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. Author manuscript; available in PMC: 2024 Jan 1.
Published in final edited form as: Addict Behav. 2022 Sep 5;136:107482. doi: 10.1016/j.addbeh.2022.107482

Posttraumatic stress symptoms and substance use among college students: Exploring interactions with sleep quality and assigned sex

Elizabeth A Lehinger a,*, Scott Graupensperger a, Frank Song b, Brittney A Hultgren a, Dara Jackson a, Mary E Larimer a,b
PMCID: PMC9760355  NIHMSID: NIHMS1852448  PMID: 36152382

Abstract

Substance use is widely recognized as a negative outcome following traumatic events and is tied to symptoms of posttraumatic stress (PTS). Sleep quality may influence the PTS and substance use association, particularly among college students who are at risk for poor sleep. The purpose of the present study was to examine the moderating effect of sleep quality on the relationship between PTS and substance use in a cohort of college students, with an exploratory aim of examining potential differences by assigned sex. A screening survey was completed by 2,767 students enrolled in a larger RCT examining various brief college student alcohol reduction strategies. Results found a significant two-way interaction between PTS symptoms and subjective sleep quality on weekly number of drinks and peak drinking occasion, where the significant positive association between PTS symptoms to weekly drinks and peak drinking occasion was only found for those who reported poor sleep quality. A similar pattern emerged for the significant two-way interaction between PTS symptoms and subjective sleep quality on cannabis use frequency. A significant three-way interaction (i.e., PTS Symptoms × Poor Subjective Sleep Quality × Assigned Sex) indicated the two-way interaction between PTS symptoms and sleep quality for both weekly drinks and cannabis use frequency was stronger among male compared to female participants. Study findings suggest sleep quality is an important factor contributing to the relation between PTS symptom severity and substance use among college students. Strategies for assessing and improving sleep quality and PTS symptoms can be incorporated into prevention and intervention efforts targeting substance use related harm for college students.

Keywords: Alcohol, Drinking, Cannabis, Marijuana, Posttraumatic stress

1. Introduction

Alcohol and cannabis use are highly prevalent among college students. National survey data assessing college students found 29.5% reported binge drinking and 21.1% reported cannabis use in the past month in 2019 (National Survey on Drug Use and Health, 2021). Trauma exposure among college students is common as well with an estimated 66% of college students reporting having experienced a traumatic event during their lifetime (Read, Ouimette, White, Colder, & Farrow, 2011). Many individuals who have experienced a traumatic event will show a natural recovery trajectory in posttraumatic stress symptoms (Steenkamp et al., 2012a, b). However, a substantial minority will report chronic subclinical levels of posttraumatic stress symptoms (PTS) that are experienced as distressing but do not meet full diagnostic criteria for posttraumatic stress disorder (PTSD; McLaughlin, Koenen, Friedman, Ruscio, Karam, Shahly, Stein, Hill, Petukhova, Alonso, Andrade, Angermeyer, Borges, de Girolamo, de Graaf, Demyttenaere, Florescu, Mladenova, Posada-Villa, & Kessler, 2015). Trauma exposure and subsequent posttraumatic stress, even at subclinical levels, are risk factors for alcohol use problems (Read et al., 2012, 2014) as well as cannabis use (Hicks et al., 2021) among college students. It is important to examine different levels of PTS symptom severity to improve our understanding of factors that contribute to substance related harms among college students, which can inform prevention and intervention efforts.

Self-medication models (Khantzian, 2003) propose that substances are used to cope with PTS symptoms. Symptom management is a commonly reported expectancy or reason for using alcohol (Pedersen, Myers, Browne, & Norman, 2014) and cannabis (Bonn- Miller, Boden, Bucossi, & Babson, 2014; Earleywine & Bolles, 2014; Kredit, Janssen, Heerdink, Egberts, & Vermetten, 2020) among individuals with PTSD. Specifically, individuals report cannabis and alcohol use reduce intrusive symptoms (Earleywine & Bolles, 2014), reduce tension (Simpson, 2003) and increase sleep quality (Kredit et al., 2020). The temporary decrease in distress is thought to lead to escalated substance use and subsequent substance-related harms (Khantzian, 2003). Poor sleep quality is one factor that may contribute to coping-motivated substance use when experiencing PTS. Sleep difficulties are widely considered a “hallmark” symptom of PTSD and can develop following trauma exposure exclusive from the development of PTSD (Sinha, 2016).The cognitive and inhibitory impairments due to inadequate sleep (e.g., Durmer & Dinges, 2005; Walker, 2008; Wong, Brower, Nigg, & Zucker, 2010) may exacerbate substance use and decrease the ability to effectively utilize protective behaviors for substance use (e.g., keeping track of the number of drinks or cannabis used) which may further increase use and problems.

