Perceptions of Heavy-Drinking College Students About a Sleep and Alcohol Health Intervention

This art icle was downloaded by: [ Yale Universit y Library] , [ Kelly DeMart ini] On: 18 June 2014, At : 11: 47 Publisher: Rout ledge I nform a Lt d Regist ered in England and Wales Regist ered Num ber: 1072954 Regist ered office: Mort im er House, 37- 41 Mort im er St reet , London W1T 3JH, UK Behavioral Sleep Medicine Publicat ion det ails, including inst ruct ions f or aut hors and subscript ion inf ormat ion: ht t p: / / www. t andf online. com/ loi/ hbsm20 Perceptions of Heavy-Drinking College Students About a Sleep and Alcohol Health Intervention a a a Lisa M. Fucit o , Kelly S. DeMart ini , Tess H. Hanrahan , Robin b c Whit t emore , H. Klar Yaggi & Nancy S. Redeker a a Depart ment of Psychiat ry Yale School of Medicine b Yale School of Nursing c Division of Pulmonary and Crit ical Care Medicine Yale School of Medicine Published online: 12 Jun 2014. To cite this article: Lisa M. Fucit o, Kelly S. DeMart ini, Tess H. Hanrahan, Robin Whit t emore, H. Klar Yaggi & Nancy S. Redeker (2014): Percept ions of Heavy-Drinking College St udent s About a Sleep and Alcohol Healt h Int ervent ion, Behavioral Sleep Medicine, DOI: 10. 1080/ 15402002. 2014. 919919 To link to this article: ht t p: / / dx. doi. org/ 10. 1080/ 15402002. 2014. 919919 PLEASE SCROLL DOWN FOR ARTI CLE Taylor & Francis m akes every effort t o ensure t he accuracy of all t he inform at ion ( t he “ Cont ent ” ) cont ained in t he publicat ions on our plat form . However, Taylor & Francis, our agent s, and our licensors m ake no represent at ions or warrant ies what soever as t o t he accuracy, com plet eness, or suit abilit y for any purpose of t he Cont ent . Any opinions and views expressed in t his publicat ion are t he opinions and views of t he aut hors, and are not t he views of or endorsed by Taylor & Francis. The accuracy of t he Cont ent should not be relied upon and should be independent ly verified wit h prim ary sources of inform at ion. 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Term s & Condit ions of access and use can be found at ht t p: / / www.t andfonline.com / page/ t erm sand- condit ions Downloaded by [Yale University Library], [Kelly DeMartini] at 11:47 18 June 2014 Behavioral Sleep Medicine, 12:1–17, 2014 Copyright © Taylor & Francis Group, LLC ISSN: 1540-2002 print/1540-2010 online DOI: 10.1080/15402002.2014.919919 Perceptions of Heavy-Drinking College Students About a Sleep and Alcohol Health Intervention Lisa M. Fucito, Kelly S. DeMartini, and Tess H. Hanrahan Department of Psychiatry Yale School of Medicine Robin Whittemore Yale School of Nursing H. Klar Yaggi Division of Pulmonary and Critical Care Medicine Yale School of Medicine Nancy S. Redeker Yale School of Nursing The purpose of this mixed methods study was to describe the sleep and psychological characteristics of heavy-drinking college students, their perceptions of sleep and sleep/alcohol interactions, and their reactions to a proposed integrated sleep and alcohol Web-based intervention. Students (N D 24) completed standardized surveys and participated in semistructured focus group interviews. Participants reported a high degree of sleep disturbance, sleep obstacles, and sleep-related consequences, which were validated by both quantitative and qualitative investigations. Sleep disturbance and sleep-related impairment were associated with more frequent drinking and greater risks from drinking. Participants perceived that alcohol has positive and negative effects on sleep latency, continuity, and quality. They expressed overall enthusiasm for the intervention but had specific content and format preferences. Sleep disturbance and heavy drinking are among the most common problems that college students in the United States report (Blanco et al., 2008; Gaultney, 2011). An estimated 70% of college students report disturbances in sleep including short sleep duration and excessive daytime sleepiness, irregular sleep schedules (i.e., inconsistent bed/wake times), and poor selfreported sleep quality (Lund, Reider, Whiting, & Prichard, 2010). More specifically, college students sleep an average of 7 hours per night (Lund et al., 2010), below recommended Correspondence should be addressed to Lisa M. Fucito, Yale School of Medicine, Department of Psychiatry, 1 Long Wharf Drive, Box 18, New Haven, CT 06511. E-mail: lisa.fucito@yale.edu 1 Downloaded by [Yale University Library], [Kelly DeMartini] at 11:47 18 June 2014 2 FUCITO ET AL. guidelines for adolescents (i.e., 8.5–9.25 hours; Bonnet & Arand, 2011), and approximately 20% report staying up all night at least monthly (Lund et al., 2010). Further, the sleep schedules of college students are highly variable. Many college students prioritize academic and social commitments over sleep during the week, and then sleep longer on the weekend. This variability in sleep scheduling puts them at increased risk for delayed sleep phase disorder (Kloss, Nash, Horsey, & Taylor, 2011). Heavy drinking in college is nearly as ubiquitous as sleep disturbance. Nearly half of all college students report heavy, episodic drinking (i.e.,  5 drinks on 1 occasion for men,  4 for women; SAMSHA, 2006). College students also have elevated rates of alcohol use disorders (i.e., roughly 20%; Blanco et al., 2008; SAMSHA, 2006), the peak onset of which occurs during young adulthood (Grant et al., 2004). Sleep disturbance and heavy drinking may function as a negative feedback loop. Alcohol consumed 30–60 minutes before bedtime reduces sleep onset latency and consolidates sleep during the first half of the night at both high and low doses but fragments sleep during the second half, particularly at high doses (Ebrahim, Shapiro, Williams, & Fenwick, 2013; Roehrs & Roth, 2001). At drinking levels frequently reported by young adults (i.e., 0.75 mg/kg, which is greater than 4 standard drinks), alcohol also decreases REM sleep and REM sleep onset (Ebrahim et al., 2013). Moreover, higher alcohol use among college students is associated with lower sleep duration, greater sleep schedule irregularity, bedtime delay, weekend oversleeping, and sleep-related impairment (DeMartini & Fucito, in press; Singleton & Wolfson, 2009). In turn, poor sleep quality may promote alcohol use to induce sleep (Brower, 2003; Roehrs & Roth, 2001). Indeed, alcohol consumption is higher among individuals who report less sleep (Roehrs & Roth, 2001). Nearly a quarter of individuals with insomnia and roughly half of alcoholics report use of alcohol to aid sleep (Brower, 2003; Johnson, Roehrs, Roth, & Breslau, 1998). Among college students, greater sleep disturbance predicts more frequent and heavier drinking and the use of alcohol to induce sleep (Kenney, LaBrie, Hummer, & Pham, 2012; Lund et al., 2010; Taylor & Bramoweth, 2010). Both sleep disturbance and heavy drinking have negative effects on mental and physical health, academic performance, and increased injury risk including motor vehicle accidents, which are among the leading causes of death in this age group (Gaultney, 2011; Hingson, Zha, & Weitzman, 2009; Millman, 2005; Park, 2004; Park, Mulye, Adams, Brindis, & Irwin, 2006; Singleton & Wolfson, 2009; Taylor & Bramoweth, 2010; Wechsler, Davenport, Dowdall, Moeykens, & Castillo, 1994). In fact, sleep disturbance may be a risk factor for developing an alcohol use disorder (Brower, 2003). In prospective studies, sleep disturbance during childhood has been shown to predict early onset of alcohol use during adolescence (Wong, Brower, Fitzgerald, & Zucker, 2004). In addition, recent evidence suggests that sleep disturbance and heavy drinking among college students in combination may have a synergistic effect (DeMartini & Fucito, in press; Kenney et al., 2012). For example, heavy-drinking college students who report greater sleep disturbance experience more alcohol-related consequences (e.g., blackouts, interpersonal problems) than their heavy-drinking counterparts who report good sleep (Kenney et al., 2012). Psychoeducational sleep interventions for college students include education about sleep stages and circadian rhythms, sleep hygiene, strategies to create a sleep-conducive environment, and relaxation and mindfulness techniques (Brown, Buboltz Jr., & Soper, 2006; Gellis, Arigo, Elliott, 2013; Kloss et al., 2011; Trockel et al., 2011). Some interventions also incorporate cognitive strategies to reduce maladaptive sleep beliefs (Brown, Buboltz, & Soper, 2002; Gellis Downloaded by [Yale University Library], [Kelly DeMartini] at 11:47 18 June 2014 SLEEP AND ALCOHOL COLLEGE INTERVENTION 3 et al., 2013; Trockel et al., 2011). These components are also part of cognitive-behavioral therapy for insomnia (CBT-I), an empirically validated treatment that results in medium to large improvements in insomnia symptoms (Edinger, Wohlgemuth, Radtke, Marsh, & Quillian, 2001; Morin et al., 2009) and is effective in brief (Edinger & Sampson, 2003) and computerbased formats (e.g., SHUTiTM, RESTORETM ; Ritterband et al., 2009; Vincent & Lewycky, 2009). Likewise, college sleep interventions have medium to large effects on sleep quality and important sleep correlates (e.g., depressive symptoms; Brown et al., 2002; Gellis et al., 2013; Trockel et al., 2011) and are effective when computer-based (Trockel et al., 2011) or delivered in brief formats (Gellis et al., 2013). Sleep hygiene education may briefly address limiting alcohol use before bedtime (Stepanski & Wyatt, 2003). However, this information alone is not effective in reducing heavy alcohol use. Individual-level alcohol interventions for college students emphasize increasing motivation and commitment to change drinking (Cronce & Larimer, 2011). Common components include education about alcohol, personalized feedback about students’ drinking patterns, alcoholrelated problems, beliefs about alcohol, and motives for drinking, and specific strategies for reducing alcohol use and alcohol-related harm (Cronce & Larimer, 2011). These interventions are efficacious for reducing alcohol use and alcohol-related problems with effect sizes in the small to medium range (Carey, Scott-Sheldon, Carey, & DeMartini, 2007; Cronce & Larimer, 2011). They are most effective if personalized feedback about the student’s drinking is provided relative to age-related norms (Cronce & Larimer, 2011). Moreover they can be delivered effectively using computer-based and brief, single session in-person formats (Bersamin, Paschall, Fearnow-Kenney, & Wyrick, 2007; Carey, Scott-Sheldon, Elliott, Bolles, & Carey, 2009; Dimeff & McNeely, 2000; Elliott, Carey, & Bolles, 2008; Hester, Delaney, & Campbell, 2012; Kypri et al., 2004; Saitz et al., 2007; Walters, Miller, & Chiauzzi, 2005). In contrast, general health interventions that do not include alcohol-specific information are not effective for reducing drinking and alcohol-related problems in college students (Carey et al., 2007; Cronce & Larimer, 2011). Therefore, a sleep intervention is not likely to be effective for reducing drinking and drinking-related consequences in college students. By the same token, alcohol interventions do not typically incorporate sleep information and a reduction in drinking may not be sufficient for improving sleep in this group. Given the substantial comorbidity of sleep disturbance and heavy drinking among college students, the bidirectional effects of sleep and alcohol use, and the fact that college students are unlikely to seek specialty alcohol treatment (Black & Coster, 1996), treatment integration makes sense. Young adulthood, and college in particular, is an ideal time to intervene on heavy drinking to prevent the development of chronic alcohol use disorders (Zucker et al., 2006). However, most heavy-drinking college students do not seek alcohol treatment and may be reluctant to change their drinking (Black & Coster, 1996). College students are, however, open to health interventions, such as sleep treatment (Orzech, Salafsky, & Hamilton, 2011) and limiting alcohol use near bedtime is already a component of sleep hygiene education (Stepanski & Wyatt, 2003). Expanding the alcohol-specific content in a sleep intervention to include personalized feedback, moderate drinking recommendations, and drinking reduction strategies may be an effective college drinking intervention, as this model does not rely on self-identification for alcohol treatment. The purpose of this mixed methods study was to understand heavy-drinking college students’ sleep and drinking behaviors and their reactions to a proposed integrated sleep and alcohol 4 FUCITO ET AL. Downloaded by [Yale University Library], [Kelly DeMartini] at 11:47 18 June 2014 Web-based intervention to inform intervention development. The aims were threefold: (a) to describe the self-reported sleep and psychosocial characteristics (i.e., depression, anxiety, health risk behaviors) of heavy-drinking college students who report having sleep concerns, (b) to characterize heavy-drinking college students’ perceptions of sleep and the association between sleep and alcohol use, and (c) to describe heavy-drinking college students’ reactions to a proposed integrated sleep and alcohol intervention. METHOD Design A mixed-methods descriptive design was used (quantitative C qualitative; Creswell & Plano Clark, 2011). Participants were heavy-drinking college students who were at risk of harm from their alcohol use and who reported sleep concerns. Participants completed standardized surveys and participated in semistructured focus group interviews. Data were collected and analyzed for each qualitative and quantitative strand individually and then integrated in the discussion. Participants and Procedure College students were recruited during the spring 2013 semester from local colleges primarily through advertisements on Facebook, flyers posted around campuses, and announcements emailed to college administrators/faculty. Advertisements targeted college students with sleep problems and stated that the purpose of the study was to conduct interviews with college students about their sleep habits and possible interventions for sleep difficulties. Study ads directed students to the study website, which informed them that the study was designed to understand the relationship between sleep disturbance and alcohol consumption in college students with the ultimate goal of developing a new sleep intervention for college students. To be eligible for the study, participants had to report the following: (a) concerns about sleep (dichotomous: yes/no), (b)  1 heavy drinking occasion(s) in the past month (i.e.,  5 drinks on 1 occasion for men;  4 for women), and (c) risk of harm from drinking based on an AUDIT-C score of  7 for men;  5 for women. Interested volunteers who clicked on Web-based advertisements or contacted study staff were first directed to the study website to complete a Web-based prescreener that took approximately 5 min. Individuals who met initial eligibility were then invited to participate in an in-person intake appointment of approximately 45–60 min, to verify final eligibility, and