Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2025 Jun 1.
Published in final edited form as: Psychol Addict Behav. 2024 Jan 25;38(4):437–450. doi: 10.1037/adb0000982

ADHD is Associated with More Alcohol Problems and Less Substance-Free Reinforcement: A Behavioral Economics Daily Diary Study of College Student Drinkers

Lauren E Oddo 1,1, Keanan J Joyner 2, James G Murphy 3, Samuel F Acuff 4, Nicholas P Marsh 5, Amanda Steinberg 6, Andrea Chronis-Tuscano 7
PMCID: PMC11116072  NIHMSID: NIHMS1947711  PMID: 38271078

Abstract

Objective:

Behavioral economic theory suggests that alcohol risk is related to elevated alcohol reinforcing efficacy (demand) combined with diminished availability of reinforcing substance-free activities, but little research has examined these reward-related processes at the daily level in association with comorbid conditions that might influence behavioral patterns and reward. Young people with attention-deficit/hyperactivity disorder (ADHD) report high levels of risky drinking, and this risk may be due in part to elevated demand for alcohol and diminished engagement in enjoyable and valued substance-free activities.

Method:

College student drinkers (N=101; 48.5% female; 68.3% white; 18–22 years old) with (n=51) and without (n=50) ADHD completed 14 consecutive daily diaries (diary entry n=1,414). We conducted a series of multilevel path models to examine (1) the associations among ADHD and average daily alcohol demand, substance-free enjoyment, and response-contingent positive reinforcement for goal-directed behaviors; (2) the associations among concurrent daily alcohol demand, substance-free reinforcement, and response-contingent positive reinforcement for goal-directed behaviors and daily alcohol use and alcohol-related negative consequences; and (3) the moderating effect of ADHD on these within-day associations.

Results:

ADHD was significantly associated with more daily alcohol-related negative consequences and less daily substance-free enjoyment and response-contingent positive reinforcement. Regardless of ADHD status, there were significant associations among behavioral economic risk factors and alcohol use and negative consequences, though effects differed within- and between-person. There were no moderating effects of ADHD on within-person associations.

Conclusions:

Results expose areas of impairment specific to drinkers with ADHD and advance theory on ADHD and hazardous drinking.

Keywords: ADHD, Alcohol Use, Daily Diary, Behavioral Economics

For many people, the college years occur during a critical developmental period marked by increased autonomy and initiation or escalation of alcohol and other drug use. Over half of full-time college students ages 18–22 endorse past-month alcohol consumption, with a substantial minority reporting past-month hazardous use characterized by heavy (i.e., consuming more than 4 or 5 drinks on any day or more than 8 or 15 drinks per week for females and males, respectively) or binge drinking (i.e., consuming 4 or more drinks in about 2 hours for women or 5 or more drinks in about 2 hours for men; NIAAA, 2023). Hazardous alcohol use can interfere with key developmental processes such as brain maturation, educational attainment, and career development, and increases the risk progression to alcohol use disorder (AUD; Murphy & Dennhardt, 2016). Thus, it is imperative to characterize those at greatest risk.

The Co-Occurrence of ADHD and Alcohol Problems

Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder that can increase risk for alcohol-related problems and disorders in adolescence and adulthood (Luderer et al., 2021). ADHD is characterized by self-regulatory deficits (Shiels & Hawk, 2010) in selecting, enacting, modifying, and maintaining appropriate behaviors over time in the context of competing reinforcers (Barkley, 2011). ADHD symptoms occur across situations, are age-inappropriate, and lead to adverse outcomes and impaired functioning in key life areas. Actuarial data suggest that adults with ADHD have, on average, 11 to 13 years reduced life expectancy compared to neurotypical peers of a similar age and heath profile, in part due to higher rates of substance use (Dalsgaard et al., 2015).

Etiological theories of ADHD suggest core disruptions in behavioral and emotional self-regulation as pathognomonic characteristics giving rise to both inattentive and hyperactive-impulsive symptoms. Given their core self-regulation deficits, those with ADHD systematically struggle in situations requiring self-motivated behavior (e.g., self-directed attention, resistance to distraction, emotional self-control) toward attaining delayed rewards, compared to situations that are inherently rewarding and immediately reinforcing (Barkley, 2011; Luman et al., 2010). The college environment could be conceptualized as a large-scale self-regulation task requiring daily self-motivated efforts in planning, organizing, and persisting towards long-term goals in the context of limited structure for adaptive, goal-directed behavior. With widely available and potent psychoactive substances whose effects on the nervous system can be inherently pleasurable and immediately rewarding, college can become a “perfect storm” for potentially harmful substance use for young people with ADHD. Relative to their peers without ADHD, college students with ADHD are more likely to meet diagnostic criteria for AUD (Rooney et al., 2012). In cross-sectional work, students with ADHD report experiencing more difficulties stopping a drinking episode (Baker et al., 2012) and higher rates of alcohol-related negative consequences (e.g., getting into fights, having memory loss, and being injured), even when students with and without ADHD do not report significantly different levels of past-month/year alcohol consumption (Mochrie et al., 2020; Rooney et al., 2012).

Behavioral Economic Contextualized Reinforcer Pathology Model

College life is replete with potentially reinforcing and rewarding stimuli. Whereas some students allocate much of their daily attention and behavior to activities that are productive over the long run, others tend to engage in behaviors that yield short-term positive effects but have the potential to undermine long-term wellbeing (Correia et al., 2010). In behavioral economic theory, hazardous alcohol use is conceptualized as a form of reinforcer pathology wherein there is a consistent tendency to overvalue alcohol, a potent immediate reinforcer, and to under-engage in alternative, substance-free reinforcers, either because those activities are not available or because they may require motivated patterns of behavior over time prior to the experience of reward (Acuff et al., 2023). Frequent and heavy alcohol use can reduce access to substance-free rewards, thereby increasing the relative reinforcing value of alcohol (Lamb et al., 2016). The neurocognitive deficits of ADHD may contribute to this process by increasing the reinforcing value of alcohol and reducing access to and preference for substance-free reinforcers; yet, to date, little research has examined these behavioral economic risks in individuals with ADHD.

Alcohol Demand

Alcohol demand characterizes the incentive value of alcohol but remains unexplored in relation to ADHD. Behavioral economic models quantify several distinct elements of alcohol demand using hypothetical alcohol purchase tasks which ask participants how many drinks they would purchase and consume across a range of drink prices (Martínez-Loredo et al., 2021). Demand curve indices of alcohol reinforcing value include peak alcohol consumption at zero cost (demand intensity), maximum alcohol expenditure across all drink price points (Omax), and the degree of sensitivity to escalating costs of alcohol (demand elasticity). Adults reporting more alcohol consumption at free and escalating prices tend to drink more alcohol (even in hypothetical scenarios), report more negative consequences, and have higher rates of AUD (Martínez-Loredo et al., 2021).

There is also evidence that alcohol demand varies considerably within individuals in response to contextual manipulations such as competing responsibilities (e.g., class, work) and social factors (e.g., the presence of peers; Acuff et al., 2020). Preliminary work also suggests that demand varies daily at the individual level and is positively linked to alcohol consumption (Aston & Merrill, 2023; Motschman et al., 2022). Yet, it is unclear if daily fluctuations in alcohol demand are uniquely associated with same-day alcohol use and negative consequences.

It is also unknown whether those with ADHD experience higher alcohol demand than those without ADHD. Alcohol is an easily accessible substance in college with inherently rewarding properties (Correia et al., 2010). Such properties, combined with the self-regulation deficits of ADHD, may make drinkers with ADHD more likely to report high alcohol demand. For example, being easily distractible with poor persistence (i.e., key inattentive symptoms) may predispose those with ADHD to overvalue alcohol relative to other reinforcers. Indeed, students who struggle to persist in their efforts towards delayed outcomes and organize their behavior to meet goals may be especially attracted to the short-term rewards and easy access of alcohol. Further, those with ADHD and high demand may engage in less regulated drinking and experience alcohol-related negative consequences, perhaps not attending to drinking quantity throughout a drinking episode. This possibility is consistent with moderating effects of ADHD constructs (i.e., tendency to act without considering consequences) on associations of trait-level demand and alcohol use, but research is mixed (Gray & MacKillop, 2014; Smith et al., 2010).

