Computer-delivered personalized feedback intervention for hazardous drinkers with elevated anxiety sensitivity: Study protocol for a randomized controlled trial

Abstract

Integrated treatments for hazardous drinking and anxiety/depression are virtually nonexistent. Anxiety sensitivity is a common mechanism for both anxiety/depression and hazardous drinking. This article presents a study protocol for the development and testing of an integrated remotely delivered anxiety sensitivity/hazardous drinking personalized feedback intervention. Hazardous drinking college students with elevated anxiety sensitivity (n = 130) will be randomly assigned to a single session of a novel integrated personalized feedback intervention for anxiety sensitivity/hazardous drinking or control condition. Follow-ups will occur at 1 week, 1 month, and 3 months. This study will serve as an initial test and proof of concept for an integrated anxiety sensitivity/hazardous drinking personalized feedback intervention.

Keywords

Alcohol anxiety health behavior Internet intervention

College drinking is a growing concern (Merrill and Carey, 2016). A majority of college students consume alcohol and those who do are inclined to drink heavily (Johnston et al., 2016). Numerous consequences of alcohol consumption among college students have been documented, including injuries (Hingson et al., 2009), memory loss (Marino and Fromme, 2015), and academic problems (El Ansari et al., 2013).

Hazardous drinking, a pattern of drinking that is characterized by problems/consequences (World Health Organization, 2015), has broad public health implications in college. Hazardous drinking is less severe and more common than alcohol use disorder (AUD) but may be more responsive to intervention (Institute of Medicine, 1990; Saunders and Conigrave, 1990). Although hazardous drinking is common among college students (Demartini and Carey, 2012; Read et al., 2016), it has been normalized due to college drinking culture (Wechsler and Nelson, 2001).

Alarmingly, only 7% of hazardous drinkers receive treatment (Cunningham et al., 2008), potentially due to barriers (e.g. stigma), which may be alleviated through the use of computerized interventions such as personalized feedback interventions (PFIs; Cunningham et al., 1993; Grant, 1997). PFIs present individuals with personal drinking profiles (e.g. consumption patterns), risk factors, consequences, and discrepant normative comparisons (e.g. amount consumed relative to peers; Walters and Neighbors, 2005). Individuals tend to hold exaggerated perceptions about the prevalence of drinking among peers (Perkins and Berkowitz, 1986), making their own drinking habits seem normative (Perkins et al., 1999). Changes in norms (or normative beliefs) can result in behavioral changes (e.g. Perkins and Berkowitz, 1986). Indeed, PFIs have been effective in reducing alcohol use among college students (Miller et al., 2013). However, the efficacy of PFIs among vulnerable subgroups of hazardous drinking students is currently unknown.

One such subgroup consists of those with comorbid mental health symptoms/disorders, which may complicate hazardous drinking treatment. Comorbidity is high between emotional symptoms/disorders and hazardous drinking (e.g. Grant et al., 2004; Kushner et al., 2000). Among college students, having an emotional disorder is associated with more severe drinking (Dawson et al., 2005). However, there are reciprocal relations between alcohol use and emotional problems that interact, influencing both trajectories over time (Kushner et al., 2000). Treatments for comorbid emotional and alcohol problems have been limited by a sequential approach (e.g. treating one problem followed by the other) with a need for integrated simultaneous treatment (Schumm and Gore, 2016).

One way to develop integrated interventions is to focus on common elements that may be maintaining the comorbid conditions, such as anxiety sensitivity (AS). Defined as the fear of anxiety-related sensations (Reiss, 1991; Reiss et al., 1986), AS is a transdiagnostic vulnerability factor involved in the etiology and maintenance of emotional disorders (Naragon-Gainey, 2010; Olatunji and Wolitzky-Taylor, 2009) and hazardous drinking (Samoluk et al., 1999; Stewart et al., 1995, 1999). AS is also a prospective predictor of AUD (Schmidt et al., 2007) and has been implicated as a common factor contributing to the comorbidity of emotional symptoms/disorders and AUD (Baillie et al., 2010; Boschloo et al., 2013; DeHaas et al., 2002; Stewart et al., 1999), making it an ideal target for comorbid alcohol–emotion intervention. AS has been shown to be modifiable in response to interventions including brief computer interventions (Keough and Schmidt, 2012), with some work showing that AS reduction is associated with reductions in non-targeted alcohol use severity (Olthuis et al., 2015; Paulus et al., 2019; Watt et al., 2008). Yet, integrative AS-drinking protocols have not yet been established.

