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. Author manuscript; available in PMC: 2024 Dec 15.
Published in final edited form as: Alcohol Clin Exp Res (Hoboken). 2023 Dec 15;47(12):2354–2365. doi: 10.1111/acer.15209

Effects of a digital cognitive behavioral therapy for insomnia on sleep and alcohol consumption in heavy drinkers: A randomized pilot study

Justin J Verlinden 1, Mairead E Moloney 2, Olga A Vsevolozhskaya 3, Lee M Ritterband 4, Fiona Winkel 1, Jessica Weafer 5,*
PMCID: PMC10842053  NIHMSID: NIHMS1939051  PMID: 38099849

Abstract

Background:

Insomnia is a well-established, prospective risk factor for Alcohol Use Disorder (AUD). As such, targeting sleep problems could serve as a novel and efficacious means for reducing problematic drinking. Here we used Sleep Healthy Using the Internet (SHUTi), a well-validated, interactive, easy to use, and self-paced digital cognitive behavioral therapy for insomnia (d-CBT-I) program to examine the impact of treatment on drinking and sleep outcomes in a sample of heavy drinkers with insomnia in a randomized, single-blind, pilot study.

Methods:

Heavy drinking men (n = 28) and women (n = 42) with insomnia were randomly assigned to complete either the SHUTi program or a control patient education (PE) program. Subjective measures of sleep and alcohol use were administered at baseline, immediately following intervention completion, three-months post-intervention, and six-months post-intervention. Sleep outcomes were assessed using the Insomnia Severity Index (ISI) and Pittsburgh Sleep Quality Index (PSQI). Drinking outcomes were assessed using the 30-Day Timeline Follow-Back (TLFB) calendar.

Results:

Data from 70 subjects (SHUTI: n = 40; control: n = 30) were analyzed. Linear mixed effects models showed that SHUTi significantly reduced insomnia symptoms (p = 0.01) and drinking outcomes (ps < 0.05) relative to the control condition over time. Trend level effects on sleep quality (p = 0.06) were also observed. No adverse events were reported.

Conclusions:

Results suggest that sleep may be an effective treatment target to reduce hazardous drinking in at-risk individuals. Further, findings provide preliminary support for the implementation of an easily accessible health behavior intervention with significant public health impact in a high-risk population.

Keywords: AUD, treatment, SHUTi, Online, web

INTRODUCTION

Alcohol use disorder (AUD) and insomnia are highly comorbid, with an estimated 36–91% of individuals with AUD suffering from insomnia (Chakravorty et al., 2016). This relationship is proposed to be bidirectional, such that problematic alcohol use leads to sleep problems and sleep problems, in turn, lead to problematic alcohol use (Koob & Colrain, 2020). Regarding the latter, evidence from both adolescent (Hasler et al., 2014; Hasler et al., 2016) and adult samples (Breslau et al., 1996; Short et al., 2019) suggest that sleep problems are a risk factor for problem drinking. As such, improving sleep could serve as a novel and efficacious means for preventing and treating problematic drinking among individuals with insomnia (Koob & Colrain, 2020).

Cognitive behavioral therapy for insomnia (CBT-I) is currently the recommended first-line insomnia treatment (Siebern & Manber, 2011) and is highly effective (van Straten et al., 2018). CBT-I aims to address problematic thoughts and behaviors regarding sleep and is comprised of four main components: cognitive therapy, sleep hygiene, sleep restriction, and stimulus control (Brower, 2015). Digital CBT-I (d-CBT-I), or online/internet-based CBT-I, follows the same principles as traditional CBT-I, but allows the individual to complete treatment at their own pace and within their own residence. As such, d-CBT-I is a more convenient and accessible treatment option, and thus could be an appealing intervention for individuals with comorbid insomnia and AUD. Importantly, despite no one-on-one interaction with a clinician, d-CBT-I has been shown to treat insomnia with similar outcomes as traditional CBT-I (Hasan et al., 2022).

To our knowledge, two studies to date have examined CBT-I (in-person or online) in samples of active heavy drinkers. In a study of in-person CBT-I, Miller et al. (2021) randomly assigned young adult binge drinkers who met DSM criteria for insomnia disorder to either CBT-I or a control Sleep Hygiene program. The CBT-I group had significantly greater improvements in insomnia severity and sleep quality relative to the Sleep Hygiene control group following treatment, but no group differences were seen in drinking outcomes (Miller et al., 2021). In an analysis of d-CBT-I, Fucito et al. (2017) randomly assigned heavy-drinking college students with sleep concerns to either a web-based sleep intervention or control condition that provided general advice for good health, including tips for sleeping and drinking. Both groups showed significant improvements in sleeping and drinking outcomes compared to baseline. However, no significant differences were found between the two interventions (Fucito et al., 2017). Together, these studies highlight the promise of CBT-I for improving sleep and reducing alcohol consumption in heavy drinkers with insomnia. Further, they provide important suggestions for future interventions, including the need for less dense and more interactive programs (Fucito et al., 2017) and better engagement and study retention (Miller et al., 2018).