Substance-Free Reinforcement

When substance-free alternatives are immediately and reliably delivered within the laboratory environment, animals reduce drug self-administration (Lamb et al., 2016). Studies with college students replicate these findings and demonstrate that drinking is substantially influenced by the presence of both immediately available alternatives (Martinetti et al., 2019) and constraints such as academic responsibilites (Joyner et al., 2019). Heavy alcohol use also tends to decrease when engagement in substance-free activities is differentially rewarded (e.g., contingency management; Dougherty et al., 2015); when a person acquires alternative sources of reward that “compete with” alcohol use (e.g., substance-free hobbies, wellness activities, academics; Murphy et al., 2006); and when substance-free behaviors are enhanced in treatment (Daughters et al., 2018; Murphy et al., 2019). In all, existing work generally uses aggregate-level data assessing overarching patterns of substance-free reinforcement, typically assessed by combining past-month ratings of average frequency of substance-free activity engagement and pleasure derived. This foundational body of work tells us that people who report more substance-free reinforcement also drink less and experience fewer negative consequences, relative to people who report less substance-free reinforcement (Acuff et al., 2019), and that successful reductions in drinking following brief intervention are associated with increases in substance-free activities (Murphy et al., 2019).

Another approach to assessing reinforcement uses self-report measures that assess more global patterns of response-contingent positive reinforcement for goal-directed activities, which will generally tend to be substance-free activities (Acuff et al., 2019). Indeed, a key piece of reinforcement includes the function of a person’s behavior patterns (Manos et al., 2011). Measures of response-contingent positive reinforcement (RCPR; Carvahlo, 2011) characterize the function of behavior (e.g., goal pursuit vs. avoidance), which align with conceptualizations of problem drinking that emphasize a larger system of reinforcement contingencies that serve to increase/decrease adaptive, goal-directed behavior. That is, certain environmental features (e.g., limited presence of adaptive sources of reinforcement, easy access to highly reinforcing substances) and maladaptive behaviors (e.g., avoidance, poor behavioral self-regulation) can interfere with deriving reinforcement from adaptive sources (Lamb et al., 2016).

Despite the importance of measuring multiple facets of reward and reinforcement in refining theory, there has been limited empirical attention to examining whether daily fluctuations in substance-free reinforcement indices (i.e., substance-free activity engagement and enjoyment) and RCPR for goal-directed behaviors differentially predict alcohol use and negative consequences within a person. In one of the few studies to disaggregate these facets, Magidson et al. (2017) found, among a nationally representative sample of adults, that greater activity enjoyment, not frequency, was associated with fewer alcohol-related problems and heavy drinking episodes. Their findings support the importance of disaggregating reinforcement and testing theory as it unfolds in a person’s life, as generalizing patterns of behavior to the individual-level is a logical facility that can mask important nuance (Curran & Bauer, 2011).

Furthermore, students with ADHD may derive limited reinforcement from substance-free activities and experience less RCPR for goal-directed activity, which may inadvertently reinforce their alcohol use. Indeed, adaptive, goal-directed behaviors require many cognitive-behavioral capabilities (e.g., adaptive coping, prosocial engagement, organization/planning skills) that characterize ADHD inattentive and/or hyperactive-impulsive symptoms (Manos et al., 2011; Nigg et al., 2020). In a study of young adult drinkers, Oddo et al., (2021a) showed that, out of a suite of reward-related risk factors, the presence of environmental suppressors to reward (e.g., unpleasant and aversive experiences) was the only shared correlate of ADHD and AUD. This study also highlighted the unique association among ADHD and low reward probability, such that young adults with elevated ADHD symptoms reported fewer reinforcing experiences and perceived more impairments in their ability to acquire rewards. Moreover, a study of RCPR as a mechanism of change in alcohol intervention for college students with ADHD showed that engagement in more adaptive, goal-directed behaviors and less avoidance over treatment predicted reductions in alcohol-related negative consequences (Oddo et al., 2021b). This work is consistent with calls for treatments to go beyond a focus on eliminating alcohol use to enhance experiences that are enjoyable, inherently reinforcement, and/or contribute to a person’s goals (McKay, 2017). Yet, inattentive (i.e., poor organization, forgetfulness, distractibility) and hyperactive-impulsive symptoms (i.e., restlessness, impatience, acting without thinking) are likely to interfere with acquiring RCPR and substance-free rewards.

Given that those with ADHD are especially sensitive to their psychosocial contexts, they may also be more vulnerable to alcohol use on days with low substance-free reinforcement and RCPR. The delayed reward from alternatives may serve as less potent opportunity costs among individuals high in ADHD-related traits (e.g., poor persistence and sustained attention/effort, maladaptive behavior allocation towards smaller, immediate rewards) (Lamb et al., 2016), as youth with ADHD are more likely to require immediate and predictable rewards to alter behavior (Nigg et al., 2020). Identification of moderating effects aligns with this priority and has the potential to test questions about “for whom” substance-free reinforcement and RCPR effectively constrain drinking and negative consequences, in turn informing personalized interventions.

The Current Study

Research to date has largely focused on behavioral risks over a long timescale, with no known work applying a behavioral economic framework to drinkers with ADHD. Proximal reports of behaviors in the form of daily diaries can reduce biased estimates and allow for statistical methodology parceling effects that occur between persons and at the individual level. Disaggregating between versus within person effects is important both for refining theory and guiding intervention approaches (i.e., the size and direction of effects may diverge at different levels of analysis; Curran & Bauer, 2011). The current study examined whether drinkers with and without ADHD differed in daily reports of alcohol demand, substance-free enjoyment and activity engagement (i.e., two substance-free reinforcement indices), and RCPR. We hypothesized that drinkers with ADHD would report more daily alcohol demand, less daily substance-free activity reinforcement, and less daily RCPR than drinkers without ADHD. We also evaluated the effects of daily alcohol demand, substance-free activity reinforcement, and RCPR on alcohol use and alcohol-related negative consequences, both across participants and at the individual level. We hypothesized that higher daily alcohol demand, lower daily substance-free reinforcement, and lower daily RCPR would be associated with higher levels of daily alcohol use and alcohol-related negative consequences. We also explored the moderating effects of ADHD on these within-person, same-day associations. Finally, we conducted separate exploratory sensitivity analyses based on ADHD symptom dimensions (i.e., total inattentive and hyperactive-impulsive symptoms) to evaluate possible relations among specific ADHD symptoms with behavioral economic indices.

Method

Participants and Procedures

Participants were 101 full-time college students with (n=51) and without (n=50) ADHD from a large public university in the Mid-Atlantic United States. See Table 1 for sample demographics. Participants predominately reported that they were white (68.3%) and lived independent of caregivers (n=87).2

Table 1.

Demographic Variables

Variable ADHD Non-ADHD Control Full Sample
Participant Demographics N (%) N (%) N (%)
Sex
 Female 22 (43.1) 27 (54.0) 49 (48.5)
 Male 29 (56.9) 23 (46.0) 52 (51.5)
Racial/Ethnic Identification
 Asian 4 (7.8) 3 (6.0) 7 (6.9)
 Black or African American 3 (5.9) 4 (8.0) 7 (6.9)
 Hispanic/Latino 7 (13.7) 5 (10.0) 12 (11.9)
 White (non-Hispanic/Latino) 36 (70.6) 33 (66.0) 69 (68.3)
 > 1 Race 1 (2.0) 5 (10.0) 6 (5.9)
Mean Age* 20.39 years 20.88 years 20.64 years
Drinking Day Descriptives
 Mean Drinking Days (SD)* 6.76 (3.23) 5.52 (2.10) 6.12 (2.75)
 Weekend Drinking Days 156 (46.0) 150 (56.2) 306 (50.5)
 Weekday Drinking Days* 183 (54.0) 117 (43.8) 300 (49.5)
 Mean Drinks per Drinking
 Day (SD)		 5.35 (4.37) 5.68 (3.94) 5.50 (4.19)
Open in a new tab

Note.

*

= p<0.05; Drinking day descriptive values represent average percentages within each group (i.e., ADHD, control, full sample). Weekend is coded as Friday-Saturday vs. Sunday-Thursday.