Previous research has supported poor sleep as a factor contributing to the relationship between PTS and substance use. Regarding alcohol use, sleep difficulties have been found to be positively associated with coping motives for alcohol use among treatment-seeking sexual assault survivors (Nishith, Resick, & Mueser, 2001). For cannabis use, a community sample of women with PTSD who used cannabis showed the association between sleep problems and coping motivations for cannabis use was stronger for those with higher PTSD symptom severity (Bonn-Miller, Babson, Vujanovic, & Feldner, 2010). A study utilizing a community sample with probable PTSD recruited from a medical cannabis dispensary showed those who reported greater sleep motives for cannabis use reported more frequent cannabis use as well (Bonn-Miller, Babson, & Vandrey, 2014). These findings indicate the relationship between PTS, sleep and substance use is likely complex, and sleep problems appear to exacerbate substance use as PTS severity increases. Overall, the association between substance use and sleep difficulties following trauma exposure has largely been examined among individuals diagnosed with PTSD (e.g., Bonn-Miller et al., 2010; Nishith et al., 2001). There is a need to understand how sleep is related to alcohol and cannabis use among individuals who may not be experiencing clinical-level PTS symptoms but are at risk for substance related harm due to subclinical levels of PTS symptoms.

In addition to being a prominent feature of post-trauma sequalae, sleep is a particularly important factor to examine among college students. Studies exploring the prevalence of sleep problems among college students in the United States found between 40 and 65% of students report patterns of sleep behavior indicative of poor sleep (Becker et al., 2018; Carney, Edinger, Meyer, Lindman, & Istre, 2006; Kenney, LaBrie, Hummer, & Pham, 2012; Lund, Reider, Whiting, & Prichard, 2010). Lifestyle behaviors typical for the college environment include improper sleep scheduling and the use of sleep-disrupting substances (Gellis, Park, Stotsky, & Taylor, 2014), such as alcohol and cannabis use (Graupensperger, Hultgren, Fairlie, Lee, & Larimer, 2022). There is substantial evidence indicating detrimental effects of alcohol and cannabis on sleep (e.g., Drazdowski, Kliewer, & Marzell, 2021; Ebrahim, Shapiro, Williams, & Fenwick, 2013; Thakkar, Sharma, & Sahota, 2015), although the literature is somewhat mixed. One short-term benefit of cannabis use on sleep is reduced sleep onset latency (Cousens & DiMascio, 1973), and this proximal benefit may help us understand the perception that cannabis improves sleep given college students report sleep improvement is a motivation for using these substances (Drazdowski et al., 2021; Goodhines, Gellis, Ansell, & Park, 2019). Those intentionally using cannabis as a sleep aid also report longer sleep duration and fewer awakenings following sleep onset (Goodhines et al., 2019). There is a robust body of literature supporting the association between poor sleep quality and alcohol (e.g., Digdon & Landry, 2013; Kenney, Lac, LaBrie, Hummer, & Pham, 2013) and cannabis use (e.g., Johnson & Breslau, 2001; Miller, Janssen, et al., 2017) among college students. However, less is known about the potential interaction between poor sleep quality and PTS symptoms on substance use. The developmental and environmental factors that put college students at risk for sleep and substance use problems warrant an examination of the interaction between sleep quality and PTS symptom severity in relation to substance use in this population.

The present study examines the moderating effect of sleep quality on the cross-sectional relationship between PTS symptoms and substance use in a large cohort of college students. This study addresses several limitations in our current understanding of sleep and substance use among individuals who have experienced a traumatic event by including individuals with a range of PTS symptom levels, and by examining alcohol and cannabis use in a college student sample. Based on prior evidence supporting directional paths from PTSD symptoms to substance use (Simpson, Stappenbeck, Luterek, Lehavot, & Kaysen, 2014), we hypothesized (1) PTS symptom severity is positively associated with alcohol and cannabis use. Given the positive association between sleep quality and increased motivation to use alcohol and cannabis (Nishith et al., 2001, Bonn-Miller et al., 2010), we hypothesize (2) sleep quality moderates the relation between PTS symptom severity and alcohol and cannabis use such that this relation is stronger among individuals with poorer sleep quality.

