Effects of Alcohol Intoxication on Sexual Decision-Making Among Men Who Have Sex With Men: Alcohol’s Influences on Self-Control Processes

Abstract

This experiment tested mechanisms linking alcohol intoxication and analogue determinants of condomless anal intercourse (CAI) in a sample of 257 men who have sex with men (MSM). The two mechanisms tested were implicit approach biases toward CAI stimuli and executive working memory. Participants were randomly assigned to three conditions (water control, placebo, or alcohol) and, following beverage administration, completed a working memory task, an approach-avoidance task of sexual versus condom stimuli, and two video role-play vignettes of high-risk sexual scenarios. Sexual arousal and CAI intentions were assessed by self-report, and behavioral skills and risk exposure were derived from participants’ role-play behavior. Estimation of four path models showed that the hypothesized mechanisms were supported for the CAI intention outcome, but the findings for the skills and risk-exposure outcome were mixed. Implications for development and enhancement of HIV prevention interventions are discussed.

Keywords

MSM alcohol condomless sex implicit associations executive function

Despite considerable advances in HIV prevention interventions, HIV and other sexually transmitted infections (STIs) remain significant public-health problems (Jiang et al., 2020). Data suggest that in the United States, the extent of the public-health problem varies by population subgroup, but the most acute problem remains in men who have sex with men (MSM; Centers for Disease Control and Prevention [CDC], 2018). Accordingly, the National Institutes of Health Office of AIDS Research 2021–2025 Strategic Plan emphasizes primary and secondary prevention efforts that target subpopulations that remain heavily affected by HIV/AIDS (National Institutes of Health, 2019). Furthermore, according to the CDC, unprotected sexual intercourse is the way that HIV most often is contracted in the United States. Therefore, increasing the effectiveness of behavioral interventions or combined behavioral and biomedical interventions first requires an understanding of mechanisms that cause, in the case of MSM, condomless anal intercourse (CAI).

Much research has been devoted to identifying variables that predict the likelihood and frequency of condomless sex and has shown that alcohol consumption is one of the most prominent among them. Research published over the past 4 decades across multiple countries has consistently shown a positive association between aggregate alcohol consumption (quantity, frequency) over a defined interval of time and aggregate frequency of occurrences of condomless sex (Williams et al., 2016). Event-level studies have likewise demonstrated an association between alcohol use and the occurrence of condomless sex, although they also have identified important contextual moderators of the association (Freeman, 2016; Wray et al., 2019). The causal influence of alcohol on sexual risk has been supported in experimental analogue studies, which have consistently shown that acute alcohol consumption increases the degree of intention to engage in condomless sex and decreases the use of strategies related to condom use (e.g., role-play performance of condom-use negotiation skills; Berry & Johnson, 2018; Scott-Sheldon et al., 2016). Alcohol is an especially important variable to consider in studying CAI in MSM because MSM have a higher prevalence of alcohol use than individuals demographically matched in the general population (Maisto & Simons, 2016; Medley et al., 2016).

Unfortunately, there is a major gap in the literature regarding empirical evidence for mechanisms underlying the alcohol–condomless sex relation (Berry & Johnson, 2018; Wray et al., 2021). This research gap is critical for at least two reasons. First, understanding mechanisms is essential to advancing knowledge and theory regarding alcohol’s effects on condomless sexual behavior. Second, knowledge of mechanisms of behavior is fundamental to developing more effective behavior-change interventions (Kazdin, 2007). This latter point is critical in the context of mechanisms underlying alcohol’s relation to condomless sex and HIV-prevention interventions that target MSM because such interventions have largely neglected to incorporate the alcohol consumption–condomless sex relation (Vagenas et al., 2015; Williams et al., 2016; Woolf-King et al., 2020).

Self-Control Mechanisms in Health Behaviors

Self-control is a key component of interventions designed to modify behaviors that affect health, such as tobacco use, excessive eating, and engaging in condomless sex. Self-control has been characterized as a set of processes that underlies one’s ability to override thoughts, feelings, and action tendencies that compete with a specific goal (e.g., choosing an activity to pursue a health goal rather than pleasure; Inzlicht & Berkman, 2015). There is substantial empirical evidence that shows an inverse relation between self-control abilities and the occurrence of behaviors that tend to result in adverse health-related consequences (Protogerou et al., 2020). Dual-process models posit that self-control may consist of two dimensions, a fast-acting, reflexive, “automatic” dimension and a slower acting, deliberative or effortful dimension (Heather, 2017; Lieberman, 2007; Osgood & Muraven, 2018; Wiers et al., 2007). The automatic, reflexive dimension emphasizes affective processes, whereas the effortful control dimension may largely consist of higher order “executive function” (EF) processes (Lieberman, 2007).

Automatic processes are based on activated associations between risk stimuli and their evaluation. For sexual stimuli, these evaluative associations may include feelings, cognitions, and action tendencies that are represented in memory and indexed with implicit measures. Research has shown that implicit measures of approach–sexual stimuli associations are correlated with sexual activity (Hinzmann et al., 2020; Hofmann, Friese, & Strack, 2009). When an individual’s EF capacities or motivation to exert self-control is compromised, behavior becomes more strongly influenced by implicit evaluations that may not align with his or her explicit self-control goals (Hofmann, Friese, & Strack, 2009). The ability to exercise self-control varies widely both between individuals (e.g., Maisto et al., 2021; Simons et al., 2010; Wills et al., 2006) and within individuals across time as a function of contextual factors (Baumeister et al., 2007; Lieberman, 2007). Two such contextual factors are alcohol consumption and sexual arousal.

Alcohol intoxication is associated with pronounced deficits in EF processes, which include working memory (WM), attention, and processing speed (Curtin & Fairchild, 2003; Pihl et al., 2003). Given this, we posit that alcohol may shift the relative influences of automatic as opposed to effortful decision-making processes on sexual behavior. When sober, individuals may tend to use deliberative processes to make decisions about sexual behavior according to personal standards and perceived costs and benefits. Such processing of goals may increase the tendency to engage in condom use. However, as degree of intoxication increases, the role of deliberative processes decreases, and automatic processes increasingly drive behavior that leads to a higher likelihood of condomless sex. In this regard, higher EF has been shown to be associated with decreased occurrence of condomless sex, but acute intoxication diminishes this effect (Ralevski et al., 2012). Accordingly, change in the influence of approach biases as a result of alcohol’s impairing effects on EF may mediate and adds to alcohol’s effects on implicit processes described earlier to influence the relation between alcohol consumption and condomless sex. However, research has yet to examine the effect of alcohol in moderating associations between implicit measures of sexual approach and sexual decision-making.

Sexual arousal has both motivational and emotional properties (Janssen, 2011; Toates, 2009) and thus is essential to consider in understanding the relations among alcohol, implicit motivation, executive function, and sexual decision-making. Experimental and observational research on alcohol and the occurrence of condomless sex in both individuals identifying as heterosexual or gay/bisexual (the latter, men only) has shown that sexual arousal can be either a mediator (George, 2019) or a moderator (Maisto et al., 2012; Maisto & Simons, 2016) of the relation between acute intoxication and having condomless sex. This work suggests that (a) increasing the influence to implicit biases toward sexual stimuli has one immediate effect of increasing sexual arousal and (b) alcohol’s effect on implicit processes and their motivating effects on sexual behaviors are enhanced with a greater degree of sexual arousal (moderator) and can mediate the association between alcohol consumption and the occurrence of condomless sex or act as a second, more proximal mediator of the occurrence of condomless sex as part of an alcohol → sexual-risk approach biases → sexual arousal → condomless sex pathway.

How best to represent these multiple control processes is difficult in the context of an experiment, which does not include sufficient time to measure a dose of alcohol’s acute effects on all of the components that have been hypothesized to constitute EF or multiple measures of automatic cognitive processes and then, in turn, to study how any changes affect decisions regarding having condomless sex. Consequently, it is necessary to select those elements that have demonstrated clearest empirical support in model testing. Overall, the literature suggests that WM is a particularly important component of EF given evidence that WM capacity is related to successful self-control efforts (e.g., Bridgett et al., 2013; Hofmann et al., 2012; Kemps et al., 2020). In this regard, people with lower WM capacity exhibit stronger associations between automatic cognitive processes and drug and alcohol use, eating sweets, and sexual behavior (Grenard et al., 2008; Hofmann et al., 2008; Houben et al., 2011), and training to improve WM has been linked to reduced alcohol use and delay discounting (Bickel et al., 2011; Houben et al., 2011).

Summary and Integration

Figure 1a depicts hypothesized interrelationships among key variables of executive WM, automatic approach biases toward sexual stimuli, acute alcohol intoxication (“beverage condition” in the experimental context), sexual arousal, and sexual risk behaviors. The purpose of this study was to conduct an alcohol challenge experiment designed to test the hypotheses described immediately above and represented in Figure 1a in a sample of MSM. In this study, MSM from a community sample were randomly assigned to one of three beverage conditions, alcohol (target blood alcohol concentration [BAC] = 0.075%), placebo, or water control, following completion of baseline assessments. After beverage consumption, participants completed measures of automatic approach biases (toward sexual stimuli) and working memory, respectively. Finally, participants watched two videos, each of which was followed by collection of role-play measures of condom-use negotiation skills, participant-determined “exposure” to sexual risk, and intentions to have condomless sex with specified characters depicted in the role-plays.

Fig. 1.

Hypothesized conceptual model and structural models of the relations among alcohol consumption, approach biases, working memory, and sexual arousal on sexual decision-making outcomes. Solid lines are hypothesized direct effects in the model. Dashed lines are moderating effects. The image in (a) is a conceptual model. The image in (b) is a structural model of condomless anal intercourse (CAI) intentions analysis, χ²(22, N = 257) = 16.88, p = .7699, comparative fit index (CFI) = 1.00. The image in (c) is a structural model of behavioral skills Prompt 1 analysis, χ²(22, N = 257) = 16.09, p = .8116, CFI = 1.00, root-mean-square error of approximation (RMSEA) = .00, 90% confidence interval (CI) = [0.00, 0.03], standardized root-mean-square residual = .017. The image in (d) is a structural model of risk-exposure analysis, χ²(22, N = 257) = 15.41, p = .8440, CFI = 1.00, RMSEA = .00, 90% CI = [0.00, 0.03], standardized root-mean-square residual = .017. Preexposure prophylaxis use, site, and age are included as covariates with paths to each endogenous variable but are omitted from the figures. Effects are standardized. Asterisks indicate significant effects (^†p < .10, *p < .05, **p < .01, ***p < .001).

