapt and react to daily presence and nightly abstinence.

Participants

Twenty male volunteers were recruited into the present study through online advertisements and flyers distributed in public areas. Interested individuals aged between 18 and 35 years old (mean age ± SD: 26.4 ± 4 years) and reporting a daily caffeine consumption between 300 and 600 mg (mean intake ± SD: 478.1 ± 102.8 mg) were included. The self-rating assessment for the daily amount of caffeine intake was structured based on Bühler et al. 42 , and the amount of caffeine content was defined according to Snel and Lorist 3 . To ensure good health, volunteers were screened by self-report questionnaires and a medical examination conducted by a physician. Additionally, all volunteers reported good sleep quality assessed with the Pittsburgh Sleep Quality Index (PSQI; score ≤ 5) 43 and showed no signs of sleep disturbances (SE > 70%, periodic leg movements < 15/h, apnea index < 10) in a PSG recorded during an adaptation night in the laboratory scheduled prior to the start of the study. To control for circadian misalignment, volunteers who reported shiftwork within 3 months and transmeridian travels (crossing > 2 time zones) within 1 month prior to study admission were excluded. Further exclusion criteria comprised body mass index (BMI) < 18 or > 26, smoking, drug use, and extreme chronotype assessed by the Morningness-Eveningness Questionnaire (MEQ; score ≤ 30 and ≥ 70) 44 . To reduce variance in the data incurred by the effect of menstrual cycle on sleep 39 and the interaction between caffeine metabolism and the use of oral contraceptives 40 , 41 , only male volunteers were studied. A detailed description of the study sample can be found in Weibel et al. 36 .

All volunteers signed a written informed consent and received financial compensation for study participation. The study was approved by the local Ethics Committee (EKNZ) and conducted according to the Declaration of Helsinki.

Design and protocol

We employed a double-blind, randomized, crossover study including a caffeine, a withdrawal, and a placebo condition. Volunteers were allocated to the order of the three conditions based on pseudo-randomization, for more details see Weibel et al. 36 . As illustrated in Fig.  4 , each condition started with an ambulatory part of 9 days, followed by a laboratory part of 43 h. In each condition, participants took either caffeine (150 mg) or placebo (mannitol) in identical appearing gelatin capsules (Hänseler AG, Herisau, Switzerland) three times daily, scheduled at 45 min, 255 min, and 475 min after awakening, for a duration of 10 days. This regimen was applied based on a previous study investigating tolerance to the effects of caffeine and caffeine cessation 18 . To enhance caffeine withdrawal in the withdrawal condition, treatment was abruptly switched from caffeine to placebo on day nine of the protocol (255 min after wake-up, 15 h before sleep recording).

Illustration of the study design. Twenty volunteers participated in a placebo, a caffeine, and a withdrawal condition during which they ingested either caffeine or placebo capsules three times daily (wake-up + 45 min, + 255 min, and + 475 min). Each condition started with an ambulatory part of 9 days and was followed by a laboratory part of 43 h. After 9 days of continuous treatment, we recorded 8 h of polysomnography (PSG), indicated as arrows, during nighttime sleep under controlled laboratory conditions. The sleep episode was scheduled to volunteers’ habitual bedtime.

During the 9 days of the ambulatory part, volunteers were asked to maintain a regular sleep-wake rhythm (± 30 min of self-selected bedtime/wake-up time, 8 h in bed, no daytime napping), verified by wrist actimetry (Actiwatch, Cambridge Neurotechnology Ltd., Cambridge, United Kingdom), and to keep subjective sleep logs. While the participants were compliant, they scheduled sleep episodes differently within the accepted range of ± 30 min. During intake of caffeine (i.e. caffeine and withdrawal condition), the ambulatory sleep episodes were on average around 25 min later as compared to placebo (results see supplements). The duration of the ambulatory part was set for 9 days based on the maximum duration of withdrawal symptoms 17 and thus, to avoid carry-over effects from the previous condition. Furthermore, volunteers were requested to refrain from caffeinated beverages and food (e.g. coffee, tea, soda drinks, and chocolate), alcohol, nicotine, and medications. Caffeine abstinence and compliance to the treatment requirements were checked by caffeine levels from the daily collection of fingertip sweat of which results are reported in the supplemental material of Weibel et al. 36 and which indicate very good adherence to the treatments.

On day nine, volunteers admitted to the laboratory at 5.5 h prior to habitual sleep time. Upon arrival, a urinary drug screen (AccuBioTech Co., Ltd., Beijing, China) was performed to ensure drug abstinence. Electrodes for the PSG were fitted and salivary caffeine levels collected. An 8-h nighttime sleep episode was scheduled at volunteers’ habitual bedtime starting 8 and 15 h after the last caffeine intake in the caffeine and withdrawal condition, respectively. The next day, volunteers rated their subjective sleep quality by the LSEQ 45 and potential withdrawal symptoms by the Caffeine Withdrawal Symptom Questionnaire (CWSQ) 46 .

To reduce potential masking effects on our outcome variables, we standardized food intake, light exposure, and posture changes throughout the laboratory part. Accordingly, volunteers were housed in single apartments under dim-light (< 8 lx) during scheduled wakefulness and 0 lx during sleep. Volunteers were asked to maintain a semi-recumbent position during wakefulness, except for restroom breaks. In addition, volunteers received standardized meals in regular intervals. Social interactions were restricted to team members and no time-of-day cues were provided throughout the in-lab protocol.

Salivary caffeine

To characterize individual caffeine levels during nighttime sleep, we report salivary caffeine levels assessed 3 h prior to the scheduled sleep episode and 5 min after wake-up. Samples were stored at 5 °C following collection, later centrifuged (3000 rpm for 10 min) and subsequently kept at − 28 °C until analyses. Liquid chromatography coupled to tandem mass spectrometry was used to analyze the levels of caffeine. One dataset in the withdrawal condition was lost.

Subjective sleep quality

Subjective sleep quality was assessed 10 min upon scheduled wake-up time with a paper and pencil version of the LSEQ 45 . Volunteers were asked to rate 10 items on visual analogue scales which are grouped into four domains (getting to sleep (GTS), quality of sleep (QOS), awake following sleep (AFS), and behavior following wakening (BFW)).

Polysomnographic recordings

PSG was continuously recorded during 8 h of nighttime sleep using the portable V-Amp device (Brain Products GmbH, Gilching, Germany). Grass gold cup electrodes were applied according to the international 10–20 system including two electrooculographic, two electromyographic, two electrocardiographic, and six electroencephalographic derivations (F3, F4, C3, C4, O1, O2). Channels were referenced online against the linked mastoids (A1, A2). Signals were recorded with a sampling rate of 500 Hz and a notch filter was online applied at 50 Hz.

Each epoch of 30 s of the recorded PSG data was visually scored according to standard criteria 47 by three trained team members blind to the condition. SWS was additionally classified into stage 3 and 4 based on Rechtschaffen and Kales 48 . The scoring agreement between the three scorers was regularly confirmed to reach > 85%.

TST was defined as the sum of the time spent in sleep stages 1–4 and rapid eye movement (REM) sleep. Sleep latency to stage 1 and 2 was calculated as minutes to the first occurrence of the corresponding sleep stage following lights off. REM sleep latency was calculated as minutes to the first occurrence of REM sleep following sleep onset. NREM sleep was calculated as sum of sleep stages 2, 3 and 4. All sleep stages are expressed as relative values (%) of TST.

Spectral analysis was performed by applying fast Fourier transformation (FFT; hamming, 0% overlapped, 0.25 Hz bins) on 4-s time windows. Artifacts were manually removed based on visual inspection, and data were log-transformed prior to spectral analyses. All-night EEG power density during NREM sleep was analyzed for each 0.25 Hz frequency bin in the range of 0.75–32 Hz recorded over the central derivations (C3, C4). SWA was defined as EEG power density between 0.75–4.5 Hz and sigma activity between 12–16 Hz. Sleep cycles were defined based on adapted rules developed by Feinberg and Floyd 49 and divided into 10 NREM and four REM sleep intervals within each cycle. Ten nights were excluded from sleep analyses due to technical problems (placebo: n  = 3; caffeine: n  = 4; withdrawal: n  = 3).

Caffeine withdrawal symptoms

Withdrawal symptoms were first assessed 35 min after wake-up and subsequently prior to each treatment administration with the self-rating CWSQ 46 . Twenty-three items are grouped into seven factors (fatigue/drowsiness, low alertness/difficulty concentrating, mood disturbances, low sociability/motivation to work, nausea/upset stomach, flu-like feelings, headache) and were rated on a 5 point scale by choosing between 1 (not at all) and 5 (extremely). Prior to analyses, eight items have been reversed scored as they were positively worded (e.g. alert or talkative) in the questionnaire. To assess caffeine withdrawal, we first calculated a sum score comprising all 23 items of the caffeine withdrawal questionnaire. Missing responses to single items were replaced by the median response of each condition over all volunteers in the respective time of assessment. In a next step, we calculated relative withdrawal symptoms in the caffeine and withdrawal condition (i.e. the difference of the withdrawal score in the caffeine and withdrawal condition respectively minus the score of the placebo condition). The data of one volunteer was lost due to technical difficulties.

Statistical analyses

Analyses were performed with the statistical package SAS (version 9.4, SAS Institute, Cary, NC, USA) by applying mixed model analyses of variance for repeated measures (PROC MIXED) with the repeated factors ‘condition’ (placebo, caffeine, withdrawal) and/or ‘time’ (levels differ per variable) and the random factor ‘subject’. The LSMEANS statement was used to calculate contrasts and degrees of freedom were based on the approximation by Kenward and Roger 50 . Post-hoc comparisons were adjusted for multiple comparisons by applying the Tukey-Kramer method. A statistical significance was defined as p  < 0.05. One dataset has been excluded from all the analyses due to non-compliance with the treatment requirements (caffeine: n  = 1).

Data availability

The present data are available upon request from the corresponding author.