Sleep Healthy Using the Internet (SHUTi), a fully automated and tailored d-CBT-I program that incorporates a heavily individualized and user-friendly platform, serves as a highly promising candidate for treating comorbid AUD and insomnia (Brooks et al., 2018). SHUTi was developed by clinicians, is well validated (Ritterband et al., 2017), and has shown efficacy in treating insomnia across a range of populations and study designs (Ritterband et al., 2012; Ritterband et al., 2017; Luyster et al., 2018; Vedaa et al., 2020; Moloney et al., 2020b; Mattos et al., 2021; Zhou et al., 2022; Ritterband et al., 2022). Furthermore, SHUTi has been shown to improve mental health outcomes related to both insomnia and AUD, including depression and anxiety (Thorndike et al., 2013; Christensen et al., 2016; Moloney et al., 2020b). User feedback has been positive, with subjects reporting that it is enjoyable and easy to use (Thorndike et al., 2008; Moloney et al., 2020a).

In this single-blind, randomized pilot study, we tested the degree to which SHUTi reduced insomnia symptoms and decreased alcohol consumption in a sample of heavy drinkers with insomnia. This study adds to previous reports suggesting that d-CBT-I may be effective in reducing alcohol consumption in heavy drinkers by testing a personalized and interactive d-CBT-I intervention. We hypothesized that heavy drinkers with insomnia who received SHUTi would report improvements in their insomnia symptoms and sleep quality and decreased alcohol consumption relative to the control program.

MATERIALS & METHODS

Design

Heavy drinkers with insomnia were randomly assigned to either the SHUTi or control (patient education; PE) condition. Assignment was single-blind. Participants were told that they would be assigned to either an intervention condition or an education-only condition and were provided details on what each condition would entail during the 9-week intervention period. However, they were not explicitly told what group they had been assigned to until the end of the study. A parallel study design was utilized using a 1:1 allocation ratio. Outcome data, including sleep and alcohol consumption measures, were collected online at four assessment points: baseline, post-treatment, 3-months post-treatment, and 6-months post-treatment. The Institutional Review Board of the University of Kentucky approved the study, and it was carried out in accordance with the Declaration of Helsinki. All participants gave informed consent and were compensated for their time. This study is registered at clinicaltrials.gov (ID: NCT04564807). A full protocol of the study can be provided upon request to the corresponding author.

Participants

Heavy drinking men and women with insomnia (n=71) were recruited through advertisements posted throughout the community and on social media. Heavy drinking was defined as having an Alcohol Use Disorders Identification Test (AUDIT) score >7 and self-reported weekly binge episodes (4+/5+ drinks in one sitting for women/men) on the Timeline Follow-Back (TLFB). Volunteers met study criteria for insomnia if they self-reported insomnia for three or more nights a week for the past three months and had an Insomnia Severity Index (ISI) score >14. Additional inclusion criteria were: fluency in English, regular access to the internet, and age range of 21–50. The minimum age of 21 was selected to exclude individuals younger than the legal drinking age and the maximum age of 50 was chosen to avoid the high rates of sleep disorders in post-menopausal women (Dancey et al., 2001; Polo-Kantola, 2008). Volunteers were excluded if they self-reported a previous diagnosis of AUD or substance use disorder, schizophrenia, bipolar disorder, other psychotic spectrum disorders, or sleep apnea. Additional exclusion criteria were: pregnancy, lactating, or plans to become pregnant in the next three months. Participants were not excluded for using sleeping aids.

Sample Size Determination.

We conducted a power analysis based on a previous study testing the effects of SHUTi in a single-arm design (Moloney et al., 2020b). A paired samples t-test of the pre- and post-ISI scores suggested a large effect size of SHUTi on sleep (d = 1.4). Assuming a similar effect on sleep and a moderate effect (d = 0.6) on alcohol consumption in the current sample, a sample size of n=24 in the SHUTi group was calculated to provide 80% power (alpha=0.05) to detect an effect of SHUTi on both sleep and drinking.