As represented in Figure 1a, intoxication was expected to result in a higher likelihood of sexual-risk behaviors via increasing automatic approach biases toward sexual stimuli and simultaneously decreasing WM capacity. Stronger automatic approach biases were expected to increase responsiveness to sexual cues in the approach-avoidance task (AAT), and the resulting increase in sexual arousal (the latter was not manipulated in this experiment but was enhanced in all participants by the experimental stimuli) was expected to further increase effects of implicit cognition on behavior. In contrast, a decrease in WM because of intoxication was expected to reduce ability to access and use more distal health-promoting information (e.g., health risks, learned skills) and thus was expected to result in stronger associations between automatic approach biases to sexual stimuli and sexual decision-making (Hofmann et al., 2008). Sexual arousal was hypothesized to be a key factor linking alcohol intoxication and sexual decision-making (Maisto et al., 2012; Norris et al., 2009). Arousal increases the impact of automatic appetitive processes and decreases effortful control of behavior (Lieberman, 2007, National Institute on Drug Abuse, 2010). Hence, it is an essential factor to incorporate sexual arousal into the model. We expected intoxication and stronger automatic approach biases to be associated with higher reported sexual arousal when the sexual-risk outcomes were measured. The increased arousal was predicted to partially mediate intoxication effects and to moderate the hypothesized effects of automatic and controlled (executive) processes. Research was approved by the Institutional Review Boards at Syracuse University (14-068) and Boston University (3468).

Method

Participants

Participants were 257 men age 21 to 50 (M = 28.14 years, SD = 6.90) who were recruited from Syracuse, New York, and Boston, Massachusetts, using flyers, advertisements, and social-networking sites (e.g., Facebook, Grindr, Tinder). Approximately 64.71% were White, 13.33% were Black, 7.45% were Asian, 0.78% were American Indian or Alaska Native, 0.39% were Hawaiian or Pacific Islander, 3.53% were mixed race, and 9.80% were designated as “other.” Approximately 18.43% were Hispanic/Latinx. Average yearly income was $32,583.60 (SD = $26,500.10; Mdn = $30,000.00).

To be included in the study, participants had to be 21 to 50 years old, be moderate or heavy drinkers (according to the quantity frequency variability; Cahalan et al., 1969), sexually active with men (at least once a month in the 3 months before study enrollment), identify as gay or bisexual (3 or higher on the Kinsey Scale; Kinsey et al., 1948), and deny being in a committed monogamous relationship. Exclusion criteria included current medical or psychiatric problems, use of a vitamin/herb or medication for which alcohol use is contraindicated, current alcohol or other substance use disorder, alcohol treatment within the past 3 years, substance use disorder or mental-health treatment in the past 3 months, or any lifetime history of treatment for bipolar disorder or schizophrenia. Seven previous articles have reported findings from this study (Anderson et al., 2020; Luehring-Jones et al., 2019; Maisto et al., 2021; Rowland et al., 2021; Simons et al., 2019, 2021; Tahaney et al., 2020).

Measures

Screening

Individuals interested in the study contacted the laboratory and completed initial telephone screening with a trained research assistant. Telephone screening assessed for age, recent sexual behavior, relationship status, alcohol-use patterns, mental-health and substance use disorder treatment, current medications, and medical conditions. If eligible, participants were invited to schedule an initial laboratory session.

Demographics

A demographics questionnaire was administered during Session 1 to collect information about participant characteristics. The items assessed age, race, ethnicity, annual income, and whether the participant was currently prescribed and taking preexposure prophylaxis (PrEP) medication.

Manipulation checks

BAC

Actual BAC was measured using breath analysis (Alcosensor FST; Intoximeters, Inc., St. Louis, MO). BAC was assessed at six time points: upon arrival to the laboratory before beverage administration, approximately 30 min after beverage administration (postalcohol-absorption period), after the WM task, after the AAT, after the set of interactive videos, and before leaving the laboratory to ensure a safe exit.

Perception of intoxication and alcohol consumption

Perception of intoxication was measured by asking participants, “How intoxicated do you think you are?” on a scale from 0 (not at all intoxicated) to 10 (the most intoxicated I’ve ever felt). Perception of the amount of alcohol that was consumed was measured by asking participants, “How many shots of vodka do you think you consumed?” on a scale from 0 to 10. These questions were administered at three time points: approximately 30 min after beverage administration, after the WM and AAT were both completed, and after the interactive videos.

Endogenous predictor variables

Sexual arousal

Before the interactive videos, current level of arousal was reported on a scale from 1 (not aroused at all) to 6 (extremely aroused). Postvideo ratings of sexual arousal were obtained after each interactive video using the same scale. The two postvideo arousal scores were averaged to form a single postvideo arousal score.

AAT

The AAT was used to assess implicit biases toward sexual stimuli versus condom stimuli (Hofmann, Friese, & Gschwendner, 2009; Wiers et al., 2011). Stimuli were randomly presented and included 20 sexual images (men engaging in sexual behavior), 20 condom images, and 20 neutral images, for a total of 132 trials. Stimuli were preceded by a 500-ms white fixation cross (+) in the center of the screen. Participants had 1,700 ms to respond to the stimulus; the intertrial interval was 1,000 ms. Each stimulus was presented in both a landscape and portrait format. Participants were instructed to pull the joystick toward them when an image in portrait format appeared while simultaneously imagining they were pulling that image to them. They were instructed to push the joystick away when an image in landscape format appeared while simultaneously imagining they were pushing that image away from them. Pushing the joystick away caused the image to recede on the screen, and pulling the joystick toward them caused the image to zoom, thus providing a sense of avoidance and approach, respectively. To maintain focus on image content and not simply the orientation, participants were instructed not to respond when the image was a tree, regardless of orientation. The instructions were followed by eight practice trials (of gray boxes rather than images), during which they received feedback on their responses. An index of sexual-stimuli approach tendency was calculated following the procedures of Zvielli and colleagues (Maisto et al., 2021; Simons et al., 2010; Wills et al., 2006; Zvielli et al., 2015). Higher scores indicate faster reaction times when approach is paired with the sexual stimuli and avoid is paired with the condom stimuli, which reflects a bias to approach sexual stimuli relative to condom stimuli. The strength of approach versus avoidance toward the sexual stimuli and condom stimuli is relevant to the behavioral outcome, and our measure quantifies the relative response bias. Split-half reliability for the bias scores indicated good reliability for both prebeverage and postbeverage estimates (Spearman-Brown prophecy reliability estimate = .83 and .84, respectively).

WM

The automated operation span (O-span) task (Unsworth et al., 2005) was used to measure WM. This task requires participants to solve a series of math operations while trying to remember a set of unrelated letters. On this task, participants are given three practice trials: (a) letters only, (b) math problems only, and (c) letters and math problems together. Response time during the practice trial is used to set that participant’s time limit for the experimental trials (M response time + 2.5 SD). The task consists of a series of blocks containing letter sets of three to seven letters that participants must remember in order. Letters are presented one at a time and alternated with a math problem. During the task, participants are shown their math accuracy as a percentage in red font at the corner of the screen. The task correlates well with other measures of WM capacity and has good internal consistency and test-retest reliability (Unsworth et al., 2005). Brief versions composed of blocks of this task have been shown to correlate highly with full versions of this measure (Foster et al., 2015). This permitted the presentation of target blocks both before and after beverage administration that could be completed along with other measures. The measure used in the pre– and post–O-Span tasks were based on total letters recalled in order (Foster et al., 2015; Unsworth et al., 2005). This served as the index of WM for prebeverage and postbeverage assessment.

Dependent variables

Behavioral skills and risk exposure

Behavioral skills and risk exposure were measured with two interactive videos developed by Maisto et al. (2012). The videos were selected to elicit moderate sexual arousal to limit the potential for overpowering the effect of the beverage manipulation. Both interactive videos produce indicators of the behavioral skills needed for safer sex in situations that would (a) be familiar to participants, (b) pose moderate difficulty to communicate feelings about condom use, and (c) elicit moderate sexual interest. The first interactive video depicted two men who had recently met, and the second depicted two men who were friends. In both situations, the men had not previously had anal sex and were eventually faced with the decision of having CAI. The videos were enacted by professional actors using a script and were filmed by professional videographers.

Each interactive video has a risk-exposure component and a behavioral-skills component. For the risk-exposure component, each video began by setting a scene in which “Jim” (the protagonist) and “Dave” (the character with whom the participants were asked to identify) meet each other. The participant was asked to make a series of binary choices (yes/no) about engaging in various increasingly high-risk sexual activities with Jim, which constituted the basis of the interactive risk-exposure measure. The choice points were (a) “Do you go with Jim to his apartment?” (b) “Do you accept a drink from Jim?” (c) “Do you go upstairs with Jim (Video 1)?” or “Do you get in the hot tub with Jim (Video 2)?” (d) “Do you have anal sex with Jim?” and (e) “Do you have unprotected, receptive anal sex with Jim?” Each affirmative response was scored as 1 point and summed to create a total score. Video 1 and Video 2 total scores were averaged together to create a single variable for analyses. The risk-exposure portion of the video terminated with the first “no” response and transitioned to the behavioral-skills component of the video.

The behavioral-skills portion of the video required participants to verbally negotiate sexual situations in an interactive role-play. Participants were asked to respond first to Jim’s comment that he desires to have CAI and that there is no cause for concern because he is safe (Prompt 1). The video then paused for 60 s to allow the participant to respond. Subsequently, Jim delivered a second, more insistent comment that reminded the participant that CAI would not be risky, would be pleasurable, and that he could be trusted (Prompt 2).

Participants’ qualitative responses to each of the prompts were audio-recorded and scored (on a scale from 0 to 2) on the following five dimensions (higher score = better communication skills): (a) use of an “I” statement of intention of safer sexual behavior or refusal of unsafe sexual behavior, (b) presence of a positive statement about Jim, (c) provision of a reason for safer sexual behavior, (d) suggestion of a specific safer alternative behavior, and (e) indications that the participant’s response was direct, serious, and clear. Each of the response dimensions was scored according to a previously established rating manual (Maisto et al., 2002, 2004). Two raters independently coded each of these five dimensions across the two prompts and the two videos. The two raters agreed on 95.15% of the codes, and the discrepancies on the remaining codes were resolved through discussion. An average behavioral-skills score across the five dimensions in the two videos was created separately for each of the two prompts.