Exploratory analyses examined the potential interaction between PTS symptoms, sleep quality, and assigned sex. Overall, the role of assigned sex in associations between PTS symptom severity, sleep, and substance use is either under-investigated or inconclusive. For example, Lee, Lee, Choi, Chung, and Jeong (2015) found men who experienced a traumatic event had a higher risk of problematic alcohol use than women who experienced a traumatic event, whereas Kachadourian, Pilver, and Potenza (2014) found trauma exposure was more strongly associated with “binge” drinking among women compared to men. For cannabis, there is evidence indicating the likelihood of being a cannabis user increases for male college students as PTS severity increases, but this relationship was not found for female college students (Rehder & Bowen, 2019). Findings on assigned sex differences in the link between alcohol use and subjective sleep quality are mixed (Inkelis, Hasler, & Baker, 2020). In terms of sex differences in the cannabis use-sleep quality link, one study found male cannabis users were more likely to experience interrupted sleep than their female counterparts during withdrawal (Cuttler, Mischley, & Sexton, 2016). Separately, assigned sex differences in alcohol consumption quantity among college students (Singleton & Wolfson, 2009), cannabis consumption quantity among adults (Calakos, Bhatt, Foster, & Cosgrove, 2017; Cuttler et al., 2016), and in the prevalence of poor subjective sleep quality among young adults (Fatima, Doi, Najman, & Mamun, 2016) have been identified. The potential differences between male and female college students in the relation between PTS symptoms, sleep quality, and substance use behavior have important implications for prevention and intervention efforts focused on reducing substance-related harm in college students.

2. Method

2.1. Participants and procedures

The present study entailed secondary data analysis of a screening survey for a large RCT study examining the efficacy of various brief college student alcohol reduction strategies (see Larimer et al., 2022). College students (N = 5,998) were randomly selected from the Registrar’s list of two west coast universities to receive email invitations to complete an online screening survey. Campus 1 is a large public university in the Pacific Northwest with an undergraduate enrollment of approximately 30,000 students. Campus 2 is a mid-sized private university in Southern California with an undergraduate enrollment of approximately 6,000 students. Data was collected during fall of 2010. For context, Washington state legalized medical cannabis in 1998 and recreational cannabis in 2013. In California, medical cannabis was legalized in 1996 and recreational cannabis was legalized in 2016.

The screening survey was at least partially completed by 2,767 (46.1%) students (Campus 1n = 1,521; Campus 2n = 1,246). Participants were between the ages of 18–25 (Mage = 19.94; 62.8% identified as female) and the racial/ethnic composition was 55.1% White non-Hispanic (NH), 18.7% Asian NH, 12.9% Hispanic, 7.1% multiracial, 2.4% Black/African American NH, 2.2% Other race/ethnicity, 1.4% Native Hawaiian/Pacific Islander NH, and 0.2% American Indian/Alaskan Native NH. Response options for the item “birth sex” were male or female. We use the term “assigned sex” in reference to this item to be consistent with the American Psychological Association’s inclusive language guidelines (Association, 2021). Participants received $15 remuneration for completing the screening survey. All aspects of the study were approved by the Institutional Review Boards of both universities, and a Federal Certificate of Confidentiality was obtained to further protect the participants.

2.2. Measures

Posttraumatic Stress Symptoms.

The Post Traumatic Stress Disorder Checklist for DSM-IV (PCL-IV; Weathers, Litz, Herman, Huska, & Keane, 1993) is a 17-item self-report measure of past-month PTS symptoms that directly correspond with the criteria for PTSD in the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition Text Revision (DSM-IV-TR; American Psychiatric Association, 2000). Each item is rated on a 5-point scale of 1 (Not at all) to 5 (Extremely) A score of 44 or higher indicates clinically significant symptoms (Blanchard, Jones-Alexander, Buckley, & Forniers, 1996). Internal consistency for the present sample was α = 0.94.

Sleep Quality.

Subjective sleep quality was measured using a single item from the Pittsburgh Sleep Quality Index (PSQI; Buysse, Reynolds, Monk, Berman, & Kupfer, 1989): “During the past month, how would you rate your sleep quality overall?” Response options ranged from 0 (Very good) to 3 (Very bad). The single item was chosen as a broad index of sleep quality given that the present study examines a non-clinical sample. Among the PSQI subscales, the subjective sleep quality subscale has demonstrated the strongest association with the global PSQI score in psychometric evaluations across community samples (Carpenter & Andrykowski, 1998; Dietch et al., 2016; Hinz et al., 2017; Raniti, Waloszeck, Schwartz, Allen, & Trinder, 2018). In addition, in past studies single item numeric scale measures of subjective sleep quality have been demonstrated to be reliable and valid measures of sleep problems (Astroszko, Bagińska, Mokosińska, & Atroszko, 2015; Cappelleri et al., 2009; Dereli & Kahraman, 2021; Snyder, Cai, DeMuro, Morrison, & Ball, 2018).

Substance Use.

