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Drug and Alcohol Dependence journal homepage: www.elsevier.com/locate/drugalcdep

Commonly used stimulants: Sleep problems, dependence and psychological distress Rowan P. Ogeil a,b,∗ , James G. Phillips c a b c

Eastern Health Clinical School, Monash University, Victoria, Australia Turning Point, Eastern Health, 54-62 Gertrude St., Fitzroy, Victoria, Australia Psychology Department, Auckland University of Technology, Auckland, New Zealand

a r t i c l e

i n f o

Article history: Received 23 March 2015 Received in revised form 21 May 2015 Accepted 21 May 2015 Available online xxx Keywords: Caffeine Nicotine Sleep problems Psychological distress Gender Stimulants

a b s t r a c t Background: Caffeine and nicotine are commonly used stimulants that enhance alertness and mood. Discontinuation of both stimulants is associated with withdrawal symptoms including sleep and mood disturbances, which may differ in males and females. The present study examines changes in sleep quality, daytime sleepiness and psychological distress associated with use and dependence on caffeine and nicotine. Methods: An online survey comprising validated tools to assess sleep quality, excessive daytime sleepiness and psychological distress was completed by 166 participants (74 males, 96 females) with a mean age of 28 years. Participants completed the study in their own time, and were not offered any inducements to participate. Results: Sleep quality was poorer in those dependent upon caffeine or nicotine, and there were also significant interaction effects with gender whereby females reported poorer sleep despite males reporting higher use of both stimulants. Caffeine dependence was associated with poorer sleep quality, increased daytime dysfunction, and increased levels of night time disturbance, while nicotine dependence was associated with poorer sleep quality and increased use of sleep medication and sleep disturbances. There were strong links between poor sleep and diminished affect, with psychological distress found to co-occur in the context of disturbed sleep. Conclusions: Stimulants are widely used to promote vigilance and mood; however, dependence on commonly used drugs including caffeine and nicotine is associated with decrements in sleep quality and increased psychological distress, which may be compounded in female dependent users. © 2015 Elsevier Ireland Ltd. All rights reserved.

1. Introduction Caffeine and nicotine are commonly used stimulants that are legally available in many countries. While stimulants enhance alertness and mood, their discontinuation is associated with withdrawal symptoms including disturbed sleep and poor affect. Given that these stimulants are used to promote alertness, and their discontinuation is associated with sleep and mood disturbances, the present paper examines how dependence is related to self-reported sleep quality and psychological distress. Caffeine is primarily ingested via dietary sources including beverages such as coffee, tea and soft-drinks. It is the most widely used psychoactive drug in the world, with 80% of the world’s population

∗ Corresponding author at: Turning Point, 54-62 Gertrude Street, Fitzroy 3065, Victoria, Australia. Tel.: +61 3 8413 8469; fax: +61 3 9416 3420. E-mail address: [email protected] (R.P. Ogeil).

estimated to have used caffeine (James, 1997). Caffeine dosages in commercially available products have increased since in the mid1990s (Reissig et al., 2009). Given this, studies of the effects of caffeine intake are warranted (Duffey and Popkin, 2007), and section III of the DSM5 includes caffeine use disorder as a condition of interest and encourages further study. Moderate caffeine consumption is often considered benign (Daly and Fredholm, 1998; James and Stirling, 1983). However, caffeine is an addictive substance that is used for its rewarding properties (Hughes et al., 1998; Strain et al., 1994) including to promote alertness (Partridge et al., 2013). Continued use of caffeine is associated with tolerance and withdrawal symptoms such as headache, fatigue, and mood disturbances even in low-moderate users who abruptly cease use (Silverman et al., 1992). Caffeine produces a variety of effects that are related to dose, sensitivity and tolerance (Strain et al., 1994). For example, low doses of caffeine (up to 200 mg) are associated with feelings of alertness, self-confidence, increased wellbeing and decreased sleepiness (Silverman and Griffiths, 1992), while higher

http://dx.doi.org/10.1016/j.drugalcdep.2015.05.036 0376-8716/© 2015 Elsevier Ireland Ltd. All rights reserved.