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Acknowledgements

The present work was performed within the framework of a project granted by the Swiss National Science Foundation (320030_163058) and was additionally funded by the Nikolaus und Bertha Burckhardt-Bürgin-Stiftung and the Janggen-Pöhn-Stiftung. Further, we thank our interns Andrea Schumacher, Laura Tincknell, Sven Leach, and all our study helpers for their help in data acquisition and all our volunteers for participating in the study. Moreover, we gratefully acknowledge the help in study organization provided by Dr. Ruta Lasauskaite and the medical screenings conducted by Dr. med. Martin Meyer and Dr. med. Corrado Garbazza.

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These authors contributed equally: Christian Cajochen and Carolin F. Reichert.

Authors and Affiliations

Centre for Chronobiology, Psychiatric Hospital of the University of Basel, Basel, Switzerland

Janine Weibel, Yu-Shiuan Lin, Joshua Kistler, Christian Cajochen & Carolin F. Reichert

Transfaculty Research Platform Molecular and Cognitive Neurosciences, University of Basel, Basel, Switzerland

Neuropsychiatry and Brain Imaging, Psychiatric Hospital of the University of Basel, Basel, Switzerland

Yu-Shiuan Lin & Stefan Borgwardt

Institute of Pharmacology and Toxicology, University of Zürich, Zürich, Switzerland

Hans-Peter Landolt

Sleep & Health Zürich, University Center of Competence, University of Zürich, Zürich, Switzerland

Laboratory Medicine, University Hospital Basel, Basel, Switzerland

Sophia Rehm & Katharina M. Rentsch

Clinical Sleep Laboratory, Psychiatric Hospital of the University of Basel, Basel, Switzerland

Helen Slawik

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C.R., C.C. and S.B. designed the study; J.W., Y.S.L. and HS collected the data; J.W., C.R. and C.C. analyzed and interpreted the data; J.W. and C.R. drafted the manuscript; C.C., Y.S.L. and H.P.L. critically revised the manuscript regarding its intellectual content; J.K., S.R. and K.R. provided the resources for the caffeine measurements and performed its analyses; all authors reviewed the present article.

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Correspondence to Christian Cajochen .

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Weibel, J., Lin, YS., Landolt, HP. et al. The impact of daily caffeine intake on nighttime sleep in young adult men. Sci Rep 11 , 4668 (2021). https://doi.org/10.1038/s41598-021-84088-x

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DOI : https://doi.org/10.1038/s41598-021-84088-x

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Overview of caffeine effects on human health and emerging delivery strategies.

1. Introduction

2. chemical structure and main natural sources of caffeine, 3. benefits of caffeine on health, 3.1. cancer, 3.2. anti-inflammatory and immunomodulation, 3.2.1. autoimmune diseases and immunomodulation, 3.2.2. ocular diseases, 3.2.3. respiratory diseases, 3.3. neurodegenerative diseases, 3.4. cardiovascular diseases, 4. caffeine impact on sports performance, 4.1. optimal dosage, 4.2. timing of intake, 4.3. abstinence, 4.4. training time vs. caffeine consumption, 4.5. physiological factors, 4.6. gender, 4.7. caffeine consumers or not, 5. future directions: nanotechnology-based delivery strategies, 6. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Saraiva, S.M.; Jacinto, T.A.; Gonçalves, A.C.; Gaspar, D.; Silva, L.R. Overview of Caffeine Effects on Human Health and Emerging Delivery Strategies. Pharmaceuticals 2023 , 16 , 1067. https://doi.org/10.3390/ph16081067

Saraiva SM, Jacinto TA, Gonçalves AC, Gaspar D, Silva LR. Overview of Caffeine Effects on Human Health and Emerging Delivery Strategies. Pharmaceuticals . 2023; 16(8):1067. https://doi.org/10.3390/ph16081067

Saraiva, Sofia M., Telma A. Jacinto, Ana C. Gonçalves, Dário Gaspar, and Luís R. Silva. 2023. "Overview of Caffeine Effects on Human Health and Emerging Delivery Strategies" Pharmaceuticals 16, no. 8: 1067. https://doi.org/10.3390/ph16081067

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• Published: 15 June 2021

Caffeine consumption, intoxication, and stress among female university students: a cross-sectional study

• Deemah A. AlAteeq   ORCID: orcid.org/0000-0003-2852-5370 1 ,
• Razan Alotaibi 1 ,
• Raneem Al Saqer 1 ,
• Njoud Alharbi 1 ,
• Maram Alotaibi 1 ,
• Reema Musllet 1 &
• Rana Alraqibah 1  

Middle East Current Psychiatry volume  28 , Article number:  30 ( 2021 ) Cite this article

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University students use caffeine to cope with stress in spite of its adverse effects. The purpose of this study is to explore caffeine consumption among university students in Saudi Arabia, as well as its correlation with stress and caffeine intoxication. This cross-sectional study examined a convenience sample of 547 students at Princess Nourah Bint Abdulrahman University (PNU). A self-administrated questionnaire was used to assess caffeine consumption in milligrams per day, stress was assessed by the perceived stress scale (PSS), and caffeine intoxication was assessed using the DSM-5 criteria.

The mean total caffeine consumption was 424.69 ± 385.31 mg/day. High levels of caffeine consumption were found among students of non-health colleges and students who were undiagnosed with psychiatric disorders ( p values <0.040 and 0.027, respectively). A significant positive correlation was found between caffeine consumption and perceived stress ( p <0.045). Only 13.26% of all participants fulfilled the DSM-5 criteria for caffeine use disorder. The majority of participants showed moderate and high stress levels (69.9% and 18.7%).

This study revealed high caffeine consumption and perceived stress levels among female undergraduate students with a significant positive association between them. The results emphasize the importance of educational campaigns about caffeine consumption and intoxication. They also encourage the development of stress management programs. Longitudinal studies need to be designed for evidence-based intervention.

Caffeine is a stimulant of the central nervous system and metabolism that is used for recreational and for medical reasons, such as decreasing physical exhaustion and increasing mental alertness [ 1 ]. Caffeine intake has positive and negative effects. The positive effects are enhanced mood and readiness, improved ability to stay conscious and alert, and strengthened exercise performance [ 2 ]. On the other hand, negative effects may occur when caffeine intake exceeds 250 mg, it can result in a condition called caffeine intoxication. Symptoms include fidgeting, excitement, insomnia, increased urination, gastrointestinal disturbance, muscle twitching, irregular or rapid heartbeat, and psychomotor agitation according to the criteria of the Diagnostic and Statistical Manual of Mental Disorders, fifth edition (DSM-5) [ 3 ].

Students experience stressful times in college due to classes, homework, exams, projects, and extracurricular activities [ 4 ]. Studies done on college students in Puerto Rico, Saudi Arabia, and Turkey showed that 49%, 49.5%, and 58.99% of them use caffeine as a coping mechanism, respectively [ 2 , 5 , 6 ]. In order to deal with this stress and fulfill academic requirements, college students may consume caffeine in the belief that it can aid their academic performance [ 4 ].

In Bahrain, the mean of daily caffeine consumption was assessed among college students. Females were consuming less than males (246 and 306 mg/day respectively) [ 7 ]. Another study assessed caffeine consumption and sleep habits among a sample of 228 students at Princess Nourah Bint Abdulrahman University (PNU) and found that most of them had high caffeine consumption, and a need for future studies concerning caffeine intoxication was suggested [ 8 ]. In this study, we aimed to estimate the level of caffeine consumption among students at health colleges and non-health colleges in PNU and to explore the correlation of caffeine consumption (including all types of caffeinated beverages) with caffeine intoxication and perceived stress.

Study design and population

This cross-sectional study examined a convenience sample of students who were Arabic speakers at the age of 18 years or above at health and non-health colleges at PNU, Riyadh, Saudi Arabia. PNU is the first women’s university in the Middle East and also the largest one. It accommodates 33,825 students and includes 18 colleges.

A self-administrated questionnaire was distributed conveniently in October 2019 to 547 students from different colleges. Equal numbers of questionnaires were distributed to health and non-health colleges (humanities, community, and science). The comparison was chosen to be between health and non-health colleges based on the significant differences in the studying years, academic system, training requirements, and health-related knowledge. Health college students have an average of 6 years of studying and non-health college students have an average of 4 years. These factors may affect the level of their perceived stress and caffeine consumption.

The consenting process started by going to the students on the campus and explaining to them the study and asks them if they are willing to participate, mentioning that their identity is confidential as it does not require a name or an ID. If they agree to participate, a copy of the questionnaire will be given to them, and they will be encouraged to read the front page thoroughly which has a statement saying “Filling this questionnaire means you agree to be part of this study.” It also has the information and purpose of the study and the contact information.

Previous literatures showed that the average daily caffeine consumptions vary from 10 to 11.2 mg/day, and the standard deviation was around 5 mg/day. Gpower software was used to calculate the minimal size required for this study, considering alpha =0.05 (level of significance of 95%) and beta to be 0.20 (power of the study is 80%), the minimal sample size required is 548.

Data collection tool

The questionnaire included four main divisions. The first one covered demographic characteristics, academic-related characteristics, and personal clinical history. The second and third divisions covered caffeine consumption and caffeine intoxication. The fourth division assessed perceived stress level by PSS-10. The first three division of the survey were first written in Arabic then translated to English by a professional translator, then reviewed and translated into Arabic by bilingual speakers. Pilot test was done for the Arabic version, and then face validation was done by sending the survey to four mental health professionals. Furthermore, the fourth division involved the Arabic version of PSS-10, which has been validated previously by Chaaya et al. The PSS-10 was translated from English into Arabic, and then it was reviewed by a bilingual psychiatrist. After that, it was back translated into English by the psychiatrist and a comparison with the original one was done [ 9 ].

Demographic characteristics, academic-related characteristics, and personal clinical history

Demographic characteristics included age, nationality, marital status, number of children, and income, which was estimated among students as having enough income, enough income with saving, not enough income, or debt. Academic-related characteristics included college, academic level (first and second-years were counted as “juniors,” whereas third, fourth, and fifth years were accounted as “seniors”), GPA, and academic satisfaction. Personal clinical history included questions about their history of diagnosed chronic diseases or psychiatric disorders, received psychiatric help, and smoking.