to assess demographic information and sleep and drinking characteristics. Eligible participants were then assigned to participate in 1 of 5 focus group interview sessions that took place immediately following or up to 3.5 weeks after intake. Focus group sessions were composed of 4–7 participants who were interviewed as a group (i.e., interview sessions 1–4); one participant who was unable to attend any group session completed an individual interview (i.e., interview session 5). In interview sessions 1–4, we asked each participant to provide his/her opinion on a given discussion topic by raising his/her hand in agreement so that we could get an estimated count of students’ perceptions and preferences. SLEEP AND ALCOHOL COLLEGE INTERVENTION 5 Quantitative Measures Downloaded by [Yale University Library], [Kelly DeMartini] at 11:47 18 June 2014 All measures were computer-based. Data were collected on sleep, alcohol use, psychosocial well-being, and health risk behaviors. Sleep. For the Web-based prescreener, potential participants indicated whether they had any concerns about their sleep using a dichotomous variable (i.e., “yes” or “no”) created for this study. The Pittsburgh Sleep Quality Index (PSQI; Buysse, Reynolds, Monk, Berman, & Kupfer, 1989), a 19-item measure, was used to assess total self-reported sleep disturbance using the global PSQI score and quantitative sleep characteristics using the sleep duration and sleep latency subscales. The PSQI is a reliable, valid tool for detecting good versus poor sleepers. Suggested cut-off scores for the total PSQI include  5 and  7 with higher scores indicating poorer self-reported sleep (Buysse et al., 1989; Gellis & Lichstein, 2009). The 8-item NIH Patient Reported Outcomes Measurement Information System Sleep-Related Impairment Short-Form (PROMIS®-SRI-SF; Yu et al., 2011), a reliable, precise measure that allows for normative comparisons, assessed qualitative aspects of sleep-wake function (e.g., perceptions of sleepiness, functional impairments due to sleep problems) using the total score. Raw total scores are converted into standardized scores using a T score with a mean score of 50 and standard deviation of 10. The PROMIS® -SRI-SF was calibrated on a U.S. sample slightly enriched for chronic illness so a score of 50 is representative of a somewhat more impaired population than the general U.S. population (Yu et al., 2011). The Composite Scale of Morningness (CSOM; Smith, Reilly, & Midkiff, 1989), a 13-item, reliable, valid measure of circadian preference, evaluated participants’ chronotype. Using the total score, participants’ chronotypes were coded as follows: (1) 44–55 D morning type, (2) 23– 43 D intermediate type, and (3) 22 or less D evening type. The Cleveland Adolescent Sleepiness Questionnaire (CASQ; Spilsbury, Drotar, Rosen, & Redline, 2007), a reliable self-report tool that corresponds with other validated sleepiness measures, assessed excessive daytime sleepiness and sleep-related impairment typical in this population using a total score (maximum possible score D 80). Alcohol. The Daily Drinking Questionnaire (DDQ; Collins, Parks, & Marlatt, 1985) assessed alcohol use starting from the month prior to and including the assessment day. We used a 7-day grid to assess typical weekly drinking based on the following standard drink definition: 12-oz. beer, 5-oz. serving of wine, or 1.5-oz. serving of hard liquor (straight or in a mixed drink), all equivalent to approximately 0.6 oz. or 14 g of pure alcohol (NIAAA, 2010). To determine the total number of drinks participants consumed during a typical week, we summed the total number of drinks reported on the grid. The Alcohol Use Disorders Identification Test (AUDIT) is a brief, standardized screener for hazardous drinking and alcohol use disorders (Bush, Kivlahan, McDonell, Fihn, & Bradley, 1998). Scores on the AUDIT Consumption Subscale (AUDIT-C) of  7 and  5 for men and women, respectively, have been identified as optimal cutoff scores for identifying at-risk drinking in college students (DeMartini & Carey, 2012). The Brief Young Adult Alcohol Consequences Questionnaire (BYAACQ) is a 24-item selfreport assessment of alcohol-related problems in the past 30 days (Kahler, Strong, & Read, 6 FUCITO ET AL. Downloaded by [Yale University Library], [Kelly DeMartini] at 11:47 18 June 2014 2005). Items are tailored for a collegiate population and include driving while intoxicated, unplanned sexual activity, and blacking out. Items were summed to create a total number of problems score for each participant. Psychosocial. The NIH PROMIS® Anxiety (7-item) & Depression (4-item) Short Forms (Pilkonis et al., 2011), both valid measures of emotional well-being that allow for normative comparisons, assessed participants’ fear, anxious misery, hyperarousal, and negative and positive mood. Raw total scores are converted into standardized scores using a T score; a mean score of 50 represents the average for the U.S. general population (standard deviation D 10). Select items from the Centers for Disease Control National College Health Risk Survey (Douglas et al., 1997), a survey of health risk behaviors that contribute to morbidity and mortality among youth and young adults, assessed participants’ potential for unintentional injury (e.g., riding as passenger with a drunk driver, driving under the influence of alcohol). Qualitative Interviews Interviews were conducted using a semistructured template and encompassed three domains: (a) perceptions of sleep difficulties, sleep-related consequences, and sleep-related barriers in college; (b) perceptions of the connection between sleep and alcohol use; (c) reactions to a proposed integrated, Web-based sleep and alcohol intervention (see Table 1). We ceased recruitment when interviews yielded no new information about the three domains and there was redundancy in subthemes. Two investigators with extensive experience providing health interventions to college students conducted the interviews (LMF, KSD) along with a research assistant. Interviews lasted approximately 90 min and were audiotaped. To protect participants’ confidentiality, participants were asked to use a pseudonym during the interview. An external transcription service then transcribed audiotaped interviews verbatim; digital interview files were uploaded using a secure, password-protected server. TABLE 1 Focus Group Interview Excerpts Domain Perceptions of sleep difficulties, sleep-related consequences, and sleep-related barriers in college Perceptions of the connection between sleep and alcohol use Reactions to a proposed integrated, Web-based sleep and alcohol intervention Sample Question “What are the barriers for you to get enough sleep in college?” “What connection, if any, do you believe there is between alcohol use and sleep disturbance among college students?” “We are interested in developing an integrated Web-based program to help college students improve their sleep and reduce negative drinking experiences. Would you be interested in this program? Would you use the program if it was available to you?” SLEEP AND ALCOHOL COLLEGE INTERVENTION 7 Downloaded by [Yale University Library], [Kelly DeMartini] at 11:47 18 June 2014 Data Analysis Quantitative methods were used to summarize participant self-report data. Pearson correlations were conducted to identify potential associations among sleep, alcohol, and psychosocial measures. It was hypothesized that more frequent and/or higher quantity of alcohol use (DDQ) and higher alcohol-related problems (AUDIT, BYACCQ) would