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doses (200–800 mg) can produce feelings of anxiety or nervousness (Evans and Griffiths, 1991). Nicotine is the primary psychoactive component of cigarette smoke. Despite smoking rates having decreased in many western countries in the past decade, between 15 and 25% of adults in Australia, New Zealand and the United States report smoking in the past year (ABS, 2011–2012; Ministry of Health, 2010; CDCP, 2015). In contrast to caffeine, smoking cigarettes is a risk factor for several diseases (Benowitz, 2010; Ezzati and Lopez, 2003). While not normally considered among the ‘harder’ stimulant drugs such as cocaine or methamphetamine, cessation is difficult (Laviolette and Van Der Kooy, 2004), and smoking has been ranked in the top three risk factors for global burden of disease (Lim et al., 2012). Nicotine is a highly addictive substance (Benowitz, 2008), with the average cigarette estimated to deliver a dose of between 10 and 30 ␮g/kg (Matta et al., 2007). In addition to acting as a positive reinforcer, nicotine has been shown to improve cognition particularly motor ability, performance and some attentional tasks (Heishman et al., 2010; Rusted et al., 2009). Cessation of nicotine is associated with withdrawal (Schneider and Jarvik, 1983) with common symptoms including irritability, anxiety, anger, difficulty concentrating, sleep disturbance, increased appetite, and weight gain (De Biasi and Dani, 2011). 1.1. Sleep studies Laboratory based studies have demonstrated that caffeine has alerting and performance enhancing properties (Roehrs and Roth, 2008). For example, acute administration of caffeine close to normal bed time results in increased sleep latency, and decreased total sleep time including decreased slow-wave sleep (Karacan et al., 1976; Nicholson and Stone, 1980). Similar sleep findings have been reported following repeated administration of caffeine (400 mg, 3 times per day for 7 days) with changes in total sleep time and slow wave sleep, with tolerance to the effect developing over the course of a week (Bonnet and Arand, 1992). In addition, there is some evidence that caffeine consumption earlier in the day will also affect sleep on subsequent nights by reducing total sleep time and reducing sleep efficiency (Landolt et al., 1995). Studies have assessed the acute effects of nicotine in smokers, during withdrawal, and during nicotine replacement therapy, as well as in non-smokers administered nicotine (Jaehne et al., 2009). Laboratory based studies in smokers have reported extended sleep latency, decreased total sleep time, extended REM sleep latency and decreased slow wave sleep following nicotine administration (Zhang et al., 2006), with these effects mirrored in subjective reports of smokers who report problems with falling asleep and daytime sleepiness (Wetter and Young, 1994). During nicotine withdrawal sleep quality is decreased, and there are increased numbers of night time awakenings and poor affect, which can last up to 20 days (Jaehne et al., 2009). Polysomnography findings between different studies examining nicotine withdrawal have been inconsistent, likely reflecting differences in the degree of nicotine dependence of participants and differences in psychometric tools used (Jaehne et al., 2009). Studies assessing sleep in non-smokers have demonstrated acute changes in sleep architecture, chiefly dose-dependent reductions in REM, followed by REM rebound in subsequent sleep episodes (Gillin et al., 1994). While these laboratory studies have demonstrated differences in sleep architecture and daytime sleepiness levels, there has been an inconsistent use of validated subjective sleep instruments in studies with drug using populations (Arnedt et al., 2007). The use of subjective tools is important for diagnosing insomnia complaints (Mayers and Baldwin, 2006; Mayers et al., 2003), given that the clinical symptoms for the diagnosis of insomnia include symptoms which need to be quantified such as: (a) difficulty initiating

or maintaining sleep, and (b) duration of the disturbance which must persist for at least 1 month (Shekleton et al., 2010). Use of validated sleep and sleepiness measures enables specific features of sleep disturbance to be characterised, and also the relationship with psychological distress to be examined given the close links between sleep and mood (Staner, 2010). 1.2. Gender Females have traditionally reported smaller consumption of caffeine (Liu et al., 2012) and to smoke less than males, however there have been changes in gender role and attitudes in many countries which are linked with an increased proportion of women smoking at rates now similar to males in many western countries (Hitchman and Fong, 2011). With changes in the amounts and patterns of use of common stimulants there may also be changes in relative harms (Becker and Hu, 2008), however studies have inconsistently considered gender differences, despite evidence that stimulants affect males and females differently (Becker et al., 2001). For example, caffeine has been shown to cause sustained changes in blood pressure in females compared with males (Hartley et al., 2004), and that females may be better at detecting changes related to caffeine administration that are related to steroid hormone circulation (Temple and Ziegler, 2011). In addition, there are subjective differences in both subjective and the reinforcing effects of nicotine (Perkins et al., 2002). While studies have examined the effects that stimulants have on sleep acutely and during withdrawal, and also their properties in promoting alertness and performance on specific tasks, the extent to which these drugs affect sleep and mood over longer periods of time and daytime functioning especially in those who are dependent is less clear. The present study examines whether there are changes in self-reported sleep quality, daytime sleepiness and psychological distress associated with use and dependence on common stimulants – caffeine and nicotine. Given the noted gender differences in response to these stimulants, we also examine the role of gender in its interaction with dependence. 2. Materials and methods 2.1. Participants The present sample comprised 166 participants (74 males, 96 females) with a mean age of 28.43 years (range 16–60 years). The majority of the sample (70%) was employed either on full-time or part-time basis, and 52% were currently studying on either a full-time or a part-time basis. Most participants had some level of postsecondary school education, with 14.5% having a trade or apprenticeship, and 57% having an undergraduate or postgraduate university qualification. 2.2. Materials Participants were asked about their frequency of use and purchase locations on average per week for caffeine and nicotine. To aid recall in determining the number of caffeine-containing beverages, participants were provided with a list of beverages and foods that contain caffeine, and asked to estimate their average daily dose. To assess participants’ dependence on these stimulants, participants completed the severity of dependence scale for both caffeine and nicotine. The Severity of Dependence (SDS) scale is a 5-item scale which asks about use of a particular substance in the previous month. It provides a short, easily administered scale which can be used to measure the degree of dependence experienced by users of different types of drugs (Gossop et al., 1995). A cut-off of 4 on the SDS has been used elsewhere to indicate dependency (e.g., Martin et al., 2006). The SDS has demonstrated both construct validity and high levels of internal consistency (Cronbach’s alpha > 8) across difference drug types including stimulants, and across different populations (Gossop et al., 1995). In the present study, high levels of reliability were obtained for nicotine (Cronbach’s alpha = .94) and caffeine (Cronbach’s alpha = .83). A cut-off of “4” on the SDS scale is commonly used to separate “non-dependent” from “dependent” substance users. Sleep was assessed using two validated measures assessing sleep quality and daytime sleepiness. The Pittsburgh Sleep Quality Index (PSQI), a 19-item scale that assesses sleep quality during the past month and contains seven subscales yielding a total score of up to 21 with global scores of >5 used to identify clinically