Procedure

All screening and study procedures took place virtually and did not involve any in-person laboratory visits. Individuals interested in participating in the study completed an online pre-screen to determine eligibility for the study. Individuals who met eligibility criteria were emailed an invitation to the study along with a link to the consent form and baseline surveys using REDCap electronic data capture tools hosted at the University of Kentucky (Harris et al., 2009; Harris et al., 2019). Baseline surveys took approximately 30 minutes to complete and included measures of insomnia severity (ISI), sleep quality (PSQI), alcohol use (AUDIT), stress (Perceived Stress Scale), and symptoms of depression (Center for Epidemiologic Studies Depression Scale). Subjects then completed a 60-minute phone interview to assess the social and environmental contexts of their past and present drinking and sleeping habits (data not reported here). Following the phone interview, a 30-day Timeline Follow-Back (TLFB) was administered to provide a detailed assessment of past-month alcohol consumption. Participants were then randomly assigned by study personnel to one of the conditions. Randomization was done using a predetermined allocation using randomizer.org. Recruitment continued until a final sample size of at least n=24 completed participants in both conditions was reached. After randomization (single-blind), participants were given access to their assigned condition. Participants in both conditions were required to fill out daily sleep diaries for 10 days within a 14-day period (data not reported here) before beginning their respective intervention.

SHUTi (Treatment Condition)

The SHUTi program is fully online, automated, interactive, and provides individualized feedback based on participants’ sleep data and other inputted information. Participants complete six cores: an overview of insomnia (“Getting Ready”), sleep restriction (“Sleep Scheduling”), stimulus control (“Sleep Practices”), cognitive restructuring (“Thinking Differently”), sleep hygiene (“Sleep Hygiene”), and relapse prevention (“Moving On”). The cores provide interactive educational and interventional content, including didactic games, quizzes, and video testimonials, based on the main pillars of CBT-I. Personalized components include sleep windows (described below), symptoms checklists, homework assignments, and goal-setting. Goal-setting exercises occur at the study start, where participants are asked to set goals for various sleep aspects (e.g., time to fall asleep, number of nighttime awakenings, etc.). At study completion participants revisit their goals, assess their progress, and are invited to work toward longer-term goals using SHUTi knowledge and tools for the remainder of their access period (one year after enrollment). Each core requires approximately 30–45 minutes to complete. Following the completion of the first core, participants must log at least five sleep diaries within the previous seven days to proceed. Sleep diaries are used to calculate sleep efficiency and suggest a personalized sleep window. These assigned bedtimes and waketimes are unique for each participant and form the basis of sleep restriction, which works by strengthening the homeostatic sleep drive, resulting in increased sleep efficiency. Sleep diaries can be completed between subsequent cores to further tailor the program, but these are not mandatory to progress through the program. For a more detailed description of the SHUTi program, see Thorndike et al. (2008). Participants had nine weeks to complete all six cores of the program. If subjects were close to completing the program after nine weeks, they were given additional time to finish. Progress (i.e., completion of sleep diaries and cores) was monitored by study personnel and email reminders were sent to subjects (in addition to daily reminder emails automatically sent by SHUTi) if subjects began to fall behind.

Online Patient Education (Control Condition)

The online PE program, the control condition that has been consistently used in previous clinical trials of SHUTi (Ritterband et al., 2017; Vedaa et al., 2020), provides access to good quality, but non-interactive and fixed content pertaining to insomnia, including the causes and consequences of insomnia and when to seek help. Over the course of nine weeks, those in the control group could access these educational materials as often as they pleased. They were not required to fill out sleep diaries during the intervention period. For more details, see Ritterband et al. (2017).

Post-Intervention

Following the intervention period, participants in both conditions completed 10 additional sleep diaries. They then completed the same online sleep, drinking, and mood surveys completed at baseline, as well as a second TLFB calendar. Finally, they completed a second phone interview to review any changes to their sleeping/drinking habits since their first phone interview and to provide feedback on the SHUTi program (data not presented here).

3-Month and 6-Month Follow-Ups

Participants completed follow-ups at 3- and 6-months post-intervention. They completed the sleep, alcohol, and mood surveys, including the TLFB, at both follow-ups. Participants also completed 10 final sleep diaries at the 6-month follow-up.

Measures

Insomnia Severity Index (ISI)

This 7-item self-report questionnaire (Bastien et al., 2001) assesses severity of insomnia. Each item requires participants to rate the severity of their symptoms on a Likert scale from 0 to 4. Scores range from 0 to 28 and are interpreted as follows: 0–7 - no clinically significant insomnia; 8–14 - subthreshold insomnia; 15–21 - moderately severe clinical insomnia; 22–28 - severe clinical insomnia. To be eligible for the current study, participants were required to score in the moderately severe to severe clinical insomnia range (>14).

Pittsburgh Sleep Quality Index (PSQI)

The PSQI is a 19-item self-report measure (Buysse et al., 1989) that provides a general index of sleep quality and sleep disturbances. Although not specifically a measure of insomnia, scores >5 indicate poor sleep.

Alcohol Use Disorders Identification Test (AUDIT)

This 10-item self-report measure (Babor et al., 1989) assesses alcohol consumption and alcohol-related problems with scores ranging from 0 (no-alcohol related problems) to 40 (most severe alcohol-related problems). A score > 7 typically indicates hazardous drinking. To be eligible for the current study, participants were required to score in the hazardous drinking range.