• Caffeine consumption

Caffeine intake per day was measured in milligrams. A table was included in the questionnaire (Table 1 ). The common caffeinated drinks had been enlisted in the table. All common caffeinated drinks were investigated, including drinks such as coffee, decaffeinated coffee, tea, cola, citrus, and energy drinks. The table included the size and the number of cup/cans per day for each drink. A reference image was attached to illustrate the size, and the amount of fluid in ounces. The participants were asked to fill out only the size and number of the drinks they regularly consume. The amount of caffeine in each drink size was calculated (Table 2 ), which were later multiplied by the number of cups/cans consumed daily. Then, the total numbers of caffeine milligrams per day were summed. Caffeine intake was examined as “low” and “high.” Low intake was considered less than 250 mg per day, while high intake was considered more than 250 mg per day. The validity and reliability of measurements have been confirmed in similar studies [ 10 , 11 ].

Caffeine intoxication

Intoxication was assessed using the criteria of DSM-5, which includes 12 symptoms. Participants were asked if they developed symptoms during or shortly after caffeine consumption. Any participant with five or more symptoms was diagnosed with caffeine intoxication according to the criteria.

Perceived stress level

Stress level was measured using the Arabic version of the PSS, which is a 10-question tool that is used to measure perception of stress over the past 30 days. The scale was developed in 1983 [ 12 ] and was modified in 1988 by Cohen [ 13 ]. It is a validated stress questionnaire with established acceptable psychometric properties [ 14 , 15 ]. A Likert-type scale was used to capture responses to the PSS (“never,” “almost never,” “sometimes,” “fairly often,” and “very often”). A score of 0-13 is considered as low stress, 14-26 is considered moderate, and 27-40 is considered high perceived stress.

Statistical analysis

Data were analyzed using SPSS 23. We described the variables as means ± the standard deviation (SD) or percentages as appropriate. A t test was used to determine the difference between quantitative variables, while the chi-squared test was used to determine the association between qualitative variables.

The total number of participants in the study was 547, and the average age was 20.30 ± 1.91 years. The majority were Saudi (98.40%) and single (96%). More than half of the participants (59.20%) reported that their income was enough, and 29.20% reported that their income was enough with saving.

Almost half of the participants were from health colleges (50.10%), whereas the other half were from non-health colleges (49.90%). More than half of the participants were junior students (61.80%). The GPA 4.50-5.00 for 42.50% of the participants, and only 9.40% of them had a GPA less than 3.50. More than half of them were either satisfied or very satisfied with their academic achievement (46.60% and 22.80%, respectively).

A minority of the participants reported that they were diagnosed with chronic diseases and psychiatric disorders (6.80% and 10.10%, respectively). Half of those who had been diagnosed with a psychiatric disorder received psychiatric help (56.36%). In addition, the majority of participants were non-smokers (93.90; Table 3 ).

The mean total caffeine consumption per day was 424.69±385.31 mg. Specialty coffee was the most consumed caffeine source with a mean of 93.06±126.99 mg, followed by regular brewed coffee, capsule coffee, and black tea with means of 62.74±114.30 mg, 55.39±114.62 mg, and 51.60±83.98 mg, respectively (Table 4 ). The mean of low caffeine consumption group (< 250 mg/day) was 126.6±68.01 mg/day, while it was 628.28±381.6 mg/day for the high caffeine consumption group (> 250 mg/day).

A high level of caffeine consumption was significantly more evident among students of non-health colleges than health college students (53.50% versus 46.50%, respectively; p <0.040). In addition, a high level of caffeine consumption was significantly more evident among students undiagnosed with psychiatric disorders than diagnosed students (87.60% versus 12.40%, respectively; p <0.027). Moreover, a high level of caffeine consumption was significantly more evident among students who experienced caffeine intoxication symptoms than asymptomatic students (75% versus 57.1%; p < 0.005). Finally, students who had a high level of caffeine consumption had significantly higher mean scores of perceived stress than students with a low level of caffeine consumption (21.40±6.38 and 20.27±6.31, respectively; p <0.045; Table 5 ).

Symptoms of caffeine intoxication

The reported caffeine intoxication symptoms in descending order were diuresis, insomnia, tachycardia or arrhythmia, gastrointestinal disturbance, restlessness, nervousness, rambling flow of thought and speech, muscle twitching, periods of inexhaustibility, psychomotor agitation, excitement, and flushed face (43.70%, 43.50%, 38.90%, 25.80%, 16.80%, 15.90%, 13.90%, 11.70%, 11%, 9.50%, 7.50%, and 5.10%, respectively). However, more than three quarters of them had no clinically significant distress or impairment of function (78%). Only 13.26% of all participants fulfilled DSM-5 criteria for caffeine use disorder.

Perceived stress

More than two-thirds of the participants showed moderate stress levels (69.9%), whereas 18.7% reported high stress levels (Table 6 ). There were significant associations between the level of perceived stress and academic satisfaction. A high level of stress was also more evident among students who were academically very unsatisfied or not satisfied than those who were satisfied or very satisfied (25.87±6.57 and 22.43±6.20 versus 20.23±5.95 and 19.33±6.17, respectively; p <0.001).

In addition, the level of perceived stress was significantly associated with students’ income; a higher level of stress was more evident among students who expressed that they were in debt or their income is not enough those who had enough income or enough income with savings (24.11±6.95 and 23.24±7.90 versus 20.99±5.75 and 20.16±6.69, respectively; p <0.008). Another significant association was found between the level of perceived stress and some personal clinical histories. A high level of stress was more evident among students who were diagnosed with psychiatric disorders than undiagnosed students (25.20±6.31 versus 20.52±6.20, respectively; p <0.001). The level of stress was also significantly higher among students diagnosed with chronic disease than undiagnosed students (23.69±6.17 versus 20.80±6.33, respectively; p <0.008). Furthermore, a high level of stress was more evident among smokers than non-smokers (24.36±7.44 versus 20.79±0.02, respectively; p <0.002). Finally, a high level of stress was more evident among students who experienced caffeine intoxication symptoms than asymptomatic students (24.12±5.92 than 20.51±6.28, respectively; p <0.001; Table 7 ).

This study represents the first Saudi university-based survey of caffeine consumption including all types of caffeinated beverages among students from both health and non-health colleges to explore the correlation with perceived stress and caffeine intoxication. The results showed that the mean total caffeine consumption was 424.69±385.31 mg/day. This could be alarming as the recommended use for healthy adult is 400 mg/day [ 16 ]. This result is comparable to that of an Egyptian study, which found that caffeine consumption was 405.47±396.43 mg/day among university students [ 17 ]. These two results are slightly higher than a Lebanese result that showed a mean total caffeine consumption of 193.32±361.81 mg/day for medical students [ 18 ]. This can be explained by the result of the current study as it showed higher caffeine consumption among non-health college student. The lower caffeine consumption level reported by students of health colleges could be due to their awareness about the side effects of caffeine. On the other hand, the total mean caffeine consumption in the current study is much higher (by at least twice) than in other studies that were conducted among another various populations, like army soldiers (285 mg/day), psychiatric patients (281±325 mg/day), office workers (205.7±34.9 mg/day), the general populations (164.5 ± 0.9 mg/day and 193 mg/day), adolescents (25.92±41.25 mg/day and 91.5 ± 4.7 mg/day), and children (76.1 ± 6.3 mg/day) [ 10 , 11 , 19 , 20 , 21 , 22 ]. This could be due to the higher level of perceived stress that was found among university students in this study as caffeine may relieve stress [ 23 ]. In addition, other numerous factors for caffeine intake among undergraduate university students were reported in the USA including improving alertness, concentration, mood, energy, and enjoying the taste [ 24 ].

Furthermore, the level of caffeine consumption was significantly lower among students diagnosed with psychiatric disorders, which could be attributed to their awareness or previous experience of the effects of excessive caffeine consumption, which increases the risk of anxiety, panic attacks, and psychotic symptoms [ 25 , 26 ]. And those who are suffering from anxiety conditions may have more caffeine sensitivity, which contribute in caffeine avoidance due to the undesirable effects [ 27 , 28 , 29 ]. It could also be attributed to their awareness or previous experience with the potential interaction of caffeine with psychotropic drugs that are used for their psychiatric conditions, which is due to the metabolism of caffeine by CYP1A2 enzyme. Caffeine can inhibit this enzyme and cause side effects that may affect their treatment plan [ 30 ]. In addition, a high level of caffeine consumption was significantly more evident among students who experienced caffeine intoxication because the more caffeine they consume, the more symptoms they experience. A related study done in the USA showed that excessive caffeine consumption can lead to caffeine intoxication [ 4 ]. And it was found that only 13% of participants experienced caffeine intoxication according to the DSM-5 criteria. This is similar to the prevalence of intoxication that was found among psychiatric patients in Italy (10.3%), which was significantly higher compared to healthy participants (2.9%). However, comparing our results with the Italian results was limited by the samples differences as the Italian study had wider age range and more severe psychiatric cases compared to our study [ 22 ].

Perceived stress was prevalent in this study. This is not surprising as similar results were found in previous studies that were conducted among university students in Saudi Arabia, Iran, and Malaysia [ 5 , 31 , 32 , 33 ]. In addition, a significant positive relationship was found between the level of caffeine consumption and the level of perceived stress. This is supported by a previous study that found a significant positive relationship between the consumption of energy drinks and stress [ 34 , 35 ]. This might be due to the beneficial effects of caffeine in maintaining cognitive function under conditions of stress and improving work performance [ 23 ].

Moreover, smoker students reported significantly higher stress levels. There are several theories on the role of stress and smoking behaviors. Smokers use cigarettes to relieve stress. However, several studies have shown that while smoking may temporarily relieve perceived stress, it actually may generate or aggravate negative emotional states and propagate negative coping strategies, leading to higher stress levels overall [ 36 ].

