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. Author manuscript; available in PMC: 2023 Nov 1.
Published in final edited form as: Cogn Behav Ther. 2022 Jun 28;51(6):486–502. doi: 10.1080/16506073.2022.2079557

COVID-Related Stress and Substance Use: Examining the Role of Sleep Disturbance

Anka A Vujanovic 1,*, Brooke Y Kauffman 1, Maya Zegel 1, Michael J Zvolensky 1,2,3
PMCID: PMC9764365  NIHMSID: NIHMS1857301  PMID: 35762877

Abstract

The current investigation employed a cross-sectional design to evaluate the associations of COVID-19 stress, sleep disturbance, and substance use among a national sample of 143 adults (57.3% male, Mage = 38.5 years, SD = 11.28), surveyed at a single time-point using Amazon’s MTurk platform. We hypothesized that COVID-19-related stress would be indirectly related to substance use outcomes (i.e., number of substance classes used daily, number of alcoholic drinks per occasion, substance use coping motives; but not substance use enhancement motives) through sleep disturbance severity. As expected, results indicated that the models examining indirect effects were statistically signficant for number of substance classes used daily and substance use coping motives. However, there was no evidence that sleep disturbance explained the relation between COVID-19-related stress and number of alcoholic drinks per occasion or substance use enhancement motives. These findings underscore the importance of sleep disturbance in efforts to better understand how COVID-19-related stress is associated with certain types of substance use behavior.

Keywords: COVID-19, stress, substance use, sleep

Introduction

The COVID-19 pandemic has caused vast morbidity and mortality, resulting in devastated individuals, families, and communities (Maital & Barzani, 2020; Xiong et al., 2020; Zvolensky et al., 2020). In addition to the stressful and potentially traumatic illness and fatality burden of COVID-19, many individuals are experiencing increased stress related to financial concerns, food insecurity, housing and job instability, and health care access (Bareket-Bojmel et al., 2020; Crayne, 2020; Gundersen et al., 2020; Ramalho et al., 2020). These uncertainties, combined with diminished social connectedness, have been related to elevated levels of stress, sleep disturbance, substance use, as well as other adverse mental health outcomes, such as depression (Pfefferbaum & North, 2020; Wang et al., 2020).

Several reports have found substance use might have increased during the COVID-19 pandemic, although rates may differ across countries or regions due to various contextual factors (e.g., financial strain) and individual differences (e.g., race, gender, age) (Acuff et al., 2022). Increases in substance use during the pandemic have been attributed, at least in part, to the psychological burden caused by stress, worry, and isolation (Czeisler et al., 2020; Rogers et al., 2020). For individuals with substance use disorders (SUD), worry about contracting COVID-19 may be elevated, at least in part, due to the greater prevalence of comorbid medical conditions among individuals with SUD compared to the general population (Berlin et al., 2020; Lagisetty et al., 2017; Wang et al., 2021; Zvolensky et al., 2020). Furthermore, elevated COVID-19 stress may exacerbate substance use. Negative reinforcement models suggest an association between negative affect and substance use (Baker et al., 2004; Khantzian, 2013), as individuals may be more likely to use substances to manage or reduce their negative affect, such as COVID-19 stress. Individuals experiencing greater stress may be more likely to use substances and to do so more frequently or intensely to regulate negative emotional states (e.g., Khantzian, 2013).

The COVID-19 pandemic has created stressful circumstances marked by chronic uncertainty for most of the international population (Norrholm et al., 2021). Notably, while the COVID-19 pandemic has exposed the global population to stress and uncertainty, only select experiences (e.g., fear of death during severe respiratory stress secondary to COVID-19 illness) meet the criteria for ‘trauma’ per the DSM-5 posttraumatic stress disorder (PTSD) Criterion A (American Psychiatric Association, 2013; Norrholm et al., 2021). Nevertheless, various types of life stressors may result in symptoms of intrusion, avoidance, negative alterations in cognitions and mood, and altered arousal and reactivity. Although these symptoms equivocally do not correspond to a PTSD diagnosis in the absence of a DSM-5 PTSD Criterion A traumatic event, such symptomatology may describe reactions to broad-based life stressors in line with DSM-5 adjustment disorders and/or ‘other trauma- and stress related disorders’ (American Psychiatric Association, 2013). Given the documented association between COVID-19-related stress and substance use (e.g., Gritsenko et al., 2020; McKay & Asmundson, 2020), it is important to evaluate behavioral factors, modifiable via intervention, that may explain that association and inform evidence-based clinical conceptualization and programming.