Two indices of past-month alcohol use were assessed: (a) number of drinks in a typical week (weekly drinks), and (b) peak number of drinks on the heaviest drinking occasion (peak drinking occasion). Weekly drinks was assessed using the Daily Drinking Questionnaire (DDQ; Collins, Parks, & Marlatt, 1985) in which students were asked to consider a typical week in the past month and indicate the number of drinks they typically consumed on each day of the week. Students’ responses were summed across the 7 days. Peak drinking occasion was measured as part of the quantity-frequency index (Marlatt et al., 1998) with an item that asked “Think of the occasion you drank the most this past month. How much did you drink?” and response options ranging from 0 to 25 + drinks. Participants were asked to report the frequency of past-month cannabis use with a single item: “How many days did you smoke marijuana during the past month?” Response options ranged from 0 (I did not smoke at all) to 30 (Every day).

2.3. Data analytic strategy

Study aims required hierarchical multiple regression analyses to estimate (a) main effects of PTS symptoms and poor subjective sleep quality on substance use behaviors, (b) two-way interactions between PTS symptoms and sleep quality, and (c) three-way interactions between PTS symptoms, sleep quality, and assigned sex.1 These effects are estimated in three separate ‘blocks’ as main effects and first-order interactions must be interpreted separately from second-order interactions (Jaccard & Turrisi, 1990). Significant interactions were plotted to enhance interpretation and were further probed by estimating simple slopes for each effect.

Each substance use outcome variable was measured on a count scale, and visual inspection of the distributions indicated evidence for zero-inflation (i.e., excessive number of zeros for alcohol/cannabis use) that require nuanced modelling approaches. Specifically, given the importance of considering non-use when addressing the present research questions, we used a two-component mixture modelling approach (i.e., zero-truncated hurdle regression) to simultaneously model (a) logistic regressions estimating the odds of any use vs. non-use and (b) truncated count regressions estimating associations among the subset of sample who did not report a zero (Feng, 2021). In models with count outcomes, coefficients are exponentiated to yield count ratios (CR) that are interpreted similar to odds ratios (i.e., CRs above 1 indicate a positive association and CRs below 1 indicate an inverse association). In each regression model, we controlled for participant age and campus (0 = public; 1 = private).

Additional analyses were conducted to determine whether the findings differed with sleep quality measured as a total of all PSQI components (see supplemental tables). The global PSQI is often used as a general estimate of sleep quality in a particular population (Mollayeva et al., 2016), and includes more objective indices of sleep such as sleep latency, duration, and efficiency. Although the current study was not focused on estimating sleep quality among college students, we included these analyses so the findings may be comparable with other studies that use the global measure of PSQI.

3. Results

3.1. Preliminary results

Descriptive statistics and bivariate correlations are displayed in Table 1. Of note, PTS symptoms were relatively low, as was expected given this non-clinical sample, with 9.8% of participants reporting clinical-level symptoms based on a cutoff of 44 (Blanchard et al., 1996). Within the past month, 30.4% of participants abstained from alcohol use and 75.6% abstained from cannabis use. Moreover, 5.2% of participants reported having never used alcohol, and 49.4% participants reported having never used cannabis. Bivariate correlations revealed significant positive associations between PTS symptoms and assigned sex, such that female participants had higher PTS symptoms, relative to male participants. Female participants also reported poorer subjective sleep quality whereas male participants had greater weekly number of drinks, peak number of drinks, and cannabis use days. Substance use variables had significant positive associations as expected.

Table 1.

Descriptive statistics and bivariate correlations among study variables.

Descriptives Bivariate correlations
Mean SD Range 1 2 3 4 5 6 7
1. Age 19.94 1.40 18–25 –
2. Assigned Sex (0 = M; 1 = F) 0.63 0.48 0–1 −0.06 –
3. Campus (0 = Public; 1 = Private) 0.45 0.50 0–1 −0.21** 0.03 –
4. PTS Symptoms (PCL Sum) 27.40 10.87 17–85 −0.01 0.08* 0.00 –
5. Poor Subjective Sleep Quality a 1.11 1.05 0–3 0.01 0.07* −0.04 0.34** –
6. Weekly Number of Drinks (DDQ) b 6.41 7.87 0–33 0.08* −0.22** 0.06 0.04 0.08* –
7. Peak Drinking Occasion (# of drinks) c 6.65 4.51 1–25 −0.01 −0.34** −0.04 0.04 0.05 0.72** –
8. Cannabis Use Days 1.90 5.62 0–30 0.02 −0.14** 0.02 0.07* 0.03 0.36** 0.25**
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Note:

a

Higher scores on sleep quality reflect poorer sleep and/or more sleep difficulties.

b

Weekly drinks was Winsorized at 3 standard deviations above the mean to reduce the influence of extreme values and spurious inflation of dispersion parameters (Tabachnick & Fidell, 2019).

c

Peak drinking occasion was only assessed of participants who reported at least one drinking occasion in the past month (n = 2161).

*

p <.01.

**

p <.001.