Timeline Follow-back (TLFB)

The TLFB (Sobell & Sobell, 1992) assesses daily patterns of alcohol consumption over the past 30 days, including the number of drinks consumed each day. The measure uses “anchor points” to structure and facilitate participants’ recall of past drinking episodes to provide a more accurate retrospective account of alcohol use during that time period. The main outcome measures included in these analyses are total number of drinks (TD), total number of drinking days (DD), and total number of heavy drinking days (4+/5+ drinks in a single day for women/men; HDD).

Perceived Stress Scale (PSS)

The PSS (Cohen et al., 1983) is a 10-item self-report measure used to gauge an individual’s global stress levels by asking non-specific questions about how overloaded, uncontrollable, or unpredictable one’s life has been in the past month. All items are scored on a scale of 0 to 4, with higher scores indicating greater psychological distress.

Center for Epidemiologic Studies Depression Scale Revised 10-Item Version (CESDR-10)

The CESDR-10 (Andresen et al., 1994) is a 10-item self-report measure that gauges a subject’s symptoms of depression. Each item is scored on a scale of 0 to 3, with higher scores indicating more severe symptoms.

SHUTi Satisfaction Scale

Participants completed a Likert scale survey (Moloney et al., 2020b) on their adherence, acceptability, and satisfaction with the treatment program (SHUTi or PE). Those in the SHUTi group completed additional questions to determine what elements of SHUTi were perceived to be the most helpful.

Baseline Sleep Diaries

Participants completed 10 sleep diaries within a 14-day period at baseline. These diaries consisted of 10 questions regarding the participant’s sleep, use of alcohol, and use of sleep aids the previous night. Diaries provided baseline measures of sleep onset latency, wake after sleep onset, total sleep time, total time spent in bed, sleep efficiency, subjective sleep quality, and use of sleep medication (Table 1).

Table 1.

Demographic and baseline measures for the SHUTi and control conditions

SHUTi (n=40) Control (n=30) Contrasts
Sex (M/F) 16/24 12/18 ns
Race ns
 American Indian or Alaska Native 1 0
 Black or African American 2 2
 White 32 25
 More than one race 5 3
Ethnicity ns
 Hispanic or Latino 1 2
 Not Hispanic or Latino 38 27
 Unknown 1 1
Lifetime Substance Use
 Amphetamine 10% 16.7% ns
 Cannabis 65% 80% ns
 Cocaine 12.5% 23.3% ns
 Hallucinogens 2.5% 13.3% ns
 Inhalents 2.5% 0% ns
 Opioids 2.5% 6.7% ns
 Sedatives (recreational use) 7.5% 6.7% ns
Age 26.1 (6.1) 24.3 (3.5) ns
Highest Level of Education* ns
 High School 2 2
 Some College 5 5
 Bachelor’s Degree 13 5
 Postgraduate Degree 2 3
ISI 19.1 (3.3) 19.7 (3.0) ns
PSQI 14.2 (2.9) 14.3 (2.6) ns
Sleep Diary Data
 Sleep Onset Latency (min) 54.7 (44.9) 54.3 (29.7) ns
 Wake After Sleep Onset (min) 28.1 (27.3) 23.0 (20.0) ns
 Total Sleep Time (min) 414.7 (74.3) 428.4 (68.1) ns
 Total Time in Bed (min) 497.4 (65.1) 502.5 (67.6) ns
 Sleep Efficiency (%) 83.0 (9.8) 84.3 (8.2) ns
 Subjective Sleep Quality (diaries) 1.9 (0.6) 1.8 (0.5) ns
 Sleep Aid Use (%) 45.9 53.3 ns
PSS 21.9 (6.1) 23.8 (6.4) ns
CESD-R 14.8 (6.7) 15.8 (5.6) ns
AUDIT 17.7 (5.6) 18.0 (7.5) ns
TLFB Total Drinks 108.8 (57.1) 88.9 (42.0) ns
TLFB Drinking Days 17.8 (6.0) 15.7 (7.2) ns
TLFB Heavy Drinking Days 10.4 (4.8) 10.4 (4.6) ns
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*

Participants’ level of education was not reported until midway through study, resulting in missing data from 33 participants. Note: Group contrasts tested by independent samples t-tests (Age, ISI, PSQI, PSS, CESD-R, AUDIT, TLFB measures, and all Sleep Diary measures except for sleep aid use) and chi square tests (sex, race, ethnicity, lifetime substance use, education, and sleep aid use). ISI = Insomnia Severity Index; PSS = Perceived Stress Scale; CESD-R = Center for Epidemiologic Studies Depression Scale Revised 10-Item Version; AUDIT = Alcohol Use Disorders Identification Test; TLFB = 30-day Timeline Follow-back. SOL, WASO, TST, TIB, SE, subjective sleep quality, and sleep aid use all derived from baseline sleep diaries. Subjective sleep quality based on scale of 0–4 with lower scores indicating worse sleep quality. Sleep aid use was defined as having reported using a sleep aid at least once during baseline diary period.