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R.P. Ogeil, J.G. Phillips / Drug and Alcohol Dependence xxx (2015) xxx–xxx significant sleep disturbance (Buysse et al., 1989). The Epworth Sleepiness Scale (ESS) was used to assess daytime sleepiness (Johns, 1991). The instrument contains 8 items assessing participants’ propensity to doze in common situations, each scored 0–3. Total scores of 10+ are used to identify clinically relevant levels of sleep-related daytime dysfunction (Johns, 1992). The Kessler Psychological Distress Scale (K-10) is a 10-item scale which provides a global measure of distress based on questions about anxiety and depressive symptoms that a person has experienced in the most recent 4-week period (Kessler et al., 2002) and has demonstrated good psychometric properties relevant to both clinical and population based health studies (Andrews and Slade, 2001). In the present study, a high level of reliability was obtained (Cronbach’s alpha = .91).

2.3. Procedure Participants were recruited via online forums including Reddit and also through online bulletin boards promoted through Monash University which advertised to the general staff and student community. Interested participants were prompted to follow a link to the study, and after reading an explanatory statement, participants completed the survey which was hosted on SurveyMonkey in their own time. The study was approved by institutional ethics committees and participants were offered no inducements to participate. The survey typically took under 30 min to complete, with participants able to access the site from any web-enabled device. 2.4. Data analyses Data were analysed using IBM SPSS version 21. ANOVA was used to assess the effects of factors (caffeine dependence, nicotine dependence, gender) on the dependent measures of sleep quality (PSQI total score) and daytime sleepiness (ESS total score). Independent measures t-tests were used to assess difference in mean scores on subscales of the PSQI for those who self-identified as dependent as opposed to those non-dependent. Multiple linear regression was used to predict psychological distress using the sleep variables (PSQI and ESS), caffeine dependence, nicotine dependence and gender.

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Table 2 Mean smoking times per day and purchases per week by males and females (SE values in brackets).

Nicotine dependent males Non-nicotine dependent males Nicotine dependent females Non-nicotine dependent females

n

Smoking times per day

Smoking purchases per week

26

14.94 (1.06)

1.64 (0.12)

48

0.75 (0.78)

0.14 (0.06)

23

8.39 (1.13)

1.26 (0.12)

69

0.67 (0.65)

0.15 (0.07)

those who were classed as nicotine dependent reported smoking 11.67 times per day, compared to those who were not dependent (0.71 times per day): F(1,162) = 139.794, p < .001, partial 2 = .46, and also reported purchasing cigarettes more often in a typical week (1.45 times) compared with those who were not dependent (0.14 times): F(1,162) = 167.306, p < .001, partial 2 = .51. Males typically reported higher levels of consumption of both caffeine (F(1,162) = 5.970, p = .016, partial 2 = .04) and nicotine (F(1,162) = 12.768, p < .001, partial 2 = .07). The associations between dose, dependence, and outcome variables are shown in Table 3. In general, variables representing dependence had higher associations with sleep quality, excessive daytime sleepiness and psychological distress than dose-related variables for both stimulants. 3.2. Stimulant use and sleep outcomes Self-reported dependence on caffeine or nicotine was associated with poorer sleep quality, but not daytime sleepiness (see Fig. 1).

3. Results 3.1. Caffeine and nicotine dependence Dependence on caffeine and nicotine was assessed using participants’ SDS scores, with a cut-off of 4 being used for both stimulants. According to the classification cut-offs, 30.1% of participants (n = 50) were classified as dependent caffeine users and 29.5% were classified as dependent nicotine users. Caffeine dependent users reported a higher mean daily dose of caffeine (418 mg) compared with those who were not caffeine dependent (285 mg): F(1,162) = 11.881, p < .001, partial 2 = .07, and reported purchasing caffeine more frequently in a typical week (2.35 times) compared with those who were not dependent (1.66 times): F(1,162) = 6.025, p = .015, partial 2 = .04 (see Table 1). As may be seen in Table 2, Table 1 Mean caffeine intake per day and purchases per week by males and females (SE values in brackets).

Caffeine dependent males Non-caffeine dependent males Caffeine dependent females Non-caffeine dependent females

n

Estimated daily dose

Caffeine purchases per week

18

462.78 (52.00)

2.08 (0.38)

56

333.85 (29.48)

1.63 (0.22)

32

372.41 (39.00)

1.68 (0.21)

60

236.45 (28.48)

2.63 (0.29)

Fig. 1. Stimulant dependence on sleep quality (panel A) and daytime sleepiness (panel B). Values are mean ± SEM.