CAI intentions

Self-reported likelihood of engaging in CAI after viewing each of the two interactive videos was measured by a 6-point rating scale anchored at “not likely at all” and “extremely likely” (George et al., 2009). A single score was created as the average CAI across the two prompts.

Procedure

Experimental study

Session 1

This session began with a research assistant verifying participant’s age using government-issued photo identification and confirming the participant’s BAC was zero when he arrived at the laboratory. Two participants showed a BAC of more than 0 at this point and were rescheduled. After consenting to participate, individuals completed self-report questionnaires to confirm eligibility. Eligible participants completed computerized behavioral tasks and additional self-report questionnaires that were not included in the analyses reported here. Participants were compensated $50 for completing Session 1, and those deemed ineligible were compensated $20.

Session 2

Participants were instructed that they may be consuming alcohol at Session 2 and thus should not drive or ride a bicycle to the laboratory. Participants were also instructed to not use any substance for 24 hr before the session and to not eat or drink anything other than water for 4 hr before the session. Compliance was assessed via self-assessment and using a breathalyzer to verify absence of alcohol consumption. Four participants who were noncompliant with presession instructions were rescheduled. Two research assistants facilitated Session 2: One (RA1) was blinded to the beverage condition, and the other (RA2) administered beverages and was not present for the assessments.

Participants were randomly assigned to one of three beverage conditions: alcohol (dose designed to raise BAC to 0.075%), placebo, or water control. Before beverage consumption, participants completed baseline measures of O-span and the AAT. Following consumption of their beverages, participants repeated the AAT and completed a second O-span task. After postbeverage completion of these two measures, current sexual arousal was assessed, and then participants watched two videos, each of which was designed to be moderately sexually arousing and to depict a scenario that required a decision to be made about engaging in CAI. Video sequence order was randomly balanced across beverage conditions. Each interactive-video role-play was followed by one role-play and two self-report measures relevant to their decisions to engage in CAI (if they actually were in the situations depicted in the videos) and post-interactive-video ratings, including sexual arousal. After the final study measure, participants who consumed alcohol were strongly discouraged from leaving the lab until two breathalyzer readings demonstrated a BAC of 0.02% or less. Participants were compensated $90 for completing Session 2. One participant vomited during the course of the beverage administration portion of the experiment and was disenrolled from the study after a breathalyzer reading of BAC less than 0.02%.

Beverage administration

On the basis of random assignment to beverage condition, RA2 told participants assigned to the alcohol and placebo conditions that they were being given an alcoholic beverage, and participants assigned to the control condition were told that they were being given water. All participants received an equivalent volume of beverage according to calculations using their reported height and recorded weight (measured in the lab). All drinks were mixed (except in the control condition) and poured in front of the participants. Those assigned to the alcohol group received 0.70 g of alcohol per kg of body weight in the form of a chilled beverage of 80-proof vodka mixed with tonic water and lime juice in a 1:4 ratio. In the placebo group, flat tonic water was substituted for vodka and poured from a vodka bottle; vodka was rubbed around the rim of the glass to enhance alcohol cues. Participants assigned to the control condition received water. Drinks were divided into three equal doses, and participants were asked to consume all within 20 min and each at the same pace. They sat alone while consuming their drinks, and RA2 intermittently (i.e., every 6–7 min) confirmed that the participant followed these instructions. General entertainment magazines that did not include mention of HIV or AIDS were available to the participants while they consumed their beverages. After finishing their drinks, participants began the 10-min “initial alcohol absorption” period. RA2 obtained breath tests after the absorption period and each subsequent computerized measure. While maintaining blinding, RA1 assessed postbeverage perceptions (i.e., perceived intoxication and perceived consumption of alcohol) via a paper questionnaire.

Analysis plan

We tested the hypothesized path models in Mplus (Version 8.4; Muthén & Muthén, 2019) with the maximum likelihood robust estimator. Four models were tested, one for each outcome: CAI intentions, Behavioral Skills Prompt 1, Behavioral Skills Prompt 2, and risk exposure. The final models are depicted in Figures 1b through 1d and Figure S1 (in the Supplemental Material available online). Although not shown in the figures, each model included site, age, and PrEP use as covariates with paths to all endogenous variables. Experimental condition was an exogenous variable represented by two dummy-coded variables reflecting the alcohol and placebo conditions. All other predictor variables were centered at their mean. Interaction terms were created by forming the cross-product of the respective centered variables. For interactions involving endogenous variables, interaction terms were allowed to freely covary with their constituent parts or, for endogenous variables, with their disturbance terms (Preacher et al., 2007). The interactions and all other exogenous variables were freely covaried. Three hypothesized interaction effects were iteratively dropped for parsimony because they were not significant in any of the models: Experimental Condition × Postbeverage AAT interaction predicting prevideo and postvideo sexual arousal and Postbeverage WM × Postbeverage AAT interaction predicting the outcomes (e.g., CAI intentions).

In summary, the models included site, age, PrEP, alcohol condition, placebo condition, prebeverage AAT, prebeverage WM, Alcohol Condition × Postbeverage AAT interaction, Placebo Condition × Postbeverage AAT interaction, Postbeverage AAT × Postvideo Sexual Arousal interaction, and Postbeverage WM × Postvideo Sexual Arousal interaction as freely covarying exogenous variables. The site, age, and PrEP covariates and the experimental condition indicators had direct paths to all endogenous variables: postbeverage AAT, postbeverage WM, prevideo and postvideo sexual arousal, and the outcome variable (e.g., CAI intentions). In addition, postbeverage AAT had direct paths to prevideo and postvideo sexual arousal and the outcome variable. Prevideo sexual arousal had a direct path to postvideo sexual arousal, which, in turn, predicted the outcome variable. Associations between both postbeverage AAT and postbeverage WM and the outcome were moderated by postvideo sexual arousal. The association between postbeverage AAT and the outcome was moderated by experimental condition. Hence, the models include direct paths from postbeverage AAT, postbeverage WM, postvideo sexual arousal, experimental condition indicators, the Experimental Condition Indicator × Postbeverage AAT interactions, Postbeverage AAT × Postvideo Sexual Arousal interaction, Postbeverage WM × Postvideo Sexual Arousal interaction, and the site, age, and PrEP covariates to the outcome (e.g., CAI intentions).

Results

Descriptive statistics

Summary statistics and correlations are presented in Table 1. PrEP use was reported by 21% of the sample, and it was positively correlated with CAI intentions and postbeverage WM and inversely correlated with sexual arousal. CAI intentions were moderately inversely correlated with behavioral skills and positively correlated with risk exposure. Sexual arousal (postvideo) was moderately positively correlated with CAI intentions and risk exposure. Approach biases (postbeverage) exhibited a modest positive correlation with prevideo sexual arousal and an inverse association with behavioral skills at Prompt 1. WM did not exhibit significant bivariate correlations with the dependent variables. Bivariate correlations indicate significant small to medium effects of alcohol versus water control on CAI intentions and both behavioral-skills outcomes (|r| range = .18–.24).

Table 1.

Summary Statistics and Correlation Matrix

	M	SD	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15
1. Age (n = 257)	28.14	6.90	—
2. Site (n = 257)	0.74	0.44	–.18**	—
3. PrEP (n = 257)	0.21	0.41	.02	.15*	—
4. Alc vs Pla (n = 172)	0.50	0.50	–.02	.00	–.06	—
5. Alc vs Wat (n = 171)	0.50	0.50	–.04	.00	–.02		—
6. Pla vs Wat (n = 171)	0.50	0.50	–.02	.00	.04			—
7. SA-1 (n = 257)	2.47	1.27	–.08	–.17**	–.13*	–.01	.01	.02	—
8. SA-2 (n = 257)	2.48	1.23	–.11	–.07	–.12*	.14	.10	–.05	.66***	—
9. CAI intent (n = 255)	2.27	1.34	–.07	–.01	.20**	.08	.18*	.11	.11	.25***	—
10. Skill-P1 (n = 196)	0.53	0.33	–.02	.05	–.01	–.14	–.24**	–.11	.02	–.04	–.44***	—
11. Skill-P2 (n = 197)	0.60	0.35	–.02	.03	–.01	–.01	–.21*	–.21*	.09	.02	–.42***	.59***	—
12. Rexp (n = 256)	3.08	1.27	–.10	.20**	.09	.14	.07	–.07	.13*	.29***	.43***	–.17*	–.21**	—
13. AAT-1 (n = 256)	435.66	166.60	.08	–.25***	–.07	–.17*	.10	.27***	.10	.06	.08	–.10	–.12	.03	—
14. AAT-2 (n = 256)	423.42	173.66	.00	–.12*	–.12	.01	.19*	.18*	.17**	.11	.05	–.20**	–.06	.12	.45***	—
15. WM-1 (n = 252)	13.55	6.96	–.14*	.20**	.10	.15*	–.01	–.16*	–.07	–.03	–.08	.08	.02	.14	–.07	–.04	—
16. WM-2 (n = 251)	29.78	12.44	–.22***	.17**	.15*	.05	–.14	–.19*	–.07	–.05	–.06	.10	.11	.10	–.13*	–.11	.53***

Note: PrEP = preexposure prophylaxis use; Alc vs Pla = alcohol-versus placebo-contrast; Alc vs Wat = alcohol-versus-water contrast; Alc vs Pla = alcohol-versus-placebo contrast; SA1 = prevideo sexual arousal; SA2 = postvideo sexual arousal; CAI intent = condomless anal intercourse intentions; Skill-P1 = Behavioral Skills Prompt 1; Skill-P2 = Behavioral Skills Prompt 2; Rexp = risk exposure; AAT-1 = prebeverage approach-avoidance biases; AAT-2 = postbeverage approach-avoidance biases; WM-1 = working memory prebeverage; WM-2 = postbeverage working memory. Site is coded as 0 = Syracuse, 1 = Boston.

p < .05. **p < .01. ***p < .001

Manipulation checks

We tested a 3 (Condition) × 3 (Time) mixed model of perceived intoxication (χ² = 680.19, N = 257, p < .0001). There were significant main effects of time, condition, and the Time × Condition interaction (ps < .0001). Pairwise contrasts indicated perceived intoxication was higher in the alcohol condition than placebo condition (contrast = 2.17, z = 9.46, p < .001) and higher in the placebo condition relative to the control condition (contrast = 2.67, z = 11.60, p < .001) at Time 1. Effects remained significant, albeit diminished, at Times 2 and 3 (Time 3 alcohol vs. placebo contrast = 1.73, z = 7.52, p < .001; placebo vs. control contrast = 1.45, z = 6.27, p < .001). Perceived intoxication exhibited a curvilinear decreasing trend in both the alcohol (time: b = −0.69, p ≤ .001; time²: b = −0.17, p = .002) and placebo conditions (time: b = −0.51, p < .001; time²: b = −0.19, p = .001). The results of the analysis of the data regarding perception of amount of alcohol consumed paralleled those of the data regarding perception of intoxication. Pairwise contrasts showed significant differences that were maintained across time (ps < .001), although effects were slightly smaller. A repeated measures analysis of BAC in the alcohol condition indicated that BAC exhibited a cubic growth trend over time (linear contrast = 0.003, z = 7.32, p < .001; quadratic = −0.000, z = −0.44, p = .693; cubic contrast = −0.002, z = −4.23, p < .001). At Time 2, BAC M = 0.065, SD = 0.02; at Time 3, BAC M = 0.065, SD = 0.01; at Time 4, BAC M = 0.072, SD = 0.01; and at Time 5, BAC M = 0.071, SD = 0.01.

