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. Author manuscript; available in PMC: 2024 Feb 1.
Published in final edited form as: Psychol Addict Behav. 2022 Dec 1;37(1):114–120. doi: 10.1037/adb0000890

Alcohol Demand Assessed Daily as a Predictor of Same Day Drinking

Elizabeth R Aston 1, Jennifer E Merrill 1
PMCID: PMC9852008  NIHMSID: NIHMS1843251  PMID: 36455005

Abstract

Objective:

In this investigation, baseline (trait) and daily (brief) alcohol purchase task (APT) indices (intensity: consumption at zero cost; Omax: maximum expenditure; breakpoint: cost suppressing consumption to zero) were used to investigate the influence of morning demand on subsequent alcohol consumption.

Methods:

Heavy drinking college students (n=92, age 18–20, 50% female) completed 28 daily morning reports including demand and prior day drinking. Hierarchical linear modeling, with days (Level 1) nested within person (Level 2) were used to test the effect of morning demand on number of drinks consumed on planned drinking days, with Level 1 (study day, survey time, weekend/weekday) and Level 2 (spending money, typical drinks) covariates. Subsequently, the relative impact on daily drinking of (a) the average of each daily demand index on planned drinking days versus (b) the matched trait demand index was assessed.

Results:

Higher morning intensity was related to increased alcohol consumption later that night. This finding held in sensitivity analyses wherein demand was assumed to be zero on unplanned drinking days. When tested individually, both aggregate daily and baseline trait intensity were significantly associated with average drinks measured daily. However, in the same model, only aggregate daily intensity was significant. Neither daily aggregate nor trait breakpoint or Omax were significant.

Conclusions:

Findings replicate previous work suggesting that brief demand (intensity) can predict same day drinking. Elevation in intensity in particular may denote greater risk for elevated alcohol consumption at subsequent episodes, thus intervention among at-risk drinkers may be possible prior to drinking initiation.

Keywords: alcohol, behavioral economics, daily demand

Introduction

Young adults report particularly high rates of heavy drinking and alcohol use disorders (AUD; Grant et al., 2017). Among underage young adults (i.e., age 18–20), approximately 10% meet criteria for an AUD (U.S. DHHS Substance Abuse, Mental Health Services Administration (SAMHSA), 2015). Moreover, those who attend college are at even higher risk for problematic drinking outcomes; this subset of young adults experience myriad alcohol-related consequences, some of which are quite severe (e.g., sexual assault, injuries; Hingson et al., 2009). Identifying and accurately measuring theoretically relevant predictors of alcohol consumption among underage college students remains a critical task, and one that can ultimately inform preventive interventions.

Behavioral economics is the integration of psychology and economics (Hursh, 1984) and may be applied to the understanding of substance use behavior, particularly that of alcohol (Murphy & MacKillop, 2006). With roots in operant conditioning (Hursh, 2014; Rachlin et al., 2018), a behavioral economic approach focuses on assessment of the rewarding value of alcohol, which develops acutely and over time as a function of the interactions between alcohol consumption behavior, experience of consequences and problems, and behavioral choice patterns (Rachlin et al., 2018). The progression to hazardous alcohol use and alcohol use disorder is an escalating pattern of repeated choice for alcohol over other non-alcohol alternatives in one’s environment (Vuchinich, 1995). Moreover, both environmental and contextual factors play a key role in the relative reinforcing efficacy of alcohol. Limited alternatives to drinking in one’s environment, as well as constraints on access to such alternatives (Rachlin et al., 1981), is consistently related to elevated demand (i.e., relative reinforcing value) for alcohol (Acuff et al., 2021). In this regard, relative alcohol value may be assessed via several well-established measures. One such measure, the hypothetical Alcohol Purchase Task (APT), assesses the relative rewarding value of alcohol across escalating price points, ultimately providing metrics of alcohol demand (Bickel et al., 2014). Providing a multidimensional conceptualization of demand for alcohol, these indices typically include intensity (i.e., alcohol consumption at zero cost), Omax (i.e., maximum expenditure for alcohol across prices), Pmax (i.e., price point associated with maximum expenditure), breakpoint (i.e., price suppressing consumption to zero), and elasticity (i.e., degree to which alcohol consumption declines with escalating price).