Participants were eligible if they were: full-time college students between the ages of 18–22, reported drinking at least 3 times per week in the past two weeks, reported at least 1 heavy drinking episode in the past two weeks (i.e., 4+/5+ drinks in ≤ 2 hours for females/males, respectively), and exceeded young adult hazardous drinking cut-offs on the Alcohol Use Disorder Identification Test (AUDIT; scores of 5+/7+ for females/males, respectively; DeMartini & Carey, 2012). Students in the non-ADHD comparison group (i.e., “controls”) were eligible if they: (1) had ≤ 3 current DSM-5 ADHD symptoms, (2) reported no history of ADHD, and (3) had never been prescribed medication for ADHD. Participants in the ADHD group met full DSM-5 diagnostic criteria for ADHD – clinically significant ADHD symptoms and multi-domain impairment by age 12 and persisting currently per rating scales and semi-structured diagnostic interviews. Approximately 67% of students (n=34) in the ADHD group reported a prior diagnosis of ADHD, diagnosed by a medical or mental health provider.3 All students were ineligible if they were currently in substance use treatment, reported a psychotic disorder or eminent suicide or homicide risk, or were not fluent in English. All participants were treated in accordance with American Psychological Association ethical guidelines for research conduct, and the institutional review board approved all study procedures.4

Participants were recruited through campus listservs, the undergraduate research participation system, and flyers posted at University Counseling, Accessibility and Disability support services, and in the proximity of campus. Interested students completed a series of screeners assessing alcohol use frequency, heavy drinking episodes, prior ADHD diagnosis and symptoms, and student status. Eligible students on the screeners were invited for a two-hour baseline session in a university-based research laboratory (n=11) or via secure online videoconference platform (n=90) due to COVID-19. Master’s- and doctoral-level assessors administered structured and semi-structured clinical interviews assessing ADHD and AUD under the supervision of a licensed clinical psychologist. Participants completed measures of alcohol/drug use and psychosocial risk and protective factors. At the end of the baseline visit, eligible participants were trained to operate the mobile daily diary and instructed to go about their daily routines without changing behavior due to study enrollment. Participants who did not meet eligibility criteria during the baseline visit were compensated, provided with appropriate referrals, and excluded from further participation (n=4).5 The next day, participants started the mobile daily diaries. They completed these diaries for 14 consecutive days, which allowed for sufficient reports to characterize typical experiences while minimizing participant burden (Eisele et al., 2022). Surveys were sent via text messages containing personal links, with up to 5 reminders per day. The first text message arrived at 8:45 AM ET and the last arrived at 4:00 PM ET.6 Surveys took between 5 to 10 minutes to complete.

Measures

Demographics.

Demographic characteristics were collected via self-report during the baseline visit. Participants reported on assigned sex at birth (male/female), gender identity, race and ethnic identity, and socioeconomic status (SES; defined to participants as “Low-income or poor” “Working-class” “Middle-class” “Upper-middle or professional-middle” “Wealthy”).

Alcohol Use Screener.

The Alcohol Use Disorders Identification Test (AUDIT; Saunders et al., 1993) was administered as a screener to determine hazardous drinking behaviors, consistent with study eligibility criteria. The AUDIT is a 10-item brief screening instrument designed to assess high-risk drinking and related impairments in the past year. A higher total score indicates more alcohol-related problems and higher risk of AUD.

Semi-Structured Adult ADHD Interview.

The Adult ADHD Clinical Diagnostic Scale (ACDS; Kessler et al., 2010) was used to assess current and childhood ADHD.7 The ACDS is a semi-structured interview administered by rigorously trained and supervised master’s- and doctoral-level assessors (n = 3). Assessors met reliability criterion on the ACDS (k > 0.80). Cases were reviewed by both an advanced clinical psychology doctoral student and a licensed clinical psychologist with extensive experience in the assessment and treatment of ADHD. ADHD diagnostic status, derived from the ACDS, was used to determine group membership (i.e., ADHD versus control), in line with inclusion/exclusion criteria described previously. Of note, the ADHD group included students with combined presentation (n=30), predominately inattentive presentation (n=20) and predominately hyperactive/impulsive presentation (n=1). Half (n=26) of students in the ADHD group were currently prescribed medication for ADHD.

Daily Alcohol Demand.

We used 3 indices of daily alcohol demand, derived from a hypothetical Alcohol Purchase Task (APT; e.g., Kaplan et al., 2018 for review) and modified from prior work (Merrill & Aston, 2020), to capture key aspects of alcohol demand. Intensity was measured by the item: “If drinks were free today/tonight, how many would you have?” (response options from 0–10+, in single drink increments). Omax was measured by the item: “What is the maximum total amount you would spend on drinking today/tonight?” (response options from $0 to $40+, in $4 increments). Breakpoint was measured with the item: “What is the maximum you would pay for a single drink today/tonight?” (response options from $0 to over $20, in $2 increments). Higher scores reflect greater alcohol demand.

Daily Substance-Free Activity Engagement.

Each day, participants were asked to approximate how much time they spent the prior day engaging in a pre-populated series of activities, while not under the influence of drugs or alcohol: substance-free social, academic, hobby, wellness/religious, and intimacy activities (see Supplemental Table 1). These domains were selected based on ADHD-related impairments (e.g., Nigg et al., 2020) and documented links among specific substance-free activities and alcohol use (Acuff et al., 2019). Higher scores indicate more time spent in these substance-free activities.

Daily Substance-Free Enjoyment.

Substance-free enjoyment was assessed via daily ratings using a 5-point Likert scale of 0 (unpleasant or neutral) to 4 (extremely pleasant), adapted from the Adolescent Reinforcement Survey Schedule (ARSS; Hallgren et al., 2016). We created a total substance-free enjoyment variable, comprised of responses to the prompt: “Yesterday, how much did you enjoy the time you spent without being under the influence of drugs or alcohol?” Higher scores indicate more daily substance-free enjoyment.

Response Contingent Positive Reinforcement (RCPR).

RCPR was assessed using a modified Behavioral Activation for Depression Scale – Short Form (BADS-SF; Manos et al., 2011). This is a 9-item questionnaire measures engagement in approach behaviors that increase the likelihood of adaptive sources of RCPR by: 1) engaging in focused, goal-directed behavior and completion of scheduled activities and 2) experiencing fewer aversive controlling stimuli and engaging in less avoidant behavior (Manos et al., 2011). Item prompts were modified for daily use, with each prompt beginning “Yesterday…” (e.g., “yesterday, I made good decisions about what types of activities and/or situations I put myself in”). All items were rated using a 7-point Likert scale from 0 (Not at all) to 6 (Completely). Higher scores indicate more RCPR.

Daily Alcohol Use.

Alcohol consumption was measured with the item: “How many standard drinks in total did you have yesterday?” The item also contained an explanation of standard drink quantities (e.g., 12oz of beer, 5oz of wine, 1.5oz of hard liquor). A daily total score indexed the total number of drinks consumed that day.

Daily Alcohol-related Negative Consequences.

Participants reported on 16 items derived from the Brief Young Adult Alcohol Consequences Questionnaire (BYAACQ; Kahler et al., 2005). Items were modified for daily use and were presented as a checklist that began with: “Please check all that apply to your drinking yesterday.” Items selected for inclusion were among the highest frequency items reported in a prior study on college student drinkers with ADHD (Meinzer et al., 2021) and theoretically linked to impairments in ADHD (e.g., “I took foolish risks while drinking”, “I failed to do something that was expected of me because of drinking”). See Supplemental Table 2 for the frequency of negative consequences endorsed in the daily diaries. Consistent with standard measurement (Kahler et al., 2005), a total score was created for each day by summing the number of endorsed items, with higher scores indicating more daily alcohol-related negative consequences.

Data Analytic Plan

In considering sample size, we followed best-practice recommendations for pilot/exploratory studies and did not formally compute a-priori power analyses (Moore et al., 2011). Following examination of descriptive statistics, a series of multilevel path models were conducted in Mplus, with day at Level 1 nested within person at Level 2. Multilevel path models accommodate the hierarchical data structure, incorporate a sandwich estimator (using maximum likelihood with robust standard errors) to help adjust for non-normality in the data, and simultaneously model multiple dependent variables (Heck & Thomas, 2020). We used cluster-mean centering to decompose between versus within subject variance. Sex and current SES were modeled as Level 2 covariates. At the daily level, day in study and a dummy variable representing weekend (i.e., Friday or Saturday vs. weekdays) were included as Level 1 covariates.8 Level 1 continuous predictors were person-mean centered. This allowed us to test whether deviations above or below a given person’s average daily levels of alcohol demand, substance-free reinforcement, and RCPR corresponded to individual-level changes in daily alcohol use and alcohol-related negative consequences.9

To test the first question, the between-person averages of the daily reports of alcohol demand (i.e., each index modeled separately), substance-free activity engagement, substance-free enjoyment, and RCPR were regressed on ADHD in separate models. In separate sensitivity models, we also regressed each of these daily variables on inattentive and hyperactive-impulsive symptoms endorsed on clinical interview (i.e., ACDS) – allowing us to explore associations with ADHD symptom presentation.10 To test the second question, within-subject daily reports of alcohol demand, substance-free activity engagement, substance-free enjoyment, and RCPR were modeled as independent predictors of same-day alcohol use and negative consequences in separate models, with alcohol use and negative consequences modeled as simultaneous outcomes. To test the third question, we specified random slopes, such that within cluster variation in the strength of associations among predictor and outcomes comprised latent variables at the between-person level, with ADHD regressed on each random slope (i.e., a cross-level interaction; Sadikaj et al., 2021). This allowed us to test whether ADHD moderated the within-person effect of daily alcohol demand, substance-free activity engagement, substance-free enjoyment, and RCPR (person-centered) on alcohol use and negative consequences. In addition, we followed-up with sensitivity analyses for inattentive and hyperactive-impulsive symptoms to explore possible moderating effects by specific ADHD symptom dimensions. Models were just identified where the number of free parameters equaled the number of variances and unique covariances, so no fit statistics are presented.