Table 2 presents the results of contrasts between alcohol condition and placebo condition for direct effects across the intermediate and sexual decision-making outcomes. Although mean differences between the alcohol and placebo groups were always in the expected rank order, they were statistically significant only for sexual arousal because alcohol predicted higher postvideo sexual arousal relative to placebo. The latter result was due to a decrease in arousal in the placebo condition. As will be discussed later, control group means also were in the predicted rank order compared with alcohol and placebo groups, but generally, statistical differences were found only for the alcohol-versus-control contrasts.

Table 2.

Alcohol Versus Placebo Contrasts

Outcome	Placebo condition	Alcohol condition	b	SE	p
CAI intentions model
CAI intention	2.28 (1.33)	2.51 (1.44)	0.13	0.10	.494
Postvideo sexual arousal	2.32 (1.86)	2.67 (1.35)	0.36	0.14	.009
Prevideo sexual arousal	2.49 (1.29)	2.47 (1.35)	−0.04	0.19	.824
Postbeverage approach avoidance	442.83 (175.18)	445.62 (186.23)	0.26	0.24	.289
Postbeverage working memory	27.79 (13.27)	29.18 (12.70)	−0.03	0.17	.842
Behavioral Skills Prompt 1 model
Behavioral Skills Prompt 1	0.53 (0.34)	0.44 (0.34)	−0.08	0.06	.159
Postvideo sexual arousal	2.32 (1.86)	2.67 (1.35)	0.36	0.14	.009
Prevideo sexual arousal	2.49 (1.29)	2.47 (1.35)	−0.04	0.19	.823
Postbeverage approach avoidance	442.83 (175.18)	445.62 (186.23)	0.26	0.24	.288
Postbeverage working memory	27.79 (13.27)	29.18 (12.70)	−0.04	0.17	.832
Behavioral Skills Prompt 2 model
Behavioral Skills Prompt 2	0.55 (0.34)	0.54 (0.38)	−0.02	0.07	.818
Postvideo sexual arousal	2.32 (1.86)	2.67 (1.35)	0.36	0.14	.009
Prevideo sexual arousal	2.49 (1.29)	2.47 (1.35)	−0.04	0.19	.823
Postbeverage approach avoidance	442.83 (175.18)	445.62 (186.23)	0.26	0.24	.288
Postbeverage working memory	27.79 (13.27)	29.18 (12.70)	−0.03	0.17	.836
Risk-exposure model
Risk exposure	2.90 (1.26)	3.26 (1.31)	0.24	0.18	.176
Postvideo sexual arousal	2.32 (1.86)	2.67 (1.35)	0.36	0.14	.009
Prevideo sexual arousal	2.49 (1.29)	2.47 (1.35)	−0.04	0.19	.824
Postbeverage approach avoidance	442.83 (175.18)	445.62 (186.23)	0.26	0.24	.290
Postbeverage working memory	27.79 (13.27)	29.18 (12.70)	−0.04	0.17	.827

Note: Values are means with standard deviations in parentheses unless otherwise indicated. Approach avoidance = approach-avoidance bias.

Structural equation models

Figures 1b through 1d present the complete results of the model tests for each of the sexual decision-making outcomes. Tables 3 through 5 summarize the results of tests of conditional indirect effects involving alcohol versus control for each of the sexual decision-making outcomes except for Behavioral Skills Prompt 2 (omitted because of the nonsignificant R² for the outcome). The remaining results of model testing are presented in Tables S1 to S6 and in Figure S1.

Table 3.

Conditional Indirect and Total Effects of Alcohol Condition for Condomless Anal Intercourse Intention Analysis

Pathway	Moderator/level	b [95% CI]
Alc → WM2 → CAI	SA2 M − 1 SD	0.08 [0.01, 0.25]
Alc → WM2 → CAI	SA2 M	0.03 [−0.01, 0.12]
Alc → WM2 → CAI	SA2 M + 1 SD	−0.02 [−0.13, 0.02]
Alc → AAT2 → CAI	SA2 M − 1 SD	−0.06 [−0.20, 0.00]
Alc → AAT2 → CAI	SA2 M	0.03 [−0.02, 0.14]
Alc → AAT2 → CAI	SA2 M + 1 SD	0.12 [0.01, 0.31]
Alc → AAT2 → SA1 → SA2 → CAI	AAT2 M − 1 SD	0.00 [−0.01, 0.00]
Alc → AAT2 → SA1 → SA2 → CAI	AAT2 M	0.01 [0.00, 0.03]
Alc → AAT2 → SA1 → SA2 → CAI	AAT2 M + 1 SD	0.02 [0.00, 0.05]
Alc → AAT2 → SA1 → SA2 → CAI	WM2 M − 1 SD	0.00 [0.00, 0.02]
Alc → AAT2 → SA1 → SA2 → CAI	WM2 M + 1 SD	0.01 [0.00, 0.05]
Total effects, Alc → CAI	SA2 M − 1 SD, AAT2 M, WM2 M	0.46 [0.08, 0.86]
Total effects, Alc → CAI	SA2 M, AAT2 M, WM2 M	0.50 [0.13, 0.88]
Total effects, Alc → CAI	SA2 M + 1 SD, AAT2 M, WM2 M	0.54 [0.13, 0.93]
Total effects, Alc → CAI	SA2 M, AAT2 M − 1 SD, WM2 M	0.26 [−0.25, 0.75]
Total effects, Alc → CAI	SA2 M, AAT2 M + 1 SD, WM2 M	0.74 [0.14, 1.41]
Total effects, Alc → CAI	SA2 M, AAT2 M, WM2 M − 1 SD	0.46 [0.10, 0.83]
Total effects, Alc → CAI	SA2 M, AAT2 M, WM2 M + 1 SD	0.54 [0.14, 0.96]

Note: Coefficients in boldface type are significant according to bias-corrected bootstrapped 95% confidence intervals. CI = confidence interval; AAT2 = postbeverage approach bias; Alc = alcohol; CAI = condomless anal intercourse; SA1 = prevideo sexual arousal; SA2 = postvideo sexual arousal; WM2 = postbeverage working memory.

Table 4.

Conditional Indirect and Total Effects of Alcohol Condition for Behavioral Skills Prompt 1 Analysis

Pathway	Moderator/level	b [95% CI]
Alc → WM2 → BSK1	SA2 M − 1 SD	0.00 [−0.04, 0.01]
Alc → WM2 → BSK1	SA2 M	−0.01 [−0.03, 0.00]
Alc → WM2 → BSK1	SA2 M + 1 SD	−0.01 [−0.04, 0.00]
Alc → AAT2 → BSK1	SA2 M − 1 SD	−0.01 [−0.06, 0.01]
Alc → AAT2 → BSK1	SA2 M	−0.02 [−0.06, 0.00]
Alc → AAT2 → BSK1	SA2 M + 1 SD	−0.03 [−0.08, 0.00]
Alc → AAT2 → SA1 → SA2 → BSK1	AAT2 M − 1 SD	0.00 [0.00, 0.01]
Alc → AAT2 → SA1 → SA2 → BSK1	AAT2 M	0.00 [0.00, 0.00]
Alc → AAT2 → SA1 → SA2 → BSK1	AAT2 M + 1 SD	0.00 [0.00, 0.00]
Alc → AAT2 → SA1 → SA2 → BSK1	WM2 M − 1 SD	0.00 [0.00, 0.00]
Alc → AAT2 → SA1 → SA2 → BSK1	WM2 M + 1 SD	0.00 [0.00, 0.00]
Total effects, Alc → BSK1	SA2 M − 1 SD, AAT2 M, WM2 M	−0.16 [−0.28, −0.04]
Total effects, Alc → BSK1	SA2 M, AAT2 M, WM2 M	−0.17 [−0.28, −0.06]
Total effects, Alc → BSK1	SA2 M + 1 SD, AAT2 M, WM2 M	−0.18 [−0.29, −0.07]
Total effects, Alc → BSK1	SA2 M, AAT2 M − 1 SD, WM2 M	−0.03 [−0.18, 0.14]
Total effects, Alc → BSK1	SA2 M, AAT2 M + 1 SD, WM2 M	−0.32 [-0.51, -0.14]
Total effects, Alc → BSK1	SA2 M, AAT2 M, WM2 M − 1 SD	−0.17 [−0.29, −0.06]
Total effects, Alc → BSK1	SA2 M, AAT2 M, WM2 M + 1 SD	−0.17 [−0.28, −0.05]

Note: Coefficients in boldface type are significant according to bias-corrected bootstrapped 95% confidence intervals. CI = confidence interval; AAT2 = postbeverage approach bias; Alc = alcohol; BSK1 = Behavioral Skills Prompt 1; SA1 = prevideo sexual arousal; SA2 = postvideo sexual arousal; WM2 = postbeverage working memory.

Table 5.