3.2. Alcohol use

Both past-month alcohol use outcomes, weekly drinks and peak drinking occasion, were modeled using zero-truncated hurdle regression, but the logistic portion of these two models estimating the odds of any alcohol use are identical so are reported only once (Table 2). In this logistic regression portion of the model, poorer subjective sleep quality was significantly associated with greater odds of any alcohol use, but the association with PTS symptoms was not statistically significant. However, in the truncated count portion of each model, PTS symptoms and poorer subjective sleep quality were both significantly associated with greater number of weekly drinks and greater peak drinking occasion, among those who engaged in at least some past-month drinking.

Table 2.

Zero-truncated hurdle regression models for past month weekly number of drinks and peak drinking occasion (N = 2,767).

Any Drinks Weekly Number of Drinks (Drinkers Only) Peak Drinking Occasion (Drinkers Only)
AOR [95% CI] p CR [95% CI] p CR [95% CI] p
Block 1: Main Effects
Age 1.470 [1.376, 1.573] <0.001 0.975 [0.964, 0.986] <0.001 0.985 [0.973, 0.998] 0.025
Assigned Sex (0 = Male; 1 = Female) 0.892 [0.746, 1.066] 0.209 0.597 [0.580, 0.616] <0.001 0.643 [0.621, 0.666] <0.001
Campus (0 = Public; 1 = Private) 1.682 [1.411, 2.007] <0.001 1.070 [1.038, 1.104] <0.001 1.040 [1.004, 1.078] 0.028
PTS Symptoms 0.943 [0.818, 1.088] 0.423 1.056 [1.031, 1.081] <0.001 1.040 [1.009, 1.070] 0.006
Poor Subjective Sleep Quality 1.109 [1.017, 1.210] 0.019 1.089 [1.072, 1.105] <0.001 1.041 [1.023, 1.059] <0.001
Block 2: Two-Way Interactions between PTSD Symptoms and Sleep Quality
PTS Symptoms × Poor Subjective Sleep Quality 0.923 [0.818, 1.041] 0.190 1.041 [1.020, 1.062] <0.001 1.049 [1.012, 1.088] 0.009
Block 3: Three-Way Interactions with Assigned Sex
PTS Symptoms × Assigned Sex 1.592 [0.972, 2.601] 0.064 0.974 [0.896, 1.060] 0.543 1.148 [1.045, 1.261] 0.004
Poor Subjective Sleep Quality × Assigned Sex 1.921 [1.197, 3.099] 0.007 0.896 [0.832, 0.965] 0.004 1.027 [0.943, 1.117] 0.537
PTS Symptoms × Poor Sleep Quality × Assigned Sex 0.727 [0.555, 0.947] 0.019 1.039 [1.000, 1.082] 0.069 0.948 [0.905, 0.995] 0.029
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Two-way interactions tested the extent to which perceived sleep quality moderated the effect of PTS symptoms on alcohol use. The interaction was non-significant in the logistic portion but was significant in the truncated count portion of each model. As shown in Fig. 1A, the effect of PTS on weekly drinks was stronger and statistically significant among those with poorer sleep quality, while this association was non-significant among those reporting better perceived sleep quality. In a similar pattern, perceived sleep quality significantly moderated the association between PTS symptoms and peak drinking occasion; simple slopes showed a significant positive association for those with poorer sleep quality, but a non-significant association for those with better perceived sleep quality (Fig. 2A).

Fig. 1.

Fig. 1.

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Simple slopes for (a) two-way interactions between sleep quality and PTS symptoms on weekly number of drinks (among drinkers only) and (b) three-way interactions between sleep quality, PTS symptoms, and assigned sex on odds of any alcohol use. CR = Count Ratio. OR = Odds Ratio. Error bands indicate 95% confidence intervals.

Fig. 2.

Fig. 2.

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Simple slopes for (a) two-way interactions between sleep quality and PTS symptoms and (b) three-way interactions between sleep quality, PTS symptoms, and assigned sex on peak number of drinks (among drinkers only). CR = Count Ratio. Error bands indicate 95% confidence intervals.

In a third and final modelling step, three-way interaction terms (i.e., PTS Symptoms × Poor Subjective Sleep Quality × Assigned Sex) estimated the extent to which the two-way interactions between PTS symptoms and perceived sleep quality may differ for males and females (Table 2). For the logistic portion (i.e., odds of any alcohol use), a significant three-way interaction was detected and probed using simple slopes (Fig. 1B). Although none of the simple slopes were significant, the plot shows a trend in which the interactions between PTS and sleep quality on odds of any alcohol use are different for males and females. For males, poorer sleep quality was associated with increased association between PTS symptoms and any alcohol use, while for females, poorer sleep quality was associated with decreased association between PTS symptoms and any alcohol use. A significant three-way interaction was also detected for the count portion of the model examining peak drinking occasion. Plotting this effect (Fig. 2B), it is seen that for males, perceived sleep quality had a noticeable moderating effect on the association between PTS symptoms and peak drinking occasion, though neither simple slope was significant, while for females, perceived sleep quality had virtually no impact on the association between PTS symptoms and peak drinking occasion. Indeed, for females, the simple slopes for both those high and low in perceived sleep quality indicated positive significant associations between PTS symptoms and peak drinking occasion.