The goal of this study is to develop and test the first PFI directly targeting hazardous drinking and AS among emotionally vulnerable hazardous college drinkers (i.e. those with elevated AS). Specifically, the PFI will integrate AS education and reduction strategies into a traditional alcohol PFI framework to reduce AS, hazardous drinking, and emotional symptoms (anxiety and depression).

Methods

Study design

This study will utilize a randomized controlled design to compare a novel integrated AS/alcohol PFI to an attention control among hazardous drinking college students with elevated AS (n = 130). The intervention consists of a single remotely delivered computer session. Follow-up assessments will be conducted 1 week, 1 month, and 3 months after the baseline.

Specific aims

This study aims to develop and test the initial efficacy of an integrated PFI for AS and hazardous drinking among college students. We hypothesize that those allocated to the PFI will evidence greater reductions in AS, hazardous drinking, anxiety, and depression, relative to those in the attention control condition.

Participants

Participants will include 130 hazardous drinking college students with elevated AS. Participants will (1) be students, (2) be at least 18 years of age, (3) have elevated AS (defined as 17 or greater on the Anxiety Sensitivity Index-3 (ASI-3; Taylor et al., 2007), consistent with suggested cutoffs for moderate to high AS (Allan et al., 2014), (4) and meet criteria for hazardous drinking, defined as 8+ for males (7+ for females) on the Alcohol Use Disorders Identification Test (AUDIT; Saunders et al., 1993). Individuals will be ineligible for the study if they report current participation in treatment for an alcohol/drug problem, including smoking cessation and mental health treatment. These criteria were selected to maintain the internal validity of the study while offering a high degree of generalizability.

Procedure

This study is funded by the National Institute on Alcohol Abuse and Alcoholism (NIAAA; F31AA024968). The Institutional Review Board of the host institution approved the study. This study is currently in the follow-up phase.

Recruitment and screening

Individuals will be recruited from host institution campus using fliers, online media (e.g. Facebook), and the university research study pool. A link to a brief screening survey will be available on all recruitment materials. Individuals will provide informed consent and answer demographic questions and eligibility measures (see below). For completing the screening survey, course credit will be offered for students participating in the university research study pool, and all individuals completing the screening survey will be eligible to win one of two $50 gift certificates. Eligible individuals will be able to earn up to $100 in gift cards for participating in the study. At each assessment point, individuals can choose to receive either a gift card or additional course credit through the university research pool: $30 for the baseline, $20 for each of the three follow-ups, and $10 bonus to individuals completing all parts of the study.

Enrollment/randomization

If eligible, individuals will be emailed information regarding participation in the study and a link to participate. Participants will be randomly assigned to the active (integrated PFI) or attention control conditions and will be blind to condition. The study links allow participants to take the study from any Internet-connected device of their choosing. The initial session consisted of questionnaires followed by online content related to the randomized condition (see below).

Integrated AS/hazardous drinking personalized feedback intervention

The novel integrated PFI will be developed and modeled from past PFIs targeting hazardous drinking (e.g. Neighbors et al., 2004). Participants will view feedback (determined via computer program/algorithm; see Supplemental material) based on their age and gender (Cunningham et al., 2006). The PFI will provide educational information outlining the role of AS in the maintenance and development of alcohol and emotional problems and drinking to cope with unpleasant emotions. Norms for AS will be based on previously published work (Allan et al., 2014); however, all individuals in the study will have elevated AS and receive feedback on the risks of elevated AS. In order to facilitate attention and engagement, the PFI will be interactive and will include multiple-choice questions related to the content at periodic intervals. Responses will be followed immediately by an indication that the participant answered correctly or incorrectly (with the correct answer provided). Strategies for effective reduction of AS (e.g. exercise, deep breathing, relaxation) will be described along with additional techniques for adaptive coping. Three 4-minute videos will be presented, each demonstrating an interoceptive exposure exercise: straw breathing, over-breathing, and head rush. In addition, the PFI will contrast participants’ drinking behavior with perceived norms and actual norms. Norms for alcohol use were based on anonymous survey data (e.g. AUDIT, drinks per day) from over 18,000 students at multiple Universities (Moyer et al., 2004). Participants will be presented with various graphical displays of their average frequency and quantity of alcohol consumption, their perceived average frequency/quantity of alcohol consumption for their age group/gender, and the actual average frequency/consumption for their age group/gender as a means of norms comparisons.