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4 Table 3 Correlations between study variables.a

PSQI K-10 Score ESS Nicotine cons Nicotine depend Caffeine cons Caffeine depend a * **

PSQI

K-10 score

ESS

Nicotine cons

Nicotine depend

Caffeine cons

Caffeine depend

1

.546** 1

.275* .328** 1

.083 .150 −.062 1

.276** .248** .066 .639** 1

.104 .006 .052 .064 .155* 1

.245* .307** .164* −.195* −.088 .280** 1

Cons = consumption (for caffeine this was self-reported mg/day, and for nicotine this was self-reported number of cigarettes per day); depend = dependence. p < .05. p < .01. Table 4 Main and interaction effects on the PSQI and ESS as identified by ANOVA. PSQI Main effects Caffeine dependence Nicotine dependence Gender Interaction effects Caffeine dependence × gender Nicotine dependence × gender Nicotine dependence × caffeine dependence × gender ESS Main effect Gender Interaction effect Nicotine dependence × gender

Statistic and effect size F(1,149) = 3.88, p = .05, partial 2 = .025 F(1,149) = 10.292, p = .002, partial 2 = .065 F(1,149) = 9.38, p = .003, partial 2 = .059 F(1,149) = 4.201, p = .04, partial 2 = .027 F(1,149) = 11.57, p = .001, partial 2 = .072 F(1,149) = 3.826, p = .05, partial 2 = .025

F(1,155) = 5.851, p = .017, partial 2 = .036 F(1,155) = 5.02, p = .027, partial 2 = .031

non-dependent users on the sub-scales of the PSQI (see Table 5). Caffeine dependence was associated with poor sleep quality, daytime dysfunction and increased sleep disturbances, while nicotine dependence was associated with poor sleep quality and increased use of sleep medication and sleep disturbances. 3.3. Psychological distress Linear regression determined that the overall model assessing psychological distress was significant (R = .594, R2 (adj.) = 331, F(5,153) = 16.17, p < .001), with the predictors together able to predict 33.1% of the variance in K-10 score (see Table 6). Sleep quality: t(153) = 5.933, p < .01 and daytime sleepiness: t(153) = 2.481, p < .05 significantly contributed to the prediction of K-10 score. Fig. 2. Interactions between gender and dependence on sleep quality (panel A) and daytime sleepiness (panel B). Values are mean ± SEM.

In addition, females with caffeine or nicotine dependence reported poorer sleep quality on the PSQI (see Fig. 2). On the PSQI, ANOVA identified a series of significant main effects and interactions (see Table 4). In summary, main effects for caffeine dependence and nicotine dependence and gender were identified. In addition, 2-way interactions between caffeine dependence and gender, and between nicotine dependence and gender and a three-way interaction between dependence on nicotine, caffeine and gender was also identified. On the ESS, ANOVA identified a significant main effect of gender and a significant interaction effect between gender and caffeine dependence. The present results suggest that problems with sleep in this group relate to sleep quality issues. To further examine the facets of sleep that were disturbed, dependent users were compared with

4. Discussion The present study found that commonly used stimulants are related to poor self-reported sleep behaviour in the past month. Specifically, sleep quality was poorer in those dependent upon caffeine or nicotine, and there were also significant interaction effects with gender, with females reporting poorer sleep quality. An interaction between gender and caffeine dependence upon excessive daytime sleepiness was also identified, however the mean scores on this instrument were lower than clinically used cut-offs (Johns, 1991) suggesting that significant impairment was not related to this facet of sleep. Despite males reporting higher levels of use of both caffeine and nicotine, female users were more likely to report problems with their sleep quality. This may reflect gender differences in: (a) the basal neurochemistry of males and females in systems affected by these stimulants; (b) differences in circulating hormone

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Table 5 Sleep quality issues associated with caffeine and nicotine dependence. Values represent means, with SEM in parenthesis. PSQI subscale

Caffeine dependent

Caffeine non-dependent

Nicotine dependent

Nicotine non-dependent

Duration Latency Quality Medication Daytime dysfunction Efficiency Disturbances

.53 (.11) 1.64 (.15) 1.56 (.10)** .54 (.14) 1.52 (.11)** .39 (.67) 1.41 (.08)**

.55 (.08) 1.46 (.09) 1.12 (.05) .47 (.09) 1.16 (.06) .51 (.08) 1.10 (.04)

.69 (.14) 1.71 (.15) 1.43 (.11)* .80 (.16)** 1.43 (.12) .55 (.12) 1.31 (.08)*

.48 (.07) 1.43 (.09) 1.18 (.06) .37 (.08) 1.20 (.06) .44 (.07) 1.14 (.04)

* **

Value is p < .05 compared with non-dependent users. Value is p < .01 compared with non-dependent users.

Table 6 Linear regression outcomes predicting psychological distress on the K-10 scale. Predictor

B

S.E

␤

t

Sr2

Gender Caffeine dependence Nicotine dependence Sleep quality (PSQI) Daytime sleepiness (ESS)

.762 2.035 2.215 1.139 .345

1.099 1.215 1.227 .192 .139

.047 .115 .126 .434 .173

.693 1.675 1.806 5.933** 2.481*

.046 .111 .119 .392 .164

Sr2 = part correlation; PSQI = Pittsburgh Sleep Quality Index; ESS = Epworth Sleepiness Scale. * p value is less than .05. ** p value is less than .01.