be associated with more selfreported sleep disturbance (PSQI total score), sleepiness (CASQ), sleep-related impairment (PROMIS® -SRI), depression, and anxiety (PROMIS® -Anxiety, PROMIS® -Depression). We anticipated that sleep outcomes would be associated with alcohol outcomes even when controlling for potential associations with depression and/or anxiety. Mann-Whitney U tests were also conducted to examine whether participants who reported ever riding with a drunk driver as a passenger, or driving under the influence, differed on sleep measures (PSQI, CASQ, PROMIS® SRI, CSOM) from their less risky counterparts. It was anticipated that health risk behaviors would be higher among participants who reported more sleep disturbance, sleepiness, and/or sleep-related impairment and among those who reported an evening chronotype. Transcribed interviews were analyzed using QSR International’s NVivo 10 (QSR International [Americas], Burlington, MA), a qualitative software program that facilitates text coding and comparison across participants. Interview transcripts are uploaded into NVivo and then text components can be assigned codes. NVivo assigns codes that are identified by the investigator. Three investigators analyzed interview content (coding team D LMF, KSD, THH) using a constant comparative method in which data was broken down into discrete units and then coded into relevant categories (Sandelowski, 2000). A preliminary thematic coding infrastructure was first derived from the three aforementioned domains of the semistructured focus group interviews; data for each question was first coded into its relevant domain. For example, a participant’s narrative about having trouble sleeping and tossing and turning throughout the night was first coded in the “sleep difficulty” domain. The coding team reviewed coded transcripts in detail to determine whether the data were initially categorized in the appropriate domain. Data within each domain were then coded, using line-by-line coding of participant’s statements. These codes were further specified into secondary themes after multiple readings of the data in which the team investigated subtopics, opposing opinions, and new insights. The relevance of domains and subthemes was evaluated using repeated comparative assessment until thematic saturation had been reached (i.e., no further themes could be derived). For example, a participant’s narrative about having trouble sleeping and tossing and turning throughout the night was further coded as “sleep difficulty, trouble staying asleep.” Coding reliability was maintained through initial group review of transcripts and thematic infrastructure development, preliminary roundtable discussions concerning the interpretation and application of themes, and group consensus on coding scheme and working definition of codes and themes. RESULTS Participants Participants were 24 undergraduate students from five local colleges and universities. Participants were 19.96 years old on average (SD D 1.52) and the majority were Caucasian (75%), and female (54%). Participants were evenly distributed across classes: freshman (21%), sophomores 8 FUCITO ET AL. TABLE 2 Participant Clinical Characteristics (N D 24) Downloaded by [Yale University Library], [Kelly DeMartini] at 11:47 18 June 2014 Sleep Pittsburgh Sleep Quality Index,a M (SD) Sleep duration in hours Sleep latency in minutes Total sleep disturbance PROMIS® : Sleep Related Impairment,b M (SD) Cleveland Adolescent Sleepiness Questionnaire,c M (SD) Chronotype (CSOM), n (%) Evening Intermediate Morning Range 6.65 38.70 9.21 61.03 40.58 (0.98) (18.95) (3.16) (7.19) (8.15) 2 (8) 22 (92) 0 (0) Alcohol Daily Drink Questionnaire (DDQ), M (SD) Typical drinks per week Typical number of drinking days per week Total binge days per week Alcohol-related consequences (BYAACQ), M (SD)d Ever drink to help with sleep, n (%) Range 24.50 (16.25) 4.04 (1.49) 2.79 (1.72) 11.70 (4.67) 8 (33) Mental Health/Injury Risk PROMIS® : Anxiety,b M (SD) PROMIS® : Depression,b M (SD) Driving after drinking, n (%) Rode with drunk driver, n (%) 5.00–8.00 15.00–270.00 4.00–17.00 48.90–75.00 27.00–58.00 3.00–65.00 2.00–7.00 0–7.00 4.00–23.00 Range 59.19 (8.69) 53.46 (9.65) 7 (29) 13 (54) 36.30–75.80 41.00–73.30 Note. a Pittsburgh Sleep Quality Index: clinical cutoff for “poor” sleepers is based on a total sleep disturbance score of  5 or  7; b PROMIS® raw scores are converted to T score based on standardization sample, a score of 50 represents U.S. general population; c Cleveland Adolescent Sleepiness Questionnaire: higher scores indicate more sleepiness for a total possible score of 80; d Brief Young Adult Alcohol Consequences Questionnaire: 1 point assigned for each consequence for a total possible score of 24. (33%), juniors (29%), and seniors (17%). A small percentage of participants reported being members of fraternities or sororities (21%). A third of the sample reported ever using alcohol to induce sleep. On average, participants reported heavy drinking on approximately 3 days per week and experiencing a high number of drinking-related consequences (see Table 2). Of particular concern, many participants reported driving after drinking (29%) and most reported riding as a passenger with a drunk driver (54%). Participants had elevated anxiety scores with an average score nearly 1 standard deviation above the general U.S. population. In contrast, their depression scores were within the average U.S. population. Associations Among Sleep, Alcohol, Psychosocial Outcomes Depression scores were significantly correlated with PROMIS® anxiety [r(24) D .80, p < .001], PROMIS® sleep-related impairment [r(24) D 74, p < .001], PSQI total sleep disturbance Downloaded by [Yale University Library], [Kelly DeMartini] at 11:47 18 June 2014 SLEEP AND ALCOHOL COLLEGE INTERVENTION 9 [r(24) D .63, p < .001], and CASQ sleepiness scores [r(24) D .41, p D .04], and there was a nonsignificant trend suggesting an association with BYACCQ alcohol-related consequences [r(23) D .39, p D .07]. In addition, both PSQI total sleep disturbance [r(24) D .41, p D .05] and PROMIS® sleep-related impairment scores [r(24) D .54, p D .01] were significantly associated with typical number of drinking days on the DDQ. Partial correlations were then conducted, controlling for PROMIS® depression scores. Only the correlation between PROMIS® sleeprelated impairment and frequency of drinking on the DDQ remained significant after controlling for depression [r(20) D .52, p D .01]. The results of Mann-Whitney U tests also revealed that participants who reported ever riding as a passenger with a drunk driver had significantly higher sleep-related impairment scores [Mean Rank D 18.50] than their less risky counterparts [Mean Rank D 10.50; z D 2.41, p D .02]. Qualitative C Quantitative Results For each domain (i.e., perceptions of sleep and sleep/alcohol interactions, reactions to integrated intervention), participants described several themes that were further divided into subthemes where relevant. Illustrative quotes for each theme are provided below. Domain 1: Perceptions of Sleep Difficulties, Sleep-Related Consequences, and Sleep-Related Barriers in College, Main Themes Common sleep problems. On average, participants reported sleep durations of 6.65 hours per night and a sleep onset latency of nearly 40 min (see Table 2). Participants also reported a high level of sleep disturbance as demonstrated by an average score of 9 on the PSQI. No participants reported a morning chronotype; most had an intermediate circadian