Results

Descriptive Statistics

Participants completed 98.20% of possible diaries (minimum completed was 12 of 14 days). There were no significant differences in completion rates between groups (ADHD: 97.90%; Controls: 98.40%; r=−0.02, p=0.50). Bivariate correlations at the within and between person levels and intraclass correlation coefficients (ICC) are presented in Table 2. ICCs ranged from 0.09–0.57, meaning that much of the variability was explained within-person.

Table 2.

Intraclass Correlations and Within- and Between-Person Correlations among Average Daily Alcohol Use, Alcohol-Related Negative Consequences, Substance-Free Enjoyment, Substance-Free Activity Engagement, RCPR, Alcohol Demand

Variable 1 2 3 4 5 6 7 8
1. Alcohol consumption − −0.07 0.04 −0.04 0.08 0.77** 0.12 −0.15
2. Alcohol-related negative consequences 0.48** − −0.36* 0.02 −0.47** 0.001 0.03 0.14
3. Substance-free enjoyment 0.03 −0.02 − 0.41** 0.65** −0.03 0.02 −0.01
4. Substance-free activity engagement −0.24** −0.23** 0.19** − 0.15 −0.14 −0.04 −0.06
5. RCPR 0.13** −0.10* 0.28** 0.11** − −0.10 −0.04 −0.10
6. Demand intensity 0.62** 0.25** 0.04 −0.20** 0.11** − 0.48* 0.18
7. Demand Omax 0.49** 0.21** 0.07* −0.15** 0.12** 0.71** − 0.84
8. Demand breakpoint 0.40** 0.15** 0.04 −0.11** 0.12** 0.59** 0.81** −
ICC 0.09 0.28 0.45 0.40 0.57 0.23 0.26 0.28
Open in a new tab

Note.

*

p<0.05,

**

p<0.01; Upper triangle represents between-person correlations (N=101), and lower triangle represents within-person correlations (n=1,414). RCPR = Response Contingent Positive Reinforcement; ICC = Intraclass correlation coefficient.

Approximately 43% of days were drinking days across the sample, with participants reporting between 1–14 drinking days over the two-week period. Participants with ADHD reported significantly more drinking days than controls (ADHD: M=6.76, SD=3.23; Controls: M=5.52, SD=2.10; p=0.03, d=0.2). Participants averaged 5.50 drinks per drinking day, which did not differ by ADHD status (ADHD: M=5.35, SD=4.37; Controls: M=5.68, SD=3.94, p=0.34). Alcohol-related negative consequences ranged from 0–10, with approximately 44% (ADHD) and 38% (Controls) of drinking days resulting in one or more consequence. Results from a multilevel path model evaluating the effect of ADHD on average daily alcohol use and negative consequences showed that ADHD was associated with more negatives consequences (b=0.37, SE=0.16, p=0.02), with no significant association between ADHD and alcohol use (b=0.28, SE=0.27, p=0.30), controlling for sex, current SES, weekend, and day in study.

Associations among ADHD and Behavioral Economic Risk Factors

See Table 3 for model results. Relative to controls, participants with ADHD reported significantly less enjoyment from substance-free activities on average (b=−0.40 SE=0.14, p=.003). Higher levels of inattentive symptoms, but not hyperactive-impulsive symptoms, were significantly associated with less daily enjoyment from substance-free activities (inattention: b=−0.06, SE=0.02, p=0.002; hyperactive-impulsive: b=−0.05, SE=0.02, p=0.06). Participants with ADHD also reported lower average levels of RCPR than did controls (b=−5.08 SE=1.33, p<.001). Higher levels of inattentive symptoms and hyperactive-impulsive symptoms were significantly associated with lower daily RCPR (inattention: b=−0.87, SE=0.19, p<0.001; hyperactive-impulsive: b=−0.91, SE=0.21, p<0.001). There were no significant associations among ADHD (nor inattentive and hyperactive-impulsive symptom dimensions) and average daily alcohol demand or total hours spent in substance-free social, academic, hobby, wellness/religious, and intimacy activities.

Table 3.

Predictors of Average Daily Substance-Free Enjoyment, Substance-Free Activity Engagement, RCPR, and Alcohol Demand

Predictor Substance-Free Enjoyment Substance-Free Activity Engagement RCPR Demand Intensity Demand Omax Demand Breakpoint
b (S.E.), p b (S.E.), p b (S.E.), p b (S.E.), p b (S.E.), p b (S.E.), p
Within Level Covariates
Weekend 0.15 (0.06), p=.01 −1.19 (0.20), p<.001 1.53 (0.43), p<.001 3.20 (0.23), p<.001 7.76 (0.66), p<.001 2.41 (0.22), p<.001
Study Day 0.002 (0.01), p=.70 −0.001 (0.03), p=.96 0.001 (0.05), p=.98 −0.06 (0.20), p=.003 −0.14 (0.07), p=.04 −0.06 (0.02), p=.01
Between Level Covariates
Sex 0.01 (0.14), p=.95 −1.42 (0.52), p=.01 2.89 (1.33), p=.03 1.00 (0.23), p=.001 0.34 (1.04), p=.76 −0.33 (0.38), p=.38
Current SES 0.12 (0.08), p=.16 0.10 (0.41), p=.81 0.86 (0.76), p=.26 −0.09 (0.23), p=.69 0.52 (0.79), p=.51 −0.10 (0.23), p=.64
Between Level Predictor
ADHD −0.40 (0.14), p=.003 −0.34 (0.54), p=.53 −5.08 (1.33), p<.001 0.18 (0.31), p=.56 −0.76 (1.03), p=.45 −0.31 (0.38), p=.40
Open in a new tab

Note.

*

p<0.05,

**

p<0.01; Weekend is coded as Friday-Saturday vs. Sunday-Thursday; RCPR = Response Contingent Positive Reinforcement

Daily Between Person Associations among Behavioral Economic Risk Factor and Same-Day Alcohol Use and Negative Consequences

Across persons, substance-free enjoyment was significantly negatively associated with alcohol-related negative consequences (b=−.30, SE=.12, p=.01), but not with alcohol use. In other words, on average, participants who derived more daily enjoyment from their substance-free activities also reported fewer alcohol-related negative consequences, compared to those who reported lower levels of substance-free enjoyment. Across persons, there were no significant associations of substance-free activity engagement and alcohol use or negative consequences. Across persons, average daily RCPR was negatively associated with alcohol-related negative consequences (b=−.04, SE=.01, p<.001) but not with alcohol use. In other words, on average, participants with more daily RCPR also reported fewer negative consequences from their alcohol use, compared to those with less RCPR.

Across persons, average daily intensity and Omax, but not breakpoint, were positively associated with alcohol use (intensity: b=.58, SE=.10, p<.001; Omax: b=.06, SE=.03, p=.03). That is, on average, participants who reported that they would consume more drinks if drinks were free (intensity) and spend more money on alcohol (Omax) also reported consuming more alcohol, compared to those who reported lower levels of intensity and Omax, respectively. There were no significant associations among average daily alcohol demand and alcohol-related negative consequences between persons.

Daily Within Person Associations among Behavioral Economic Risk Factor and Alcohol Use and Negative Consequences

See Table 4 for model results. Within person, more time spent in substance-free activities was associated with less alcohol use (b=−.19, SE-.04, p<.001) and fewer alcohol-related negative consequences (b=−.05, SE=.02, p=.002). That is, when an individual reported spending more time in substance-free social, academic, hobby, wellness/religious, and intimacy activities on a given day than they typically did, they reported consuming less alcohol and experiencing fewer negative consequences than they typically did. Within person, there were no significant associations among substance-free enjoyment and alcohol use or negative consequences. Within person, higher average daily RCPR was associated with more alcohol use (b=.04, SE=.02, p=.02) but fewer alcohol-related negative consequences (b=−.02, SE=.01, p=.01). In other words, when an individual reported more RCPR than they typically did, they reported consuming more alcohol but experiencing fewer negative consequences.

Table 4.