Conditional Indirect and Total Effects of Alcohol Condition for the Risk-Exposure Analysis

Pathway	Moderator/level	b [95% CI]
Alc → WM2 → REXP	SA2 M − 1 SD	0.00 [−0.11, 0.06]
Alc → WM2 → REXP	SA2 M	−0.02 [−0.10, 0.01]
Alc → WM2 → REXP	SA2 M + 1 SD	−0.04 [−0.13, 0.00]
Alc → AAT2 → REXP	SA2 M − 1 SD	0.04 [−0.04, 0.20]
Alc → AAT2 → REXP	SA2 M	0.07 [0.00, 0.21]
Alc → AAT2 → REXP	SA2 M + 1 SD	0.10 [0.00, 0.26]
Alc → AAT2 → SA1 → SA2 → REXP	AAT2 M − 1 SD	0.01 [0.00, 0.03]
Alc → AAT2 → SA1 → SA2 → REXP	AAT2 M	0.01 [0.00, 0.03]
Alc → AAT2 → SA1 → SA2 → REXP	AAT2 M + 1 SD	0.01 [0.00, 0.04]
Alc → AAT2 → SA1 → SA2 → REXP	WM2 M − 1 SD	0.01 [0.00, 0.03]
Alc → AAT2 → SA1 → SA2 → REXP	WM2 M + 1 SD	0.01 [0.00, 0.04]
Total effects, Alc → REXP	SA2 M − 1 SD, AAT2 M, WM2 M	0.20 [−0.19, 0.60]
Total effects, Alc → REXP	SA2 M, AAT2 M, WM2 M	0.21 [−0.16, 0.60]
Total effects, Alc → REXP	SA2 M + 1 SD, AAT2 M, WM2 M	0.22 [−0.16, 0.61]
Total effects, Alc → REXP	SA2 M, AAT2 M − 1 SD, WM2 M	−0.06 [−0.52, 0.40]
Total effects, Alc → REXP	SA2 M, AAT2 M + 1 SD, WM2 M	0.48 [−0.13, 1.17]
Total effects, Alc → REXP	SA2 M, AAT2 M, WM2 M − 1 SD	0.20 [−0.17, 0.58]
Total effects, Alc → REXP	SA2 M, AAT2 M, WM2 M + 1 SD	0.22 [−0.16, 0.63]

Note: Coefficients in boldface type are significant according to bias-corrected bootstrapped 95% confidence intervals. CI = confidence interval; AAT2 = postbeverage approach bias; Alc = alcohol; REXP = risk exposure; SA1 = prevideo sexual arousal; SA2 = postvideo sexual arousal; WM2 = postbeverage working memory.

CAI intentions

The structural model is shown in Figure 1b. The model was a good fit to the data, χ²(22, N = 257) = 16.88, p = .7699, RMSEA = .00, 90% confidence interval [CI] = [0.00, 0.04], CFI = 1.00, standardized root-mean-square residual (SRMR) = .018, and accounted for 22% of the variance in CAI intentions. With respect to the covariates, PrEP was associated with higher CAI intentions (β = 0.27, p < . 001) and higher WM (postbeverage: β = 0.10, p = .037). The Boston site was associated with less arousal (prevideo: β = −0.16, p = .037). Age was associated with lower WM (postbeverage: β = −0.15, p = .010). Other covariate effects were not significant. These covariate effects, except for any related to the decision-making outcome, are the same for all subsequent models and are not repeated in later description of model testing results.

Select conditional indirect effects involving alcohol-versus-control contrasts are reported in Table 3. On average, alcohol condition (relative to water control) was indirectly associated with CAI intentions via approach biases and sexual arousal. Alcohol’s relation to CAI was stronger when approach biases were higher. There were other significant indirect effects as well that supported the hypothesized conditional mechanisms. For example, when arousal was elevated, there was a significant indirect alcohol effect via automatic control processes. In contrast, when arousal was low, alcohol’s effect was mediated through diminished EF.

Behavioral Skills Prompt 1

The structural model is shown in Figure 1c. The model was a good fit to the data, χ²(22, N = 257) = 16.09, p = .8116, RMSEA = .00, 90% CI = [0.00, 0.03], CFI = 1.00, SRMR = .017, and accounted for 12% of the variance in behavioral skills at Prompt 1. There were no significant relations between covariates and Behavioral Skills Prompt 1.

Indirect effects

Select conditional indirect effects involving the alcohol-versus-control contrasts are reported in Table 4. Alcohol condition had significant inverse total indirect effects on Behavioral Skills Prompt 1 when either sexual arousal or approach biases were elevated (i.e., M + 1 SD). This is due primarily to the significant specific indirect effect of alcohol condition → approach biases → behavioral skills when sexual arousal was at the mean + 1 SD. Other specific indirect effects were not significant. The total effect of alcohol on Behavioral Skills Prompt 1 was significant except under conditions when approach biases were low (M − 1 SD). Specific indirect effects and total indirect of the placebo condition versus water control were generally not significant. The total effect of placebo versus water control was significant only when approach biases were elevated (M + 1 SD).

Behavioral Skills Prompt 2

The structural model is shown in Figure S1 in the Supplemental Material. The model was a good fit to the data, χ²(22, N = 257) = 17.41, p = .7405, RMSEA = .00, 90% CI = [0.00, 0.04], CFI = 1.00, SRMR = .018. However, the model R² was not significant (R² = .07, p = .058). Therefore, additional results of model testing are not presented here but, as noted, are available in the Supplemental Material.

Risk exposure

The structural model is shown in Figure 1d. The model was a good fit to the data, χ²(22, N = 257) = 15.41, p = .8440, RMSEA = .00, 90% CI = [0.00, 0.03], CFI = 1.00, SRMR = .017, and accounted for 19% of the variance in risk exposure. With respect to the effect of covariates on risk exposure, PrEP was associated with more risk exposure (β = 0.12, p = .037). The Boston site was associated with more risk exposure (β = 0.21, p = .002). Other covariate effects on risk exposure were not significant.

Select conditional indirect effects involving the alcohol-versus-control contrasts are reported in Table 5. The placebo condition did not have significant indirect or total effects on risk exposure. On the other hand, the alcohol condition exhibited some significant positive indirect effects on risk exposure via approach biases and sexual arousal. However, the total indirect and total effects of alcohol condition (vs. water control) were not significant.

Discussion

This experiment tested two mechanisms derived from a dual-process model of self-control linking alcohol intoxication and analogue factors contributing to CAI among MSM. First, alcohol intoxication was hypothesized to increase CAI risk factors via increases in automatic, reflexive, control processes, operationalized as implicit approach biases toward MSM sexual stimuli versus condom stimuli. Second, alcohol intoxication was hypothesized to increase CAI risk factors via decreases in controlled, reflective processes, operationalized as executive WM. Consistent with dual-process theories, arousal was hypothesized to increase the effect of automatic processes on behavior and decrease the effect of controlled processes on behavior (Lieberman, 2007; Metcalfe & Mischel, 1999; National Institute on Drug Abuse, 2010). Thus, mechanisms linking alcohol intoxication and CAI risk factors were expected to be conditional on level of subjective sexual arousal, an important factor underlying sexual behavior and linked to alcohol effect’s on sexual behavior (Maisto et al., 2012; Maisto & Simons, 2016). Consistent with these predictions, the results showed the following. First, alcohol condition (vs. water control) was indirectly associated with higher CAI intentions via increases in implicit approach biases when subjective sexual arousal was elevated (M + 1 SD) but not when arousal was low (M − 1 SD). This same pattern held for the Behavioral Skills Prompt 1 and risk-exposure outcomes. Second, alcohol condition was indirectly associated with higher CAI intentions via impairments in executive WM when subjective sexual arousal was low (M − 1 SD) but not when arousal was elevated (M + 1 SD). The finding that impairment of WM mediated alcohol’s relation to CAI only when arousal was low suggests that, at higher levels of arousal, the influence of EF on sexual decision-making declines. No other indirect or conditional indirect effects involving alcohol and WM were significant for outcomes besides CAI.

It is important to view the findings of this study in the context of a study conducted recently by Wray et al. (2021), who reported the results of an alcohol challenge experiment that was similar in design and conceptualization to this study but whose pattern of CAI results did not fully align with those of this study. In the Wray et al. study, 121 heavy, “high-risk” drinking MSM ages 21 to 50 were randomly assigned to one of three (alcohol [BAC target = 0.08%], placebo, or water control) beverage conditions and after beverage consumption completed tasks of attention bias to MSM-related sexual stimuli and inhibitory control, respectively. After task performance, participants viewed the same two sexual-risk videos that were used in this study and completed CAI ratings after each. No other sexual decision-making outcomes were reported. Consistent with this study’s findings, Wray et al. found that alcohol (vs. water; no alcohol vs. placebo contrasts were reported) was associated with increased CAI and that sexual-arousal ratings over the course of the experiment were positively related to CAI. However, contrary to their hypotheses, Wray et al. found no direct effects of alcohol on the performance of either task and no indirect or conditional indirect effects involving alcohol, attention bias, inhibitory control, and CAI. The discrepancies in results between Wray et al. and this study could be due to several differences between them, including use of two different behavioral tasks, neither of which showed an alcohol effect despite participants’ reaching the target BAC of 0.08% on average, including being on PrEP as an exclusion criterion and a smaller sample size in Wray et al., which was reduced further for analysis to 83 because of task-performance-related attrition.

The results of the present study involving alcohol consumption cannot be interpreted as a pharmacological effect of alcohol but rather as the combined influence of expectations that alcohol will be consumed and the consumption of a moderate (peak BAC averaging about 0.072%) dose of alcohol. This interpretation follows from direct alcohol condition versus placebo condition contrasts that show no differences between them in any of the primary sexual decision-making or task-performance outcomes. Such a pattern of findings has also been observed in some alcohol challenge/HIV-related sexual decision-making (Berry & Johnson, 2018) and in alcohol–cognitive-task-performance (Hoffman & Nixon, 2015) studies. It is plausible that these results were due to small effect sizes/insufficient statistical power, given that the ordering of the outcome means for the beverage conditions was consistent with expectations. Unfortunately, it is difficult to identify other plausible explanations for the absence of differences between the alcohol and placebo conditions in this study. For example, the dose of alcohol used was similar to that used in our own and past experiments that did show alcohol–placebo differences in sexual decision-making outcomes in both MSM and heterosexual samples. Furthermore, the sexual decision-making outcomes in this study are similar to those used in our and others’ prior research and have been shown to be sensitive to beverage-condition manipulations. Likewise, participants’ BAC when performing the postbeverage consumption O-span was sufficiently high to impair performance on a complex WM task such as the O-span. From a practical standpoint, however, failure to find pure pharmacological effects of alcohol is of less significance because in real-life situations, drinking alcohol typically is inseparable from knowledge and associated expectancies of outcomes of its consumption in real-life sexual decision-making situations.