levels affecting the response to these stimulants; or (c) differences in the sensitivity of males and females to the effects of nicotine and caffeine. Caffeine’s CNS alerting effects result from its ability to act as an antagonist at adenosine receptors, given that adenosine acts as an antagonist of neuronal activity and promotes sleep (Fisone et al., 2004). Indeed, wakefulness is associated with reductions in adenosine triphosphate (ATP) within the CNS, leading to increased CNS levels of extracellular adenosine (Åkerstedt and Nilsson, 2003). Work in rodents has suggested that there are sex differences in the receptor signalling of the adenosine receptor A1 subtype, and that this may cause differences relevant to drug abuse (Butler et al., 2008). Alternatively caffeine consumption at levels equivalent to 300 mg/day has been shown to be related to changes in hormonal cycling, specifically with an increased risk of a short cycle length (Fenster et al., 1999). Indeed, these levels are comparable to the self-report data collected in the present study. Females may also be more sensitive to the withdrawal effects associated with caffeine. A population based study reported heightened symptoms of withdrawal in a greater proportion of females (5.5%) compared with males (0.9%) that interfered with their daily functioning (Dews et al., 1999). Nicotine is rapidly absorbed into the circulation following inhalation of cigarette smoke and acts upon nicotinic cholinergic receptors within the CNS, facilitating the release of a series of neurotransmitters including dopamine which is central to the reinforcing effects (Benowitz, 2008, 2010). Differences in the selfreported sleep (Ogeil et al., 2013) and behavioural responses to other drugs of abuse affecting dopamine pathways such as cocaine have been reported (Festa et al., 2004), and may be mediated by differences in basal monoamine levels given that females have greater DA transporter availability and D2 receptor binding potential compared with males (Kaasinen et al., 2001; Lavalaye et al., 2000). While findings on the relationship between nicotine use and oestrus phase have been inconsistent, there are associations between cycles and dependence on nicotine (Perkins et al., 1999). Alternatively the effects may be related to dopamine facilitation given that females have been shown to be more sensitive to amphetamine during the late follicular phase which is characterised by high levels of oestrogen and low levels of progesterone (Justice and De Wit,

2000; White et al., 2002). This mechanism is plausible given that that both the number of DA receptors and the levels of DA transporters in the brain vary as a function of oestrogen distribution in the brain (Attali et al., 1997). Finally, there are reports that females are more sensitive to some of the subjective effects of nicotine, including negative mood, and enhanced “jitteriness” (Sofuoglu and Mooney, 2009), and these may be related to a series of pharmacokinetic processes including slower metabolism of nicotine in females compared with males (Benowitz and Hatsukami, 1998), or differences in body size given that females are typically smaller than males. Further research is needed to determine the relevant mechanisms which result in gender differences in response to caffeine and nicotine in longer term outcomes related to sleep quality. Following identification of sleep quality disturbances in our sample, the subscales of the PSQI were identified in order to ascertain which facets were disturbed. Caffeine dependence was associated with poorer sleep quality, increased daytime dysfunction and increased levels of night time disturbance, while nicotine dependence was associated with poorer sleep quality and increased use of sleep medication and sleep disturbances. This suggests that while both of these drugs promote overall vigilance, they are also associated with subtle differences in how they affect sleep quality. These differences may in part be explained by withdrawal phenomenon which occurs during the night, owing to the relative half-lives of each of these stimulants. Indeed, during dependence cholinergic receptors have been found to be responsive during sleep periods (Benowitz, 2010). As nicotine has a half-life of 1–2 h, and caffeine has a half-life of 3–7 h, withdrawal is likely to occur earlier than for caffeine. Although the overall levels of sleep disturbance seem comparable in the present analysis, the nicotine dependent participants report greater use of medication to help them sleep, suggesting difficulties in initiating and maintaining sleep. In addition we found strong links between self-reported psychological distress and poor sleep outcomes supporting previous research demonstrating this link in both population based samples (Scott et al., 2013), and in drug-using cohorts such as ecstasy users (Ogeil et al., 2013). Both caffeine and nicotine are used to improve mood, and their withdrawal is associated with poor mood (De Biasi and Dani, 2011; Evans and Griffiths, 1991). The present study suggests that psychological distress is likely to occur in the context of

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disturbed sleep, and also daytime dysfunction and reinforces the need to address sleep complaints in people who have co-morbid sleep and mood problems, given that the scores for sleep quality attained in the present sample exceed the commonly used cut-off to detect clinical disturbance (Buysse et al., 1989). Indeed, while it is commonly co-morbid with mood disturbances, sleep problems are not simply a symptom of depression (Staner, 2010) and may require separate treatment (Carney et al., 2007) in people that are dependent on stimulants, given that daytime sleepiness was a significant predictor of distress. 4.1. Limitations Questionnaire studies assessing drug use are limited in their capacity to verify the amounts and frequency of drug use by participants. While accurate recall of smoking rates in current smokers is generally high (Krall et al., 1989), inferring a relative dose of nicotine is difficult given that cigarettes vary in their nicotine content and intake varies as a function of individual smoking behaviour (e.g., volume and frequency of puffs; Benowitz et al., 2006), hence the focus on the number of cigarettes smoked, purchases made per week and use of a validated scale to infer dependence in the present study. Given the wide variability in caffeine content between different beverage types and foods there has been difficulty in estimating the daily dose that individuals typically consume (Roehrs and Roth, 2008). However, population research in the United States has suggested that a typical adult user consumes 280 mg per day which corresponds to the average daily consumption reported by our participants in the non-dependent group. In addition, our typical daily dose reported by those dependent (418 mg) corresponds to laboratory studies which have modelled the physiological arousal of insomnia through repeated administration of 400 mg, 3 times per day (Bonnet and Arand, 1992). This suggests that when provided with contextual cues to aid recall, people are able to provide estimates of consumption which are consistent with controlled laboratory studies and population estimates of consumption. Future research should examine other factors that may impact upon the relationship between subjective measures of sleep and mood and stimulant use. For example, the pattern of consumption in those dependent versus not dependent and the time of last consumption of caffeine or nicotine prior to a sleep episode could be examined, given that there may be variations in relative effectiveness across the circadian cycle (Benowitz et al., 1982; Smith, 2002). 5. Conclusion The present study found that sleep quality was poorer in those dependent upon caffeine or nicotine compared to those non-dependent, and that this effect was heightened in females. Examining the facets of sleep quality associated with this disturbance revealed difference based on drug type, with caffeine users more likely to report daytime dysfunction, and nicotine users more likely to have used a medication to help them sleep in the past month. In addition, we demonstrated that there are close links between poor sleep outcomes and psychological distress. Collectively, these findings suggest that misuse of commonly available and used stimulants have detrimental impacts on sleep quality and, in turn, on psychological distress. Role of funding sources This research was conducted without specific funding from the public, commercial or not-for-profit sectors.