Attention information control

Participants in the control group will receive facts about the student body, retrieved from various official University sources. To increase attention, videos (freely available on YouTube.com) will be shown and interactive questions about the University will be asked. Correct responses will be validated and inaccurate responses will be corrected. Similar control conditions have been utilized in previous trials of PFIs (Buckner et al., 2019; Neighbors et al., 2010).

Follow-up

Participants will receive emails with links to questionnaires on three occasions after the baseline session: 1 week, 1 month, and 3 months post baseline. Participants will be sent automated prompts to complete the follow-ups and notified when follow-ups will expire. Each follow-up link will be active for 7 days. Individuals missing any follow-ups will still be eligible and contacted for later follow-ups to maximize data retention.

Assessments

Demographics

Demographic questions including sex, age, sexual orientation, race/ethnicity, educational level, and history of substance/mental health treatment will be used at screening. Items from this measure will be used to describe the sample and determine eligibility.

Anxiety Sensitivity Index-3 (ASI-3)

The ASI-3 is an 18-item self-report measure of AS (Taylor et al., 2007). Items are rated on a 5-point Likert-type scale from 0 (very little) to 4 (very much). The ASI-3 shows good convergent and discriminant validity (Taylor et al., 2007) as well as invariance across sex, age, race/ethnicity, and sexual minority status (Jardin et al., 2018). The ASI-3 will be administered at screening, baseline, and all follow-ups.

Alcohol Use Disorders Identification Test (AUDIT)

The AUDIT is used to identify past year hazardous drinking (Saunders et al., 1993). The AUDIT has strong psychometric properties (e.g. Saunders et al., 1993). Ten items are rated on various scales from 0 (e.g. never) to 4 (e.g. daily or almost daily). Total scores of 8+ (men) and 7+ (women), respectively, are used to identify hazardous drinkers. The AUDIT will be administered in the screening. The AUDIT-C (Bush et al., 1998) is an indicator of hazardous drinking among college students (Verhoog et al., 2019) with efficient and accurate measurement (De Meneses-Gaya et al., 2009; Reinert and Allen, 2002) and good test–retest reliability (Jeong et al., 2017). AUDIT-C items are not linked to the past-year timeframe, and thus will be administered at baseline and all follow-ups to assess changes in hazardous drinking severity over time.

Mood Anxiety Symptom Questionnaire–Short Form (MASQ-SF)

The MASQ-SF is a measure of emotional symptoms; 30 items are rated on a scale from 1 (not at all) to 5 (extremely) (Wardenaar et al., 2010). The MASQ-SF yields three subscales: general distress (MASQ-GD), anhedonic depression (MASQ-AD), and anxious arousal (MASQ-AA). The MASQ-SF has demonstrated convergent validity with other measures of anxiety/depression (Wardenaar et al., 2010). The MASQ-AA and MASQ-AD will be used to measure anxiety and depressive symptoms, respectively. Although social anxiety is strongly related to alcohol use (Buckner et al., 2008; Ham and Hope, 2005), multiple domains of anxiety co-occur with alcohol use (Grant et al., 2004). Thus, the current study will assess anxiety from a transdiagnostic perspective (Norton and Paulus, 2017); this approach allows for a streamlined assessment albeit at the expense of specificity into multiple anxiety domains. The MASQ will be administered at baseline and all follow-ups.