Sleep disturbance, which refers to both quantitative (e.g., sleep duration, sleep latency, and number of arousals) and qualitative (e.g., depth and restfulness) aspects of sleep (Buysse et al., 1989), provides one such avenue pertinent to this association. During the COVID-19 pandemic, sleep disturbances have been well-documented across numerous populations (Jahrami et al., 2021; Marelli et al., 2021). Sleep disturbance is an established symptom of stress and can be triggered by changes in one’s routine, such as working from home or a national lockdown (Gupta et al., 2020; Wright et al., 2020). Sleep disturbance is linked to various mental health problems, including stress, anxiety, depression, PTSD, and substance use (Conroy & Arnedt, 2014; Germain et al., 2017; Hall Brown & Mellman, 2014; Lind et al., 2017; Richards et al., 2020; Smith et al., 2018; Valentino & Volkow, 2020) and these psychiatric conditions have been exacerbated by the COVID-19 pandemic (Fu et al., 2020; Huang & Zhao, 2020).

Theoretically, COVID-19 stress may be related to sleep disturbance (Haydon & Salvatore, 2021; Hyun et al., 2021), which may in turn, be associated with increased substance use and greater substance use to cope with negative emotional states (MacMillan et al., 2021; Valentino & Volkow, 2020). Stress has been related to sleep disturbance among adults during the COVID-19 pandemic (e.g., Duong, 2021; Emery et al., 2021). Furthermore, both self-reported sleep problems and experimentally induced sleep restriction are related to decreased emotion regulation as indexed via self-report and neuroimaging studies (Palmer & Alfano, 2017). Lower capacities to regulate negative emotional states relevant to COVID-19 stress may thus lead to increased attempts to use substances to ‘self-medicate,’ or cope with negative emotional states (MacMillan et al., 2021). A growing body of research has documented associations among stress, sleep disturbance, and substance use, wherein both elevated stress and substance use are associated with sleep disturbance via transactional and bidirectional pathways (e.g., Carey et al., 2011; Gardani et al., 2021; Geoffroy et al., 2020; Lind et al., 2017; Smith et al., 2018; Vujanovic & Back, 2019). Fewer studies have evaluated the role of sleep disturbance in associations between stress and substance use (Chakravorty et al., 2018; Teeters et al., 2021). Furthermore, to the best of our knowledge, no published studies to date have examined the associations of COVID-19-related stress, sleep disturbance, and substance use and motives, concurrently in a single theoretical model and in the context of the pandemic.

The present investigation sought to extend extant literature and evaluate the associations of COVID-19-related stress, sleep disturbance, and substance use among a nationally recruited sample of adults. We hypothesized that COVID-19-related stress would be associated with substance use (i.e., number of substance classes used daily, number of alcoholic drinks per occasion) and substance use coping motives through sleep disturbance severity. This effect was not expected for substance use enhancement motives, which was included to provide a test of explanatory specificity regarding use motives. Covariates included COVID-19 diagnosis, number of medical conditions, and depressive symptom severity (Bueno-Notivol et al., 2021; Czeisler et al., 2020; Ko et al., 2020; Lai et al., 2015; Razzaghi et al., 2020). In models examining substance use motives, the alternate motive variable was also included as a covariate to ensure specificity of effects (i.e., not attributable to shared variance with other motives).

Methods

Participants

Participants for the current cross-sectional study included 143 adults (58% male, Mage = 38.5 years, SD = 11.28) recruited through Amazon’s MTurk online platform system and surveyed at a single-time point. Regarding racial composition, 66.4% (n = 95) identified as White, 13.3% (n = 19) as Black or African American, 11.2% (n = 16) as Asian, 4.2% (n = 6) as Multiracial, 2.1% (n = 3) as Alaska Native or American Indian, 0.7% (n = 1) as other, and 2.1% (n = 3) preferred not to respond. A total of 21% (n = 30) identified as Spanish, Hispanic, or Latinx. In terms of the highest education level achieved, 0.7% (n = 1) indicated completing some high school, 6.3% (n = 9) indicated completing high school (or equivalent), 11.2% (n = 16) indicated obtaining some college, 9.8% (n = 14) indicated obtaining an Associate’s degree, 51% (n = 73) indicated obtaining a Bachelor’s degree, 18.2% (n = 26) indicated obtaining a Master’s degree, and 2.8% (n = 4) indicated obtaining a doctoral degree. The current medical conditions endorsed in the current sample are as follows: diabetes (16.8%; n = 24), asthma (15.4%; n = 22), hypertension (15.4%; n = 22), cardiovascular disease (3.5%; n = 5), autoimmune disease (2.8%; n = 4), cerebrovascular disease (1.4%; n = 2), kidney disease (1.4%; n = 2), liver disease (1.4%; n = 2), malignant tumor (0.7%; n = 1), respiratory disease/condition (0.7%; n = 1), and other (2.8%; n = 4). A total of 66.4% (n = 95) of participants met criteria for sleep disturbances (PSQI total score ≥ 5). A total of 46.2% (n = 66) of participants reported they drink alcohol, and they drink on average 3.6 standard drinks (SD = 4.24) on any given occasion. Regarding (non-alcohol) substance use among the current sample: 29.4% (n = 42) endorsed daily or more frequent cigarette or e-cigarette use, 7.7% (n = 11) endorsed daily or more frequent marijuana use, 3.5% (n = 5) endorsed daily or more frequent stimulant use, and 2.1% (n = 3) daily or more frequent endorsed opioid use.