Contributors RPO and JGP designed the study and wrote the protocol. JGP led the data collection. RPO conducted the literature searches and provided summaries of previous research studies relevant for the manuscript. Both RO and JGP conducted relevant statistical analysis. Author RO wrote the first draft of the manuscript. Both authors contributed to and have approved the final manuscript. Conflict of interest None declared. Acknowledgment R.P.O. is supported by a National Health and Medical Research Council Peter Doherty Early Career Fellowship (APP1071725). References Australian Bureau of Statistics, 2011–2012. Australian Health Survey. Canberra, Australia. Åkerstedt, T., Nilsson, P., 2003. Sleep as restitution: an introduction. J. Intern. Med. 254, 6–12. Andrews, G., Slade, T., 2001. Interpreting scores on the Kessler Psychological Distress Scale (K10). Aust. N. Z. J. Public Health 25, 494–497. Arnedt, J.T., Conroy, D.A., Brower, K.J., 2007. Treatment options for sleep disturbances during alcohol recovery. J. Addict. Dis. 26, 41–54. Attali, G., Weizman, A., Gil-Ad, I., Rehavi, M., 1997. Opposite modulatory effects of ovarian hormones on rat brain dopamine and serotonin transporters. Brain Res. 756, 153–159. Becker, J.B., Hu, M., 2008. Sex differences in drug abuse. Front. Neuroendocrinol. 29, 36–47. Becker, J.B., Molenda, H., Hummer, D.L., 2001. Gender differences in the behavioral responses to cocaine and amphetamine. Ann. N.Y. Acad. Sci. 937, 172–187. Benowitz, N.L., 2008. Clinical pharmacology of nicotine: implications for understanding, preventing, and treating tobacco addiction. Clin. Pharmacol. Ther. 83, 531–541. Benowitz, N.L., 2010. Nicotine addiction. N. Engl. J. Med. 362, 2295–2303. Benowitz, N.K., Kuyt, F., Peyton, J., 1982. Circadian blood nicotine concentrations during cigarette smoking. Clin. Pharmacol. Ther. 32, 758–764. Benowitz, N.L., Peyton, J., Herrera, B., 2006. Nicotine intake and dose response when smoking reduced-nicotine content cigarettes. Clin. Pharmacol. Ther. 80, 703–714. Benowitz, N.L., Hatsukami, D., 1998. Gender differences in the pharmacology of nicotine addiction. Addict. Biol. 3, 383–404. Bonnet, M.H., Arand, D.L., 1992. Caffeine use as a model of acute and chronic insomnia. Sleep 15, 526–536. Butler, T.R., Smith, K.J., Self, R.L., Braden, B.B., Prendergast, M.A., 2008. Sex differences in the neurotoxic effects of adenosine A1 receptor antagonism during ethanol withdrawal: reversal with an A1 receptor agonist or an NMDA receptor antagonist. Alcohol. Clin. Exp. Res. 32, 1260–1270. Buysse, D.J., Reynolds 3rd, C.F., Monk, T.H., Berman, S.R., Kupfer, D.J., 1989. The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research. Psychiatry Res. 28, 193–213. Carney, C.E., Segal, Z.V., Edinger, J.D., Krystal, A.D., 2007. A comparison of rates of residual insomnia symptoms following pharmacotherapy or cognitive-behavioral therapy for major depressive disorder. J. Clin. Psychiatry 68, 254–260. Center for Disease Control and Prevention, 2015. Current Cigarette Smoking Among Adults in the United States. Atlanta, GA, USA. Daly, J.W., Fredholm, B.B., 1998. Caffeine – an atypical drug of dependence. Drug Alcohol Depend. 51, 199–206. De Biasi, M., Dani, J.A., 2011. Reward, addiction, withdrawal to nicotine. Ann. Rev. Neurosci. 34, 105–130. Dews, P.B., Curtis, G.L., Hanford, K.J., O’Brien, C.P., 1999. The frequency of caffeine withdrawal in a population-based survey and in a controlled, blinded pilot experiment. J. Clin. Pharmacol. 39, 1221–1232. Duffey, K.J., Popkin, B.M., 2007. Shifts in patterns and consumption of beverages between 1965 and 2002. Obesity 15, 2739–2747. Evans, S.M., Griffiths, R.R., 1991. Dose-related caffeine discrimination in normal volunteers: individual differences in subjective effects and self-reported cues. Behav. Pharmacol. 2, 345–356. Ezzati, M., Lopez, A.D., 2003. Estimates of global mortality attributable to smoking in 2000. Lancet 362, 847–852. Fenster, L., Quale, C., Waller, K., Windham, G.C., Elkin, E.P., Benowitz, N., Swan, S.H., 1999. Caffeine consumption and menstrual function. Am. J. Epidemiol. 149, 550–557. Festa, E.D., Russo, S.J., Gazi, F.M., Niyomchai, T., Kemen, L.M., Lin, S.N., Foltz, R., Jenab, S., Quinones-Jenab, V., 2004. Sex differences in cocaine-induced