3.3. Cannabis use

Main effects for the cannabis use model (Table 3) indicated a strong association between PTS symptoms and any cannabis use as well as cannabis use frequency among the subset who did use cannabis. However, the association between cannabis use and perceived sleep quality was less straightforward. Poorer sleep quality was associated with greater odds of any past month cannabis use, but among those who did use, poorer sleep quality was associated with fewer cannabis use days. The significant two-way interaction between PTS symptoms and sleep quality indicated that the effect of PTS symptoms on cannabis use frequency was stronger for those with relatively better sleep quality, though simple slopes (Fig. 3A) shows significant positive associations for those with relatively poorer sleep quality as well. The three-way interaction (i.e., PTS Symptoms × Poor Subjective Sleep Quality × Assigned Sex) was also significant, indicating that the moderating effect of sleep quality on the association between PTS symptoms and cannabis use frequency differs between males and females. Specifically, plotting this three-way interaction (Fig. 3B) shows that sleep may only moderate this association for males, among which PTS symptoms had a much stronger effect on cannabis use frequency for those with relatively better sleep quality, but an inverse association for those with poorer sleep quality. For females, the association between PTS symptoms and cannabis use frequency was positive regardless of perceived sleep quality.

Table 3.

Zero-truncated hurdle regression models for past month cannabis use frequency (N = 2,767).

Any Cannabis Use (Past Month) Cannabis Use Days (Cannabis Users Only)
AOR [95% CI] p CR [95% CI] p
Block 1: Main Effects
Age 1.003 [0.940, 1.071] 0.919 1.030 [1.009, 1.052] 0.004
Assigned Sex (0 = Male; 1 = Female) 0.648 [0.539, 0.778] <0.001 0.578 [0.546, 0.610] <0.001
Campus (0 = Public; 1 = Private) 1.190 [0.991, 1.429] 0.063 1.018 [0.962, 1.078] 0.535
PTS Symptoms 1.310 [1.137, 1.509] <0.001 1.136 [1.089, 1.185] <0.001
Poor Subjective Sleep Quality 1.134 [1.037, 1.240] 0.006 0.945 [0.918, 0.973] <0.001
Block 2: Two-Way Interactions between PTSD Symptoms and Sleep Quality
PTS Symptoms × Poor Subjective Sleep Quality 0.958 [0.849, 1.081] 0.485 0.932 [0.900, 0.966] <0.001
Block 3: Three-Way Interactions with Assigned Sex
PTS Symptoms × Assigned Sex 1.172 [0.717, 1.920] 0.528 1.035 [0.905, 1.184] 0.614
Poor Subjective Sleep Quality × Assigned Sex 1.228 [0.770, 1.963] 0.389 0.826 [0.716, 0.953] 0.009
PTS Symptoms × Poor Sleep Quality × Assigned Sex 0.907 [0.701, 1.172] 0.457 1.113 [1.035, 1.198] 0.004
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Note. Subjective sleep quality is scored such that higher scores indicate greater sleep difficulties. Hurdle models are two-component mixture models consisting of (1) a logistic regression model estimating odds of any cannabis use in the past month and (2) a zero-truncated count model estimating associations among non-zeros (i.e., past-month cannabis users). AOR = Adjusted Odds Ratios and CR = Count Ratios.

Fig. 3.

Fig. 3.

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Simple slopes for (a) two-way interactions between sleep quality and PTS symptoms and (b) three-way interactions between sleep quality, PTS symptoms, and assigned sex cannabis use days (among cannabis only). CR = Count Ratio. Error bands indicate 95% confidence intervals.

4. Discussion

The aim of this study was to examine the moderating effect of sleep quality on the relationship between PTS symptoms and substance use among college students. We found a two-way interaction between PTS symptoms and subjective sleep quality on weekly drinks. Similarly, we found a two-way interaction between PTS symptoms and subjective sleep quality on peak drinking occasion. These findings indicate the relation of PTS symptom severity to weekly drinks and peak drinking occasion was stronger among individuals who reported poorer subjective sleep quality. We also found a two-way interaction between PTS symptoms and subjective sleep quality on cannabis use frequency where the relation of PTS symptoms and cannabis use frequency was stronger among individuals who reported relatively better subjective sleep quality. Exploratory analyses found a three-way interaction between PTS symptoms, subjective sleep quality, and assigned sex for any alcohol use, peak drinking occasion among alcohol users, and cannabis use frequency among cannabis users. For any alcohol use and peak drinking occasion, the interaction between PTS and poor sleep quality was present only for male participants. For cannabis use frequency, the interaction between PTS and better sleep quality was present only for male participants.