Same Day Effects of Alcohol Demand, Substance-Free Enjoyment and Engagement, and RCPR on Alcohol Use and Alcohol-Related Negative Consequences

Predictor Alcohol Consumption Negative Consequences
b SE p-value b SE p-value
Within Level Predictors
 Substance-free enjoyment −0.053 0.142 0.71 −0.067 0.086 0.44
 Substance-free activity
 engagement		 −0.190 0.041 0.000 −0.052 0.017 0.002
 RCPR 0.041 0.018 0.02 −0.021 0.008 0.01
 Demand Intensity 0.69 0.06 <0.001 0.07 0.03 0.01
 Demand Omax 0.16 0.02 <0.001 0.02 0.01 0.02
 Demand Breakpoint 0.33 0.05 <0.001 0.02 0.03 0.48
Between Level Predictors
 Substance-free enjoyment 0.106 0.145 0.46 −0.300 0.115 0.01
 Substance-free activity
 engagement		 0.019 0.043 0.68 −0.023 0.035 0.52
 RCPR 0.003 0.015 0.84 −0.041 0.012 <0.001
 Demand Intensity 0.58 0.10 <0.001 0.04 0.06 0.44
 Demand Omax 0.06 0.03 0.03 0.01 0.02 0.64
 Demand Breakpoint 0.04 0.07 0.63 0.05 0.06 0.43
Open in a new tab

Note. For ease of visual presentation, predictors are simultaneously included in one table, but each model was run separately. Covariates are not depicted. All models included covariates sex (male/female), self-reported current socioeconomic status, and a dummy variable of Friday-Saturday vs. Sunday-Thursday. RCPR = Response Contingent Positive Reinforcement.

In addition, within person, greater alcohol demand was significantly associated with more alcohol use (intensity: b=.69, SE=.06, p<.001; Omax: b=.16, SE=.02, p<.001; breakpoint: b=.33, SE=.05, p<.001). In other words, when an individual reported that they would consume more drinks that day if drinks were free (intensity), spend more money on alcohol (Omax), and pay more for a single drink (breakpoint) than they typically did, they reported consuming more alcohol that day. Additionally, within person, greater demand intensity (b=.07, SE=.03, p=.01) and Omax (b=.02, SE=.01, p=.02) but not breakpoint, predicted more negative consequences.

Moderating Effects of ADHD

There were no significant moderating effects of ADHD on within-person daily associations among each behavioral economic risk factor and alcohol use and negative consequences (see Table 5 for model results). There were no significant moderating effects of the inattentive and hyperactive-impulsive symptom dimensions.

Table 5.

Between Level Moderation of ADHD on the Within Level Same-Day Associations Among Behavioral Economic Predictors and Alcohol Consumption and Alcohol-Related Negative Consequences

Interaction Term Alcohol Consumption Negative Consequences
b SE p-value b SE p-value
Substance-free enjoyment*ADHD 0.29 0.30 0.34 −0.11 0.16 0.48
Substance-free activity engagement*ADHD 0.05 0.08 0.55 0.01 0.04 0.73
RCPR*ADHD −0.02 0.04 0.64 −0.02 0.02 0.20
Demand Intensity*ADHD 0.03 0.09 0.73 0.04 0.05 0.40
Demand Omax*ADHD 0.06 0.03 0.10 0.02 0.01 0.16
Demand Breakpoint*ADHD 0.20 0.12 0.09 0.02 0.04 0.55
Open in a new tab

Note. For ease of visual presentation, predictors are simultaneously included in one table, but each model was run separately. Covariates are not depicted. All models included covariates sex (male/female), self-reported current socioeconomic status, and a dummy variable of Friday-Saturday vs. Sunday-Thursday. RCPR = Response Contingent Positive Reinforcement.

Discussion

There is a need for novel, theoretically grounded approaches to inform prevention and intervention of AUD among college students with ADHD. Behavioral economic theory articulates a reinforcer pathology model whereby individuals engaging in hazardous alcohol use tend to overvalue alcohol as a reinforcer and under-engage in substance-free activities due to limited availability and/or to a preference for the immediate rewards of alcohol use (Acuff et al., 2023). This is the first study to map associations among these factors and alcohol use and negative consequences with a daily diary approach in those with and without ADHD.

Descriptive models showed that drinkers with ADHD experienced more alcohol-related negative consequences than drinkers without ADHD. This finding replicates prior work (e.g., Lee et al., 2011; Rooney et al., 2012) using robust daily diary methodology, including a control group engaging in hazardous drinking, and simultaneously accounting for average daily alcohol use in modeling negative consequences. Future work should evaluate the specific facets of alcohol-related impairment in drinkers with ADHD, which might further illuminate areas of intervention (Wang et al., 2021). Of note, our high compliance rates provide proof-of-concept support for daily dairy methodology in populations with difficulties with follow-through (e.g., young people with ADHD). Such methodology has tremendous potential in providing insights that inform theory as well as interventions delivered to the right people at the right time.

Associations among ADHD and Behavioral Economic Risk Factors

ADHD and Substance-Free Reinforcement

Drinkers with ADHD, and those with higher levels of inattentive and hyperactive-impulsive symptoms, reported more daily avoidance and aversive experiences and lower levels of engagement in focused, goal-directed behavior and completion of scheduled activities (i.e., less RCPR). Those with ADHD, and those higher levels of inattentive symptoms in particular, also reported deriving less enjoyment in their daily substance-free lives. Perhaps drinkers with ADHD perceive efforts to engage in important, goal-directed approach behaviors as burdensome and effortful or are less confident in their ability to deploy cognitive-behavioral strategies to achieve effortful rewards. Indeed, both inattentive and hyperactive-impulsive symptoms may interfere with sustained effort towards attaining rewards, especially in the context of easy access to alcohol and other immediately reinforcing substances. This possibility is consistent with prior work showing that individuals with ADHD demonstrate more avoidant coping and difficulties in task persistence, effort regulation, and social skills compared to neurotypical peers (Nigg et al., 2020). Relatedly, the constellation of ADHD-related impairment may represent a chronic stressor (Combs et al., 2015) and interfere with enjoying substance-free experiences, as substance-free activities often recruit effortful, goal-directed, and prosocial behaviors. Further, our exploratory sensitivity analyses suggest that inattentive symptoms, in particular, may drive the association among ADHD and substance-free enjoyment. Interestingly, a key component to life satisfaction is being mindful, or brining one’s attention to the present moment with non-judgmental observation of thoughts, emotions, and sensations (Kong, Wang, & Khao, 2014). Novel adult ADHD interventions include mindfulness training, which holds promise in improving self-regulation of attention (Cairncross & Miller, 2016; Mitchell et al., 2014). Our findings also extend those of Knouse and colleagues (2008), who showed that community adults with elevated ADHD symptoms reported more daily distress, higher levels of negative affect, and less satisfaction in daily living. Indeed, prior work shows that ADHD-related impairments in social and academic domains can limit a person’s perceptions of their life options, potential, and sense of direction (Ramsay, 2002). Exposure to chronic stressors is associated with difficulties in task persistence and executive functioning (Seldin et al., 2019; Wolff et al., 2020), which may be a mechanism underlying lower engagement in valued and enjoyable substance-free activities. It is also notable that currently being prescribed ADHD medication did not alter these associations, suggesting that ADHD-related impairment may affect RCPR and substance-free reinforcement even among students prescribed medication to treat ADHD. Future research should directly test whether RCPR and substance-free enjoyment are mechanisms contributing to harmful drinking in ADHD, and whether medication-related variables (e.g., adherence) buffer these effects.

Contrary to predictions, however, college drinkers with ADHD did not differ from controls in the amount of time spent in substance-free social, academic, hobby, wellness/religious, and intimacy activities. It is possible that drinkers with ADHD in our study engaged in these substance-free activities at aversive, stressful, and/or otherwise inopportune times (e.g., selecting to go to dinner with friends [substance-free activity] instead of completing an important assignment [substance-free activity]). Two people may engage in the same substance-free behavior (e.g., going to dinner with friends), but the function of those behaviors could be adaptive (e.g., establishing or strengthening social relationships) or maladaptive (e.g., avoidance of schoolwork). Similarly, drinkers with ADHD may participate in social or other recreational activities but experience rejection from others and/or lack the skills to navigate those situations in ways that results in mutually enjoyable interactions. This possibility helps to explain the seemingly contradictory finding that ADHD was related to lower RCPR and substance-free enjoyment but not in time allocation, such that the difference lies in less goal-directed and enjoyable behavior vs. simply less discrete behavior in specific categories. It is imperative that research further scrutinize facets of reinforcement to refine our understanding of impairments that map onto nodes of intervention.

ADHD and Alcohol Demand

Contrary to predictions and to the observed group differences in substance-free reinforcement and RCPR, we did not find significant differences among drinkers with and without ADHD in average daily alcohol demand (intensity, Omax, and breakpoint). This suggests that the greater frequency of alcohol problems reported by those with ADHD relative to heavy-drinking counterparts without ADHD may not necessarily be due to underlying differences in alcohol reinforcing efficacy, and is inconsistent with previous research linking psychiatric comorbidity to elevated demand (Minhas et al., 2020). There are several explanations for these findings, warranting additional investigation. It may be that drinkers with ADHD endorse elevated alcohol demand in certain situations, only. For example, drinkers with ADHD may experience more alcohol demand when involved in drinking contexts that are accompanied by salient reinforcers (e.g., drinking games, romantic partners) or minimal environmental constraints (e.g., alternative substance-free reinforcers, next-day responsibilities). Additionally, perhaps those with ADHD experience more moment-to-moment fluctuations in impulsivity domains (Pedersen et al., 2019), which may situationally amplify alcohol demand. Interestingly, findings did not differ among those with more hyperactive/impulsive symptoms, suggesting that these non-significant findings are not solely attributable to differences in symptom presentations. Further, as approximately half of students with ADHD in the study were prescribed medication for ADHD, it is also possible that medication effectively tempered alcohol-related reinforcement though we did not observe this effect in our models. Certainly, medication for ADHD has been shown to reduce risk for later substance use among people with ADHD (Chang et al., 2014). Future research should identify contextual correlates of alcohol demand in drinkers with ADHD as well as possible medication and treatment effects.