Please cite this article in press as: Ogeil, R.P., Phillips, J.G., Commonly used stimulants: Sleep problems, dependence and psychological distress. Drug Alcohol Depend. (2015), http://dx.doi.org/10.1016/j.drugalcdep.2015.05.036

G Model DAD-5609; No. of Pages 7

ARTICLE IN PRESS R.P. Ogeil, J.G. Phillips / Drug and Alcohol Dependence xxx (2015) xxx–xxx

behavioral responses, pharmacokinetics, and monoamine levels. Neuropharmacol 46, 672–687. Fisone, G., Borgkvist, A., Usiello, A., 2004. Caffeine as a psychomotor stimulant: mechanism of action. Cell. Mol. Life Sci. 61, 857–872. Gillin, J.C., Lardon, M., Ruiz, C., Golshan, S., Salin-Pascual, R., 1994. Dose-dependent effects of transdermal nicotine on early morning awakening and rapid eye movement sleep time in nonsmoking normal volunteers. J. Clin. Psychopharmacol. 14, 264–267. Gossop, M., Darke, S., Griffiths, P., Hando, J., Powis, B., Hall, W., Strang, J., 1995. The Severity of Dependence Scale (SDS): psychometric properties of the SDS in English and Australian samples of heroin, cocaine and amphetamine users. Addiction 90, 607–614. Hartley, T.R., Lovallo, W.R., Whitsett, T.L., 2004. Cardiovascular effects of caffeine in men and women. Am. J. Cardiol. 93, 1022–1026. Heishman, S., Kleykamp, B., Singleton, E., 2010. Meta-analysis of the acute effects of nicotine and smoking on human performance. Psychopharmacol 210, 453–469. Hitchman, S.C., Fong, G.T., 2011. Gender empowerment and female-to-male smoking prevalence ratios. Bull. World Health Org. 89, 195–202. Hughes, J.R., Oliveto, A.H., Liguori, A., Carpenter, J., Howard, T., 1998. Endorsement of DSM-IV dependence criteria among caffeine users. Drug Alcohol Depend. 52, 99–107. Jaehne, A., Loessl, B., Bárkai, Z., Riemann, D., Hornyak, M., 2009. Effects of nicotine on sleep during consumption, withdrawal and replacement therapy. Sleep Med. Rev. 13, 363–377. James, J.E., 1997. Understanding Caffeine: A Biobehavioral Analysis. Sage, Thousand Oaks, CA, USA. James, J.E., Stirling, K.P., 1983. Caffeine: a survey of some of the known and suspected deleterious effects of habitual use. Br. J. Addict. 78, 251–258. Johns, M.W., 1991. A new method for measuring daytime sleepiness: the Epworth sleepiness scale. Sleep 14, 540–545. Johns, M.W., 1992. Reliability and factor analysis of the Epworth Sleepiness Scale. Sleep 15, 376–381. Justice, A.J., De Wit, H., 2000. Acute effects of d-amphetamine during the early and late follicular phases of the menstrual cycle in women. Pharmacol. Biochem. Behav. 66, 509–515. Kaasinen, V., Nagren, K., Hietala, J., Farde, L., Rinne, J.O., 2001. Sex differences in extrastriatal dopamine D2-like receptors in the human brain. Am. J. Psychiatry 158, 308–311. Karacan, I., Thornby, J.I., Anch, M., Booth, G.H., Williams, R.L., Salis, P.J., 1976. Dose-related sleep disturbances induced by coffee and caffeine. Clin. Pharmacol. Ther. 20, 682–689. Kessler, R.C., Andrews, G., Colpe, L.J., Hiripi, E., Mroczek, D.K., Normand, S-L.T., Walters, E.E., Zaslavsky, A.M., 2002. Short screening scales to monitor population prevalences and trends in non-specific psychological distress. Psychol. Med. 32, 959–976. Krall, E.A., Valadian, I., Dwyer, J.T., Gardner, J., 1989. Accuracy of recalled smoking data. Am. J. Public Health 79, 200–202. Landolt, H.-P., Werth, E., Borbély, A.A., Dijk, D.-J., 1995. Caffeine intake (200 mg) in the morning affects human sleep and EEG power spectra at night. Brain Res. 675, 67–74. Lavalaye, J., Booij, J., Reneman, L., Habraken, J.B., van Royen, E.A., 2000. Effect of age and gender on dopamine transporter imaging with [123I]FP-CIT SPET in healthy volunteers. Eur. J. Nucl. Med. 27, 867–869. Laviolette, S.R., Van Der Kooy, D., 2004. The neurobiology of nicotine addiction: bridging the gap from molecules to behaviour. Nat. Rev. Neurosci. 5, 55–65. Lim, S.S., Vos, T., Flaxman, A.D., Danaei, G., Shibuya, K., Adair-Rohani, H., et al., 2012. A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet 380, 2224– 2260. Liu, R., Guo, X., Park, Y., Huang, X., Sinha, R., Freedman, N.D., Hollenbeck, A.R., Blair, A., Chen, H., 2012. Caffeine intake, smoking, and risk of Parkinson disease in men and women. Am. J. Epidemiol. 175, 1200–1207. Martin, G., Copeland, J., Gates, P., Gilmour, S., 2006. The Severity of Dependence Scale (SDS) in an adolescent population of cannabis users: reliability, validity and diagnostic cut-off. Drug Alcohol Depend. 83, 90–93.