Analyses

All statistical analyses were performed using IBM SPSS Statistics for MacOS, Version 28.0. Group differences were tested across condition (SHUTi vs control) in demographic and baseline measures using between group t-tests and chi-square tests. We used an intention-to-treat approach as recommended by Witkiewitz et al. (2015) for randomized controlled trials of alcohol treatments, such that all participants who were enrolled in either intervention were included in analyses, regardless of their completion status. Separate linear mixed effects models for repeated measures (Hedeker & Gibbons, 2006) were conducted to determine the degree to which condition (SHUTi vs. control) interacted with timepoint (baseline, post-intervention, 3-month follow-up, and 6-month follow-up) to predict sleep (ISI and PSQI) and drinking (30-day TLFB TD, DD, and HDD) outcomes. These models were chosen because they are robust to missing data and account for nesting within data. In each model, timepoints (level 1) were nested within participant (level 2) and included random subject intercepts. PSS and CESD-R scores were grand mean centered and included as covariates to control for the well-established effects of stress and depression on sleep and drinking (Tsuno et al., 2005; Kim & Dimsdale, 2007; Breese et al., 2011; McHugh & Weiss, 2019) and because of previous evidence suggesting that SHUTi may lead to improvements in depression and global stress levels (Thorndike et al., 2013; Christensen et al., 2016; Moloney et al., 2020b). Sensitivity analyses were conducted with these covariates excluded from the models. SHUTi and baseline assessments were coded as the reference conditions for condition and timepoint, respectively. The effects of interest were the condition x timepoint interactions for each outcome measure. Significant interactions were probed by: 1) conducting between group t-tests at each timepoint and 2) analyzing main effects of timepoint separately for each group. We determined the main effect of timepoint for the control condition by re-running the model with the control group coded as the reference. Significance for all comparisons was set at a 2-sided alpha level of 0.05.

Unconditional models were used to determine intraclass correlation coefficients (ICCs) for each sleep and drinking outcome measure. For sleep outcome measures, 10% of variance for insomnia severity and 16% of variance for global sleep quality occurred between individuals. For drinking measures, 72% of variance for total drinks, 62% of variance for drinking days, and 67% of variance for heavy drinking days occurred between individuals. The remaining variance for each outcome measure occurred within individuals across time.

RESULTS

Participants

Participants were recruited between September 2020 and September 2021. As outlined in Figure 1, 233 volunteers were deemed eligible based on their responses from the pre-screen. Of these, 133 consented to participate. 44 were eliminated after either not completing the phone interview or not completing the TLFB. Following screening, 18 additional participants were excluded for not meeting the following criteria [ISI scores <15 (n=6); AUDIT scores <8 (n=6); less than weekly binge episodes (n=5); and sleep apnea (n=1)]. The remaining 71 participants completed the phone interview and TLFB and were confirmed to be eligible. Participants were randomly assigned to either the SHUTi (n = 41) or PE control condition (n = 30). Of these, n=28 in the SHUTi group and n=27 in the control group completed at least one of the post-intervention questionnaires.

Figure 1.

Figure 1.

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Consolidated Standards of Reporting Trials (CONSORT) flow chart of study enrollment.

Demographic and baseline data are provided separately by condition in Table 1. One participant in the SHUTi condition was identified as an outlier (TLFB measures > 3 standard deviations above the mean at baseline and the 3- and 6-month follow-ups) and removed from subsequent analyses, resulting in a final sample size of n=40 in the SHUTi condition and n=30 in the control condition (age range 21–44). Sensitivity analyses containing the outlier are reported below. The groups did not differ in terms of demographics or on any of the baseline sleep, alcohol, stress, or mood measures. Both groups contained a higher percentage of women, were relatively young, were well above the threshold for moderately severe clinical insomnia (ISI > 14), reported poor sleep quality (PSQI > 5), had PSS scores indicative of moderate stress, did not have clinically significant symptoms of depression (CESD-R < 16), and had mean AUDIT scores suggestive of moderate to severe AUD (AUDIT > 14). Neither group reported any adverse events during the study.