Data analysis

Analyses will be conducted using Mplus (version 7.4; Muthén and Muthén, 2015) using maximum likelihood estimation with robust standard errors (i.e. MLR estimation). Maximum likelihood accounts for missing data in a manner as good as, if not better than, multiple imputation (Allison, 2003). To evaluate change in dependent variables (alcohol consumption (AUDIT-C), AS (ASI-3), anxiety (MASQ-AA), and depression (MASQ-AD) over the course of the four time periods (baseline, 1-week follow-up, 1-month follow-up, 3-month follow-up), latent growth curve modeling will be used. Slopes will be estimated for each outcome, representing the average trajectory of intraindividual change in each construct. Intercepts will be centered at the baseline. Both linear and non-linear (e.g. quadratic) slopes will be tested with each outcome.

Growth models will be fit for each outcome, with treatment condition (0 = control; 1 = PFI) included as a predictor of intercept and slope, to examine treatment-related differences in baseline values and rate of change. Model fit will be assessed using root mean square error of approximation (RMSEA), comparative fit index (CFI), Tucker–Lewis index (TLI), and standardized root mean square residual (SRMR). Cohen’s d, calculated as the mean difference divided by the pooled standard deviation, will be used as an estimate of effect size for changes and will be examined to indicate at what time point change occurred for descriptive purposes (e.g. between baseline and 1-week follow-up).

Statistical power

Although no effect size estimates are available for PFI among emotionally vulnerable drinkers, the anticipated effect size is based on extant work with single-session PFI for alcohol (d = 0.22; Riper et al., 2009). Using G*Power 3.1 (Faul et al., 2007), it was determined that in order to detect a d of 0.22 using repeated measures ANOVA with an interaction of time by group, with an alpha of .05 and power of .80 among two groups and measures repeated across four time points, 114 individuals would be required. Given latent growth curve modeling has greater power to detect effects than ANOVA (Xitao and Xiaotao, 2005), we are confident this estimate will be sufficient. Furthermore, we anticipate that recruiting 130 will be more than adequate to accomplish the aims of the proposed study with anticipated attrition of 10%, consistent with past work utilizing web-based PFIs among students with a 3-month follow-up (Lewis et al., 2014). Indeed, a recent study (Buckner et al., 2019) of PFI for alcohol use among students was successfully implemented with a similar sample size (n = 122 used for analysis) despite having only 1 follow-up; the current study will have 3 follow-ups, thus increasing power to detect effects (Bakeman and Robinson, 2005).

Discussion

Among college students, alcohol use, broadly, and hazardous drinking, specifically, are tremendous public health problems (DeMartini and Carey, 2012; Merrill and Carey, 2016; Read et al., 2016). Yet, a small fraction of hazardous drinking students receive treatment (Cunningham et al., 2008). Furthermore, emotionally vulnerable hazardous drinking students represent a particularly at risk and understudied subgroup that are not only more difficult to treat, but also unlikely to utilize currently available treatments (Baker et al., 2012; Book et al., 2009; Boschloo et al., 2012; Burns et al., 2005; Haver and Gjestad, 2005; Kushner et al., 2005; Mills et al., 2009). Extending from a wide body of work on computer-delivered interventions for hazardous drinking students (Miller et al., 2013), the proposed low-cost and low-effort novel intervention has the potential to broadly reach these vulnerable drinkers. By capitalizing on separate lines of scholarly work establishing the potential of PFIs for college drinkers (e.g. Buckner et al., 2019; Neighbors et al., 2019) and AS reduction treatments (Smits et al., 2008), the proposed PFI for hazardous drinking and AS represents the first integrated computer-delivered intervention for emotionally vulnerable college drinkers. Theoretically, implementing feedback and education on AS in addition to AS reduction strategies into a hazardous drinking PFI framework may more effectively reduce drinking and emotional outcomes in this population. To date, no PFIs have been evaluated among emotionally vulnerable hazardous drinkers. If successful, this study would represent the first intervention for emotionally vulnerable hazardous college drinkers.