preference. In focus group discussions, many participants stated that they have difficulty falling asleep or staying asleep. Most also indicated sleeping for short durations and maintaining sleep schedules that differed on weekdays and weekends. Most participants also described their sleep quality at college as poor: I find myself tossing and turning a lot through the night, waking up having to go to the bathroom in the middle of the night or getting really hot at night, so just like tossing and turning and not being able to go back to sleep. On the weekends, say I drank the night before, I might get up at 12 or 1, but then during the week I will probably get up at 7:20 if I have an 8 a.m. So I think it just really depends on what is going on. Sleep-related consequences. Participants reported high scores on measures of sleepiness and sleep-related impairment (see Table 2). These quantitative findings corresponded with participants’ focus group discussions. The most common sleep-related consequence participants discussed was excessive daytime sleepiness (e.g., falling asleep in class, missing classes due to oversleeping). Many participants also stated that sleep disturbance reduced their efficiency and productivity and caused negative mood changes. Some also indicated that it interfered with their ability to practice good self-care (i.e., exercise, nutrition): 10 FUCITO ET AL. I fall asleep during class : : : like I can feel it too. I’m just sitting there, trying to take my notes, and all of a sudden like my eyes, my eyelids start getting really heavy. Downloaded by [Yale University Library], [Kelly DeMartini] at 11:47 18 June 2014 If I obviously got better sleep and slept better throughout the night, I would be able to wake up, concentrate more, and just be able to get things done quicker and more proficient and stuff like that, so I feel like if I had better sleep, it would just be easier for me. Sleep barriers. In focus groups, some participants stated that technology (i.e., TV, computers, smart phones) interfered with their sleep. Many participants also identified several college-specific environmental factors as major sleep barriers such as roommate disruptions, uncontrollable dorm room temperatures, dorm room lighting, and ambient noise. Many respondents also stated that both positive and negative aspects of the “college experience” (e.g., socializing, stress) disrupt sleep: My roommate’s up like [until] 3:00 or 4:00 in the morning. She’s on the phone all the time and I think that makes me wake [up] because I’m used to sleeping in this quiet and dark place and she’s used to sleeping with TV all night so it’s like we have a different lifestyle. This is supposed to be the best 4 years of your life, so why sleep? Sleep-promoting strategies. The most common strategy participants identified to promote sleep in interviews was the use of over-the-counter (OTC) or prescription sleep aids or illicit drugs. Sleep aids included antihistamines, nighttime OTC cold remedies or pain medications, benzodiazepines, and opiates. Few participants discussed current use of alcohol to induce sleep. Many participants indicated that they use behavioral strategies to promote sleep such as white noise machines, room-darkening shades, smart phone sleep applications, exercise, or relaxation techniques: I usually need a fan too, just background noise. It can’t be dead silent. And then also I would hear my suitemates if it’s not noisy in my room. ZzzquilTM works, I think. I tried drinking tea like non-caffeine tea just to kind of calm me down but that doesn’t work so I usually take ZzzquilTM as well or like other prescribed medication. Domain 2: Perceptions of Alcohol/Sleep Interactions, Main Themes Falling asleep. Many participants stated during interviews that they fall asleep faster after a night of drinking either due to the direct sedating effects of alcohol or because they are less likely to delay sleep because of use of smartphones, computers, or other technology: Yeah, like you will be out and you will get back and just be so tired. You can change quickly, go to bed, and fall asleep. I won’t even go on my phone. I will just go to sleep. If I drink, I sleep so well. I don’t wake up so, for me, somehow drinking on the weekends helps me to get sleep. I couldn’t sleep during the weekdays and weekends I party and drink and I feel better, actually, because I can sleep well. SLEEP AND ALCOHOL COLLEGE INTERVENTION 11 Downloaded by [Yale University Library], [Kelly DeMartini] at 11:47 18 June 2014 Staying asleep. In focus groups, participants were split in terms of their perceived effects of alcohol on sleep continuity. A few participants indicated that alcohol helps them stay asleep until their preferred wake time or later in the morning. Conversely, many others stated that alcohol wakes them up much earlier than they preferred and causes hangovers. As a result, the latter participants indicated that they attempt to go back to sleep later in the morning and then have difficulty being active during the rest of the day. I feel like I will have the worst hangover and maybe I will wake up at like 8 but I won’t be able to get out of my bed until 11 or 12. It’s just the worst feeling. If I for some chance have a class, I won’t feel like taking notes at all. And I won’t really comprehend everything as well that I’m learning. Overall sleep quality. Participants were also divided on their perceptions of how alcohol affects sleep quality in interview discussions. Some discussed positive effects of alcohol on sleep quality (e.g., facilitates falling/staying asleep), whereas many others indicated that alcohol had a negative effect (e.g., causes vivid dreams, frequent awakenings, shallow sleep): I think that I get worse quality of sleep when I drink so I’ll wake up at like 7:00 in the morning, even if I stayed out until 2:00 or 3:00 the night before just because I’m sobering up and then I’ll have difficulties falling back asleep. Domain 3: Integrated Intervention Perceptions and Preferences, Main Themes Interest. Most participants expressed being open to trying an integrated sleep and alcohol intervention during interviews, but some stated that their overall interest would depend on the specific content and structure of the program. Some expressed ambivalence about their willingness to remain engaged in the program for several weeks: I think a lot of college students, they’re probably quite aware that their sleep pattern is poor but they think that’s just like a consequence of their lifestyle that they have and they’re not prepared to sacrifice their schoolwork/social experience. : : : But if there’s something : : : whereby they could see how poor their sleep might be : : : I think it would be pretty successful for the web-based [program]. Preferred content. During focus group interviews, most participants expressed interest in the sleep and sleep/alcohol interaction proposed content of the intervention. Many were also interested in learning more about the association between sleep and alcohol use, and a few thought information about the combined risks of sleep deprivation and heavy alcohol use would be very helpful. Many participants were less enthusiastic about the brief alcohol intervention unless it had a sufficient rationale for sleep, emphasized harm reduction, and provided content that was uniquely different from other alcohol interventions students already receive. For the treatment of sleep problems and heavy alcohol use, most participants preferred specific, tailored feedback and personalized