Perceived stress was found to be significantly higher among students who were diagnosed with psychiatric disorder or chronic disease. This is not surprising as it is evident that stress is a risk factor for various psychiatric and medical conditions [ 37 , 38 , 39 , 40 ]. Research shows that almost every system in the body can be influenced by chronic stress. When chronic stress goes unreleased, it suppresses the body’s immune system and ultimately manifests as illness. If stress continues and the body is unable to cope, there is likely to be a breakdown of bodily resources [ 41 ].

Limitations

This is the first Saudi university-based survey of caffeine consumption among students from both health and non-health colleges that included all types of caffeinated beverages. The results provided valuable information about caffeine consumption, caffeine intoxication, and stress. However, the convenience sampling and female participants limit the generalizability of the study. Although all common caffeinated drinks were investigated in this study, other possible sources of caffeine such as caffeine pills and chocolate were not included. In addition, even if intoxication symptoms listed in the survey were developed during or shortly after caffeine intake, it was difficult to differentiate between caffeine intoxication and symptoms of other medical or psychiatric conditions. Furthermore, a cross-sectional study cannot identify causality relationships.

Caffeine is highly consumed by female undergraduate students, mostly specially coffee, and the level is significantly higher among students of non-health colleges. In addition, caffeine consumption levels are positively and significantly correlated with perceived stress levels, which were prevalent among the students. However, only 13.26% of all participants fulfilled DSM-5 criteria for caffeine use disorder which was associated with high level of stress. This emphasizes the importance of educational campaigns about caffeine consumption and intoxication. Furthermore, this study could be useful for future university education and stress management planning. It could also be used as a primary resource for future investigations. However, longitudinal studies need to be designed for evidence-based intervention. Further studies also need to involve both sexes and postgraduate students.

Availability of data and materials

All data and material of this study are available upon request from the corresponding author.

Abbreviations

Perceived stress scale

The Diagnostic and Statistical Manual of Mental Disorders, fifth edition

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Acknowledgements

The authors would like to thank Prof. Amel Fayed and Prof. Halah Elmershardi.

This research was funded by the Deanship of Scientific Research at Princess Nourah Bint Abdulrahman University through the Fast-track Research Funding Program. The funding body has no role in study design, data collection, data analysis, data interpretation, or manuscript writing.

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RMA and NA were responsible of designing the study. MA and RM were in charge of collecting and entering the data. And they helped in the analysis. RMA and RIA were responsible of interpreting and analyzing of the data. DA was responsible of the general process, editing, and publication of paper. All authors took a part in writing, revising and approving the final manuscript. The authors read and approved the final manuscript.

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AlAteeq, D.A., Alotaibi, R., Al Saqer, R. et al. Caffeine consumption, intoxication, and stress among female university students: a cross-sectional study. Middle East Curr Psychiatry 28 , 30 (2021). https://doi.org/10.1186/s43045-021-00109-5

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New Study Finds Chronic High Caffeine Consumption May Heighten Risk for Cardiovascular Disease

The findings will be presented at acc asia 2024.

Aug 15, 2024

Contact: Julie Boyland, [email protected] ,

NEW DELHI (Aug 15, 2024) -

From coffee to tea, caffeinated beverages are an integral part of morning routines across the globe, but these popular drinks can be harmful when enjoyed in excess. According to a new study being presented at ACC Asia 2024 in Delhi, India, drinking over 400 mg of caffeine per day on most days of the week could increase the susceptibility of otherwise healthy individuals to cardiovascular disease.

“Regular caffeine consumption could disturb the parasympathetic system, leading to elevated blood pressure and heart rates,” said lead author Nency Kagathara, MBBS, Department of Internal Medicine, Zydus Medical College and Hospital, Dahod, India. “Our study sought to determine the effects of chronic caffeine consumption on heart health, specifically the recovery of heart rate and blood pressure.”

Chronic caffeine consumption was defined as the intake of any caffeinated drinks five days per week for more than one year. The study focused on tea, coffee and aerated beverages such as Coke, Pepsi, Redbull, Sting and Monster. The authors evaluated a randomized group of 92 normotensive and healthy individuals between the ages of 18 and 45 years. All participants had their blood pressure and pulse measured and underwent a three-minute step test. Blood pressure and heart rates were measured at one minute and five minutes after the test. The authors recorded information on each participant’s sociodemographic data and daily caffeine intake.

The results found that 19.6% of the participants consumed more than 400 mg of caffeine every day, which translates to about four cups of coffee, 10 cans of soda or two energy drinks. Chronic caffeine consumption at 400 mg daily was shown to significantly impact the autonomic nervous system, raising the heart rate and blood pressure over time.

Researchers said the highest daily caffeine intakes were observed in participants who were female, employed in business and management roles, and living in urban areas.

Those who consumed the highest amounts, chronic caffeine consumption of more than 600 mg of caffeine per day, had significantly elevated heart rates and blood pressure after five minutes of rest following the step test

“Due to its effect on the autonomic nervous system, regular caffeine consumption could put otherwise healthy individuals at risk of hypertension and other cardiovascular events,” said Kagathara. “Increasing awareness of these risks is vital to improve heart health for all.”

High blood pressure, also known as hypertension, is associated with an increased risk of coronary artery disease, heart failure, chronic kidney disease, and dementia. Hypertension weakens your heart over time and is a leading risk factor for heart disease. In addition to caffeine intake, there are several factors that could contribute to high blood pressure, such as alcohol consumption, smoking, age, family medical history, and salt consumption. Increasing physical activity, following a nutritious diet and other lifestyle changes could help lower blood pressure and reduce the risk of heart disease.

The American College of Cardiology (ACC) is the global leader in transforming cardiovascular care and improving heart health for all. As  the preeminent source of professional medical education for the entire cardiovascular care team since 1949,  ACC   credentials cardiovascular professionals in over 140 countries who meet stringent qualifications and leads in the formation of health policy, standards and guidelines .  Through its world-renowned family of JACC Journals, NCDR registries, ACC Accreditation Services, global network of Member Sections, CardioSmart patient resources and more, the College is committed to ensuring a world where science, knowledge and innovation optimize patient care and outcomes. Learn more at www.ACC.org or follow @ACCinTouch .

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New study finds chronic high caffeine consumption may heighten risk for cardiovascular disease

by American College of Cardiology

From coffee to tea, caffeinated beverages are an integral part of morning routines across the globe, but these popular drinks can be harmful when enjoyed in excess. According to a new study presented at ACC Asia 2024 in Delhi, India, drinking over 400 mg of caffeine per day on most days of the week could increase the susceptibility of otherwise healthy individuals to cardiovascular disease.

"Regular caffeine consumption could disturb the parasympathetic system, leading to elevated blood pressure and heart rates ," said lead author Nency Kagathara, MBBS, Department of Internal Medicine, Zydus Medical College and Hospital, Dahod, India. "Our study sought to determine the effects of chronic caffeine consumption on heart health , specifically the recovery of heart rate and blood pressure."

Chronic caffeine consumption is defined as the intake of any caffeinated drinks five days per week for more than one year. The study focused on tea, coffee and aerated beverages such as Coke, Pepsi, Redbull, Sting and Monster.

The authors evaluated a randomized group of 92 normotensive and healthy individuals between the ages of 18 and 45 years. All participants had their blood pressure and pulse measured and underwent a three-minute step test. Blood pressure and heart rates were measured at one minute and five minutes after the test. The authors recorded information on each participant's sociodemographic data and daily caffeine intake.

The results found that 19.6% of the participants consumed more than 400 mg of caffeine every day, which translates to about four cups of coffee, 10 cans of soda or two energy drinks. Chronic caffeine consumption at 400 mg daily was shown to significantly impact the autonomic nervous system, raising the heart rate and blood pressure over time.

Researchers said the highest daily caffeine intakes were observed in participants who were female, employed in business and management roles, and living in urban areas.

Those who consumed the highest amounts, chronic caffeine consumption of more than 600 mg of caffeine per day, had significantly elevated heart rates and blood pressure after five minutes of rest following the step test

"Due to its effect on the autonomic nervous system , regular caffeine consumption could put otherwise healthy individuals at risk of hypertension and other cardiovascular events," said Kagathara. "Increasing awareness of these risks is vital to improve heart health for all."

High blood pressure, also known as hypertension, is associated with an increased risk of coronary artery disease, heart failure, chronic kidney disease, and dementia.

Hypertension weakens your heart over time and is a leading risk factor for heart disease. In addition to caffeine intake, there are several factors that could contribute to high blood pressure , such as alcohol consumption, smoking, age, family medical history, and salt consumption.

Increasing physical activity , following a nutritious diet and other lifestyle changes could help lower blood pressure and reduce the risk of heart disease.

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Drinking too much caffeine could be bad for your heart health, new study finds. Here's how to know if you're overdoing it.

You may wake up in the morning desperate for a cup of coffee (or four) to kickstart your day. However, a new study says that overdoing it on caffeine may have implications for your heart.

The new findings, from Zydus Medical College and Hospital in Dahod, India, says that regularly drinking more than 400 mg of caffeine per day could increase the likelihood that otherwise healthy individuals will develop heart disease. For reference, 400 mg is, per the Mayo Clinic, the equivalent of roughly four cups of brewed coffee , 10 cans of soda or two energy drinks.

The small study looked at 92 healthy individuals between the ages of 18 and 45, 19.6% of whom were considered chronic caffeine users. Researchers found that those consuming more than 400 mg of caffeine daily had elevated heart rates and blood pressure. This effect was even more pronounced in those consuming over 600 mg daily, which is equivalent to about six to seven cups of brewed coffee.

The study authors say that caffeine’s impact on the autonomic nervous system (which helps control things like your heart rate and blood pressure) could put undue stress on the heart, as well as other parts of the body. Hypertension, or high blood pressure , increases the risk of health issues such as heart attacks, strokes, heart failure, aneurysms and chronic kidney disease — and is often referred to as the “silent killer.”

What experts say

Caffeine is a stimulant. Dr. Jamie Alan , associate professor of pharmacology and toxicology at Michigan State University, previously told Yahoo Life that consuming caffeine “will increase heart rate, open up the lungs and increase wakefulness.” (That last effect is why so many people reach for a coffee in the morning.)