Taken together, the current study suggests sleep quality is an important factor that may contribute to the relation between PTS symptom severity and alcohol and cannabis use among college students. One interpretation is that poor sleep quality may exacerbate the use of alcohol to cope with PTS, although coping was not examined in the current study. This interpretation is consistent with prior research that found the relationship between poor sleep quality and alcohol consequences was moderated by coping motivations (Kenney, Paves, Grimaldi, & LaBrie, 2014). Alternatively, the effect of poor sleep quality on the relationship between PTS and alcohol use may be due to factors attributable to the college environment rather than coping motives. It may be that college students with PTS are more likely to engage in behaviors that negatively impact sleep quality (e.g., attending late-night parties). The findings for cannabis use frequency are consistent with previous findings where college students report a motivation for using cannabis is to improve sleep (Drazdowski et al., 2021; Goodhines et al., 2019). Subjective reports of sleep quality may also be more influenced by ease of falling asleep and extension of deep sleep, which are particularly impacted by cannabis use (Schierenbeck, Riemann, Berger, & Hornyak, 2008), whereas ongoing fatigue and REM suppression effects of cannabis on sleep may have less impact on self-reported sleep quality. Future research would benefit from more detailed assessment of sleep symptoms and addition of physiological sleep monitoring to assess these aspects of cannabis’ impact on the sleep cycle in relation to PTS symptom severity and substance use outcomes.

Furthermore, we examined whether these findings held when using the global PSQI score, rather than perceived sleep quality, and found the two-way interactions with PTS on weekly number of drinks and peak drinking occasion were no longer significant (see supplementary materials). The global PSQI score includes several subscales, such as use of sleep medication, sleep duration, and sleep efficiency, that may not be relevant to associations between PTS and substance use behaviors. While the global score may provide more information about sleep impairment, psychometric studies of the PSQI do not support a single-factor model with the full PSQI and there is no consensus on the best-fitting structure (Mollayeva et al., 2016). A more parsimonious examination of perceived sleep quality using the single item in the PSQI may be the most relevant to the current study given the sample was not recruited for sleep impairment. The best fitting factor structure of the PSQI varies across different populations (Mollayeva et al., 2016), and future studies should determine the optimal measurement of sleep indices necessary to examine the impact of sleep quality on PTS and substance use among college students specifically.

The observed assigned sex differences for the moderation effects suggest good sleep quality is a potential protective factor that buffers the relation between PTS symptom severity and weekly drinks and peak drinking occasion, but this protective factor may be more salient for male college students. This was an exploratory aim of the study given relatively little is known about assigned sex differences in the relation of these variables. It could be that PTS and sleep quality are more closely tied together for male college students such that males with good sleep quality do not experience the negative impact of PTS symptoms on any alcohol use and peak drinking occasion to the same extent as males with poor sleep quality. The high number of drinks consumed during the past month peak drinking occasion for male participants with poor sleep quality is particularly important considering the increased risk of negative consequences associated with drinking levels that exceed binge drinking (i.e., 5 + drinks for males; Read, Beattie, Chamberlain, & Merrill, 2008). In contrast to alcohol use, male students with good sleep quality who use cannabis appear to be more likely to experience the negative impact of PTS symptoms on cannabis use frequency. There may be factors other than sleep (e.g., emotion dysregulation, distress intolerance) that impact the relationship between PTS symptom severity and weekly drinks for female college students, such that female college students are more likely to drink to cope with trauma related distress regardless of their sleep quality. Female participants also appeared to have a stronger relation between PTS symptoms and alcohol use, both for weekly drinks and peak drinking occasion, compared to men. It may be that the significant three-way interactions are driven by this stronger relation. Given there are assigned sex and gender differences across the variables in this study, such that women have a greater likelihood of developing PTSD following a traumatic event (Tolin & Foa, 2008) and likelihood of reporting poor sleep quality (Fatima et al., 2016), future research should examine potential underlying mechanisms of these differences to enhance our understanding of sleep quality and alcohol use behavior following traumatic events.