Effect of SHUTi on Sleep

Linear mixed effects models showed a significant condition × timepoint interaction for the ISI (p = 0.011), and a trend level effect for the PSQI (p = 0.060) (Table 2). Figure 2 and Table S1 show that individuals in the SHUTI and control conditions did not differ in ISI or PSQI scores at baseline (between group t-tests - ISI: t = 0.72, p = 0.475, 95% CI [−2.17, 1.02]; PSQI: t = 0.15, p = 0.882, 95% CI [−1.44, 1.24]). As hypothesized, however, those in the SHUTi condition reported significantly lower ISI and PSQI scores than those in the control condition at post-intervention (ISI: t = 3.50, p < 0.001, 95% CI [−6.70, −1.82]; PSQI: t = 3.06, p = 0.004, 95% CI [−4.96, −1.03]) and at the 3-month follow-up (ISI: t = 3.51, p = 0.001, 95% CI [−7.23, −1.96]; PSQI: t = 3.52, p < 0.001, 95% CI [−5.72, −1.56]). At the 6-month follow-up, the SHUTi condition sustained significantly lower ISI scores compared to controls (t = 2.44, p = 0.005, 95% CI [−6.61, −1.23]). However, the groups no longer differed in PSQI scores (t = 1.43, p = 0.159, 95% CI [−3.85, 0.65]), due to a decrease in PSQI scores in the control group. Additionally, both conditions showed significantly improved sleep from baseline across timepoint, although the effect was larger in the SHUTi condition [SHUTi (Table 2): main effects of timepoint for ISI (t = 9.24, p < 0.001, 95% CI [−3.71, −2.41]) and PSQI (t = 7.75, p < 0.001, 95% CI [−2.44, −1.45]); Control: main effects of timepoint for ISI (t = 5.27, p < 0.001, 95% CI [−2.57, −1.17]) and PSQI (t = 4.78, p < 0.001, 95% CI [−1.81, −0.75])].

Table 2.

Linear mixed effects models testing effects of condition and timepoint on sleep

Estimate SE |t| p
ISI
 Intercept 16.12 0.62 26.11 <0.001
 PSS 0.05 0.07 0.68 0.495
 CESDR 0.37 0.08 4.86 <0.001
 Timepoint −3.06 0.33 9.24 <0.001
 Group 0.61 0.92 0.66 0.508
 Timepoint x Group 1.19 0.46 2.56 0.011
PSQI
 Intercept 11.96 0.50 24.14 <0.001
 PSS 0.04 0.05 0.75 0.455
 CESDR 0.28 0.06 4.78 <0.001
 Timepoint −1.94 0.25 7.75 <0.001
 Group 0.42 0.74 0.56 0.575
 Timepoint x Group 0.66 0.35 1.90 0.060
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Note: Bold font indicates significant effects (p ≤ 0.05).

Figure 2.

Figure 2.

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Mean ratings on the Insomnia Severity Index (ISI) and Pittsburgh Sleep Quality Index (PSQI) by condition at baseline, post-intervention, and at the 3-month and 6-month follow-ups. ISI and PSQI scores were improved in both the SHUTi and control conditions (main effect of timepoint; ps < 0.001). Further, ISI scores were significantly lower in the SHUTi compared to control condition at each post-intervention and follow-up assessment (ps < 0.01). PSQI scores were significantly lower in the SHUTi compared to control condition at the post-intervention and 3-months follow-up assessment (ps < 0.01). Horizontal bars reflect significance of the main effect of timepoint for each group. Horizontal bars with vertical tick marks reflect group differences at individual timepoints, assessed by between-groups t-tests. ***: p ≤ 0.001; **: p < 0.01.

The percentage of participants in each group who met study criteria for insomnia (ISI scores > 14) were determined at each post-intervention timepoint. In the SHUTi condition, 1 participant met criteria for insomnia at post-intervention, 2 met criteria at the 3-month follow-up, and 1 met criteria at the 6-month follow-up. By contrast, in the control condition, 9 participants met criteria at post-intervention, 7 at the 3-month follow-up, and 6 at the 6-month follow-up.

Effect of SHUTi on Drinking

Linear mixed effects models showed significant condition × timepoint interactions for 30-day TLFB TD and DD (ps ≤ 0.031) and a trend level effect for HDD (p = .053) (Table 3). Figure 3 shows a more pronounced decrease in alcohol consumption measures across timepoint in the SHUTi condition compared to the control condition. This was confirmed by significant decreases in alcohol consumption across timepoint for all drinking measures in the SHUTi condition [Table 3; main effects of time for TD (t = 5.47, p < 0.001, 95% CI [−16.70, −7.84]), DD (t = 4.89, p < 0.001, 95% CI [−2.65, −1.12]), and HDD (t = 4.70, p < 0.001, 95% CI [−1.75, −0.71])]. By contrast, a significant main effect of time was observed for TD in the control condition (t = 2.01, p = 0.047, 95% CI [−9.38, −0.07]), but not for DD (t = 1.85, p = 0.066, 95% CI [−1.55, 0.05]) or HDD (t = 1.97, p = 0.051, 95% CI [−1.09, 0.00])]. Follow-up t-tests showed no significant differences between the groups on any of the drinking measures at any timepoint (ps ≥ 0.100; Table S2).