This study demonstrated how alcohol may alter and combine with implicit associations, WM, and sexual arousal in relation to in-the-moment decision-making about having condomless sex. As George (2019) noted, this is the point at which HIV-prevention interventions aim ultimately to be effective. Nevertheless, such interventions have predominantly been derived from a social-cognitive perspective and have not addressed the dynamics of in-the-moment sexual decision-making (Johnson, 2019). Although many HIV-prevention interventions have met formal empirical criteria for being “evidence-based” (Protogerou et al., 2020), a considerable amount of variance in outcomes of HIV-prevention intervention trials is not explained by intervention effects. The data of this and prior experimental and other event-level studies (e.g., ecological momentary assessment studies; Maisto et al., 2021; Simons et al., 2018) suggest that interventions with a focus on in-the-moment dynamics to complement social-cognitive interventions that emphasize skills building and determinants more distal from making decisions about sex could increase the efficacy of HIV-prevention interventions. This is especially the case because drinking alcohol can alter mediators and moderators of relations between alcohol consumption and decision-making outcomes and results in a less than perfect correlation between intentions expressed when not intoxicated and intentions while intoxicated. Johnson (2019) formally recognized this conclusion by proposing a model of determinants of sexual intentions and behavior into “motivation” (preconsumption) and consumption phases.

The importance of developing intervention models on the basis of in-the-moment sexual decision-making has been discussed in the literature for decades (Gold et al., 1991; Maisto & Simons, 2016). Unfortunately, it seems that for the most part, such interventions have not been established, particularly to account for the proximal effects of alcohol on sexual decision-making. George (2019) provided an excellent review and discussion of interventions that have attempted to address in-the-moment effects of alcohol. This work suggests that at least two types of intervention approaches may be relevant and may be combined. The first is to use strategies to modify the learned associations that underlie implicit biases toward sexual stimuli and away from condom use. The literature on cognitive bias modification (CBM) is relevant in this regard (Friese et al., 2011). Although there have been recent efforts to improve procedures (Wiers et al., 2020), reviews of CBM addiction (alcohol and tobacco use, respectively) studies have shown that they often are below current standards for clinical-trial methodology and vary considerably in efficacy findings that average in the small effect size range (Boffo et al., 2019). As Wiers et al. (2020) noted, this is a body of research still in its early stages that would benefit from methodological improvement. The second approach has been to focus on the in-the-moment event itself via use of education (about how alcohol can change critical determinants of sexual decision-making) and techniques such as imagery, vignette or role-play simulation, and discussion/review and processing of past incidents when a decision to have condomless sex was made while under the influence of alcohol (George, 2019). The testing of “microinterventions” (Strauman et al., 2013) is another possibility that has been applied in interventions for mood disorders (Strauman et al., 2013) and alcohol-related sexual aggression (Davis et al., 2021). One possible application to alcohol and decision-making about condomless sex is an intervention that focuses on the key mechanism of implicit approach associations regarding condomless sex. Such an intervention could be administered and then evaluated by requesting that participants complete analogue sexual decision-making tasks such as those used in this study under conditions in which participants are either intoxicated (or are instructed that they are consuming alcohol but are not) or sober with feedback and discussion following each test. Such an approach takes into account that alcohol changes mechanisms underlying its association with having condomless sex and individualizes the intervention as well or makes it more “patient centered” (Pantelic et al., 2018).

There are a few limitations of this study that should be considered in interpreting its findings. Most important, it was an experimental analogue and for ethical reasons did not measure outcomes of actual in-the-moment sexual decision-making. Therefore, the generalizability to real sexual events is an empirical question. Another limitation is that the design of this study did not take into account the increased availability of PrEP to individuals such as MSM, who are considered “high risk” for contracting HIV. In fact, 21% of our participants reported at baseline that they were prescribed PrEP (we have no data on how adherent the participants were to their prescriptions). In this study, “taking PrEP” was used as a covariate in testing the structural equation models and showed a significant direct relation to one of the four main sexual decision-making outcomes, CAI intentions. Whether PrEP is important in estimating the relevance of this study’s findings is unclear because its availability has increased substantially in the past few years, and the concept of what is “sexual risk” when adhering to a PrEP regimen differs from when not taking PrEP (Maisto et al., 2021). Accordingly, the relevance of the sexual decision-making outcomes used in this study to current efforts at HIV prevention is not straightforward. In fact, the issue of the relevance of this study’s data to sexual decision-making is multifaceted. Essentially strict adherence to a PrEP regimen lowers the likelihood of HIV transmission to a partner virtually to zero (Mayer & Allan Blitz, 2019), thereby greatly decreasing the importance of factors that affect the decision to have CAI, although PrEP does not provide protection against contracting other STIs. Unfortunately, the large majority of individuals who are at high risk to contract HIV do not initiate PrEP uptake (Jenness et al., 2018) or maintain it if they do (Mayer & Allan-Blitz, 2019). Furthermore, there are racial and ethnic subgroup differences (disparities) in MSM who initiate PrEP (Kanny et al., 2019). As a result, multiple modalities of PrEP delivery are being developed with the aim of increasing its uptake and maintenance, but their success in doing so is still under evaluation (Beymer et al., 2019). Therefore, this study’s findings for now may be viewed as highly relevant to a large majority of MSM, particularly in specific racial and ethnic subgroups. However, this conclusion is subject to change with patterns of PrEP uptake and use and as research findings emerge.

In summary, the results of this study show that implicit approach associations, EF (WM), and subjective sexual arousal are critical mediators and moderators of the alcohol-CAI association in MSM. In particular, alcohol has direct effects on implicit approach biases to sexual stimuli, WM, and sexual arousal, and implicit associations and arousal combine to potentiate alcohol’s relation to decisions regarding unprotected sex in MSM so that the latter is most likely to occur when sexual arousal is high and implicit associations are strong. In this study, WM played a less pervasive role in that its relation to CAI was most evident when arousal was lower. This pattern of findings has clear implications for development and enhancement of HIV-prevention interventions.

The results of this experiment also suggest several directions for future research. An overarching question is the generalizability of this study’s results to populations other than MSM. The theoretically derived mechanisms whose actions were tested in this experiment have not been considered population specific. Nevertheless, the replicability of this study’s findings in populations besides MSM is a topic for future research. Another priority is further clarification of the role of EF in combination with acute alcohol effects and implicit associations in sexual decision-making. As our and Wray et al.’s (2021) data show, such investigations will require a conceptualization of EF that has empirical support in the cognitive science literature and then a derivative operationalization that can be used in the context of either an alcohol challenge experiment or, more complex, in the natural environment in a longitudinal ecological momentary assessment study, for example. Related to this point, WM was related to CAI only when sexual arousal was lower. This pattern is consistent with dual-process models and suggests that to the extent that alcohol acts to increase arousal, it somewhat paradoxically may decrease the impact of alcohol-induced impairments in EF because “cold cognition” may be a less powerful influence on decision-making when aroused. In contrast, alcohol in this study increased both risk-promoting implicit biases and arousal, which further enhanced the impact of such automatic processes. These results are consistent with and enhance the findings of previous experimental analogue studies on the effects of alcohol, implicit biases, and sexual arousal and the mechanisms of their action on decisions about having unprotected sex. Future research should clarify EF and consider other factors at the event level, including partner characteristics, setting variables, and individual differences variables. Finally, ultimately this research is geared to increasing the evaluation of an increased number of HIV-prevention interventions that incorporate the role of alcohol in combination with mechanisms underlying its association with in-the-moment decisions about sex and implementing those that are empirically supported.

Supplemental Material

sj-pdf-1-cpx-10.1177_21677026221079780 – Supplemental material for Effects of Alcohol Intoxication on Sexual Decision-Making Among Men Who Have Sex With Men: Alcohol’s Influences on Self-Control Processes

Supplemental material, sj-pdf-1-cpx-10.1177_21677026221079780 for Effects of Alcohol Intoxication on Sexual Decision-Making Among Men Who Have Sex With Men: Alcohol’s Influences on Self-Control Processes by Stephen A. Maisto, Jeffrey S. Simons, Tibor P. Palfai, Dezarie Moskal, Alan Z. Sheinfil and Kelli D. Tahaney in Clinical Psychological Science

Footnotes

Transparency

Action Editor: Tamika C. Zapolski

Editor: Jennifer L. Tackett

Author Contributions

S. A. Maisto, J. S. Simons, and T. P. Palfai developed the study concept and design. Testing and data collection were performed by D. Moskal, A. Z. Sheinfil, and K. D. Tahaney. J. S. Simons performed the data analysis and interpretation. S. A. Maisto, J. S. Simons, D. Moskal, and A. Z. Sheinfil drafted the manuscript, and T. P. Palfai and K. D. Tahaney provided critical revisions. All of the authors approved the final manuscript for submission.

ORCID iDs

Dezarie Moskal

Alan Z. Sheinfil

Supplemental Material

Additional supporting information can be found at

References

Anderson

K. R.

Palfai

T. P.

Maisto

S. A.

Simons

J. S.

(2020). Drinking motives mediate the associations between urgency and hazardous/harmful alcohol use among moderate-to-heavy drinking men who have sex with men (MSM). Addictive Behaviors, 110, Article 106520. https://doi.org/10.1016/j.addbeh.2020.106520

Baumeister

R. F.

Vohs

K. D.

Tice

D. M.

(2007). The strength model of self-control. Current Directions in Psychological Science, 16(6), 351–355. https://doi.org/10.1111/j.1467-8721.2007.00534.x

Berry

M. S.

Johnson

M. W.

(2018). Does being drunk or high cause HIV sexual risk behavior? A systematic review of drug administration studies. Pharmacology Biochemistry and Behavior, 164, 125–138. https://doi.org/10.1016/j.pbb.2017.08.009

Beymer

M. R.

Holloway

I. W.

Pulsipher

Landovitz

R. J.

(2019). Current and future PrEP medications and modalities: On demand, injectables, and topicals. Current HIV AIDS Reports, 16, 349–358.