Of the 143 participants, 7% (n = 10) endorsed a positive diagnosis of COVID-19. Of those individuals with a positive diagnosis, 2.1% (n = 3) reported mild symptoms (i.e., could still do normal activities), 4.2% (n = 6) reported moderate symptoms (i.e., sick but not admitted to hospital for > 24 hour), and 0.7% (n = 1) reported severe symptoms (i.e., admitted to hospital for > 24 hours). An additional 9.8% (n = 14) of individuals reported that they think they may have COVID-19 but have not been tested or diagnosed. In terms of COVID-19 exposure, 11.9% (n = 17) indicated exposure to someone who has COVID-19, 9.8% (n = 14) reported exposure to someone who was awaiting COVID-19 testing results, and 9.1% (n = 13) reported traveling to/from an area/community with a COVID-19 outbreak (e.g., China, Italy) within the past three months (data from the current study was collected from April 2020 through May 2020). A total of 5.6% (n = 8) of individuals reported someone in their family, household, or close to them died from COVID-19.

Participants from 38 U.S. states participated in the study. Most participants resided in New York (n = 15, 10.5%). In addition, 9.8% resided in California (n = 14) and Texas (n = 14), 7% in Pennsylvania (n = 10), 6.3% in Florida (n = 9), 4.9% in Indiana (n = 7), 4.2% in Georgia (n = 6) and Illinois (n = 6), 3.5% in New Jersey (n = 5) and Washington (n = 5), 2.8% in Colorado (n = 4), Kentucky (n = 4), and Ohio (n = 4), 2.1% in Hawaii (n = 3) and North Carolina (n = 3), 1.4% in Alabama (n = 2), Idaho (n = 2), Maryland (n = 2), Michigan (n = 2), Minnesota (n = 2), Mississippi (n = 2), Nevada (n = 2), New Hampshire (n = 2), Tennessee (n = 2), Utah (n = 2), and Virginia, and 0.7% in Alaska (n = 1), Arkansas (n = 1), Connecticut (n = 1), Iowa (n = 1), Kansas (n = 1), Louisiana (n = 1), Maine (n = 1), Missouri (n = 1), New Mexico (n = 1), Oklahoma (n = 1), Oregon (n = 1), and Rhode Island (n = 1).

Measures

Demographics.

A demographics questionnaire was utilized to obtain information regarding participant age, sex, race, and ethnicity to describe the sample. Sex was evaluated as potential covariate in the current study.

COVID-19 Screening.

A COVID-19 screening questionnaire was used to provide information relevant to COVID-19 diagnosis and exposure of the current sample. COVID-19 diagnosis (0 = No, 1 = Yes) was utilized as a covariate in the current study.

Depression.

As a measure of depression, the 5-item Overall Depression Severity and Impairment Scale was used (ODSIS; Bentley et al., 2014). Participants were asked to indicate from 0 to 4 the degree to which they experienced depressive symptoms over the past week (possible range 0 – 20). In past work, the ODSIS has demonstrated good psychometric properties (Bentley et al., 2014). In the current study, the total score was used as a covariate and yielded excellent internal consistency (α = .94).

COVID-19 Stress.