strategies to change their behaviors. Some participants also stated that it would be useful for the intervention to provide tools that could be used outside of the Web-based program (e.g., personalized blood alcohol chart): 12 FUCITO ET AL. As long as it didn’t like appear deceptive, I think if I went on a website to help my sleeping patterns and then it turned into an alcohol thing, I would kind of be, like, this isn’t right. Downloaded by [Yale University Library], [Kelly DeMartini] at 11:47 18 June 2014 The combination would be nice because we already know what alcohol is gonna do to us and we’ve been told since we were like 5 years old by our parents that drinking is bad and we know about that, it’s just the combination would be new information to us. Like the example, you might actually be more likely to fall off the curb when you’re walking if you might have sleep deprivation and you’re drinking. Preferred structure/format. In focus group interviews, most participants preferred an intervention with brief assessments that could tailor health information to them specifically rather than general health information. Most also preferred interventions that would allow modules or information to be accessed quickly based on choice rather than requiring content to be accessed in a particular order. Many participants favored the provision of new, brief tips of the day to keep interest; some also liked the ability to track one’s progress. Many participants thought the option for peer support or clinician contact (via phone or e-mail) would be helpful. Almost no participants preferred content to be delivered via video clips. I think if you have one of those interactive surveys where it gets your personalized information, I don’t think have the survey too long, maybe just where you could get a basic thing of what’s wrong with you. I think kind of like SnappleTM caps, too, like little fun facts that would be interesting so you just go on to the website and it’s something at the top of the page that’s changed every day. DISCUSSION The purpose of the study was to understand the sleep and psychosocial characteristics of college students who are heavy drinkers and their perceptions of sleep and sleep/alcohol interactions to inform the development of an integrated Web-based sleep and alcohol intervention. Participants reported a high degree of sleep disturbance in line with prior studies with college students (Lund et al., 2010). Typical sleep problems included short sleep duration, irregular sleep schedules (i.e., inconsistent bed/wake times on weekdays vs. weekends), poor self-reported sleep quality, and difficulty initiating or maintaining sleep. Of note, participants’ sleepiness and sleep-related impairment scores exceeded those of normative samples. For instance, their Cleveland Adolescent Sleepiness Questionnaire average score of 40.58 exceeded the sleepiness level reported by adolescents with known sleep problems (Spilsbury et al., 2007). In a prior study, adolescents with sleep apnea and primary snoring had average CASQ scores of 37.7 and 35.0 respectively, compared with adolescents without a known sleep problem who had average scores of 12.2. In regard to sleep-related impairment, participants scored on average 1 standard deviation higher than the general U.S. population. Participants also identified a number of barriers that interfered with sleep, many of which were typical of the college experience. A common sentiment among participants was that academics and social activities are prioritized over sleep. Participants also reported a number of sleep-related consequences as a result of their sleep problems, the most common being excessive daytime sleepiness that interfered with school performance. Other sleep-related impairments included negative Downloaded by [Yale University Library], [Kelly DeMartini] at 11:47 18 June 2014 SLEEP AND ALCOHOL COLLEGE INTERVENTION 13 mood and poor self-care. These findings are consistent with prior studies on sleep barriers and sleep-related impairment in college (Lund et al., 2010). To promote sleep, participants reported relying primarily on pharmacological interventions (i.e., OTC or prescription sleep aids, illicit drugs). Some participants also tried technology-based sleep aids (e.g., smart phone apps), relaxation techniques, and/or strategies to make their sleep space more sleep-conducive. In general, participants seemed to accept that sleep disturbance was a fact of college life. In this college sample selected for current heavy drinking and sleep concerns, those who reported more sleep disturbance and sleep-related impairment drank alcohol more frequently and took more health risks. This finding confirms recent research that showed poor self-reported sleep quality was associated with greater harm from drinking among college students who reported heavy drinking (Kenney et al., 2012). Sleep disturbance may render heavy drinking college students more vulnerable to alcohol-related risks due to its impairment of executive functioning (Kenney, Lac, Labrie, Hummer, & Pham, 2013). Three themes best described participants’ perceptions of the connection between sleep and alcohol use. Participants indicated that alcohol affects sleep latency, sleep continuity, and sleep quality. Many participants discussed how alcohol helps them fall asleep faster. While some stated that alcohol helped them sleep longer and better, others perceived that their sleep is disrupted following alcohol consumption. Several factors may account for the differences in participants’ perceptions of alcohol and sleep interactions. Participants with longer drinking histories may have become tolerant to alcohol’s sedating effects and thus more likely to experience sleep disruptions from drinking. Tolerance to alcohol’s sedating effects occurs quickly (Ebrahim et al., 2013); alcohol consumed close to bedtime increases sleep onset latency and decreases total sleep time with chronic, heavy use (Brower, 2003). In addition, the amount of alcohol consumed is important for determining when the negative effects of alcohol on sleep occur. Alcohol is metabolized at a constant rate that decreases breath alcohol concentrations (BAC) by approximately .01–.02 per hour (Roehrs & Roth, 2001). At this rate, participants who go to sleep with a BAC level of .08 would have metabolized all of the alcohol 4–5 hours after going to sleep (Roehrs & Roth, 2001), and may report waking up after drinking at a much earlier time than preferred. In contrast, participants who go to sleep with higher BAC levels would still be metabolizing alcohol upon waking and may be able to sleep until their preferred wake time. Therefore, these individuals may not perceive that alcohol has the same negative effects on sleep. It is also possible that some students may delay bedtime on drinking nights and consequently experience greater homeostatic sleep pressure on those nights (Borbely, 1982). These students may then misattribute the effect of greater sleep pressure to alcohol. Our study design did permit an examination of these potential explanations. Future studies are needed to clarify the mechanisms underpinning college students’ perceptions of alcohol and sleep interactions. Reactions to the integrated intervention were positive overall. Most participants expressed enthusiasm for the integrated intervention. Their overall commitment to the program, however, varied depending on the content and structure of the intervention. Most wanted the content to be personalized to their unique health profile. Further, they