There are several strengths of the current design. First, it focuses on college drinkers, an extremely prevalent, yet underserved group. Second, it specifically targets a potentially vulnerable and understudied group of emotionally vulnerable (i.e. high AS) drinkers, for which we are unaware of any current interventions. Third, the study is built upon strong theory and numerous past studies utilizing PFI’s among college drinkers and computer-based interventions for AS, respectively, and represents a novel attempt to integrate them into one cohesive treatment. Fourth, as with other PFIs and computer interventions, this approach is low cost and has broad reach. PFIs are widely disseminable and reduce barriers such as stigma, access to clinics/care, and cost (Cunningham et al., 1993; Grant, 1997). Fifth, this intervention will be delivered remotely to maximize generalizability; individuals will receive the intervention from computer-devices of their choosing, in their own homes rather than under artificial laboratory conditions. Sixth, the study will be conducted in one of the most diverse cities in the United States. As such, we expect a racially/ethnically diverse sample.

There are several limitations of the current design that should be noted. First, although the randomized design is a strength, the study is limited by an inactive control condition. As this is an initial proof of concept study, the goal is to demonstrate a signal for the proposed intervention; if successful, additional studies can compare the integrated AS/hazardous drinking PFI to a traditional hazardous drinking only PFI among emotionally vulnerable college drinkers. Second, the remote nature of the study results in the potential for bias or lack of attention to the intervention/study measures. We believe the benefits of this approach in terms of ecological validity outweigh the costs that result in having individuals participate under varied and unsupervised conditions. Although past work suggests that in person PFIs are more effective (Rodriguez et al., 2015), given the aim of wide distribution of PFIs, we wanted to test the novel intervention under “real-life” conditions. Third, due to the lack of laboratory visits, the study relies solely on self-report and is subject to various biases (e.g. recall, impression management).

In sum, this proposed intervention has the potential to reach an important and vulnerable subgroup of college drinkers. Utilizing brief and remotely delivered personalized feedback, the proposed intervention is low cost and in a format that can be widely disseminable in the future. This represents a meaningful step forward in the way of integrated mood/anxiety–alcohol interventions.

Supplemental Material

Figure_1_Supplementary – Supplemental material for Computer-delivered personalized feedback intervention for hazardous drinkers with elevated anxiety sensitivity: Study protocol for a randomized controlled trial

Supplemental material, Figure_1_Supplementary for Computer-delivered personalized feedback intervention for hazardous drinkers with elevated anxiety sensitivity: Study protocol for a randomized controlled trial by Daniel J Paulus, Matthew W Gallagher, Clayton Neighbors and Michael J Zvolensky in Journal of Health Psychology

Footnotes

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by NIAAA grant (F31 AA 024968) awarded to the first author.

ORCID iDs

Daniel J Paulus

Michael J Zvolensky

Supplemental material

Supplemental material for this article is available online.

References

Allan

Korte

Capron

, et al. (2014) Factor mixture modeling of anxiety sensitivity: A three-class structure. Psychological Assessment 26(4): 1184–1195.

Allison

(2003) Missing data techniques for structural equation modeling. Journal of Abnormal Psychology 112(4): 545–557.

Baillie

Stapinski

Crome

, et al. (2010) Some new directions for research on psychological interventions for comorbid anxiety and substance use disorders. Drug and Alcohol Review 29(5): 518–524.

Bakeman

Robinson

(2005) Understanding Statistics in the Behavioral Sciences. London: Psychology Press.

Baker

Thornton

Hiles

, et al. (2012) Psychological interventions for alcohol misuse among people with co-occurring depression or anxiety disorders: A systematic review. Journal of Affective Disorders 139(3): 217–229.

Book

Thomas

Dempsey

, et al. (2009) Social anxiety impacts willingness to participate in addiction treatment. Addictive Behaviors 34(5): 474–476.

Boschloo

Vogelzangs

Van den Brink

, et al. (2012) Predictors of the 2-year recurrence and persistence of alcohol dependence. Addiction 107(9): 1639–1640.

Boschloo

Vogelzangs

Van den Brink

, et al. (2013) The role of negative emotionality and impulsivity in depressive/anxiety disorders and alcohol dependence. Psychological Medicine 43(6): 1241–1253.

Buckner

Neighbors

Walukevich-Dienst

, et al. (2019) Online personalized normative feedback intervention to reduce event-specific drinking during Mardi Gras. Experimental and Clinical Psychopharmacology 27(5): 466–473.

10.

Buckner

Schmidt

Lang

, et al. (2008) Specificity of social anxiety disorder as a risk factor for alcohol and cannabis dependence. Journal of Psychiatric Research 42(3): 230–239.