Caffeine may also raise blood pressure, albeit temporarily, according to the Mayo Clinic . Contrary to what this new study found, this effect is more commonly seen in people who don’t consume caffeine regularly and therefore have not developed a tolerance for the stimulant. While it’s unclear why some people experience this, some researchers believe it may have to do with caffeine causing the adrenal glands to release more adrenaline (raising blood pressure) or caffeine blocking a hormone that causes arteries to widen. Despite this effect, people with high blood pressure aren’t typically told to cut out caffeine entirely.

Dr. Gregory Marcus , a cardiologist and professor of medicine at the University of California, San Francisco, tells Yahoo Life that these findings remind us that commonly consumed substances like caffeine may have effects on our heart and blood vessels. However, he notes that it’s possible that the effects attributed to caffeine could have been caused by other factors, such as age or fitness level.

“It is important to remember that while blood pressure and heart rate are important determinants of health, the best way to determine health effects is to examine actual cardiovascular outcomes, such as rates of stroke, heart attack and heart rhythm disturbances,” Marcus says.

There have been studies that suggest coffee could be good for the heart. For example, one study from 2023 found that regular caffeinated coffee consumption is associated with a decreased risk of hypertension, heart failure and atrial fibrillation . Research from 2021 also found that caffeinated coffee may reduce the risk of heart failure .

What is considered too much caffeine?

Dr. Brynna Connor , a specialist in family medicine, tells Yahoo Life that the Food and Drug Administration recommends no more than 400 mg of caffeine per day . “Anything beyond that is considered too much, and people may start to experience symptoms such as headaches, irritability and jitters,” she says.

She adds, however, that the severity of these symptoms depends on each individual’s sensitivity to caffeine and how quickly it is metabolized. “While the average half-life of caffeine is between three and seven hours, people with a slower metabolism or liver problems may experience the effects of caffeine longer, and more acutely, as the stimulant would take longer to pass and subsequently stay in their bloodstream longer.”

Connor warns that caffeine in quantities of 1,200 mg, especially if consumed in a short time frame, can be “toxic and lead to severe side effects such as seizures.” That is the equivalent of 12 to 15 cups of coffee — or about three of Panera’s now discontinued Charged Lemonades.

Marcus says that it’s hard to say what amount of caffeine is universally applicable because caffeine tolerance varies greatly among individuals due to genetic variants that cause us break down caffeine at different speeds and to the amount of caffeine we regularly consume.

People who have caffeine on a regular basis become more tolerant of its effects because our bodies ramp up the systems that get rid of caffeine, Marcus explains. Someone who doesn’t drink much caffeine may feel ill after drinking just one espresso, he says, but someone who drinks caffeinated beverages on a regular basis may experience minimal effects after even several cups of coffee.

“Ultimately, individuals should listen to their body, and learn to recognize the amount of caffeine for them that results in uncomfortable sensations, such as feeling tremulous or shaky, nervous or experiencing a racing heart,” Marcus says.

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Caffeine consumption and self-assessed stress, anxiety, and depression in secondary school children

Previous research suggests that effects of caffeine on behaviour are positive unless one is investigating sensitive groups or ingestion of large amounts. Children are a potentially sensitive subgroup, and especially so considering the high levels of caffeine currently found in energy drinks. The present study used data from the Cornish Academies Project to investigate associations between caffeine (both its total consumption, and that derived separately from energy drinks, cola, tea, and coffee) and single-item measures of stress, anxiety, and depression, in a large cohort of secondary school children from the South West of England. After adjusting for additional dietary, demographic, and lifestyle covariates, positive associations between total weekly caffeine intake and anxiety and depression remained significant, and the effects differed between males and females. Initially, effects were also observed in relation to caffeine consumed specifically from coffee. However, coffee was found to be the major contributor to high overall caffeine intake, providing explanation as to why effects relating to this source were also apparent. Findings from the current study increase our knowledge regarding associations between caffeine intake and stress, anxiety, and depression in secondary school children, though the cross-sectional nature of the research made it impossible to infer causality.

Introduction

Dose-dependent effects of caffeine on behaviour.

Short-term effects of caffeine consumption include enhanced mood and alertness ( Ferré, 2008 ; Kaplan et al., 1997 ; Lorist and Tops, 2003 ), improved exercise performance ( Doherty and Smith, 2004 ), increased blood pressure ( Riksen et al., 2009 ), improved ability to remain awake and mentally alert after fatigue ( Smit and Rogers, 2002 ), faster information processing speed and reaction time, and heightened awareness and attention ( Cysneiros et al., 2007 ). When consumed in moderation it appears that there are no serious adverse health effects associated with its use by adults ( Nawrot et al., 2003 ) or children ( Higdon and Frei, 2006 ; Mandel, 2002 ). However, it has been advised that those who are highly sensitive should not consume >400 mg/d, in order to avoid headaches, drowsiness, anxiety, and nausea ( Nawrot et al., 2003 ). A sensitive individual might experience adverse effects at a lower dose than less sensitive individuals. Children are often considered as sensitive individuals because of their size and developing central nervous system. This is concerning because many children and adolescents are frequent caffeine consumers (for instance, a recent US study found 73% of children to consume caffeine on a given day; Branum et al., 2014 ). It is important, therefore, to identify thresholds above which negative effects might occur. In the context of the current study, the thresholds in question relate to the group as a whole, with potential sensitivity to caffeine being defined by the participants being children.

The relatively recent introduction of ‘energy drinks’ to the consumer market has been highlighted as a cause for concern (e.g. Reissig et al., 2009 ). Energy drinks are soft drinks that manufacturers claim boost performance and endurance ( Meadows-Oliver and Ryan-Krause, 2007 ), with the main active ingredient being caffeine ( McLellan and Lieberman, 2012 ). These products are often strategically marketed towards the young consumer ( Reissig et al., 2009 ), with 30–50% of adolescents and young adults now known to consume them ( Seifert et al., 2011 ). Energy drinks have also been associated with behavioural problems ( Richards et al., 2015a ), and a number of serious health complications ( Reissig et al., 2009 ).

A potential avenue by which energy drink use may negatively affect health is through their association with risk-taking behaviours (see Arria et al., 2014 ). Miller (2008a) , for instance, reported that the frequency of energy drink consumption in US undergraduates was positively associated with smoking, drinking, alcohol problems, use of illicit prescription drugs and marijuana, sexual risk-taking, fighting, seatbelt omission, and taking risks on a dare. However, it should be noted that such effects might also be explainable by personality characteristics of high users of energy drinks (for example, adherence to a ‘toxic jock’ identity; Miller, 2008b ), rather than necessarily to the products themselves.

Another potential route that energy drinks may negatively affect health is through caffeine’s capacity to disrupt sleep. Energy drink use has been associated with daytime sleepiness and weekly ‘jolt and crash’ episodes ( Kristjánsson et al., 2011 ; Malinauskas et al., 2007 ), though the products also appear to be used to counter the effects of insufficient sleep ( Malinauskas et al., 2007 ). Although findings such as these may implicate energy drinks in particular, Kristjánsson et al. (2013) have reported that caffeine consumption itself is positively associated with self-reported violent behaviour and conduct disorder. Furthermore, James et al. (2011) observed a strong inverse relationship between caffeine intake and academic attainment, 32% of which was explained by mediating effects of daytime sleepiness and other licit substance use. Due to findings such as these it is considered to be of particular importance to investigate the effects of caffeine from difference sources, as well as its overall intake.

Associations between caffeine intake and stress, anxiety, and depression

The consumption of caffeinated beverages is known to be a coping strategy used by college students in the management of stressful academic situations ( Lazarus, 1993 ; Thoits, 1995 ), with 49% of a representative stratified sample of Puerto Rican students reporting caffeinated products to be useful for coping with stress ( Ríos et al., 2013 ). Pettit and DeBarr (2011) have also reported a positive relationship between energy drink consumption and perceived stress levels in undergraduate students. Though the use of caffeine is moderately related to a range of psychiatric and substance use disorders in the general population, the relationships appear not to be causal ( Kendler et al., 2006 ), and results between studies are equivocal (for a review of the area see Lara, 2010 ). Discerning the nature and direction of relationships between such variables becomes even more difficult when considering the self-medication hypothesis (e.g. Khantzian, 1997 ). The idea here is that people may self-medicate with legal and/or illicit substances, with evidence having already been provided to suggest that some individuals with mental health problems use caffeinated energy drinks for such purposes ( Chelben et al., 2008 ).

In some cases positive effects of caffeine have been observed. For instance, low doses have been shown to reduce anxiety and elevate mood ( Haskell et al., 2005 ; Lieberman et al., 1987 , 2002 ; Smith, 2009a ; Smith et al., 1999 ). Smith (2009b) also reported that caffeine consumption was associated with reduced risk of depression compared with non-consumption in a population study.

Negative effects of caffeine on stress and mental health have also been observed. Gilliland and Andress (1981) , for instance, reported higher anxiety levels in moderate and high caffeine consumers compared with abstainers in a student sample. Case reports also suggest that mania can be induced by a high intake of caffeine ( Ogawa and Ueki, 2003 ) or energy drinks ( Sharma, 2010 ). These results are supported by the finding of Kaplan et al. (1997) , that 250 mg of caffeine can increase elation in healthy volunteers, whereas 500 mg increases irritability. Other studies, however, have reported null findings. James et al. (1989) , for instance, found no relationships between caffeine intake and anxiety or depression in medical students.

In the general population, negative effects of caffeine are usually observed in relation to excessive intake. At extremely high doses its consumption can induce a condition known as ‘caffeinism’. Symptoms include anxiety, nervousness, restlessness, insomnia, excitement, psychomotor agitation, dysphoria, and a rambling flow of thoughts and speech ( Gilliland and Andress, 1981 ; Greden, 1974 ), which have been considered to mimic a clinical picture known as ‘mixed mood state’ ( Lara, 2010 ).