The research team adapted the Posttraumatic Stress Disorder Checklist for DSM-5 (PCL-5; Blevins et al., 2015) to a 20-item COVID-19 stress self-report measure to assess stress-related symptoms due to COVID-19. The items were derived verbatim from the PCL-5, a 20-item self-report measure of PTSD symptom severity, with the intention to measure general stressor-related symptoms. The modification included an addendum to the instructions, which asked respondents to complete the questionnaire regarding their response to the COVID-19 pandemic, specifically. Respondents were asked to rate symptom severity, including symptoms of intrusions, avoidance, negative alterations in cognitions and mood, and arousal and reactivity, from 0 (Not at all) to 4 (Extremely). The total score (possible range 0 −80) was utilized in the current study as an independent variable (α = .97).

Sleep Disturbance.

The 19-item Pittsburgh Sleep Quality Index (PSQI; Buysse et al., 1989) was utilized in the current study as a measure of sleep disturbances experienced over the past month. The total score is comprised of seven components, including subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbance, use of sleeping medication, and daytime dysfunction. In the current study, the total score (possible range 0 −21) was utilized as a mediator variable. The PSQI has demonstrated sound psychometric properties in past work (Mollayeva et al., 2016) and demonstrated acceptable internal consistency in the current study (α = .77).

Substance Use.

To assess current substance use since the COVID-19 outbreak, a 7-item substance use questionnaire was utilized (Rogers et al., 2020). Participants were asked if they drink alcohol as well as how much they typically drink on any occasion (e.g., number of drinks: a standard drink = 12 oz of beer, 5 oz of wine, or 1.5oz of liquor). Participants also were asked to indicate current substance use frequency from 0 (less than monthly) to 5 (more than once daily) for marijuana, e-cigarettes, stimulants, opioids, and an ‘other’ category. The number of cigarettes smoked per day was also assessed. A composite score was created to capture number of substance classes used daily (coded: 0 = less than daily [or not at all] and 1 = used daily or more frequently); endorsement of daily cigarette smoking was included. Number of substance classes used daily was utilized as a criterion variable in the current study. Number of standard alcoholic drinks typically consumed on any given occasion was also utilized as a criterion variable in the current study.

Substance Use Coping Motives.

To assess substance use coping motives and substance use enhancement motives, the research team created a 10-item self-report questionnaire derived from the original 32-item Substance Use Motives Measure (Biolcati & Passini, 2019). Participants were asked to indicate whether they used substances for coping (e.g., “To forget your worries”) or enhancement (e.g., “To get high”) motives from 1 (Almost never/never) to 5 (Almost always/always). The 4-item substance use coping motives subscale (possible range 4 – 20) and the 6-item substance use enhancement motives subscale (possible range 6 – 20) were evaluated as criterion variables in the present study. The substance use coping motives subscale (α = .94) and the substance use enhancement motives subscale (α = .92) demonstrated excellent internal consistency in the current study.

Procedure

For the current study, participants were recruited and screened for eligibility criteria through Amazon’s MTurk online platform system. Extant work has demonstrated the value of MTurk in research (e.g., demographic diversity of samples) relative to other data collection methodologies (e.g., undergraduate samples; Behrend et al., 2011; Buhrmester et al., 2018; Burnham et al., 2018). Participants were deemed eligible for the current study if they were between the ages of 18 and 65. Exclusion criteria included inability to complete required self-report surveys, lack of proficiency in English, and an inability to provide consent. Individuals with an interest in participating were redirected to Qualtrics, a reliable and valid online survey management system, to complete the survey. Participants who provided informed consent and completed the entirety of the survey were compensated $4 through their MTurk worker account. Amazon MTurk workers can choose their preferred mode of compensation (either US dollars or Amazon.com gift cards). A number of investigator-selected quality assurance checks were included in the survey, such as requiring participants to have a > 90% approval rate (i.e., participant met quality assurance inclusion criteria set forth by researchers > 90% of the time when completing MTurk research assignments), location matching to collected IP address, and speed checks to ensure participants did not complete the survey in less than half the median response time. The study protocol was approved by the relevant Institutional Review Board.

Analytic Strategy

First, sample descriptive statistics and zero-order correlations were examined among all study variables. Only variables with significant correlations with at least one criterion variable of interest were included in the test of indirect effects. Next, the PROCESS macro, a conditional modeling program in SPSS (Hayes, 2013), was utilized to examine the indirect effects. Please see Figure 1. Specifically, sleep disturbance was examined as an indirect factor between COVID-19 stress and four criterion variables: (1) number of substance classes used daily, (2) number of alcoholic drinks per occasion, (3) substance use coping motives, and (4) substance use enhancement motives. Covariates for all models included COVID-19 diagnosis (0 = No, 1 = Yes), total number of medical conditions, and depressive symptom severity (ODSIS total score; e.g., Bueno-Notivol et al., 2021; Czeisler et al., 2020; Ko et al., 2020; Lai et al., 2015; Razzaghi et al., 2020). In models examining substance use motives, the alternate motive variable was also included as a covariate to ensure specificity of effects. Skewness and kurtosis of relevant variables were evaluated.