11.

Burns

Teesson

O’Neill

(2005) The impact of comorbid anxiety and depression on alcohol treatment outcomes. Addiction 100(6): 787–796.

12.

Bush

Kivlahan

McDonell

, et al. (1998) The AUDIT alcohol consumption questions (AUDIT-C): An effective brief screening test for problem drinking. Archives of Internal Medicine 158(16): 1789–1795.

13.

Cunningham

Humphreys

Kypri

, et al. (2006) Formative evaluation and three-month follow-up of an online personalized assessment feedback intervention for problem drinkers. Journal of Medical Internet Research 8(2): e5.

14.

Cunningham

Neighbors

Wild

, et al. (2008) Ultra-brief intervention for problem drinkers: Research protocol. BMC Public Health 8: 298.

15.

Cunningham

Sobell

, et al. (1993) Barriers to treatment: Why alcohol and drug abusers delay or never seek treatment. Addictive Behaviors 18(3): 347–353.

16.

Dawson

Grant

Stinson

, et al. (2005) Psychopathology associated with drinking and alcohol use disorders in the college and general adult populations. Drug and Alcohol Dependence 77(2): 139–150.

17.

De Meneses-Gaya

Zuardi

Loureiro

, et al. (2009) Alcohol Use Disorders Identification Test (AUDIT): An updated systematic review of psychometric properties. Psychology & Neuroscience 2(1): 83–97.

18.

DeHaas

RAB

Calamari

Bair

(2002) Anxiety sensitivity and the situational antecedents to drug and alcohol use: An evaluation of anxiety patients with substance use disorders. Cognitive Therapy & Research 26(3): 335–353.

19.

Demartini

Carey

(2012) Optimizing the use of the AUDIT for alcohol screening in college students. Psychological Assessment 24(4): 954–963.

20.

El Ansari

Stock

Mills

(2013) Is alcohol consumption associated with poor academic achievement in university students? International Journal of Preventive Medicine 4(10): 1175–1188.

21.

Faul

Erdfelder

Lang

A-G

, et al. (2007) G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods 39(2): 175–191.

22.

Grant

(1997) Barriers to alcoholism treatment: Reasons for not seeking treatment in a general population sample. Journal of Studies on Alcohol 58(4): 365–371.

23.

Grant

Stinson

Dawson

, et al. (2004) Prevalence and co-occurrence of substance use disorders and independent mood and anxiety disorders: Results from the National Epidemiologic Survey on Alcohol and Related Conditions. Archives of General Psychiatry 61(8): 807–816.

24.

Ham

Hope

(2005) Incorporating social anxiety into a model of college student problematic drinking. Addictive Behaviors 30(1): 127–150.

25.

Haver

Gjestad

(2005) Phobic anxiety and depression as predictor variables for treatment outcome: A LISREL analysis on treated female alcoholics. Nordic Journal of Psychiatry 59(1): 25–30.

26.

Hingson

Zha

Weitzman

(2009) Magnitude of and trends in alcohol-related mortality and morbidity among US college students ages 18-24, 1998-2005. Journal of Studies on Alcohol and Drugs, Supplement 16: 12–20.

27.

Institute of Medicine (1990) Broadening the Base of Treatment for Alcohol Problems. Washington, DC: National Academy Press.

28.

Jardin

Paulus

Garey

, et al. (2018) Towards a greater understanding of anxiety sensitivity across groups: The construct validity of the Anxiety Sensitivity Index-3. Psychiatry Research 268: 72–81.

29.

Jeong

Park

Lim

, et al. (2017) Psychometric properties of the Alcohol Use Disorders Identification Test-Consumption (AUDIT-C) in public first responders. Substance Use & Misuse 52(8): 1069–1075.

30.

Johnston

O’Malley

Bachman

, et al. (2016) Monitoring the Future National Survey Results on Drug Use, 1975-2015: College Students and Adults Ages, vol. 2. Ann Arbor, MI: Institute for Social Research, pp. 19–55.

31.

Keough

Schmidt

(2012) Refinement of a brief anxiety sensitivity reduction intervention. Journal of Consulting and Clinical Psychology 80(5): 766–772.