preferred sleep-related content and information about sleep/alcohol interactions to general alcohol content. Participants also preferred an intervention that was easy to navigate by choice order, added new content daily, and offered a way to monitor progress and the option for support from peers or a clinician. Participants indicated that they would be much more interested and committed to the interven- Downloaded by [Yale University Library], [Kelly DeMartini] at 11:47 18 June 2014 14 FUCITO ET AL. tion if it included this preferred content and structure. These findings are consistent with prior college student health research that suggests both the content and format of Web-based health interventions may need to employ innovative strategies to engage this cohort (Kwan, Faulkner, & Bray, 2013). Moreover, models of Web-based intervention development emphasize the need to involve targeted users to ensure interventions are relevant, easy to use, and meet users’ needs (Skinner, Maley, & Norman, 2006). Potential study limitations should be noted. We studied a small sample of college students in New England whose sleep disturbance, perceptions of sleep/alcohol interactions, and integrated intervention preferences may not be representative of all heavy-drinking college students in the United States. The design does not enable us to quantify themes, make predictions about college students’ behavior relevant to the proposed intervention, or determine the relative importance of themes by college students’ clinical characteristics. We chose a primarily qualitative study design in order to describe college students’ experiences and identify key themes that characterize their reactions to the proposed intervention. The quantitative findings should be interpreted with caution given the small sample size. An integrated intervention that includes sleep and alcohol content may engage heavydrinking college students and warrants further investigation. Given that most heavy-drinking college students do not seek alcohol treatment (Black & Coster, 1996), novel intervention strategies to engage this population are needed. Sleep may serve as an important gateway topic for intervening on heavy alcohol use among these high-risk college students. Moreover, improving sleep in this highly sleep-disturbed group would have additional benefits for overall functioning and mental health/injury risk outcomes. ACKNOWLEDGMENTS We would like to thank Stephanie S. O’Malley, PhD, for her assistance with study design, and Corey R. Roos, BA, for his assistance with study implementation. FUNDING This research was supported by grants from the National Institutes of Health: K23AA020000 (LMF), P20NR014126 (HKY, NSR, RW), T32AA015496 (KSD); and by the State of Connecticut, Department of Mental Health and Addiction Services. REFERENCES Bersamin, M., Paschall, M. J., Fearnow-Kenney, M., & Wyrick, D. (2007). Effectiveness of a web-based alcoholmisuse and harm-prevention course among high- and low-risk students. Journal of American College Health, 55(4), 247–254. Black, D. R., & Coster, D. C. (1996). Interest in a stepped approach model (SAM): Identification of recruitment strategies for University alcohol programs. Health Education and Behavior, 23(1), 98–114. Downloaded by [Yale University Library], [Kelly DeMartini] at 11:47 18 June 2014 SLEEP AND ALCOHOL COLLEGE INTERVENTION 15 Blanco, C., Okuda, M., Wright, C., Hasin, D. S., Grant, B. F., Liu, S. M., & Olfson, M. (2008). Mental health of college students and their non-college-attending peers: Results from the National Epidemiologic Study on Alcohol and Related Conditions. Archives of General Psychiatry, 65(12), 1429–1437. Bonnet, M. H., & Arand, D. L. (2011). How much sleep do adults need? Retrieved from http://www.sleepfoundation. org/article/how-sleep-works/how-much-sleep-do-we-really-need Borbely, A. A. (1982). A two process model of sleep regulation. Human Neurobiology, 1, 195–204. Brower, K. J. (2003). Insomnia, alcoholism and relapse. Sleep Medicine Reviews, 7(6), 523–539. Brown, F. C., Buboltz, W. C., Jr., & Soper, B. (2002). Relationship of sleep hygiene awareness, sleep hygiene practices, and sleep quality in university students. Behavioral Medicine, 28(1), 33–38. Brown, F. C., Buboltz, W. C., Jr., & Soper, B. (2006). Development and evaluation of the Sleep Treatment and Education Program for Students (STEPS). Journal of American College Health, 54(4), 231–237. Bush, K., Kivlahan, D. R., McDonell, M. B., Fihn, S. D., & Bradley, K. A. (1998). The AUDIT alcohol consumption questions (AUDIT-C): An effective brief screening test for problem drinking. Ambulatory Care Quality Improvement Project (ACQUIP). Alcohol Use Disorders Identification Test. Archives of Internal Medicine, 158(16), 1789–1795. Buysse, D. J., Reynolds, C. F., 3rd, Monk, T. H., Berman, S. R., & Kupfer, D. J. (1989). The Pittsburgh Sleep Quality Index: A new instrument for psychiatric practice and research. Psychiatry Research, 28(2), 193–213. Carey, K. B., Scott-Sheldon, L. A. J., Carey, M. P., & DeMartini, K. S. (2007). Individual-level interventions to reduce college student drinking: A meta-analytic review. Addictive Behaviors, 32(11), 2469–2494. Carey, K. B., Scott-Sheldon, L. A. J., Elliott, J. C., Bolles, J. R., & Carey, M. P. (2009). Computer-delivered interventions to reduce college student drinking: A meta-analysis. Addiction, 104(11), 1807–1819. Collins, R. L., Parks, G. A., & Marlatt, G. A. (1985). Social determinants of alcohol consumption: The effects of social interaction and model status on the self-administration of alcohol. Journal of Consulting and Clinical Psychology, 53(2), 189–200. Creswell, J. W., & Plano Clark, V. L. (2011). Designing and conducting mixed methods research. Los Angeles, CA: Sage. Cronce, J. M. & Larimer, M. E. (2011). Individual-focused approaches to the prevention of college student drinking. Alcohol Research and Health, 34(2), 210–221. DeMartini, K. S., & Carey, K. B. (2012). Optimizing the use of the AUDIT for alcohol screening in college students. Psychological Assessment, 24(4), 954–963. DeMartini, K. S., & Fucito, L. M. (in press). Variations in sleep characteristics and sleep-related impairment in at-risk college drinkers: A latent profile analysis. Health Psychology. Dimeff, L. A., & McNeely, M. (2000). Computer-enhanced primary care practitioner advice for high-risk college drinkers in a student primary care setting. Cognitive and Behavioral Practice, 7(1), 82–100. Douglas, K. A., Collins, J. L., Warren, C., Kann, L., Gold, R., Clayton, S., : : : Kolbe, L. J. (1997). Results from the 1995 National College Health Risk Behavior Survey. Journal of American College Health, 46(2), 55–66. Ebrahim, I. O., Shapiro, C. M., Williams, A. J., & Fenwick, P. B. (2013). Alcohol and sleep I: Effects on normal sleep. Alcoholism: Clinical and Experimental Research, 37(4), 539–549. Edinger, J. D., & Sampson, W. S. (2003). A primary care “friendly” cognitive behavioral insomnia therapy. Sleep, 26(2), 177–182. Edinger, J. D., Wohlgemuth, W. K., Radtke, R. A., Marsh, G. R., & Quillian, R. E. (2001). Cognitive behavioral therapy for treatment of chronic primary insomnia: A randomized controlled trial. Journal of the American Medical Association, 285(14), 1856–1864. Elliot, C., Carey, K. B., & Bolles, J. R. (2008). Computer-based interventions for college drinking: A qualitative review. Addictive Behaviors, 33(8), 994–1005. Gaultney, J. F. (2011). The prevalence of sleep disorders in college students: Impact on academic performance. Journal of American