7

Matta, S., Balfour, D., Benowitz, N., Boyd, R.T., Buccafusco, J., Caggiula, A., Craig, C., Collins, A., Damaj, M.I., Donny, E., Gardiner, P., Grady, S., Heberlein, U., Leonard, S., Levin, E., Lukas, R., Markou, A., Marks, M., McCallum, S., Parameswaran, N., Perkins, K., Picciotto, M., Quik, M., Rose, J., Rothenfluh, A., Schafer, W., Stolerman, I., Tyndale, R., Wehner, J., Zirger, J., 2007. Guidelines on nicotine dose selection for in vivo research. Psychopharmacology (Berl.) 190, 269–319. Mayers, A.G., Baldwin, D.S., 2006. The relationship between sleep disturbance and depression. Int. J. Psychiatry Clin. Pract. 10, 2–16. Mayers, A.G., Van Hooff, J.C., Baldwin, D.S., 2003. Assessing subjective sleep reports in the first-degree relatives of antidepressant-treated depressed outpatients. Hum. Psychopharmacol. 18, 207–214. Ministry of Health, 2010. Drug Use in New Zealand: Key results of the 2007/08 New Zealand Alcohol and Drug Use Survey. Wellington, New Zealand. Nicholson, A.N., Stone, B.M., 1980. Heterocyclic amphetamine derivatives and caffeine on sleep in man. Br. J. Clin. Pharmacol. 9, 195–203. Ogeil, R.P., Rajaratnam, S.M., Broadbear, J.H., 2013. Male and female ecstasy users: differences in patterns of use, sleep quality and mental health outcomes. Drug Alcohol Depend. 132, 223–230. Partridge, B., Bell, S., Lucke, J., Hall, W., 2013. Australian university students’ attitudes towards the use of prescription stimulants as cognitive enhancers: perceived patterns of use, efficacy and safety. Drug Alcohol Rev. 32, 295–302. Perkins, K., Jacobs, L., Sanders, M., Caggiula, A., 2002. Sex differences in the subjective and reinforcing effects of cigarette nicotine dose. Psychopharmacology 163, 194–201. Perkins, K.A., Donny, E., Caggiula, A.R., 1999. Sex differences in nicotine effects and self-administration: review of human and animal evidence. Nicotine Tob. Res. 1, 301–315. Reissig, C.J., Strain, E.C., Griffiths, R.R., 2009. Caffeinated energy drinks – a growing problem. Drug Alcohol Depend. 99, 1–10. Roehrs, T., Roth, T., 2008. Caffeine: sleep and daytime sleepiness. Sleep Med. Rev. 12, 153–162. Rusted, J.M., Sawyer, R., Jones, C., Trawley, S.L., Marchant, N.L., 2009. Positive effects of nicotine on cognition: the deployment of attention for prospective memory. Psychopharmacology 202, 93–102. Schneider, N., Jarvik, M., 1983. Time course of smoking withdrawal symptoms as a function of nicotine replacement. Psychopharmacology 82, 143–144. Scott, D., Paterson, J.L., Happell, B., 2013. Poor sleep quality in Australian adults with comorbid psychological distress and physical illness. Behav. Sleep Med. 12, 331–341. Shekleton, J.A., Rogers, N.L., Rajaratnam, S.M., 2010. Searching for the daytime impairments of primary insomnia. Sleep Med. Rev. 14, 47–60. Silverman, K., Evans, S.M., Strain, E.C., Griffiths, R.R., 1992. Withdrawal syndrome after the double-blind cessation of caffeine consumption. New Engl. J. Med. 327, 1109–1114. Silverman, K., Griffiths, R.R., 1992. Low-dose caffeine discrimination and self-reported mood effects in normal volunteers. J. Exp. Anal. Behav. 57, 91–107. Smith, A., 2002. Effects of caffeine on human behavior. Food Chem. Toxicol. 40, 1243–1255. Sofuoglu, M., Mooney, M., 2009. Subjective responses to intravenous nicotine: greater sensitivity in women than in men. Exp. Clin. Psychopharmacol. 17, 63–69. Staner, L., 2010. Comorbidity of insomnia and depression. Sleep Med. Rev. 14, 35–46. Strain, E.C., Mumford, G.K., Silverman, K., Griffiths, R.R., 1994. Caffeine dependence syndrome evidence from case histories and experimental evaluations. JAMA 272, 1043–1048. Temple, J.L., Ziegler, A.M., 2011. Gender differences in subjective and physiological responses to caffeine and the role of steroid hormones. J. Caffeine Res. 1, 41–48. Wetter, D.W., Young, T.B., 1994. The relation between cigarette smoking and sleep disturbance. Prev. Med. 23, 328–334. White, T.L., Justice, A.J., de Wit, H., 2002. Differential subjective effects of D-amphetamine by gender, hormone levels and menstrual cycle phase. Pharmacol. Biochem. Behav. 73, 729–741. Zhang, L., Samet, J., Caffo, B., Punjabi, N.M., 2006. Cigarette smoking and nocturnal sleep architecture. Am. J. Epidemiol. 164, 529–537.

Please cite this article in press as: Ogeil, R.P., Phillips, J.G., Commonly used stimulants: Sleep problems, dependence and psychological distress. Drug Alcohol Depend. (2015), http://dx.doi.org/10.1016/j.drugalcdep.2015.05.036