Associations among Behavioral Economic Risk Factors and Alcohol Use and Consequences

Substance-Free Reinforcement

Regardless of ADHD status, students who derived less enjoyment from their daily substance-free activities also reported more alcohol-related negative consequences, compared to those who reported higher levels of daily substance-free enjoyment. Consistent with previous research, this between-person finding suggests that those who tended to derive less enjoyment from substance-free activities also tended to experience more negative consequences, relative to those who tended to derive more enjoyment from substance-free activities (Morris et al., 2017). Consistent with behavioral economic theoretical assumptions, students who derive less daily enjoyment from substance-free experiences may be less concerned about the negative impact of their drinking on substance-free functioning. In a recursive cycle, experiences of alcohol-related negative consequences may also interfere with ability to access enjoyable substance-free activities (e.g., due to relationship or academic impairment), thereby further increasing the relative reinforcing efficacy of alcohol (Murphy & Dennhardt, 2016). Future research should evaluate the magnitude and direction of these associations over time. Additionally, interventions for drinkers with ADHD may work to identify and scaffold substance-free experiences that are inherently rewarding and pleasurable or that increase the response cost associated with heavy drinking (Fazzino et al., 2019; Oddo et al., 2021b).

Interestingly, our within-person findings regarding RCPR were somewhat counterintuitive. That is, more RCPR (than a person’s typical levels) corresponded to more alcohol use but fewer negative consequences. It is important to note that the RCPR measure used in the current study did not specify substance-free behaviors and instead captured broader patterns of goal-directed activation and escape/avoidance; thus, it is possible that we also captured certain substance-related activities. Indeed, socializing is an adaptive and goal-directed activity for young adults that may be facilitated by and involve drinking (Acuff, Stoops, & Strickland, 2021). This possibility is bolstered by the modest correlation among RCPR and time spent in substance-free activities, suggesting these are related but distinct constructs and that method variance may be substantial. Nevertheless, it is noteworthy that participants with greater amounts of RCPR experienced lower levels of alcohol-related negative consequences. Similarly, regardless of ADHD status, participants who reported spending more time in substance-free activities than they typically did also reported consuming less alcohol and experiencing fewer negative consequences. Of note, we specifically asked about substance-free activities related to social connection, goal-directed behavior, and health/wellness. Therefore, our results extend prior research that has evaluated aggregate levels of substance-free behavior, abstracted to typical frequency of substance-free activity engagement over longer timescale (Acuff et al., 2019). Findings are consistent with the idea that substance-free activities have the potential to “compete with” alcohol use (Murphy et al., 2006; Lamb et al., 2016), with our findings specifying that this is only evident within a person. Our results map onto intervention strategies that facilitate change within a person by enhancing specific categories of substance-free activities (Murphy et al., 2019; Murphy et al., 2021). Thus, this study is strengthened by analyses parceling effects across persons and at the individual level – an essential step in generalizing findings to interventions whose mechanisms of action occur within a person in their daily lives.

Alcohol Demand

Regardless of ADHD status, participants who reported that they would consume more drinks that day if drinks were free (intensity), spend more money on alcohol (Omax), and pay more for a single drink (breakpoint) than they typically did also reported consuming more alcohol. Additionally, across-persons, those with more average daily demand intensity and Omax, only, reported more average daily alcohol use compared those with less intensity and Omax. A similar pattern was observed with alcohol-related negative consequences at the individual level, only, with higher demand intensity and Omax associated with more negative consequences. Our results generally align with Motschman and colleagues (2022), who found at both the day- and event-levels that higher intensity, Omax, and breakpoint were associated with more total alcohol use, yet they did not evaluate negative consequences. The current study supports the utility of evaluating both alcohol use and negative consequences in models of daily alcohol demand, as each demand metric is theoretically distinct and can be used in daily analyses to refine theory.

Indeed, cross-sectional principal component analyses have shown that alcohol demand consists of a two-factor structure, with one factor indexing a pure metric of drug value under no/minimal cost and the other factor indexing sensitivity to escalating drug price. The intensity metric loads onto the value factor whereas breakpoint loads onto the cost sensitivity factor; Omax is shown to load onto both factors (Hardy et al., 2021; MacKillop et al., 2009). These two-factor structures are considered to map onto theoretical accounts of addiction: a compulsion-based account whereby individuals do not incorporate the costs of alcohol into their decisions to drink, versus a value-based choice account whereby individuals place extremely high value on alcohol, which effectively overrides any alcohol-related cost consideration (Hardy et al., 2021). The current findings are consistent with a value-based choice account, as intensity and Omax showed the strongest and most consistent relations with alcohol use and negative consequences.

Moderating Effects of ADHD

Finally, our results showed no significant moderating effects of ADHD. That is, drinkers with and without ADHD did not appear to differ in the magnitude of daily within-person associations between (1) alcohol demand (2) substance-free indices or (3) RCPR and alcohol use and negative consequences. Instead, findings suggest “upstream effects” of ADHD on select behavioral economic risk factors, such that ADHD contributes to impairment in domains of substance-free reinforcement and RCPR, more broadly. Findings support future research on the ways a person’s ADHD interferes with deriving substance-free enjoyment and RCPR. Such work has the potential to refine treatments, which may be less efficacious for those with ADHD (Carey et al., 2007), elevated demand, and/or low substance-free reinforcement (Murphy et al., 2015).

Limitations

We did not assess specific next-day contingencies that could shape drinking behavior. Our sample was largely collected during COVID-19, likely coinciding with a decline in rewards. Still, most students still lived independently of caregivers, all students met drinking inclusion criteria, in-person classes were held, and residence halls and facilities were open. We also did not include a full suite of alcohol-related negative consequences due to our desire to keep surveys brief; thus, we may have failed to fully capture the frequency of consequences. Finally, our sample contained a large group of male and female students who were White, non-Latina/o/e, attending a university in a large, metropolitan area. Replication with broader gender, racial, geographic, and economic representation is essential.

Conclusion

This study supports the application of daily diary methodology to evaluating a behavioral economic model of alcohol use and negative consequences in college student drinkers with and without ADHD. On average, drinkers with ADHD experienced more daily alcohol-related negative consequences and less daily substance-free enjoyment and RCPR. Daily substance-free enjoyment and RCPR were each negatively associated with alcohol-related negative consequences, regardless of ADHD status. Findings also highlight daily substance-free activity engagement and alcohol demand as relevant correlates of alcohol use and negative consequences. Taken together, this study shows upstream effects of ADHD on daily behavioral economic risk factors, with clear implications for AUD prevention and intervention. Future research identifying daily associations among environmental triggers and alcohol problems in an ecologically valid manner has tremendous potential to inform the development of adaptive interventions delivered to the right people at the right time.

Supplementary Material

Supplemental tables

Public Health Significance.

This study indicates that college student drinkers with ADHD experience more alcohol-related negative consequences and higher rates of impairment in their daily substance-free lives, relative to their peers without ADHD. This study also highlighted significant daily effects of alcohol demand, substance-free reinforcement, and positive reinforcement for goal-directed behaviors on alcohol use and/or alcohol-related negative consequences, regardless of ADHD status.

Acknowledgments

This project was funded by a grant from the National Institute on Alcohol Abuse and Alcoholism F31AA027937, awarded to Lauren Oddo and supported by a grant from the National Institute on Drug Abuse R36 DA050049, awarded to Keanan Joyner. This study was not preregistered.

Footnotes

2

Ninety students completed the study during COVID-19 (September 2020 – May 2021). During this period, university dorms were open, and classes were held both virtually and in person.

3

All participants, including those who were diagnosed with ADHD for the first time, were provided with a packet with on- and off-campus referrals to therapy, academic, and psychiatry supports. In addition, participants were provided with the contact information for a specialty ADHD clinic for college students at the university.

4

Study analysis code is available upon reasonable request to the first author.

5

Four participants were excluded at baseline due to elevated ADHD symptoms not at full diagnostic threshold.