Larger effects of caffeine seem to occur in sensitive individuals, with psychiatric patients appearing to make up one such group. Higher sensitivity to the anxiogenic effects of high doses (typically >400 mg), for instance, has been observed in patients with panic disorder ( Boulenger et al., 1984 ; Charney et al., 1985 ), generalised panic disorder ( Bruce et al., 1992 ), and to a lesser extent, depression ( Lee et al., 1988 ). Similar findings have also been made in patients with performance social anxiety disorder (though not generalised social anxiety disorder; Nardi et al., 2009 ), and excessive intake may interfere with the recovery of patients with bipolar disorder and manic-type mood episodes ( Caykoylu et al., 2008 ; Dratcu et al., 2007 ; Tondo and Rudas, 1991 ).

Another potentially sensitive subgroup is that of young consumers. Certain psychiatric symptoms appear to occur at an alarming rate in this group. For example, the prevalence of major depressive disorder is known to range from 0.4% to 8% in adolescents ( Birmaher et al., 1996 ; Fleming and Offord, 1990 ; Roberts et al., 1995 ), with approximately 30% reporting at least one current symptom of a major depressive episode ( Roberts et al., 1995 ). Depressive symptoms have also been found to correlate positively with coffee consumption in middle- and high-school students ( Fulkerson et al., 2004 ), and positive associations with the Children’s Depression Inventory have been reported in both children and adolescents ( Luebbe and Bell, 2009 ). However, as with research in adults, some studies have also reported null findings. Luebbe and Bell (2009) , for instance, found no relationship between anxiety and caffeine in children and adolescents.

Aims of the current research

The general lack of research relating to the effects of caffeine on stress, anxiety, and depression in children is an area that the current paper will try to address. In order to do this, the Diet and Behaviour Scale (DABS; Richards et al., 2015b ), a measure of intake of food and drinks (including caffeinated products) that may affect psychological outcomes, was administered to a large cohort of secondary school children from the South West of England. The current paper used the DABS for two purposes: (1) to provide estimates of weekly caffeine intake from energy drinks, cola, tea, and coffee, and (2) so that additional aspects of diet could be controlled for in multivariate analyses.

Along with the DABS, single-item measures of self-assessed stress, anxiety, and depression were administered. Single items were chosen as they have been shown to be valid and reliable, allowing for the identification of overall risk whilst reducing the time costs associated with administering multi-item measures ( Williams and Smith, 2012 ). The items themselves came from the Wellbeing Process Questionnaire ( Williams, 2014 ), have been validated against full measures, demonstrated to correlate well, and appear to be as sensitive as the full-length measures with which they were compared ( Williams, 2015 ; Williams and Smith, 2013 ).

It was hypothesised that high consumption of caffeine would be associated with high stress, anxiety, and depression, and that such relationships would not be dependent on the source from which caffeine was obtained. However, as no interventions were conducted, and data presented here are only cross-sectional in nature, it should be acknowledged that it is not possible to infer causality or the direction of relationships observed.

The Cornish Academies Project was a large-scale longitudinal programme of research designed to investigate dietary effects on school performance, general health, and stress, anxiety, and depression in secondary school children. Two cross-sections of data were collected from three academies in the South West of England. The first cross-section (T1) was collected 6 months prior to the second (T2). The current paper presents analyses using data from the latter cross-section only, as information relating to stress, anxiety, and depression were not collected at the former.

Participants

In total, 3071 secondary school pupils were asked to take part in the Cornish Academies Project at T1; 2610 (85%) agreed to participate. At T2, the cohort consisted of 3323 pupils, and 2307 completed the questionnaires. A relatively balanced sex ratio (48.5% male, 51.5% female), and an age range of 11–17 ( M = 13.6, SD = 1.49) were observed (for a more detailed description of the sample see Richards et al., 2015b ).

Apparatus/materials

The DABS ( Richards et al., 2015b ) is a 29-item questionnaire developed for the purpose of assessing intake of common dietary variables with an onus on functional foods, and foods and drinks of current concern. The DABS contains 18 questions that assess frequency of consumption on a five-point scale (1 = never, 2 = once a month, 3 = once or twice a week, 4 = most days [3–6], 5 = every day), and 11 questions to assess amounts typically consumed. It has been associated with a four-factor structure in secondary school children labelled Junk Food, Caffeinated Soft Drinks/Gum, Healthy Foods, and Hot Caffeinated Beverages (see Richards et al., 2015b ).

Because caffeine content is known to vary considerably between energy drink products ( Reissig et al., 2009 ), participants were asked to state the brand names of those that they consumed. This measure was included in order to increase the accuracy of estimating caffeine consumption. In addition to this, as diet may reflect general lifestyle (e.g. Akbaraly, 2009 ), five further questions were administered. Three items were used to gauge exercise frequency (mildly energetic, moderately energetic, and vigorous), with answers being given on a four-point scale (1 = three times a week or more, 2 = once or twice a week, 3 = about once to three times a month, 4 = never/hardly ever). In addition to this, participants were asked to state how many hours per night they typically spent asleep, and to give an indication of how good they perceived their general health to have been over the previous 6 months (1 = very good, 2 = good, 3 = fair, 4 = bad, 5 = very bad). Participants were then asked to state how frequently they had experienced stress, anxiety, and depression over the previous 6 months, on a five-point scale (1 = not at all, 2 = rarely, 3 = sometimes, 4 = frequently, 5 = very frequently), though no clinical evaluations were made. No further descriptions of ‘stress’, ‘anxiety’, or ‘depression’ were provided as it was assumed that participants would understand the concepts at hand.

Design and procedure

Schoolteachers administered the DABS as well as the lifestyle, stress, anxiety, and depression questions to the pupils at their respective academies. Demographic information was acquired through the School Information Management System (SIMS) and stored in a confidential database in Cardiff. This information included age, sex, school attendance, number of detentions/behavioural points received, English and maths attainment at Key Stage 3/Key Stage 4, school year, ethnicity, presence/absence of a special educational needs (SEN) status, eligibility/ineligibility to receive free school meals (FSM; a proxy indication of socioeconomic status; Shuttleworth, 1995 ), whether or not English was spoken as an additional language, and whether or not children were cared for by a non-parental guardian.

All questionnaire and demographic data were anonymised prior to being merged into a single database. Ethical clearance was granted by Cardiff University’s School of Psychology Ethics Committee, and informed consent was acquired from all participants (as well as their parents) before data were collected. All data analysis was conducted using IBM SPSS Statistics Version 20.

Statistical analysis

The representativeness of the sample was investigated by comparing SIMS data for those who completed the questionnaires with that of those who did not, though frequency data relating to stress, anxiety, and depression are not provided here because they have already been reported elsewhere (see Richards and Smith, 2015 ). Weekly caffeine consumption was calculated from the DABS items used to measure the amount of consumption of energy drinks, cola, tea, and coffee. Linear-by-linear trends were then investigated between total weekly caffeine intake and stress, anxiety, and depression, and were followed up with binary logistic regression analyses (using the ‘enter’ method), so that additional variance from diet, demography, and lifestyle could be controlled for statistically. In order to investigate interactions between caffeine use and sex, multivariate analyses were conducted for males and females separately. It was also deemed important to examine the effects of each individual source of caffeine (i.e. that consumed specifically from energy drinks, cola, tea, and coffee). As with the analyses of total weekly caffeine intake, these effects were initially investigated using linear-by-linear trends, and then with binary logistic regression to control for additional covariates (though in this instance separate analyses were not conducted for males and females).

Demographic and lifestyle variables

Considerable variance in demographic background and lifestyle was observed within the sample; for frequency data, see Table 1 . Participants’ average number of sleep hours and frequency of exercise (a single-factor analysed variable derived from items measuring mild, moderate, and vigorous exercise) that are used as control variables in the current study have been described elsewhere (see Richards et al., 2015b ).

Frequency information for demographic variables.

Representativeness of the sample

A relatively high response rate of 88.4% was observed for completion of the DABS. To investigate how representative the sample was in reference to the academies from which it came, Chi-square tests were conducted to determine if SIMS data for those who completed the DABS differed from SIMS data of those that did not. It should be noted that a similar analysis presented in Richards et al. (2015b) relates to T1 from the Cornish Academies Project, whereas that presented here relates to T2.

It was found that the academy a pupil came from was significantly related to their likelihood of responding to the questionnaires, with Academy 1 and Academy 3 providing fewer respondents, and Academy 2 providing more respondents, than expected, χ2 (2, N = 3323) = 241.172, p < .001. The school year that a participant came from was also related to their likelihood of completing the questionnaires, χ2 (4, N = 3049) = 34.681, p < .001. A significant linear-by-linear association was observed, with the likelihood of responding being negatively associated with school year, χ2 (1, N = 3049) = 30.245, p < .001. Children with a SEN status were also less likely to answer the questionnaire, χ2 (1, N = 3083) = 23.142, p < .001, as were children who were eligible to receive FSM, χ2 (1, N = 3049) = 25.116, p < .001.

Associations between total weekly caffeine intake and stress, anxiety, and depression

Univariate associations between total weekly caffeine intake and stress, anxiety, and depression.

Single items from the DABS were used to estimate weekly caffeine intake, with the following values being assigned: cup of coffee (80 mg), cup of tea (40 mg), can of cola (25 mg), can of energy drink (133 mg). The values used for coffee, tea, and cola, were based on updated versions of those reported by Brice and Smith (2002) , which were themselves based on values provided by Barone and Roberts (1996) and Scott et al. (1989) ; the value used for energy drinks was the mean caffeine content of the three brands most commonly reported by the current sample (which together accounted for 53.2% of all cases). Caffeine totals consumed from coffee, tea, energy drinks, and cola were then added together to create a variable for total weekly consumption. It was found that caffeine intake was higher in males than females, both in total amount, as well as in that consumed from energy drinks, cola, and coffee (though there was no difference regarding caffeine consumed from tea; for descriptive statistics, see Table 2 ). Total weekly caffeine was subsequently recoded into a categorical variable consisting of the following six consumption groups: 0 mg/w, 0.1–250 mg/w, 250.1–500 mg/w, 500.1–750 mg/w, 750.1–1000 mg/w, >1000 mg/w.

Descriptive statistics and sex differences for self-reported stress, anxiety and depression, and weekly caffeine intake as calculated from the DABS.