Figure 1.

Figure 1.

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Model of the total (c), direct (c’), and indirect effect (ab) of COVID-19 stress on substance use through sleep disturbances.

Note. a path = Effect of X on M; b path = Effect of M on Y; c paths = Total effect of X on Y; c’ path = Direct effect of X on Y controlling for M.

The indirect effects confidence intervals (CIs) were subjected to 5,000 bootstrap re-samplings and 95-percent CIs were estimated (Hayes, 2009; Preacher & Hayes, 2004; Preacher & Hayes, 2008). Statistical significance of effects were determined if the CIs around their product do not contain zero (Preacher & Hayes, 2008). Completely standardized point estimates (CSE) were utilized as a measure of effect size and were interpreted as small (.014) medium (.36) and large (.51; Cheung, 2009). To test the interpretation of our results, competing models were run in which (1) the predictor and mediator variable were switched and (2) the outcome and predictor were switched. These results were compared to the original models (Preacher & Hayes, 2008). Covariates in the reverse models were consistent with those employed in the original models.

Results

Sample descriptive statistics and bivariate correlations among all study variables are presented in Table 1. Notably, participant data that did not pass instituted quality assurance checks were removed (n = 4). Furthermore, participants with missing data (n = 31) were removed from final analyses. Only participants with complete data on all study variables were utilized in the current study (N = 143). COVID-19 stress was significantly related to sleep disturbance (r = .60, p < .001), number of substance classes used daily (r = .19, p < .05), substance use coping motives (r = .71, p < .001), and substance use enhancement motives (r = .62, p < .001). Sleep disturbance was significantly related to number of substance classes used daily (r = .29, p < .001), substance use coping motives (r = .58, p < .001), and substance use enhancement motives (r = .61, p < .001).

Table 1.

Descriptive statistics and Bivariate Correlations Between Study Variables (N = 143)

Variable Observed Ranges Mean/n (SD/%) 1 2 3 4 5 6 7 8 9 10
1. Sex 83 (58%) -
2. COVID-19 Diagnosis 10 (7%) −.07 -
3. # of Medical Conditions 0 – 4 0.62 (0.93) −.07 .41*** -
4. Depressive Symptom Severity 0 – 20 5.69 (5.44) −.01 .30*** .38*** -
5. COVID-19 Stress 0 – 73 27.96 (21.41) −.01 .36*** .40*** .66*** -
6. Sleep Disturbance 0 – 17 6.98 (4.18) .15 .31*** .41*** .50*** .60*** -
7. # of Substance Classes Used Daily 0 – 4 .43 (.76) .01 .32*** .17** .25** .19* .29*** -
8. # Alcoholic Drinks per Occasion 4 – 28 1.73 (3.43) −.02. .10 −.02 .02 −.02 −.04 .17* -
9. Substance Use Coping Motives 4 – 20 9.29 (4.98) .03 .33*** .33*** .52*** .71*** .58*** .34*** .11 -
10. Substance Use Enhancement Motives 6 – 30 14.55 (7.27) −.06 .31*** .32*** .41*** .61*** .48*** .35*** .19* .86*** -
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Note.

***

p < .001,

**

p < .01

*

p < .05.

Sex: % listed as males (Coded: 0 = Male, 1 = Female); COVID-19 Diagnosis: % listed as positive COVID-19 Diagnosis (Coded 0 = No, 1 = Yes); Depressive Symptom Severity = Overall Depression Severity and Impairment Scale (ODSIS; Bentley et al., 2014); Sleep Disturbances = Pittsburgh Sleep Quality Index (PSQI; Buysse et al., 1989).

Please see Table 2 for a summary of results. In terms of number of substance classes used daily, the total effects model with COVID-19 stress and covariates only was statistically significant (R2 = .13, F[4, 138] = 5.08, p < .001). The full effects model with the addition of sleep disturbances was statistically significant (R2 = .16, F[5, 137] = 5.14, p < .001). In the test of the indirect effect model, results revealed that COVID-19 stress was indirectly associated with number of substance classes used daily via sleep disturbances (a*b = 0.01, SE = 0.01, CI95% = 0.00, 0.01, CSE = .10).