6

To optimize recall and compliance, we instructed participants to complete their surveys between 8:45 AM ET and 4:00 PM ET, times that coincided with before the start of most university classes and before the start of most local happy hours. Mean time of daily survey enrollment was 11:06 AM ET (range 8:45 AM – 4:00 PM). Of note, select participants (n = 3) completed their surveys after 4:00 PM ET due to survey technical difficulties.

7

As the ACDS was originally developed based upon DSM-4 diagnostic criteria for ADHD, we made select adjustments in the current student to align with current diagnostic nosology. Specifically, childhood onset was considered prior to age 12 and symptom threshold for adults was 5 current symptoms (APA, 2013).

8

We explored the distribution of drinking prior to coding, with Friday and Saturday showing the highest prevalence of drinking relative to the other days of the week; thus, we created a dummy coded variable for Friday – Saturday (i.e., weekend) and Sunday – Thursday.

9

Of note, we evaluated all models with ADHD medication status (i.e., currently prescribed versus not currently prescribed medication to treat ADHD) as a covariate; results of all models remained consistent. We report results without ADHD medication status to optimize parsimony.

10

As ADHD inattentive and hyperactive-impulsive symptoms were highly correlated (r = 0.79), we conducted each of these exploratory sensitivity analyses separately (i.e., clinical reports of inattentive symptoms vs. hyperactive-impulsive symptoms on the ACDS) to prevent model instability concerns.

Contributor Information

Lauren E. Oddo, University of Maryland, College Park.

Keanan J. Joyner, University of California, Berkeley

James G. Murphy, University of Memphis

Samuel F. Acuff, University of Memphis

Nicholas P. Marsh, University of Maryland, College Park

Amanda Steinberg, University of Maryland, College Park.

Andrea Chronis-Tuscano, University of Maryland, College Park.