College Health, 59(2), 91–97. Gellis, L. A., Arigo, D., & Elliott, J. C. (2013). Cognitive refocusing treatment for insomnia: A randomized controlled trial in university students. Behavior Therapy, 44(1), 100–110. Gellis, L. A., & Lichstein, K. L. (2009). Sleep hygiene practices of good and poor sleepers in the United States: An internet-based study. Behavior Therapy, 40(1), 1–9. Grant, B. F., Dawson, D. A., Stinson, F. S., Chou, S. P., Dufour, M. C., & Pickering, R. P. (2004). The 12-month prevalence and trends in DSM-IV alcohol abuse and dependence: United States, 1991–1992 and 2001–2002. Drug and Alcohol Dependence, 74(3), 223–234. Downloaded by [Yale University Library], [Kelly DeMartini] at 11:47 18 June 2014 16 FUCITO ET AL. Hester, R. K., Delaney, H. D., & Campbell, W. (2012). The college drinker’s check-up: Outcomes of two randomized clinical trials of a computer-delivered intervention. Psychology of Addictive Behaviors, 26(1), 1–12. Hingson, R. W., Zha, W., & Weitzman, E. R. (2009). Magnitude of and trends in alcohol-related mortality and morbidity among U.S. college students ages 18–24, 1998–2005. Journal of Studies on Alcohol and Drugs Supplement, 16, 12–20. Johnson, E. O., Roehrs, T., Roth, T., & Breslau, N. (1998). Epidemiology of alcohol and medication as aids to sleep in early adulthood. Sleep, 21, 178–186. Kahler, C. W., Strong, D. R., & Read, J. P. (2005). Toward efficient and comprehensive measurement of the alcohol problems continuum in college students: The brief young adult alcohol consequences questionnaire. Alcoholism: Clinical and Experimental Research, 29(7), 1180–1189. Kenney, S. R., LaBrie, J. W., Hummer, J. F., & Pham, A. T. (2012). Global sleep quality as a moderator of alcohol consumption and consequences in college students. Addictive Behaviors, 37(4), 507–512. Kenney, S. R., Lac, A., Labrie, J. W., Hummer, J. F., & Pham, A. (2013). Mental health, sleep quality, drinking motives, and alcohol-related consequences: A path-analytic model. Journal of Studies on Alcohol and Drugs, 74(6), 841–851. Kloss, J. D., Nash, C. O., Horsey, S. E., & Taylor, D. J. (2011). The delivery of behavioral sleep medicine to college students. Journal of Adolescent Health, 48(6), 553–561. Kwan, M., Faulkner, G., & Bray, S. (2013). Evaluation of active transition, a website-delivered physical activity intervention for university students: Pilot study. JMIR Research Protocols, 2(1), e16. Kypri, K., Saunders, J. B., Williams, S. M., McGee, R. O., Langley, J. D., Cashell-Smith, M. L., & Gallagher, S. J. (2004). Web-based screening and brief intervention for hazardous drinking: A double-blind randomized controlled trial. Addiction, 99(11), 1410–1417. Lund, H. G., Reider, B. D., Whiting, A. B., & Prichard, J. R. (2010). Sleep patterns and predictors of disturbed sleep in a large population of college students. Journal of Adolescent Health, 46(2), 124–132. Millman, R. P. (2005). Excessive sleepiness in adolescents and young adults: Causes, consequences, and treatment strategies. Pediatrics, 115(6), 1774–1786. Morin, C. M., Vallières, A., Guay, B., Ivers, H., Savard, J., Mérette, C., : : : Baillargeon, L. (2009). Cognitive behavioral therapy, singly and combined with medication, for persistent insomnia: A randomized controlled trial. Journal of the American Medical Association, 301(19), 2005–2015. National Institute on Alcohol Abuse and Alcoholism. (2010). Rethinking drinking: Alcohol and your health. Retrieved from http://rethinkingdrinking.niaaa.nih.gov/WhatCountsDrink/HowMuchIsTooMuch.asp Orzech, K. M., Salafsky, D. B., & Hamilton, L. A. (2011). The state of sleep among college students at a large public university. Journal of American College Health, 59(7), 612–619. Park, C. L. (2004). Positive and negative consequences of alcohol consumption in college students. Addictive Behaviors, 29(2), 311–321. Park, M. J., Mulye, T. P., Adams, S. H., Brindis, C. D., & Irwin, C. E. (2006). The health status of young adults in the United States. Journal of Adolescent Health, 39, 305–317. Pilkonis, P. A., Choi, S. W., Reise, S. P., Stover, A. M., Riley, W. T., Cella, D., & Group, P. C. (2011). Item banks for measuring emotional distress from the Patient-Reported Outcomes Measurement Information System (PROMIS® ): Depression, anxiety, and anger. Assessment, 18(3), 263–283. Roehrs, T., & Roth, T. (2001). Sleep, sleepiness, and alcohol use. Alcohol Research and Health, 25(2), 101. Sandelowski, M. (2000). Whatever happened to qualitative description? Research in Nursing and Health, 23(4), 334– 340. Saitz, R., Palfai, T. P., Freedner, N., Winter, M. R., MacDonald, A., Lu, J., : : : Dejong, W. (2007). Screening and brief intervention online for college students: The iHealth study. Alcohol and Alcoholism, 42(1), 28–36. Singleton, R. A., & Wolfson, A. R. (2009). Alcohol consumption, sleep, and academic performance among college students. Journal of Studies on Alcohol and Drugs, 70(3), 355–363. Skinner, H. A., Maley, O., & Norman, C. D. (2006). Developing internet-based eHealth promotion programs: The Spiral Technology Action Research (STAR) model. Health Promotion Practice, 7(4), 406–417. Smith, C. S., Reilly, C., & Midkiff, K. (1989). Evaluation of three circadian rhythm questionnaires with suggestions for an improved measure of morningness. Journal of Applied Psychology, 74(5), 728–738. Spilsbury, J. C., Drotar, D., Rosen, C. L., & Redline, S. (2007). The Cleveland adolescent sleepiness questionnaire: A new measure to assess excessive daytime sleepiness in adolescents. Journal of Clinical Sleep Medicine, 3(6), 603–612. Downloaded by [Yale University Library], [Kelly DeMartini] at 11:47 18 June 2014 SLEEP AND ALCOHOL COLLEGE INTERVENTION 17 Stepanski, E. J., & Wyatt, J. K. (2003). Use of sleep hygiene in the treatment of insomnia. Sleep Medicine Reviews, 7(3), 215–225. Substance Abuse and Mental Health Services Administration. (2006). Results from the 2005 National Survey on Drug Use and Health: National findings. (Vol. NHDSA Series H-30). Rockville, MD: Office of Applied Studies. Taylor, D. J., & Bramoweth, A. D. (2010). Patterns and consequences of inadequate sleep in college students: Substance use and motor vehicle accidents. Journal of Adolescent Health, 46(6), 610–612. Vincent, N., & Lewycky, S. (2009). Logging on for better sleep: RCT of the effectiveness of online treatment for insomnia. Sleep, 32(6), 807–815. Walters, S. T., Miller, E., & Chiauzzi, E. (2005). Wired for wellness: e-interventions for addressing college drinking. Journal of Substance Abuse Treatment, 29(2), 139–145. Wechsler, H., Davenport, A., Dowdall, G., Moeykens, B., & Castillo, S. (1994). Health and behavioral consequences of binge drinking in college. A national survey of students at 140 campuses. Journal of the American Medical Association, 272(21), 1672–1677. Wong, M. M., Brower, K. J., Fitzgerald, H. E., & Zucker, R. A. (2004). Sleep problems in early childhood and early onset of alcohol and other drug use in adolescence. Alcoholism: Clinical and Experimental Research, 28(4), 578–587. Yu, L., Buysse, D. J., Germain, A., Moul, D. E., Stover, A., Dodds, N. E., : : : Pilkonis, P. A. (2011). Development of short forms from the PROMIS® sleep disturbance and Sleep-Related Impairment item banks. Behavioral Sleep Medicine, 10(1), 6–24. Zucker, R. A., Wong, M. M., Clark, D. B., Leonard, K. E., Schulenberg, J. E., Cornelius, J. R., : : : Puttler, L. I. (2006). Predicting risky drinking outcomes longitudinally: What kind of advance notice can we get? Alcoholism: Clinical and Experimental Research, 30(2), 243–252.