Self-assessed stress, anxiety, and depression were all found to be significantly higher in females compared with males (for descriptive statistics, see Table 2 ). The single-item measures were then dichotomised, with those answering with 1 or 2 (‘not at all’ or ‘rarely’ experienced stress, anxiety, or depression) making up the above average mental health group, and those answering with 3, 4, or 5 (‘sometimes’, ‘frequently’, or ‘very frequently’ experienced stress, anxiety, or depression) making up the below average mental health group.

Linear-by-linear associations were investigated between the dichotomous variables for stress, anxiety, and depression, and the categorical variable created from total weekly caffeine intake. The analysis found the >1000 mg/w condition to be associated with high stress, anxiety, and depression. In addition to this, consuming 0.1–250 mg/w was associated with low stress, and non-consumption was associated with low depression, though the latter effect was not significant. For linear-by-linear associations and cross-tabulations between total weekly caffeine intake and stress, anxiety, and depression, see Table 3 .

Cross-tabulations between total weekly caffeine intake and stress, anxiety, and depression.

Note. Mean weekly caffeine intake for each consumption group was as follows: 0 mg M = 0 mg ( SD = 0), 0.1–250 mg/w M = 117.83 ( SD = 69.32), 250.1–500 mg/w M = 355.94 ( SD = 70.61), 500.1–750 mg/w M = 616.37 ( SD = 69.99), 750.1–1000 mg/w M = 865.09 ( SD = 72.71), >1000 mg/w M = 1651.74 ( SD = 750.33).

Multivariate associations between total weekly caffeine intake and stress, anxiety, and depression

The analyses described in the previous section indicate that being a very high consumer of caffeine is a predictor of high levels of stress, anxiety, and depression. It was therefore deemed important to further investigate such effects at the multivariate level, so that additional variance could be controlled for statistically. In order to do this, binary logistic regression analyses (using the ‘enter’ method) were conducted upon the dependent variables of stress, anxiety, and depression. The same categorical variable for total weekly caffeine intake described in the previous section was used, and the non-consumption group was set as the comparison. The additional covariates entered were diet (the DABS subscale scores for Junk Food and Healthy Foods; Caffeinated Soft Drinks/Gum and Hot Caffeinated Beverages were not entered as they were comprised of caffeinated products; for a description of these variables see Richards et al., 2015b ), demography (sex, school, school year, presence/absence of a SEN status, and the eligibility/ineligibility to receive FSM), and lifestyle (sleep hours, exercise frequency, and school attendance). It was, however, deemed inappropriate to attempt to control for ethnicity, whether English was spoken as an additional language, and whether or not the child was cared for by a non-parental guardian, due to the numbers present in the minority groups being particularly small.

After controlling for covariates, the overall effect of caffeine on stress was not significant, Wald = 6.252, p = .283, and none of the consumption groups differed from the non-consumption group. However, total weekly caffeine intake remained a significant predictor of anxiety, Wald = 12.39, p = .03. This effect reflected increased risk of high anxiety occurring in the >1000 mg/w group, though none of the other conditions differed significantly from the non-consumers. For odds ratios and 95% confidence intervals for the multivariate association between total weekly caffeine intake and anxiety, see Figure 1 .

Odds ratios and 95% confidence intervals for multivariate associations between total weekly caffeine intake and anxiety.

The effect of caffeine on depression also remained significant after controlling for covariates, Wald = 14.682, p = .012. In this case increased risk was associated with each of the consumption groups compared with the non-consumers (though the effect relating to the 250.1–500 mg/w group was only marginally significant, and the effect relating to the 500.1–750 mg/w group was not significant). For odds ratios and 95% confidence intervals for the multivariate associations between caffeine and depression, see Figure 2 .

Odds ratios and 95% confidence intervals for multivariate associations between total weekly caffeine intake and depression.

Sex differences in associations between total weekly caffeine intake and stress, anxiety, and depression

Due to the large sample size available, and because sex differences in responses to caffeine in adolescents have been reported (e.g. Temple and Ziegler, 2011 ), it was deemed meritorious to investigate interactions between sex and caffeine intake. To do this, the same methodology outlined in the previous section was used (i.e. binary logistic regression analyses were conducted, and the same covariates were entered), except that the caffeine*sex interaction term was included instead of the main effects of caffeine and sex. Significant interactions were observed for each of the outcome variables: stress, Wald = 31.927, p < .001, anxiety, Wald = 50.341, p < .001, depression, Wald = 45.038, p < .001.

In order to further investigate the interactions between sex and caffeine intake on stress, anxiety, and depression, separate multivariate analyses were conducted in males and females. The overall effect of caffeine on stress was not significant in males, Wald = 5.193, p = .393, or females, Wald = 4.243, p = .515, though males who consumed >1000 mg/w were marginally more likely to report high stress compared with controls, OR = 1.891, 95% CI [.943, 3.792], p = .073. The effect of caffeine on anxiety was not significant in females, Wald = 8.307, p = .14, and none of the consumption groups differed significantly from the control. In males, however, the effect was significant, Wald = 13.186, p = .022. This reflected increased risk of high anxiety in the 0.1–250 mg/w, 250.1–500 mg/w, and >1000 mg/w conditions, with the effect being most apparent in the last condition. For odds ratios and 95% confidence intervals for the multivariate association between total weekly caffeine intake and anxiety in males see Figure 3 .

Odds ratios and 95% confidence intervals for multivariate associations between total weekly caffeine intake and anxiety in males.

The overall effect of caffeine on depression in males was not significant, Wald = 7.882, p = .163. However, each caffeine consumption group was associated with increased risk compared with the control (though the effect relating to the 500.1–750 mg/w group was only marginally significant, and the effect in the 750.1–1000 mg/w condition was not significant). The overall effect in females was significant, Wald = 13.137, p = .022, and reflected increased risk in both the 750.1–1000 mg/w and >1000 mg/w groups. For odds ratios and 95% confidence intervals for the multivariate associations between total weekly caffeine intake and depression in males and females, see Figures 4 and ​ and5, 5 , respectively.

Odds ratios and 95% confidence intervals for multivariate associations between total weekly caffeine intake and depression in males.

Odds ratios and 95% confidence intervals for multivariate associations between total weekly caffeine intake and depression in females.

Associations between individual caffeine sources and stress, anxiety, and depression

Univariate associations between individual caffeine sources and stress, anxiety, and depression.

In order to determine whether the source of caffeine was important regarding the relationships reported in the previous section, caffeine from energy drinks, cola, tea, and coffee were recoded into three groups (non-consumption, low consumption, and high consumption), and linear-by-linear associations were investigated in relation to stress, anxiety, and depression. Because the distributions were skewed, the cut-off points to define what constituted ‘low consumption’ and ‘high consumption’ were determined in a manner that assigned relatively balanced numbers of participants to each group. These distinctions are shown in Table 4 ; essentially ‘low consumption’ related to one can of energy drink, one can of cola, two cups of coffee, and three cups of tea per week, and ‘high consumption’ related to any values in excess of these.

Cross-tabulations between weekly caffeine intake from energy drinks, cola, coffee, and tea, and stress, anxiety, and depression.

Note. Caffeine amounts listed relate to weekly consumption.

Caffeine consumed from energy drinks and tea was not associated with stress, anxiety, or depression. Interestingly, although consumption of caffeine from cola was not associated with anxiety or depression, its non-consumption was associated with high stress levels, and being a low consumer was associated with low stress levels.

Positive linear relationships were observed between caffeine consumption from coffee and stress, anxiety, and depression (for linear-by-linear associations and cross-tabulations between stress, anxiety, and depression, and caffeine consumed from individual sources, see Table 4 ). However these associations are likely explained by coffee being the major contributor to high overall caffeine intake. This is reflected in the observation that those above the median for caffeine intake from coffee consumed more total caffeine than did those above the median for each of the other sources: caffeine from coffee low M = 261.42 ( SD = 331.82), high M = 827.65 ( SD = 748.51); caffeine from energy drinks low M = 247.63 ( SD = 382.38), high M = 674.24 ( SD = 649.38); caffeine from tea low M = 225.97 ( SD = 365.43), high M = 640.55 ( SD = 633.11); caffeine from cola low M = 295.12 ( SD = 448.63), high M = 486.88 ( SD = 585).

Multivariate associations between individual caffeine sources and stress, anxiety, and depression

In order to further investigate associations between caffeine from different sources and stress, anxiety, and depression, the non-consumption/low consumption/high consumption variables for caffeine from energy drinks, cola, tea, and coffee were entered together into binary logistic regression analyses using the ‘enter’ method. The same dietary, demographic, and lifestyle variables that were controlled for in the multivariate analyses of total weekly caffeine intake were again entered as covariates here.

Low consumption of caffeine from energy drinks was associated with high stress, though the overall effect was not significant. Both low and high consumption of caffeine from cola, on the other hand, were significantly associated with low stress. Low caffeine from energy drinks and high caffeine from coffee were both marginally associated with high anxiety, though neither effect was significant overall. Low consumption of caffeine from tea was associated with high depression, and the overall effect was significant. High caffeine consumption from coffee was also associated with high depression, though in this case the overall effect was not significant. For odds rations, 95% confidence intervals, and p -values for all multivariate level associations between individual caffeine sources and stress, anxiety, and depression, see Table 5 .

Multivariate associations between individual sources of caffeine and stress, anxiety, and depression.

The current study aimed to present cross-sectional data from the Cornish Academies Project to investigate associations between caffeine consumption and stress, anxiety, and depression in secondary school children. Based on findings from the literature it was predicted that excessive caffeine intake would be associated with high stress, anxiety, and depression, and that such effects would not be dependent on any particular source of caffeine. In addition to this, separate analyses were conducted in males and females in order to investigate interactions between caffeine and sex.

Relationships between total weekly caffeine intake and stress, anxiety, and depression

Initial positive relationships were observed between total weekly caffeine intake and stress, anxiety, and depression. After adjusting for dietary, demographic, and lifestyle covariates, the effect on stress disappeared. However, consuming >1000 mg/w remained a predictor of high anxiety, and caffeine consumption in general appeared to be associated with higher instances of depression compared with non-consumption (although the effect was also most pronounced in those who consumed >1000 mg/w).