Bickel

W. K.

Landes

R. D.

Hill

P. F.

Baxter

(2011). Remember the future: Working memory training decreases delay discounting among stimulant addicts. Biological Psychiatry, 69(3), 260–265. https://doi.org/10.1016/j.biopsych.2010.08.017

Boffo

Zerhouni

Gronau

Q. F.

van Beek

R. J. J.

Nikolaou

Marsman

Wiers

R. W.

(2019). Cognitive bias modification for behavior change in alcohol and smoking addiction: Bayesian meta-analysis of individual participant data. Neuropsychology Review, 29(1), 52–78. https://doi.org/10.1007/s11065-018-9386-4

Bridgett

D. J.

Oddi

K. B.

Laake

L. M.

Murdock

K. W.

Bachmann

M. N.

(2013). Integrating and differentiating aspects of self-regulation: Effortful control, executive functioning, and links to negative affectivity. Emotion, 13(1), 47–63. https://doi.org/10.1037/a0029536

Cahalan

Cisin

I. H.

Crossley

H. M.

(1969). American drinking practices: A national study of drinking behavior and attitudes. Monographs of the Rutgers Center of Alcohol Studies, 6, 260.

Centers for Disease Control and Prevention. (2018). Diagnoses of HIV Infection in the United States and Dependent Areas, 2018 (Updated). HIV Surveillance Report (Vol. 31). U.S Department of Health and Human Services. https://www.cdc.gov/hiv/library/reports/hiv-surveillance/vol-31/index.html

10.

Curtin

J. J.

Fairchild

B. A.

(2003). Alcohol and cognitive control: Implications for regulation of behavior during response conflict. Journal of Abnormal Psychology, 112(3), 424–436. https://doi.org/10.1037/0021-843X.112.3.424

11.

Davis

K. C.

Neilson

E. C.

Kirwan

Eldridge

George

W. H.

Stappenbeck

C. A.

(2021). Alcohol-involved sexual aggression: Emotion regulation as a mechanism of behavior change. Health Psychology, 40(12), 940–950. https://doi.org/10.1037/hea0001048

12.

Foster

J. L.

Shipstead

Harrison

T. L.

Hicks

K. L.

Redick

T. S.

Engle

R. W.

(2015). Shortened complex span tasks can reliably measure working memory capacity. Memory & Cognition, 43(2), 226–236. https://doi.org/10.3758/s13421-014-0461-7

13.

Freeman

R. C.

(2016). Toward development of enhanced preventive interventions for HIV sexual risk among alcohol-using populations: Confronting the ‘Mere pause from thinking.’ AIDS and Behavior, 20(1), 1–18. https://doi.org/10.1007/s10461-015-1179-7

14.

Friese

Hofmann

Wiers

R. W.

(2011). On taming horses and strengthening riders: Recent developments in research on interventions to improve self-control in health behaviors. Self and Identity, 10(3), 336–351. https://doi.org/10.1080/15298868.2010.536417

15.

George

W. H.

(2019). Alcohol and sexual health behavior: “What we know and how we know it.” The Journal of Sex Research, 56(4–5), 409–424. https://doi.org/10.1080/00224499.2019.1588213

16.

George

W. H.

Davis

K. C.

Norris

Heiman

J. R.

Stoner

S. A.

Schacht

R. L.

Hendershot

C. S.

Kajumulo

K. F.

(2009). Indirect effects of acute alcohol intoxication on sexual risk-taking: The roles of subjective and physiological sexual arousal. Archives of Sexual Behavior, 38, 498–513.

17.

Gold

R. S.

Skinner

M. J.

Grant

P. J.

Plummer

D. C.

(1991). Situational factors and thought processes associated with unprotected intercourse in gay men. Psychology & Health, 5(4), 259–278. https://doi.org/10.1080/08870449108400427

18.

Grenard

J. L.

Ames

S. L.

Wiers

R. W.

Thush

Sussman

Stacy

A. W.

(2008). Working memory capacity moderates the predictive effects of drug-related associations on substance use. Psychology of Addictive Behaviors, 22(3), 426–432. https://doi.org/10.1037/0893-164X.22.3.426

19.

Heather

(2017). Overview of addiction as a disorder of choice and future prospects. In Heather

Segal

(Eds.), Addiction and choice: Rethinking the relationship (pp. 463–482). Oxford University Press.

20.

Hinzmann

Borg

de Jong

P. J.

(2020). Implicit measures in clinical sex research: A critical evaluation. Sexual Medicine Reviews, 8(4), 531–541. https://doi.org/10.1016/j.sxmr.2020.06.005

21.

Hoffman

L. A.

Nixon

S. J.

(2015). Alcohol doesn’t always compromise cognitive function: Exploring moderate doses in young adults. Journal of Studies on Alcohol and Drugs, 76(6), 952–956. https://doi.org/10.15288/jsad.2015.76.952

22.

Hofmann

Friese

Gschwendner

(2009). Men on the “Pull.” Social Psychology, 40(2), 73–78. https://doi.org/10.1027/1864-9335.40.2.73

23.

Hofmann

Friese

Strack

(2009). Impulse and self-control from a dual-systems perspective. Perspectives on Psychological Science, 4(2), 162–176. https://doi.org/10.1111/j.1745-6924.2009.01116.x

24.

Hofmann

Gschwendner

Friese

Wiers

R. W.

Schmitt

(2008). Working memory capacity and self-regulatory behavior: Toward an individual differences perspective on behavior determination by automatic versus controlled processes. Journal of Personality and Social Psychology, 95(4), 962–977. https://doi.org/10.1037/a0012705

25.

Hofmann

Schmeichel

B. J.

Baddeley

A. D.

(2012). Executive functions and self-regulation. Trends in Cognitive Sciences, 16(3), 174–180. https://doi.org/10.1016/j.tics.2012.01.006

26.

Houben

Wiers

R. W.

Jansen

(2011). Getting a grip on drinking behavior: Training working memory to reduce alcohol abuse. Psychological Science, 22(7), 968–975. https://doi.org/10.1177/0956797611412392

27.

Inzlicht

Berkman

(2015). Six questions for the resource model of control (and some answers). Social and Personality Psychology Compass, 9(10), 511–524. https://doi.org/10.1111/spc3.12200

28.

Janssen

(2011). Sexual arousal in men: A review and conceptual analysis. Hormones and Behavior, 59(5), 708–716. https://doi.org/10.1016/j.yhbeh.2011.03.004

29.

Jenness

S. M.

Maloney

K. M.

Smith

D. K.

Hoover

K. W.

Goodreau

S. M.

Rosenberg

E. S.

Weiss

K. M.

Liu

A. Y.

Rao

Sullivan

P. S.

(2018). The PrEP care continuum and racial disparities in HIV incidence among men who have sex with men. BioRxiv. https://doi.org/10.1101/249540

30.

Jiang

Zhou

Tang

(2020). Maintaining HIV care during the COVID-19 pandemic. The Lancet HIV, 7(5), e308–e309. https://doi.org/10.1016/S2352-3018(20)30105-3

31.

Johnson

H. L.

(2019). Modifying sexual risk health behavior change models to incorporate alcohol-related factors. Sexual Addiction and Compulsivity, 26, 126–136. https://doi.org/10.1080/10720162.2019.1608879

32.

Kanny

Jeffries

W. L.

IV Chapin-Bardales

Denning

Cha

Finlayson

Wejnert

, & National HIV Behavioral Surveillance Study Group. (2019). Racial/ethnic disparities in HIV preexposure prophylaxis among men who have sex with men—23 urban areas, 2017. Morbidity and Mortality Weekly Report, 68(37), 801–806. https://dx-doi-org.web.bisu.edu.cn/10.15585/mmwr.mm6837a2

33.

Kazdin

A. E.

(2007). Mediators and mechanisms of change in psychotherapy research. Annual Review of Clinical Psychology, 3(1), 1–27. https://doi.org/10.1146/annurev.clinpsy.3.022806.091432

34.

Kemps

Goossens

Petersen

Verbeken

Vervoort

Braet

(2020). Evidence for enhancing childhood obesity treatment from a dual-process perspective: A systematic literature review. Clinical Psychology Review, 77, Article 101840. https://doi.org/10.1016/j.cpr.2020.101840

35.

Kinsey

A. C.

Pomeroy

W. B.

Martin

C. E.

(1948). Sexual behavior in the human male. Saunders.

36.

Lieberman

M. D.

(2007). Social cognitive neuroscience: A review of core processes. Annual Review of Psychology, 58(1), 259–289. https://doi.org/10.1146/annurev.psych.58.110405.085654

37.

Luehring-Jones

Palfai

T. P.

Tahaney

K. D.

Maisto

S. A.

Simons

(2019). Pre-exposure prophylaxis (PrEP) use is associated with health risk behaviors among moderate-and heavy-drinking MSM. AIDS Education and Prevention, 31(5), 452–462. https://doi.org/10.1521/aeap.2019.31.5.452

38.

Maisto

S. A.

Carey

M. P.

Carey

K. B.

Gordon

C. M.

(2002). The effects of alcohol and expectancies on risk perception and behavioral skills relevant to safer sex among heterosexual young adult women. Journal of Studies on Alcohol, 63(4), 476–485.

39.

Maisto

S. A.

Carey

M. P.

Carey

K. B.

Gordon

C. M.

Schum

J. L.

Lynch

K. G.

(2004). The relationship between alcohol and individual differences variables on attitudes and behavioral skills relevant to sexual health among heterosexual young adult men. Archives of Sexual Behavior, 33(6), 571–584. https://doi.org/10.1023/B:ASEB.0000044741.09127.e6

40.

Maisto

S. A.

Palfai

Vanable

P. A.

Heath

Woolf-King

S. E.

(2012). The effects of alcohol and sexual arousal on determinants of sexual risk in men who have sex with men. Archives of Sexual Behavior, 41(4), 971–986. https://doi.org/10.1007/s10508-011-9846-x

41.

Maisto

S. A.

Simons

J. S.

(2016). Research on the effects of alcohol and sexual arousal on sexual risk in men who have sex with men: Implications for HIV prevention interventions. AIDS and Behavior, 20(1), 158–172. https://doi.org/10.1007/s10461-015-1220-x

42.