Table 3.

Linear mixed effects models testing effects of condition and timepoint on drinking

Estimate SE |t| p
Total Drinks
 Intercept 95.90 6.65 14.43 <0.001
 PSS 0.43 0.51 0.85 0.396
 CESD-R 1.33 0.59 2.24 0.026
 Timepoint −12.27 2.24 5.47 <0.001
 Group −17.30 10.03 1.72 0.089
 Timepoint x Group 7.55 3.02 2.50 0.014
Drinking Days
 Intercept 17.04 1.06 16.04 <0.001
 PSS 0.04 0.09 0.45 0.652
 CESD-R 0.13 0.10 1.31 0.191
 Timepoint −1.88 0.39 4.89 <0.001
 Group −2.01 1.60 1.26 0.212
 Timepoint x Group 1.13 0.52 2.18 0.031
Heavy Drinking Days
 Intercept 9.41 0.74 12.66 <0.001
 PSS 0.06 0.06 1.00 0.320
 CESD-R 0.13 0.07 1.94 0.054
 Timepoint −1.23 0.26 4.70 <0.001
 Group −0.18 1.12 0.16 0.870
 Timepoint x Group 0.69 0.35 1.95 0.053
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Note: Bold font indicates significant effects (p ≤ 0.05).

Figure 3.

Figure 3.

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Mean ratings of past 30-day Timeline Follow-back (TLFB) measures [total number of heavy drinking days (HDD); total number of drinking days (DD); and total number of drinks (TD)] by condition at baseline, post-intervention, and at the 3-month and 6-month follow-ups. Alcohol consumption was significantly reduced on all measures in the SHUTi condition (main effect of timepoint; ps < 0.001). By contrast, only TD (main effect of timepoint; p = 0.047) was significantly reduced in the control condition. Horizontal bars reflect significance of the main effect of timepoint for each group. ***: p ≤ 0.001; *: p < 0.05.

Sensitivity Analyses

As previously mentioned, one participant in the SHUTi condition was an outlier (drinking levels > 3 SD from the mean at baseline and the 3-month and 6-month follow-up timepoints). Sensitivity analyses which included the participant were conducted for all models using the same statistical analyses previously outlined. Condition × timepoint interactions for ISI, PSQI, TD, and HDD were not impacted by the inclusion of the outlier. However, the condition x timepoint interaction for DD became trend level (p = 0.064) when the outlier was included in the sample.

Sensitivity analyses which excluded the covariates of PSS and CESDR were also conducted for all models using the same statistical analyses previously described. Condition × timepoint interactions were unchanged in all of the models.

Use, Adherence, & Feedback

During the 9-week intervention period, individuals enrolled in the SHUTi condition logged in between 0 and 46 times, with a median login count of 26. Ten individuals took longer than 63 days to complete the program (range of additional days: 2 – 35, median = 14), during which time they logged in between 1 and 25 times (median = 7.5). A total of 68.3% (28 of 41) in the SHUTi condition completed all 6 cores of the program. In contrast, 17.1% (7 of 41) did not complete any of the cores, 4.9% (2 of 41) completed through the first core, 4.9% (2 of 41) completed through the third core, 2.4% (1 of 41) completed through the fourth core, and 2.4% (1 of 41) completed through the fifth core.

Results of user feedback are shown in Table S3. With the exception of self-esteem, a greater percentage of the SHUTi group reported that their treatment made aspects of their life “somewhat” or “a lot better” compared to the control group, although insomnia symptoms were the only aspect which reached statistical significance (between group t-test; t = 2.84, p = 0.006, 95% CI [0.17, 0.96]). Table S4 shows that the majority of participants in the SHUTi condition reported adhering to all major strategies and techniques consistently or most of the time. Additionally, the majority of participants reported all but one of the major strategies and techniques given by SHUTi to be very or moderately helpful. No adverse events or harms were reported.

DISCUSSION

This pilot study assessed the degree to which a well-validated d-CBT-I program improved insomnia symptoms and drinking outcomes in a sample of heavy drinkers with insomnia. We replicated previous studies showing that SHUTi reduces symptoms of insomnia (Ritterband et al., 2012; Ritterband et al., 2017; Luyster et al., 2018; Vedaa et al., 2020; Moloney et al., 2020b; Mattos et al., 2021; Zhou et al., 2022; Ritterband et al., 2022) and extended these findings to demonstrate that SHUTi is effective among a novel population of heavy drinkers. Moreover, we showed for the first time that d-CBT-I can lead to significant reductions in alcohol consumption among heavy drinkers with insomnia. These findings build on a growing body of work considering impaired sleep as a target for alcohol reduction interventions. This has important implications for prevention and treatment strategies geared towards addressing problematic drinking.