32.

Kushner

Abrams

Borchardt

(2000) The relationship between anxiety disorders and alcohol use disorders: A review of major perspectives and findings. Clinical Psychology Review 20(2): 149–171.

33.

Kushner

Abrams

Thuras

, et al. (2005) Follow-up study of anxiety disorder and alcohol dependence in comorbid alcoholism treatment patients. Alcoholism: Clinical and Experimental Research 29(8): 1432–1443.

34.

Lewis

Patrick

Litt

, et al. (2014) Randomized controlled trial of a web-delivered personalized normative feedback intervention to reduce alcohol-related risky sexual behavior among college students. Journal of Consulting and Clinical Psychology 82(3): 429–440.

35.

Marino

Fromme

(2015) Alcohol-induced blackouts and maternal family history of problematic alcohol use. Addictive Behaviors 45: 201–206.

36.

Merrill

Carey

(2016) Drinking over the lifespan: Focus on college ages. Alcohol Research: Current Reviews 38(1): 103–114.

37.

Miller

Leffingwell

Claborn

, et al. (2013) Personalized feedback interventions for college alcohol misuse: An update of Walters & Neighbors (2005). Psychology of Addictive Behaviors 27(4): 909–920.

38.

Mills

Deady

Proudfoot

, et al. (2009) Guidelines on the Management of Co-Occurring Alcohol and Other Drug and Mental Conditions in Alcohol and Other Drug Treatment Settings. Sydney, NSW, Australia: University of New South Wales.

39.

Moyer

Rikard

Van Sickle

, et al. (2004) The Electronic CHECKUP TO GO. San Diego, CA: San Diego State University.

40.

Muthén

(2015) Mplus User’s Guide (7th edn). Los Angeles, CA: Muthén & Muthén.

41.

Naragon-Gainey

(2010) Meta-analysis of the relations of anxiety sensitivity to the depressive and anxiety disorders. Psychological Bulletin 136(1): 128–150.

42.

Neighbors

DiBello

Young

, et al. (2019) Personalized normative feedback for heavy drinking: An application of deviance regulation theory. Behaviour Research and Therapy 115: 73–82.

43.

Neighbors

Larimer

Lewis

(2004) Targeting misperceptions of descriptive drinking norms: Efficacy of a computer-delivered personalized normative feedback intervention. Journal of Consulting and Clinical Psychology 72(3): 434–447.

44.

Neighbors

Lewis

Atkins

, et al. (2010) Efficacy of web-based personalized normative feedback: A two-year randomized controlled trial. Journal of Consulting and Clinical Psychology 78(6): 898–911.

45.

Norton

Paulus

(2017) Transdiagnostic models of anxiety disorder: Theoretical and empirical underpinnings. Clinical Psychology Review 56: 122–137.

46.

Olatunji

Wolitzky-Taylor

(2009) Anxiety sensitivity and the anxiety disorders: A meta-analytic review and synthesis. Psychological Bulletin 135(6): 974–999.

47.

Olthuis

Watt

Mackinnon

, et al. (2015) CBT for high anxiety sensitivity: Alcohol outcomes. Addictive Behaviors 46: 19–24.

48.

Paulus

Gallagher

Raines

, et al. (2019) Intraindividual change in anxiety sensitivity and alcohol use severity 12-months following smoking cessation treatment. Behaviour Research and Therapy 116: 10–18.

49.

Perkins

Berkowitz

(1986) Perceiving the community norms of alcohol use among students: Some research implications for campus alcohol education programming. International Journal of the Addictions 21(9–10): 961–976.

50.

Perkins

Meilman

Leichliter

, et al. (1999) Misperceptions of the norms for the frequency of alcohol and other drug use on college campuses. Journal of American College Health 47(6): 253–258.

51.

Read

Radomski

Haas

, et al. (2016) Identification of hazardous drinking with the Young Adult Alcohol Consequences Questionnaire: Relative operating characteristics as a function of gender. Psychological Assessment 28(10): 1276–1289.

52.

Reinert

Allen

(2002) The Alcohol Use Disorders Identification Test (AUDIT): A review of recent research. Alcoholism: Clinical and Experimental Research 26(2): 272–279.