Table 2.

Indirect effect of COVID-19 Stress on Substance Use Criterion Variables via Sleep Disturbance

Y Path R 2 b SE t p CI(l) CI(u)
1 COVID-19 Stress → Sleep Disturbances (a) .41*** 0.08 0.02 4.78 < .001 0.05 0.12
Sleep Disturbances → # of Substances Classes Used Daily (b) .16*** 0.04 0.02 2.20 .030 0.00 0.08
COVID-19 Stress → # of Substances Classes Used Daily (c’) −0.00 0.00 −1.08 .281 −0.01 0.00
COVID-19 Stress → # of Substances Classes Used Daily (c) .13*** −0.00 0.00 −0.27 .787 −0.01 0.01
COVID-19 Stress → Sleep Disturbances → # of Substances Classes Used Daily (a*b) 0.00 .0.00 0.00 0.01
2 Sleep Disturbances → Number of Alcoholic Drinks Per Occasion (b) .02 −0.05 .09 −0.58 .562 −0.23 0.13
COVID-19 Stress → Number of Alcoholic Drinks Per Occasion (c’) −0.01 0.02 −0.36 .718 −0.05 0.03
COVID-19 Stress → Number of Alcoholic Drinks Per Occasion (c) .02 −0.01 0.02 −0.63 531 −0.05 0.03
COVID-19 Stress → Sleep Disturbances → Number of Alcoholic Drinks Per Occasion (a*b) −0.00 0.01 −0.02 0.01
3 Sleep Disturbances → Substance Use Coping Motives (b) .81*** 0.15 0.06 2.57 .022 0.04 0.27
COVID-19 Stress → Substance Use Coping Motives (c’) 0.05 0.01 3.47 < .001 0.02 0.08
COVID-19 Stress → Substance Use Coping Motives (c) .80*** 0.06 0.01 4.30 < .001 0.03 0.09
COVID-19 Stress → Sleep Disturbances → Substance Use Coping Motives (a*b) 0.01 0.01 0.00 0.02
4 Sleep Disturbances → Substance Use Enhancement Motives (b) .74*** −0.08 0.10 −0.74 .458 −0.28 0.13
COVID-19 Stress → Substance Use Enhancement Motives (c’) 0.01 0.02 0.29 .773 −0.04 0.06
COVID-19 Stress → Substance Use Enhancement Motives (c) .74*** 0.00 0.02 0.14 .889 −0.04 0.05
COVID-19 Stress → Sleep Disturbances → Substance Use Enhancement Motives (a*b) 0.02 0.02 −0.00 0.06
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Note.

***

p < .001,

**

p < .01

*

p < .05.

Path a is equal in all cases Y1–3; therefore, it presented only once to avoid redundancies. N for analysis is 143 cases. The standard error and 95% CI for the indirect effects (a*b) are obtained through bootstrapping with 5,000 re-samples. a path = Effect of X on M; b paths = Effect of M on Yi; c’ paths = Direct effect of X on Yi controlling for M; c paths = Total effect of X on Yi.

In terms of number of alcoholic drinks consumed per occasion, the total effects model with COVID-19 stress and covariates only was not statistically significant (R2 = .02, F[4, 138] = 0.62, p = .646). The full effects model with the addition of sleep disturbances was also not statistically significant (R2 = .02, F[5, 137] = 0.56, p = .727). In the test of the indirect effect model, result revealed that COVID-19 stress was not indirectly associated with number of drinks via sleep disturbances (a*b = −0.00, SE = 0.01, CI95% = −0.02, 0.01, CSE = −.03). To address skewness of the alcohol-related variable, we employed logarithmic transformation; the pattern of results and magnitude of effects remained consistent.

Regarding substance use coping motives, the total effects model with COVID-19 stress and covariates only was statistically significant (R2 = .80, F[5, 137] = 107.63, p < .001). With the addition of sleep disturbances in the full effects model, the model was also statistically significant (R2 = .81, F[6, 136] = 94.47, p < .001). For the indirect effect model, COVID-19 stress was indirectly related to substance use coping motives via sleep disturbances (a*b = 0.01, SE = 0.01, CI95% = 0.00, 0.02, CSE = .04).