Though the above findings mainly replicated those reported in adults (e.g. Gilliland and Andress, 1981 ; Pettit and DeBarr, 2011 ), the effects appeared to occur at lower doses, which is most likely a reflection of the lower bodyweight of children compared with adults. One finding that differed considerably from those made in adult populations was that of depression. Smith (2009b) observed caffeine consumption to be beneficial compared with its abstinence, whereas the opposite pattern of results was observed here. This finding is therefore likely to highlight differences between the populations studied.

As significant interactions between total weekly caffeine intake and sex were observed in relation to each of the outcome variables, separate multivariate analyses were conducted for males and females. No association between caffeine and anxiety appeared in females; in males, higher instances of anxiety occurred in the 0.1–250 mg/w, 250.1–500 mg/w, and >1000 mg/w conditions, with the largest effect occurring in the last group. For depression, effects occurred in both males and females. In males, increased risk was associated with each group that consumed caffeine compared with non-consumers (though consuming 500.1–750 mg/w was only a marginally significant predictor, and consuming 750.1–1000 mg/w was not significantly related). In females, consuming either 750.1–1000 mg/w or >1000 mg/w was significantly associated with higher reporting of depression. These observations are consistent with other findings, such as caffeine having been shown to produce greater arousal effects in young males compared with females ( Adan et al., 2008 ), and to have a higher propensity for reinforcement in adolescent males compared with females ( Temple et al., 2009 ). Though it may be that male adolescents are more vulnerable to harmful effects of caffeine than are females, these results may also reflect sexually dimorphic personality characteristics, or the observation that overall caffeine consumption in the current study was higher in males than in females.

When individual caffeine sources were investigated, negative effects were observed in relation to coffee, tea, and energy drinks, though they were not consistent across variables and often only marginally statistically significant. One relationship of particular interest was however observed: both low (0.1–25 mg/w) and high (>25 mg/w) levels of caffeine consumed from cola were associated with low stress. This finding may reflect reports of students using caffeinated products to cope with stress (e.g. Ríos et al., 2013 ). However, the general lack of consistent findings from this analysis as a whole suggests that, when investigating its effects on stress, anxiety, and depression, caffeine is best examined in terms of total intake rather than by differentiating between individual sources.

Methodological limitations and directions for future research

Though the current study has addressed a gap in the literature, several methodological limitations should be considered when interpreting the findings. One such limitation is that the participants who completed the questionnaires were not fully representative of the schools from which they came. However, taking a multivariate approach to data analysis in which demographic and lifestyle variables could be controlled for statistically is deemed to have been an effective method for addressing this issue. Nevertheless, as the population studied came from a very specific demographic group (i.e. 11–17-year-old White children from the South West of England), further research is needed that focuses on more representative samples.

Another limitation of the current research was that the chronicity of caffeine use was not taken into account. For instance, a weekly cycle of caffeine use in adolescents was reported by Pollak and Bright (2003) , in which consumption peaked during the weekend (Saturday), and was lowest in the middle of the week (Wednesday). Coupled with the observations that adolescents sometimes use caffeinated products to delay sleep onset (e.g. Calamaro et al., 2009 ) and to counteract the effects of sleepiness during the day ( Malinauskas et al., 2007 ), it is possible that the timing of administration of the questionnaire may have been of importance.

A further limitation of the current study is that it utilised a cross-sectional design. This means that all effects observed here are correlational, and that causation cannot be inferred. Therefore the possibility of reverse-causation, or indeed bi-directionality, cannot be disregarded. For instance, high caffeine consumption may be a cause of high stress, anxiety, and depression, but suffering from such conditions may also lead towards the high consumption of caffeinated products as a coping strategy. Furthermore, it is possible that the effects observed here are attributable to personality characteristics associated with caffeine users, rather than to their use of caffeine. Future research should therefore aim to conduct intervention studies in order to investigate the nature of these relationships further.

Conclusions

The current study has presented results that suggest caffeine consumption may be associated with stress, anxiety, and depression in secondary school children, though the effect on stress disappeared after additional dietary, demographic, and lifestyle variance was controlled for statistically. The effects observed also appeared to differ between males and females. Though caffeine consumption was associated with anxiety in males at the multivariate level, no such observation was made in females. Furthermore, though the effects relating to depression occurred in both sexes, the threshold at which they appeared was lower in males than it was in females.

Initial analyses of individual caffeine sources implied that coffee may have been responsible for the effects observed in relation to total caffeine intake, but further investigations suggested this not to have been the case, and that they were likely attributable to caffeine consumption in general rather than to any particular source. The study also identified very high caffeine intake (>1000 mg/w) to be a risk factor associated with anxiety and depression, although effects were sometimes detected at lower doses. These findings may therefore be a concern for public health and school policy, and should be considered an important area for further investigation.

Acknowledgments

The authors would like to acknowledge the contribution of The Waterloo Foundation for funding the research. In addition, the authors wish to express their gratitude for the on-going support and collaboration with Pool Academy, Penrice Community College, and Treviglas Community College, as well as to thank each of the teachers and pupils who made the research possible.

Declaration of Conflicting Interests: The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding: The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The current research was supported by a grant from The Waterloo Foundation (grant number: 503692).

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You Might Be Consuming Too Much Caffeine — Here's How Much Is Actually in Your Favorite Drinks

There's caffeine hiding in more places than you'd think.

Korin Miller has spent nearly two decades covering food, health, and nutrition for digital, print, and TV platforms. Her work has appeared in Women's Health, SELF, Prevention, The Washington Post, and more.

How does caffeine impact the body?

Do all caffeine sources impact your body the same way, how much caffeine is too much, signs you’ve had too much caffeine.

Food & Wine / Getty Images

Caffeine is having a moment, and not for the best reasons. The drug has been linked to two deaths after it showed up in large quantities in a since-discontinued lemonade offering from chain bakery Panera Bread, and has even come under fire from lawmakers for appearing in energy drinks marketed towards by children. 

But despite all the negative attention, caffeine is still a popular element in plenty of products Americans consume on a regular basis. Case in point: Nearly 70% of U.S. adults said in a recent survey that they had a cup of coffee within the previous 24 hours, which is the highest number of daily coffee drinkers reported in the past two decades. Americans also eat about 10 pounds of chocolate a year, and there are plenty of other less obvious sources of caffeine we’re tossing back. 

But doctors say it’s important to be aware of what exactly it is you’re consuming when you have caffeinated products, as well as why you don’t want to go overboard. Here’s how caffeine impacts your body, along with exactly how much is in a range of popular products, from a shot of espresso to a can of Red Bull.

Caffeine stimulates your central nervous system, causing you to feel more awake and energetic than you would feel otherwise. “Caffeine reduces the effects of adenosine, a signal that makes you feel sleepy, by blocking the adenosine receptors,” explains Rob M. van Dam, Ph.D. , nutrition researcher and professor in the departments of Exercise and Nutrition Sciences and Epidemiology, Milken School of Public Health, at The George Washington University.

But caffeine is also a diuretic (meaning, it can cause you to pee more), increases the release of acid in your stomach, can interfere with the absorption of calcium in your body, and increase your blood pressure, per MedlinePlus . Caffeine reaches its peak level in your blood after an hour of consuming it, although you can feel the effects for up to six hours. 

Caffeine doesn’t impact everyone the same way. “If someone has ADHD, they might feel sleepy after caffeine,” says Jamie Alan, Ph.D., Pharm.D ., an associate professor of pharmacology and toxicology at Michigan State University. She also points out that some people may also be more sensitive to the effects of caffeine than others.

The opposite can also be true, too, especially if you continue to use caffeine over time. The adenosine receptors in your body become less sensitive to caffeine as you continuously expose them to the drug, says Alan. As a result, you can build up a tolerance for caffeine over time.

It’s generally assumed by doctors that all caffeine sources impact your body the same way. “It doesn’t matter how you consume caffeine — eating or drinking — the effect is the same,” says Alan. 

However, van Dam notes that emerging research shows some components in coffee may partly inhibit the effects of caffeine, although more studies are needed to explore these relationships further. “Still, it is helpful to keep track of the total amount of caffeine consumed during the day from all sources to avoid consuming too much,” he says. 

These are some of the most popular caffeine sources that you may come across on a regular basis, and how much caffeine each contains:

But there are also super-charged products that have hit the market. Those can include:

Of course, caffeine shows up in some foods, too. Dark chocolate (70% to 85%), for example, contains more than 22 milligrams of caffeine per ounce, according to the U.S. Department of Agriculture (USDA). Given that many people eat more than an ounce of chocolate at a time, you can have a significant amount of caffeine from dark chocolate without realizing it. 

This is a surprisingly tricky question to answer. Most adults can safely have up to 400 milligrams of caffeine a day, according to the Food and Drug Administration (FDA). “That is similar to four to five 8-fluid ounce cups of coffee,” says van Dam. “However, the speed with which caffeine is metabolized in the body differs substantially from person to person.”

Caffeine recommendations are significantly lower for pregnant people, with the American College of Obstetricians and Gynecologists (ACOG) suggesting having no more than 200 milligrams per day for expectant moms. People with certain heart and cardiovascular conditions may also have lower caffeine thresholds, says Alan.

You’ll usually develop uncomfortable symptoms if you have too much caffeine. Those can include, per the FDA:

• Trouble sleeping
• Anxiousness
• Elevated heart rate
• Upset stomach
• Feeling unhappy

“Very large doses can cause seizures, hallucinations, and agitation,” says Alan. 

If you feel like your heart rate is too fast or it seems like it has an abnormal rhythm, Alan recommends seeking medical attention. But if you feel mostly OK after having too much caffeine, van Dam says you should be fine to ride it out. “Typically, waiting it out and reducing caffeine intake in the future is sufficient,” he says. 

“You can also take an antacid to settle your stomach or eat something bland, like oatmeal or a banana,” says Alan. Taking a walk or doing light exercise may also help reduce the effects of caffeine, she points out. And, of course, doing your best in the future to have less caffeine.

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