Maisto

S. A.

Simons

J. S.

Palfai

T. P.

Moskal

Luehring-Jones

(2021). Daily associations among alcohol intoxication, partner familiarity, participant effortful control, urgency, and PrEP uptake on sexual behavior in men who have sex with men. Archives of Sexual Behavior, 50, 2843–2860. https://doi.org/10.1007/s10508-020-01852-2

43.

Mayer

K. H.

Allan-Blitz

L. T.

(2019). PrEP 1.0 and beyond: Optimizing a biobehavioral intervention. Journal of Acquired Immunity Deficiency Syndrome, 82, S113–S117.

44.

Medley

G. R. N.

Lipari

Bose Cribb

D. S.

Kroutil

L. A.

McHenry

(2016). Sexual orientation and estimates of adult substance use and mental health: Results from the 2015 national survey on drug use and health. Substance Abuse and Mental Health Services Administration. https://www.samhsa.gov/data/sites/default/files/NSDUH-SexualOrientation-2015/NSDUH-SexualOrientation-2015/NSDUH-SexualOrientation-2015.pdf

45.

Metcalfe

Mischel

(1999). A hot/cool-system analysis of delay of gratification: Dynamics of willpower. Psychological Review, 106(1), 3–19. https://doi.org/10.1037/0033-295x.106.1.3

46.

Muthén

L. K.

Muthén

B. O.

(2017). Mplus user’s guide (8th ed.).

47.

National Institutes of Health. (2019). NIH strategic plan for HIV and HIV-related research FY 2021–2025. https://www.oar.nih.gov/sites/default/files/NIH_StrategicPlan_FY2021-2025.pdf

48.

National Institute on Drug Abuse. (2010). Drugs, brains, and behavior: The science of addiction. U.S. Department of Health and Human Services.

49.

Norris

Stoner

S. A.

Hessler

D. M.

Zawacki

Davis

K. C.

George

W. H.

Morrison

D. M.

Parkhill

M. R.

Abdallah

D. A.

(2009). Influences of sexual sensation seeking, alcohol consumption, and sexual arousal on women’s behavioral intentions related to having unprotected sex. Psychology of Addictive Behaviors, 23(1), 14–22. https://doi.org/10.1037/a0013998

50.

Osgood

J. M.

Muraven

(2018). Self-control and alcohol consumption. In de Ridder

Adriaanse

Fujita

(Eds.), The Routledge international handbook of self-control in health and well-being (pp. 251–263). Routledge/Taylor & Francis Group.

51.

Pantelic

Stegling

Shackleton

Restoy

(2018). Power to participants: A call for person-centred HIV prevention services and research. Journal of the International AIDS Society, 21(Suppl. 7), Article e25167. https://doi.org/10.1002/jia2.25167

52.

Pihl

R. O.

Paylan

S. S.

Gentes-Hawn

Hoaken

P. N. S.

(2003). Alcohol affects executive cognitive functioning differentially on the ascending versus descending limb of the blood alcohol concentration curve. Alcoholism: Clinical and Experimental Research, 27(5), 773–779. https://doi.org/10.1097/01.ALC.0000065434.92204.A1

53.

Preacher

K. J.

Rucker

D. D.

Hayes

A. F.

(2007). Addressing moderated mediation hypotheses: Theory, methods, and prescriptions. Multivariate Behavioral Research, 42(1), 185–227. https://doi.org/10.1080/00273170701341316

54.

Protogerou

McHugh

R. K.

Johnson

B. T.

(2020). How best to reduce unhealthy risk-taking behaviours? A meta-review of evidence syntheses of interventions using self-regulation principles. Health Psychology Review, 14(1), 86–115. https://doi.org/10.1080/17437199.2019.1707104

55.

Ralevski

Perry

E. B.

Jr. D’Souza

D. C.

Bufis

Elander

Limoncelli

Vendetti

Dean

Cooper

T. B.

McKee

Petrakis

(2012). Preliminary findings on the interactive effects of IV ethanol and IV nicotine on human behavior and cognition: A laboratory study. Nicotine & Tobacco Research, 14(5), 596–606. https://doi.org/10.1093/ntr/ntr258

56.

Rowland

B. H.

Palfai

T. P.

Simons

J. S.

Maisto

S. A.

(2021). Working memory moderates the relation between implicit alcohol associations and heavy episodic drinking in moderate-to-heavy drinking men who have sex with men. Addictive Behaviors, 123, Article 107053. https://doi.org/10.1016/j.addbeh.2021.107053

57.

Scott-Sheldon

L. A.

Carey

K. B.

Cunningham

Johnson

B. T.

Carey

M. P.

(2016). Alcohol use predicts sexual decision-making: A systematic review and meta-analysis of the experimental literature. AIDS and Behavior, 20 (Suppl. 1), 19–39. https://doi.org/10.1007/s10461-015-1108-9

58.

Simons

J. S.

Dvorak

R. D.

Batien

B. D.

Wray

T. B.

(2010). Event-level associations between affect, alcohol intoxication, and acute dependence symptoms: Effects of urgency, self-control, and drinking experience. Addictive Behaviors, 35(12), 1045–1053. https://doi.org/10.1016/j.addbeh.2010.07.001

59.

Simons

J. S.

Maisto

S. A.

Palfai

T. P.

(2019). Using the experience sampling method to study sexual risk behavior among men who have sex with men (MSM). The Journal of Sex Research, 56(9), 1147–1154. https://doi.org/10.1080/00224499.2019.1663479

60.

Simons

J. S.

Maisto

S. A.

Palfai

T. P.

(2021). External validity of analogue sexual risk laboratory data. Experimental and Clinical Psychopharmacology. Advance online publication. https://doi.org/10.1037/pha0000537

61.

Simons

J. S.

Simons

R. M.

Maisto

S. A.

Hahn

A. M.

Walters

K. J.

(2018). Daily associations between alcohol and sexual behavior in young adults. Experimental and Clinical Psychopharmacology, 26(1), 36–48. https://doi.org/10.1037/pha0000163

62.

Strauman

T. J.

Goetz

E. L.

Detloff

A. M.

MacDuffie

K. E.

Zaunmüller

Lutz

(2013). Self-regulation and mechanisms of action in psychotherapy: A theory-based translational perspective. Journal of Personality, 81(6), 542–553. https://doi.org/10.1111/jopy.12012

63.

Tahaney

K. D.

Palfai

T. P.

Luehring-Jones

Maisto

S. A.

Simons

J. S.

(2020). Working memory moderates the association between condom use intentions and behavior among moderate-to-heavy drinking men who have sex with men. Journal of Behavioral Medicine, 43(4), 666–671. https://doi.org/10.1007/s10865-019-00090-9

64.

Toates

(2009). An integrative theoretical framework for understanding sexual motivation, arousal, and behavior. The Journal of Sex Research, 46(2–3), 168–193. https://doi.org/10.1080/00224490902747768

65.

Unsworth

Heitz

R. P.

Schrock

J. C.

Engle

R. W.

(2005). An automated version of the operation span task. Behavior Research Methods, 37(3), 498–505. https://doi.org/10.3758/BF03192720

66.

Vagenas

Azar

M. M.

Copenhaver

M. M.

Springer

S. A.

Molina

P. E.

Altice

F. L.

(2015). The impact of alcohol use and related disorders on the HIV continuum of care: A systematic review. Current HIV/AIDS Reports, 12(4), 421–436. https://doi.org/10.1007/s11904-015-0285-5

67.

Wiers

R. W.

Bartholow

B. D.

van den Wildenberg

Thush

Engels

R. C. M. E.

Sher

K. J.

Grenard

Ames

S. L.

Stacy

A. W.

(2007). Automatic and controlled processes and the development of addictive behaviors in adolescents: A review and a model. Pharmacology Biochemistry and Behavior, 86(2), 263–283. https://doi.org/10.1016/j.pbb.2006.09.021

68.

Wiers

R. W.

Eberl

Rinck

Becker

E. S.

Lindenmeyer

(2011). Retraining automatic action tendencies changes alcoholic patients’ approach bias for alcohol and improves treatment outcome. Psychological Science, 22(4), 490–497. https://doi.org/10.1177/0956797611400615

69.

Wiers

R. W.

Van Dessel

Köpetz

(2020). ABC training: A new theory-based form of cognitive-bias modification to foster automatization of alternative choices in the treatment of addiction and related disorders. Current Directions in Psychological Science, 29(5), 499–505. https://doi.org/10.1177/0963721420949500

70.

Williams

E. C.

Hahn

J. A.

Saitz

Bryant

Lira

M. C.

Samet

J. H.

(2016). Alcohol use and human immunodeficiency virus (HIV) infection: Current knowledge, implications, and future directions. Alcoholism: Clinical and Experimental Research, 40(10), 2056–2072. https://doi.org/10.1111/acer.13204

71.

Wills

T. A.

Walker

Mendoza

Ainette

M. G.

(2006). Behavioral and emotional self-control: Relations to substance use in samples of middle and high school students. Psychology of Addictive Behaviors, 20(3), 265–278. https://doi.org/10.1037/0893-164X.20.3.265

72.

Woolf-King

S. E.

Sheinfil

A. Z.

Ramos

Foley

J. D.

Moskal

Firkey

Kellen

Maisto

S. A.

(2020). A conceptual model of alcohol use and adherence to antiretroviral therapy: Systematic review and theoretical implications for mechanisms of action. Health Psychology Review, 16(1). https://doi.org/10.1080/17437199.2020.1806722

73.

Wray

T. B.

Luo

Pérez

A. E.

Carr

D. J.

Monti

P. M.

(2019). Using Smartphone survey data and machine learning to identify situational and contextual risk factors for HIV risk behavior among men who have sex with men who are not on PrEP. Prevention Science, 20(6), 904–913. https://doi.org/10.1007/s11121-019-01019-z

74.

Wray

T. B.

Monti

P. M.

Celio

M. A.

Pérez

A. E.

(2021). Cognitive-emotional mechanisms of alcohol intoxication-involved HIV-risk behavior among men who have sex with men (MSM). Experimental and Clinical Psychopharmacology, 29(2), 178–190. https://doi.org/10.1037/pha0000377

75.

Zvielli

Bernstein

Koster

E. H. W.

(2015). Temporal dynamics of attentional bias. Clinical Psychological Science, 3(5), 772–788. https://doi.org/10.1177/2167702614551572

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.31 MB