For substance use enhancement motives, the total effects model with covariates and COVID-19 stress was statistically significant (R2 = .74, F[5, 137] = 77.44, p < .001). The full effects model with sleep disturbances was also statistically significant (R2 = .74, F[6, 136] = 64.41, p < .001). Results for the indirect effect model revealed that COVID-19 stress was not indirectly associated with substance use enhancement motives through sleep disturbances (a*b = −0.00, SE = 0.01, CI95% = −0.02, 0.01, CSE = −.01).

Reverse models were run in which the proposed predictor variable (COVID-19 stress) and the proposed statistical mediator (sleep disturbance) were reversed and the criterion variables were kept the same (Preacher & Hayes, 2004). The indirect effect of the reverse model for number of substance classes used daily, number of alcoholic drinks per occasion, and substance use enhancement motives were not statistically significant (a*b = −0.01, SE = 0.01, CI95% = −0.02, 0.01, CSE = −.04., a*b = −0.01, SE = 0.03, CI95% = −0.07, 0.04, CSE = −.02, and a*b = 0.01, SE = 0.02, CI95% = −0.05, 0.05, CSE = .00, respectively). For substance use coping motives, the indirect effects of the reverse model was statistically significant (a*b = 0.06, SE = 0.03, CI95% = 0.01, 0.14, CSE = .05).

Models were also examined in which the proposed outcomes were set as the predictor variables, sleep disturbance as the statistical mediator and COVID-19 stress as the criterion variable in four separate models. Models were significant when number of substance classes used daily (a*b = 1.40, SE = 0.75, CI95% = 0.15, 3.11, CSE = .05), substance use coping motives (a*b = 0.29, SE = 0.16, CI95% = 0.02, 0.64, CSE = .07), and substance use enhancement motives (a*b = 0.19, SE = 0.11, CI95% = 0.03, 0.43, CSE = .07) were the predictor variables. Indirect effects were not evident with number of alcoholic drinks per occasion (a*b = −0.11, SE = 0.16, CI95% = −0.29, 0.33, CSE = − .02) as the predictor variable.

Discussion

The present investigation evaluated associations among COVID-19 stress, sleep disturbance, and substance use among a nationally recruited sample of adults recruited in the early months of the COVID-19 pandemic in 2020. Results were generally consistent with hypotheses. Specifically, COVID-19 stress was indirectly associated with (a) number of substance classes used daily and (b) substance use coping motives. Sleep disturbance accounted for the association between COVID-19 stress and substance use, such that greater stress was related to more severe sleep disturbance, which in turn, was associated with a greater number of (non-alcohol) substances used daily and greater tendency to use substances to cope with negative emotional states. Individuals experiencing COVID-19-related stress may use more substance classes daily to regulate negative emotional experiences, in part, due to experiencing increased sleep disruption. Given the cross-sectional design, directionality of relations cannot be inferred; the significant indirect effects demonstrate only that sleep disturbance accounted for statistical variance in the association between COVID-19 stress and substance use.

Contrary to hypothesis, COVID-19 stress was not indirectly associated with number of alcoholic drinks per occasion through sleep disturbance. This finding may be due to the nature of the alcohol use variable used in the present study, as we inquired about the “typical occasion” of alcohol use rather than daily use. The nature of the variable thus may obscure heavier drinking days or binge-drinking episodes. Furthermore, only 46.2% (n = 66) of participants in this sample reported using alcohol; the subsample of alcohol users reported consuming an average of 3.6 standard drinks (SD = 4.24) on any given occasion. It is thus possible that the indirect effects model would be more relevant to hazardous drinkers or clinical samples with alcohol use disorders.

As expected, COVID-19 stress was not indirectly associated with substance use enhancement motives through sleep disturbance, offering explanatory specificity of the a priori indirect effects model to coping-oriented use motives. Although COVID-19 stress and sleep disturbance were significantly associated with enhancement motives at the bivariate level (r’s = .61 and .48, respectively), sleep disturbance did not statistically explain the association between COVID-19 stress and enhancement motives.