SHUTi is the first CBT-I intervention to result in reductions in alcohol consumption among active heavy drinkers with insomnia. Although previous CBT-I studies have demonstrated some efficacy in improving sleep and reducing drinking in this population (Fucito et al., 2017; Miller et al., 2021), SHUTi is the first to demonstrate significant reductions in alcohol consumption over and above control conditions. This is especially noteworthy given that the control condition in this study was not a wait-list or no treatment control, but instead involved patient education that was expected to at least partially improve sleep. One possible explanation for the discrepancies in findings is differences in quantity and frequency of alcohol consumption across samples. In the current study, all participants were required to engage in weekly binge episodes, resulting in a sample with a higher frequency of binge drinking than in previous samples. Additionally, it could be that the highly interactive and personalized nature of SHUTi, including the use of daily diaries, interactive sleep cores, and individualized feedback, led to the significant effects observed here that were not seen in previous d-CBT-I studies. Indeed, according to user feedback from this sample of predominately young adults, the interactive and online nature of the SHUTi platform encouraged and resulted in participant engagement, adherence, and satisfaction.

These findings have several important clinical implications. First, the encouraging effects of SHUTi on reducing alcohol consumption lend further credence to the idea that sleep can be an intervention target for AUD. Given that poor sleep is an established risk factor for problematic drinking in both adolescents (Hasler et al., 2014; Hasler et al., 2016) and adults (Breslau et al., 1996; Short et al., 2019), our results suggest that SHUTi could be an efficacious intervention to slow down or prevent the progression of AUD in these at-risk individuals. Second, d-CBT-I has the potential to reach individuals with AUD who otherwise would not seek treatment. Several factors contribute to low rates of treatment-seeking among individuals with AUD (Cohen et al., 2007), including lack of access, stigma, and a desire to not quit drinking entirely (Keyes et al., 2010; Park-Lee et al., 2016; Priester et al., 2016). d-CBT-I may act as a more convenient alternative that allows heavy drinkers, particularly the large percentage of heavy drinkers who suffer from comorbid insomnia, to improve their sleep, and, as our study suggests, reduce their drinking without engaging in total abstinence. Furthermore, the ability to complete d-CBT-I within one’s own residence and without seeking a clinical encounter may help reduce any fears of stigma stemming from seeking treatment. Finally, d-CBT-I could be an especially attractive treatment option for women with AUD, as women are more likely to suffer from insomnia (Zhang & Wing, 2006; Zhang et al., 2016), show a stronger association between poor sleep quality and alcohol-related problems (Verlinden et al., 2022), and they are more likely to seek treatment in non-alcohol specific settings (Erol & Karpyak, 2015).

This study had several limitations. First, only subjective measures of drinking and sleep were used. Although all measures are well-validated and widely used, they remain vulnerable to self-report bias. Second, this study did not collect detailed information regarding caregiving and marriage status, education, or sleep aid use despite their possible confounding effects. Third, sleep apnea diagnosis was based on self-report, meaning some individuals in our sample may have been at high risk of obstructive sleep apnea, a sleep disorder that SHUTi is not designed to address. Finally, this study was underpowered to assess how treatment efficacy may differ across a variety of subject variables, including sex, age, and circadian preferences. These limitations can be addressed in future studies that include larger and more diverse sample sizes, objective measures of alcohol consumption and sleep (e.g., wrist alcohol biosensors and actigraphy watches), and detailed measures of caregiving and marriage status, circadian rhythms, education, recreational drug use, sleep aid use, and sleep apnea.

In conclusion, the SHUTi program, an online, automated version of CBT-I, significantly improved insomnia symptoms and drinking outcomes compared to controls in a sample of heavy drinkers with insomnia. These findings are particularly novel and support the hypothesis that an intervention focused on improving sleep can positively impact drinking behaviors. This line of research has the potential to inform innovative and highly efficacious self-administered interventions for reducing alcohol consumption among heavy drinkers with insomnia. As such, these findings support the implementation of an easily accessible health behavior intervention with significant public health impact in a high-risk population.

Supplementary Material

Supinfo

Acknowledgements & Funding:

This research was supported by University of Kentucky Substance Use Priority Research Area (SUPRA) Pilot Award Funds (MPIs JW and MM) and National Institute on Alcohol Abuse and Alcoholism grants T32 AA027488, R21 AA029201 (MPIs JW and MM) and R01 AA028503 (JW). All authors declare no conflict of interest.

Footnotes

ClinicalTrials.gov Identifier: NCT04564807; Registered 9/22/2020

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