53.

Reiss

(1991) Expectancy model of fear, anxiety, and panic. Clinical Psychology Review 11(2): 141–153.

54.

Reiss

Peterson

Gursky

, et al. (1986) Anxiety sensitivity, anxiety frequency and the prediction of fearfulness. Behaviour Research and Therapy 24(1): 1–8.

55.

Riper

Van Straten

Keuken

, et al. (2009) Curbing problem drinking with personalized-feedback interventions: A meta-analysis. American Journal of Preventive Medicine 36(3): 247–255.

56.

Rodriguez

Neighbors

Rinker

, et al. (2015) Remote versus in-lab computer-delivered personalized normative feedback interventions for college student drinking. Journal of Consulting and Clinical Psychology 83(3): 455–463.

57.

Samoluk

Stewart

Sweet

, et al. (1999) Anxiety sensitivity and social affiliation as determinants of alcohol consumption. Behavior Therapy 30(2): 285–303.

58.

Saunders

Aasland

Babor

, et al. (1993) Development of the Alcohol Use Disorders Identification Test (AUDIT): WHO collaborative project on early detection of persons with harmful alcohol consumption-II. Addiction 88(6): 791–804.

59.

Saunders

Conigrave

(1990) Early identification of alcohol problems. Canadian Medical Association Journal (CMAJ) 143(10): 1060–1069.

60.

Schmidt

Buckner

Keough

(2007) Anxiety sensitivity as a prospective predictor of alcohol use disorders. Behavior Modification 31(2): 202–219.

61.

Schumm

Gore

(2016) Simultaneous treatment of co-occurring posttraumatic stress disorder and substance use disorder. Current Treatment Options in Psychiatry 3(1): 28–36.

62.

Smits

JAJ

Berry

Tart

, et al. (2008) The efficacy of cognitive-behavioral interventions for reducing anxiety sensitivity: A meta-analytic review. Behaviour Research and Therapy 46(9): 1047–1054.

63.

Stewart

Peterson

Pihl

(1995) Anxiety sensitivity and self-reported alcohol consumption rates in university women. Journal of Anxiety Disorders 9(4): 283–292.

64.

Stewart

Samoluk

MacDonald

(1999) Anxiety sensitivity and substance use and abuse. In: Taylor

(ed.) Anxiety Sensitivity: Theory, Research, and Treatment of the Fear of Anxiety (The LEA Series in Personality and Clinical Psychology). Mahwah, NJ: Lawrence Erlbaum, pp. 287–319.

65.

Taylor

Zvolensky

Cox

, et al. (2007) Robust dimensions of anxiety sensitivity: Development and initial validation of the Anxiety Sensitivity Index-3. Psychological Assessment 19(2): 176–188.

66.

Verhoog

Dopmeijer

de Jonge

, et al. (2019) The use of the Alcohol Use Disorders Identification Test —Consumption as an indicator of hazardous alcohol use among university students. European Addiction Research 26: 1–9.

67.

Walters

Neighbors

(2005) Feedback interventions for college alcohol misuse: What, why and for whom? Addictive Behaviors 30(6): 1168–1182.

68.

Wardenaar

Van Veen

Giltay

, et al. (2010) Development and validation of a 30-item short adaptation of the Mood and Anxiety Symptoms Questionnaire (MASQ). Psychiatry Research 179(1): 101–106.

69.

Watt

Stewart

Conrod

, et al. (2008) Personality-based approaches to treatment of co-morbid anxiety and substance use disorder. In: Stewart

Conrod

(eds) Anxiety and Substance Use Disorders (Series in Anxiety and Related Disorders). New York: Springer, pp. 201–219.

70.

Wechsler

Nelson

(2001) Binge drinking and the American college students: What’s five drinks? Psychology of Addictive Behaviors 15(4): 287–291.

71.

World Health Organization (2015) Lexicon of alcohol and drug terms published by the World Health Organization. Available at: http://www.who.int/substance_abuse/terminology/who_lexicon/en/

72.

Xitao

Xiaotao

(2005) Power of latent growth modeling for detecting linear growth: Number of measurements and comparison with other analytic approaches. The Journal of Experimental Education 73(2): 121–139.

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