Comparative analyses indicated that sleep disturbance was not indirectly related to number of substance classes used daily, number of alcohol drinks per occasion, nor enhancement motives for substance use through COVID-19 stress. Further, models demonstrated that sleep disturbance was significantly related to coping motives for substance use through COVID-19 stress. The present findings suggest that sleep disturbance may be indirectly related to coping motives for substance use through COVID-19 stress rather than frequency of use or enhancements motives for substance use per se. Relatedly, number of substance classes used daily as well as coping and enhancement motives for substance use were indirectly related to COVID-19 stress through sleep disturbance. Regular (daily) use of various substances and use to regulate mood (e.g., coping with negative mood, enhancing mood) can lead to sleep disturbance (e.g., Valentino & Volkow, 2020), which may lead to elevations in stress (Palmer & Alfano, 2017). These findings underscore the need for longitudinal or experimental designs to ascertain directionality of these relations. Indeed, these relations are likely to be bidirectional and transactional. Nevertheless, these findings suggest pertinent future directions to disentangle these commonly co-occurring associations more clearly, particularly during times of international crisis such as pandemics. This study builds upon work that has documented the role of sleep disturbance in the association between stress and substance use (e.g., Brower & Perron, 2010; Conroy & Arnedt, 2014; Marcks et al., 2010; Taylor et al., 2005) by extending that work to the examination of COVID-19 stress and mental health during the pandemic. Developing sleep interventions for individuals experiencing COVID-19 stress might thus be indicated to reduce substance use, pending further replication and extension of this work.

A few additional findings are worthy of brief discussion. First, approximately 66.4% of the sample met probable criteria for disturbed sleep per the suggested PSQI cut-off of 5 or greater, suggesting that sleep disturbances may be common during the pandemic. Second, while only 7% (n = 10) of the sample reported a positive COVID-19 diagnosis, a positive diagnosis was moderately, positively (r’s = .30 - .41) associated with all variables of interest, except for number of alcohol drinks per occasion. Furthermore, depressive symptom severity was associated with all criterion variables of interest, except for number of alcohol drinks per occasion, underscoring that depressive mood is related to COVID-19 stress (e.g., Ettman et al., 2020), sleep disturbance (e.g., Fang et al., 2019), substance use (e.g., Lai et al., 2015; Stewart et al., 2016) and coping motives for use (e.g., Vujanovic et al., 2017).

The study has several limitations. First, the study utilized a cross-sectional design and data were collected early in the COVID-19 pandemic (April 2020 through May 2020). Thus, causality and temporality cannot be assumed, and the associations of COVID-19 stress, sleep disturbance, and substance use at other time epochs during the pandemic may differ. Future work should include longitudinal designs to evaluate associations over time. Second, the study relied exclusively on self-report measures of the constructs of interest. Some of the self-report measures, such as those querying substance use and COVID-19 stress, were developed in the early stages of the pandemic, prior to the availability of alternative pandemic-related measures, to gather descriptive data. Further, issues of method variance and social desirability bias may have affected results. Future work should integrate interview-based measures of symptoms and objective indices of sleep (e.g., actigraphy) and substance use (e.g., urinalysis, cotinine saliva test) to examine the constructs of interest more rigorously. Relatedly, this study did not include measures of trauma exposure or potentially traumatic events experienced due to COVID-19, and therefore, the findings cannot be extended to PTSD symptoms. Third, the investigation assessed number of standard alcoholic drinks consumed on a ‘typical’ occasion, obscuring our ability to understand the daily or weekly fluctuations of alcohol use among this sample. Future work that integrates queries about frequency and intensity of alcohol use is necessary to examine whether the present findings are replicated. Fourth, the sample reported relatively low levels of COVID diagnosis (7%), exposure (11.9%) possible exposure (9.8%), or family exposure or diagnosis (5.6%), limiting generalizability to populations diagnosed with COVID-19 and those manifesting long-term symptoms. Fifth, the sample was comprised of community adults recruited via the Amazon MTurk platform and may not be representative of national or clinical samples of adults with SUD, PTSD, sleep disorders, or anxiety disorders. While utilizing the Amazon MTurk platform was conducive to national sampling, additional checks for data quality should be implemented in future work. For example, utilizing a combination of attention checks and human checks (e.g., captchas and open-ended questions) while setting a higher approval rate (e.g., >99%) might increase data quality. Finally, the sample was relatively small, predominantly White (66.4%), and reported obtaining at least a Bachelor’s degree or greater (72%). Future work should extend this work to racial/ethnic minority populations who report socioeconomic disadvantage and thus are likely to have endured a greater mental health burden related to the COVID-19 pandemic (e.g., Shim, 2020).

Overall, the study documents associations between COVID-19 stress and substance use during the pandemic and elucidates the significant role of sleep disturbance in that association. Future work is needed to understand the nature of these associations more conclusively to inform treatment programs relevant to this pandemic and future global disasters that address the role of sleep disruption in stress-substance use relations.

Acknowledgements

Research reported in this publication was supported by the National Institute on Minority Health and Health Disparities (NIMHD) of the National Institutes of Health (NIH) to the University of Houston under Award Number U54MD015946. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

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