Trauma exposure and PTS symptom severity are important risk factors for problematic drinking among college students (Read et al., 2012). The present findings suggest sleep quality may be an additional important factor to consider in prevention or intervention efforts targeting alcohol-related harm. Assessing sleep quality, trauma exposure, and PTS symptom severity among college students who consume alcohol is an important first step to identifying appropriate intervention targets, particularly for male college students who appear to be more negatively impacted by poor sleep quality. Second, personalized feedback intervention and prevention programs such as Brief Alcohol Screening and Intervention for College Students (BASICS; Dimeff, Baer, Kivlahan, & Marlatt, 1999, Marlatt et al., 1998) are effective in reducing college student alcohol use and alcohol-related negative consequences (Miller et al., 2013). Programs such as BASICS could be adapted to incorporate personalized feedback and coping skills for sleep difficulties and PTS symptoms. Furthermore, college students may be more willing to engage in interventions that address sleep compared to alcohol focused interventions (e.g., Fucito et al., 2015). For college students experiencing subclinical PTS symptoms, addressing sleep may reduce the negative impact of PTS symptoms on alcohol use behavior.

4.1. Limitations and future directions

Although the results of this study provide important information about the potential role of sleep quality in PTS symptom severity and substance use behavior among college students, several limitations should be considered. First, the cross-sectional design precludes our ability to draw causal conclusions. Longitudinal studies that examine the predictive relationship between sleep difficulties and alcohol and cannabis use at the daily level are needed to increase our understanding of the functional relationship between sleep quality, PTS symptoms, and substance use behavior among college students who have experienced a traumatic event. Second, the measurement of sleep quality in the current study was limited to a single, four-point self-report item assessing subjective sleep quality. The current study did not include objective or physiological measurements of sleep quality (e.g., polysomnography). Other studies assessing subjective sleep quality using a single item have also used scales with greater variability (Astroszko et al., 2015; Cappelleri et al., 2009; Dereli & Kahraman, 2021; Snyder et al., 2018). Sleep functioning is complex and can be assessed with multiple components such as sleep duration, sleep efficiency, and daytime dysfunction for clinical or treatment-seeking populations. Although we view subjective sleep quality as an important first step in this line of research, future studies should examine which indices of sleep have the strongest effect on the relation between PTS symptoms and alcohol and cannabis use to provide direction for interventions. Third, while the current study addressed an important gap in the literature by examining assigned sex differences, future studies should examine gender differences, other demographic characteristics, and trauma characteristics such as severity and type of trauma in the relationship between sleep, substance use, and PTS symptoms. This study did not include an assessment that determined whether the symptoms endorsed on the PCL are tied to a Criterion A traumatic event per DSM-IV diagnostic guidelines (APA, 2000). Although this study focused on moderators, future research could seek to understand underlying mechanisms, such as coping-related motives for substance use, using longitudinal mediation models. Future studies would also benefit from a more specific assessment of cannabis use including the type, particularly THC and CBD levels, quantity, and method of administration. Finally, we examined outcomes related to alcohol and cannabis use separately, but did not measure students’ use of these substances simultaneously (i.e., SAM use). Given recent interest in associations between sleep and SAM use (Graupensperger et al., 2021), there may be rationale for future studies to examine relations between PTS symptoms and sleep on simultaneous use of alcohol and cannabis.

4.2. Conclusion

The current study provides evidence that poor sleep quality strengthens the relationship between PTS symptom severity and alcohol use among college students. Our findings indicate there are important sex differences in the relationship between sleep quality, PTS symptoms, and alcohol use, increasing our understanding of how male and female participant’s drinking behavior may be differentially impacted by trauma and poor sleep. Sleep quality is a modifiable target that can be easily incorporated into existing alcohol use interventions for college students, such as incorporating evidence-based approaches like cognitive behavioral therapy for insomnia (Taylor et al., 2014). Future studies should determine the extent to which improvements in sleep influence weekly alcohol consumption and heavy episodic drinking.

Supplementary Material

Supplemental Tables

Acknowledgement

This research was funded by the National Institute on Alcohol Abuse and Alcoholism (R01AA012547, R01AA027499, R56AA012547, R37AA012547, K01AA027771, and T32AA007455). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

Footnotes

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

1

PTSD symptoms (PCL-IV) are traditionally sum-scored to enable cutoff scores and clinical diagnoses; however, sum scores perform poorly in statistical models given the large dispersion causing difficulties in interpreting coefficients and because sum scores are not able to discern missing responses from responses with a score of ‘0’. Therefore, statistical models included a mean composite score of the PCL-IV.

CRediT authorship contribution statement

Elizabeth A. Lehinger: Conceptualization, Writing – original draft. Scott Graupensperger: Conceptualization, Formal analysis, Writing – original draft. Frank Song: Writing – original draft. Brittney A. Hultgren: Conceptualization, Writing – review & editing. Dara Jackson: Writing – original draft. Mary E. Larimer: Investigation, Resources, Writing – review & editing, Funding acquisition.

Appendix A. Supplementary material

Supplementary data to this article can be found online at https://doi.org/10.1016/j.addbeh.2022.107482.

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