References

  1. Acuff SF, Dennhardt AA, Correia CJ, & Murphy JG. (2019). Measurement of substance-free reinforcement in addiction: A systematic review. Clinical Psychology Review, 70, 79–90. 10.1016/j.cpr.2019.04.003 [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Acuff SF, MacKillop J, & Murphy JG. (2023). A contextualized reinforcer pathology approach to addiction. Nature reviews psychology, 2(5), 309–323. 10.1038/s44159-023-00167-y [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Acuff SF, Stoops WW, & Strickland JC. (2021). Behavioral economics and the aggregate versus proximal impact of sociality on heavy drinking. Drug and alcohol dependence, 220, 108523. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Aston ER, & Merrill JE. (2023). Alcohol demand assessed daily as a predictor of same day drinking. Psychology of Addictive Behaviors, 37(1),114–120. 10.1037/adb0000890 [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Baker L, Prevatt F, & Proctor B. (2012). Drug and Alcohol Use in College Students With and Without ADHD. Journal of Attention Disorders, 16(3), 255–263. 10.1177/1087054711416314 [DOI] [PubMed] [Google Scholar]
  6. Barkley RA, & Murphy KR. (2011). The Nature of Executive Function (EF) Deficits in Daily Life Activities in Adults with ADHD and Their Relationship to Performance on EF Tests. Journal of Psychopathology and Behavioral Assessment, 33(2), 137–158. 10.1007/s10862-011-9217-x [DOI] [Google Scholar]
  7. Carey KB, Henson JM, Carey MP, & Maisto SA. (2007). Which Heavy Drinking College Students Benefit from a Brief Motivational Intervention? Journal of Consulting and Clinical Psychology, 75(4), 663–669. 10.1037/0022-006X.75.4.663 [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Chang Z, Lichtenstein P, Halldner L, D’Onofrio B, Serlachius E, Fazel S, Långström N, & Larsson H. (2014). Stimulant ADHD medication and risk for substance abuse. Journal of Child Psychology and Psychiatry, and Allied Disciplines, 55(8), 878–885. 10.1111/jcpp.12164 [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Correia CJ, Murphy JG, Irons JG, & Vasi AE. (2010). The behavioral economics of substance use: Research on the relationship between substance use and alternative reinforcers. Journal of Behavioral Health and Medicine, 1(3), 216–237. 10.1037/h0100553 [DOI] [Google Scholar]
  10. Curran PJ, & Bauer DJ. (2011). The Disaggregation of Within-Person and Between-Person Effects in Longitudinal Models of Change. Annual Review of Psychology, 62, 583–619. 10.1146/annurev.psych.093008.100356 [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Dalsgaard S, Østergaard SD, Leckman JF, Mortensen PB, & Pedersen MG. (2015). Mortality in children, adolescents, and adults with attention deficit hyperactivity disorder: A nationwide cohort study. The Lancet, 385(9983), 2190–2196. 10.1016/S0140-6736(14)61684-6 [DOI] [PubMed] [Google Scholar]
  12. Daughters SB, Magidson JF, Anand D, Seitz-Brown CJ, Chen Y, & Baker S. (2018). The effect of a behavioral activation treatment for substance use on post-treatment abstinence: A randomized controlled trial. Addiction, 113(3), 535–544. 10.1111/add.14049 [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. DeMartini KS, & Carey KB. (2012). Optimizing the Use of the AUDIT for Alcohol Screening in College Students. Psychological Assessment, 24(4). 10.1037/a0028519 [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Dougherty DM, Lake SL, Hill-Kapturczak N, Liang Y, Karns TE, Mullen J, & Roache JD. (2015). Using Contingency Management Procedures to Reduce At-Risk Drinking in Heavy Drinkers. Alcoholism: Clinical and Experimental Research, 39(4), 743–751. 10.1111/acer.12687 [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Eisele G, Vachon H, Lafit G, Kuppens P, Houben M, Myin-Germeys I, & Viechtbauer W. (2022). The Effects of Sampling Frequency and Questionnaire Length on Perceived Burden, Compliance, and Careless Responding in Experience Sampling Data in a Student Population. Assessment, 29(2), 136–151. 10.1177/1073191120957102 [DOI] [PubMed] [Google Scholar]
  16. Fazzino TL, Bjorlie K, & Lejuez CW. (2019). A systematic review of reinforcement-based interventions for substance use: Efficacy, mechanisms of action, and moderators of treatment effects. Journal of Substance Abuse Treatment, 104, 83–96. 10.1016/j.jsat.2019.06.016 [DOI] [PubMed] [Google Scholar]
  17. Gray JC, & MacKillop J. (2014). Interrelationships among individual differences in alcohol demand, impulsivity, and alcohol misuse. Psychology of Addictive Behaviors, 28(1), 282–287. 10.1037/a0032766 [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Gupte-Singh K, Singh RR, & Lawson KA. (2017). Economic Burden of Attention-Deficit/Hyperactivity Disorder among Pediatric Patients in the United States. Value in Health, 20(4), 602–609. 10.1016/j.jval.2017.01.007 [DOI] [PubMed] [Google Scholar]
  19. Hallgren KA, Greenfield BL, & Ladd BO. (2016). Psychometric Properties of the Adolescent Reinforcement Survey Schedule – Alcohol Use Version with College Student Drinkers. Substance Use & Misuse, 51(7), 812–822. 10.3109/10826084.2016.1155609 [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Hardy L, Bakou AE, Shuai R, Acuff SF, MacKillop J, Murphy CM, Murphy JG, & Hogarth L. (2021). Associations between the Brief Assessment of Alcohol Demand (BAAD) questionnaire and alcohol use disorder severity in UK samples of student and community drinkers. Addictive Behaviors, 113, 106724. 10.1016/j.addbeh.2020.106724 [DOI] [PubMed] [Google Scholar]
  21. Joyner KJ, Meshesha LZ, Dennhardt AA, Borsari B, Martens MP, & Murphy JG. (2019). High opportunity cost demand as an indicator of weekday drinking and distinctly severe alcohol problems: A behavioral economic analysis. Alcoholism: Clinical and Experimental Research, 43(12), 2607–2619. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Kahler CW, Strong DR, & Read JP. (2005). Toward Efficient and Comprehensive Measurement of the Alcohol Problems Continuum in College Students: The Brief Young Adult Alcohol Consequences Questionnaire. Alcoholism: Clinical and Experimental Research, 29(7), 1180–1189. 10.1097/01.ALC.0000171940.95813.A5 [DOI] [PubMed] [Google Scholar]
  23. Kaplan BA, Foster RNS, Reed DD, Amlung M, Murphy JG, & MacKillop J. (2018). Understanding alcohol motivation using the alcohol purchase task: A methodological systematic review. Drug and Alcohol Dependence, 191, 117–140. 10.1016/j.drugalcdep.2018.06.029 [DOI] [PubMed] [Google Scholar]
  24. Kessler RC, Green JG, Adler LA, Barkley RA, Chatterji S, Faraone SV, Finkelman M, Greenhill LL, Gruber MJ, Jewell M, Russo LJ, Sampson NA, & Brunt DLV. (2010). Structure and Diagnosis of Adult Attention-Deficit/Hyperactivity Disorder: Analysis of Expanded Symptom Criteria From the Adult ADHD Clinical Diagnostic Scale. Archives of General Psychiatry, 67(11), 1168–1178. 10.1001/archgenpsychiatry.2010.146 [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Knouse LE, Mitchell JT, Brown LH, Silvia PJ, Kane MJ, Myin-germeys I, & Kwapil TR. (2008). The expression of adult ADHD symptoms in daily life: An application of experience sampling methodology. Journal of Attention Disorders, 652–663. 10.1177/1087054707299411 [DOI] [PubMed] [Google Scholar]
  26. Lamb RJ, Maguire DR, Ginsburg BC, Pinkston JW, & France CP. (2016). Determinants of choice, and vulnerability and recovery in addiction. Behavioural Processes, 127, 35–42. 10.1016/j.beproc.2016.04.001 [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Luderer M, Ramos Quiroga JA, Faraone SV, Zhang-James Y, & Reif A. (2021). Alcohol use disorders and ADHD. Neuroscience & Biobehavioral Reviews, 128, 648–660. 10.1016/j.neubiorev.2021.07.010 [DOI] [PubMed] [Google Scholar]
  28. Luman M, Tripp G, & Scheres A. (2010). Identifying the neurobiology of altered reinforcement sensitivity in ADHD: A review and research agenda. Neuroscience & Biobehavioral Reviews, 34(5), 744–754. 10.1016/j.neubiorev.2009.11.021 [DOI] [PubMed] [Google Scholar]
  29. MacKillop J, Murphy JG, Tidey JW, Kahler CW, Ray LA, & Bickel WK. (2009). Latent structure of facets of alcohol reinforcement from a behavioral economic demand curve. Psychopharmacology, 203(1), 33–40. 10.1007/s00213-008-1367-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Magidson JF, Robustelli BL, Seitz-Brown CJ, & Whisman MA. (2017). Activity enjoyment, not frequency, is associated with alcohol-related problems and heavy episodic drinking. Psychology of Addictive Behaviors, 31(1), 73–78. 10.1037/adb0000220 [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Manos RC, Kanter JW, & Luo W. (2011). The Behavioral Activation for Depression Scale–Short Form: Development and Validation. Behavior Therapy, 42(4), 726–739. 10.1016/j.beth.2011.04.004 [DOI] [PubMed] [Google Scholar]
  32. Martinetti MP, Caughron RL, Berman HL, André J, Sokolowski MBC, Wiley S, & Naassila M. (2019). The Behavioral Economics of Alcohol Demand in French and American University Students. Alcoholism, clinical and experimental research, 43(3), 531–544. 10.1111/acer.13954 [DOI] [PubMed] [Google Scholar]
  33. Martínez-Loredo V, González-Roz A, Secades-Villa R, Fernández-Hermida JR, & MacKillop J. (2021). Concurrent validity of the Alcohol Purchase Task for measuring the reinforcing efficacy of alcohol: An updated systematic review and meta-analysis. Addiction, 116(10), 2635–2650. 10.1111/add.15379 [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. McKay JR. (2017). Making the hard work of recovery more attractive for those with substance use disorders. Addiction (Abingdon, England), 112(5), 751–757. 10.1111/add.13502 [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Meinzer MC, Oddo LE, Vasko JM, Murphy JG, Iwamoto D, Lejuez CW, & Chronis-Tuscano A. (2021). Motivational interviewing plus behavioral activation for alcohol misuse in college students with ADHD. Psychology of Addictive Behaviors, No Pagination Specified-No Pagination Specified. 10.1037/adb0000663 [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Motschman CA, Amlung M, & McCarthy DM. (2022). Alcohol demand as a predictor of drinking behavior in the natural environment. Addiction. 10.1111/add.15822 [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Morris V, Amlung M, Kaplan BA, Reed DD, Petker T, & MacKillop J. (2017). Using crowdsourcing to examine behavioral economic measures of alcohol value and proportionate alcohol reinforcement. Experimental and clinical psychopharmacology, 25(4), 314–321. 10.1037/pha0000130 [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Minhas M, Oshri A, Amlung M, Dennhardt A, Ferro M, Halladay J, Munn C, Tucker J, Murphy J, & MacKillop J. (2020). Latent Profile Analysis of Heavy Episodic Drinking in Emerging Adults: A Reinforcer Pathology Approach. Alcoholism, clinical and experimental research, 44(10), 2130–2140. 10.1111/acer.14438 [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Murphy JG, Barnett NP, & Colby SM. (2006). Alcohol-related and alcohol-free activity participation and enjoyment among college students: A behavioral theories of choice analysis. Experimental and Clinical Psychopharmacology, 14(3), 339–349. 10.1037/1064-1297.14.3.339 [DOI] [PubMed] [Google Scholar]
  40. Murphy JG, Campbell KW, Joyner KJ, Dennhardt AA, Martens MP, & Borsari B. (2021). Trajectories of reward availability moderate the impact of brief alcohol interventions on alcohol severity in heavy-drinking young adults. Alcoholism: Clinical and Experimental Research, 45(10), 2147–2159. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Murphy JG, & Dennhardt AA. (2016). The behavioral economics of young adult substance abuse. Preventive Medicine, 92, 24–30. 10.1016/j.ypmed.2016.04.022 [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Murphy JG, Dennhardt AA, Martens MP, Borsari B, Witkiewitz K, & Meshesha LZ. (2019). A randomized clinical trial evaluating the efficacy of a brief alcohol intervention supplemented with a substance-free activity session or relaxation training. Journal of Consulting and Clinical Psychology, 87(7), 657–669. 10.1037/ccp0000412 [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Murphy JG, Dennhardt AA, Martens MP, Yurasek AM, Skidmore JR, MacKillop J, & McDevitt-Murphy ME. (2015). Behavioral Economic Predictors of Brief Alcohol Intervention Outcomes. Journal of Consulting and Clinical Psychology, 83(6), 1033–1043. 10.1037/ccp0000032 [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Nigg JT, Sibley MH, Thapar A, & Karalunas SL. (2020). Development of ADHD: Etiology, Heterogeneity, and Early Life Course. Annual Review of Developmental Psychology, 2(1), 559–583. 10.1146/annurev-devpsych-060320-093413 [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Oddo LE, Acuff SF, Arenson MB, Oshri A, Chronis-Tuscano A, MacKillop J, & Murphy JG. (2021). Unique and Transdiagnostic Dimensions of Reward Functioning in Attention-Deficit/Hyperactivity Disorder and Alcohol Use Disorder Symptoms. Alcohol and Alcoholism, agab070. 10.1093/alcalc/agab070 [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Oddo LE, Meinzer MC, Tang A, Murphy JG, Vasko JM, Lejuez CW, & Chronis-Tuscano A. (2021). Enhanced Brief Motivational Intervention for College Student Drinkers With ADHD: Goal-Directed Activation as a Mechanism of Change. Behavior Therapy, 52(5), 1198–1212. 10.1016/j.beth.2021.01.007 [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Ramsay JR. (2002). A cognitive therapy approach for treating chronic procrastination and avoidance: Behavioral activation interventions. Journal of Group Psychotherapy Psychodrama and Soiometry, 55(2–3), 79–93. [Google Scholar]
  48. Rooney M, Chronis-Tuscano A, & Yoon Y. (2012). Substance Use in College Students With ADHD. Journal of Attention Disorders, 16(3), 221–234. 10.1177/1087054710392536 [DOI] [PubMed] [Google Scholar]
  49. Sacks JJ, Gonzales KR, Bouchery EE, Tomedi LE, & Brewer RD. (2015). 2010 National and State Costs of Excessive Alcohol Consumption. American Journal of Preventive Medicine, 49(5), e73–e79. 10.1016/j.amepre.2015.05.031 [DOI] [PubMed] [Google Scholar]
  50. Saunders JB, Aasland OG, Babor TF, Fuente JRDL, & Grant M. (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. 10.1111/j.1360-0443.1993.tb02093.x [DOI] [PubMed] [Google Scholar]
  51. Seldin K, Lengua LJ, & King KM. (2019). The relation between stress and impulsivity during the first year of college. Journal of Personality. [DOI] [PubMed] [Google Scholar]
  52. Shiels K, & Hawk LW. (2010). Self-regulation in ADHD: The role of error processing. Clinical Psychology Review, 30(8), 951–961. 10.1016/j.cpr.2010.06.010 [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Smith A, Martens M, Murphy J, Buscemi J, Yurasek A, & Watrous J. (2010). Reinforcing Efficacy Moderates the Relationship Between Impulsivity-Related Traits and Alcohol Use. Experimental and Clinical Psychopharmacology, 18, 521–529. 10.1037/a0021585 [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplemental tables
NIHMS1947711-supplement-Supplemental_tables.docx (14.9KB, docx)

RESOURCES