Alcohol demand plays a critical role in hazardous alcohol use and the experience of alcohol related problems and consequences (Aston et al., in press), and it may be implicated in the progression to alcohol use disorder (Hogarth & Hardy, 2018). Alcohol demand has been associated with impulsivity (Gray & Mackillop, 2013), depression symptoms (Meshesha et al., 2020), and alcohol craving (Motschman, Griffin, et al., 2022), and has been shown to predict subsequent success in response to pharmacologic (Bujarski et al., 2012) and therapeutic (Murphy et al., 2015) treatment. Of more recent interest is the notion of alcohol demand as a mutable construct, able to be influenced and manipulated by internal and external influences including impending next-day responsibilities (Berman & Martinetti, 2017; Skidmore & Murphy, 2011), induction of stress (Amlung & MacKillop, 2014; Owens, Ray, et al., 2015), and presence of other substances (Ramirez et al., 2020). Similarly, there has been burgeoning interest in the utilization of repeated demand assessment, both in the laboratory (Amlung et al., 2015; Owens, Ray, et al., 2015) and in daily life (Merrill & Aston, 2020; Motschman, Amlung, et al., 2022).

Repeated demand assessment is achieved via employment of brief demand items rather than administration of a full trait-level APT. In this regard, Owens and colleagues (2015) launched a study wherein a brief 3-item version of the APT, assessing intensity, Omax, and breakpoint, was used to examine the impact of alcohol cue exposure in the laboratory. Similarly, Amlung and colleagues (2015) used brief APT items to assess craving for alcohol following administration of alcohol versus placebo beverages in the laboratory. In both studies, alcohol demand assessed via brief items increased as a function of alcohol cue-exposure and increases in craving for alcohol. Building on these initial studies employing brief alcohol demand assessment, our laboratory was the first to integrate examination of brief alcohol demand and ecological momentary assessment (EMA) methodology to assess daily demand for alcohol in the environment over a longer time period (i.e., 1-month; Merrill & Aston, 2020). Daily brief alcohol demand assessed over 1-month was associated with its corresponding trait-APT index, demonstrating that repeated brief alcohol demand assessment is a reliable and valid tool for assessing the rewarding value of alcohol in one’s natural environment. Moreover, brief demand for alcohol, specifically intensity, was impacted by the experience of more negative consequences the prior day, again establishing alcohol demand’s lability in response to external stimuli.

Motschman and colleagues (2022) continued this line of work, conducting the first study to assess momentary state-dependent alcohol demand in daily life in an effort to predict alcohol consumption, maintenance of consumption in an episode, and quantity consumed among young adults. They found that greater demand for alcohol at the momentary level was related to increased likelihood for alcohol consumption, increased propensity of continuation of drinking across an episode, and greater alcohol consumption quantity. Thus, alcohol demand is clearly alterable not only due to the experience of negative alcohol-related consequences (Merrill & Aston, 2020), but also based on one’s moment-to-moment interactions with their environment (Motschman, Amlung, et al., 2022), likely due to fluctuating context, affect, and subjective intoxication, among other variables. The present investigation was designed to replicate and extend this work by confirming that brief demand predicts alcohol consumption quantity, and determining whether this relation is better predicted by trait or brief alcohol demand.

Specifically, in a sample of underage college students who endorsed alcohol use, we used daily brief alcohol purchase task (APT) indices (i.e., intensity: consumption at zero cost; Omax: maximum expenditure; breakpoint: cost suppressing consumption to zero) to examine the impact of demand on subsequent (same day) drinking. First, daily demand indices were examined as predictors of number of drinks consumed on any day where participants indicated that they planned to drink that day (whether or not they actually did so). Second, we tested the hypothesis that metrics of daily demand (aggregated across time) would better predict alcohol use compared to their corresponding baseline trait index.

Methods

Participants

Participants were 92 (50% female) heavy drinking college students who endorsed at least one planned drinking event on morning assessments over the course of the 28-day study. One hundred participants completed the protocol; however, eight were removed prior to analyses (four reported zero alcohol consumption, three reported no morning plans to drink that day [and therefore did not report demand for drinking that day] during the investigation, and one only purchased alcohol at zero cost on the baseline trait APT). Inclusion criteria were as follows: age 18–20 years, smartphone and data plan access, undergraduate enrollment at a local 4-year university, and either (a) weekly heavy episodic drinking (4+ drinks in an episode [women]/5+ drinks in an episode [men]) or (b) endorsement of ≥1 (of 10 measured) negative alcohol-related consequence in the prior two weeks. Exclusion criteria were as follows: illicit substance use apart from cannabis in prior two weeks or current engagement in substance use disorder treatment. All study activities and procedures were approved by the Brown University Institutional Review Board.

Procedures

Recruitment and orientation.

Potential participants were recruited both using flyers displayed on and around local college campuses and via advertisements on social media websites including a link to a web-based screener (<5 minutes). Eligible participants were paid $25 for completion of a baseline questionnaire and attendance at an in-person orientation session, at which time they provided informed consent for the daily assessment phase of the study, were trained in reporting of standard drinks (1.5 oz liquor, 5 oz wine, 12 oz beer), downloaded a mobile application (MetricWire, Inc) for daily report delivery onto their smartphones, and completed practice reports.

Daily assessment protocol.

A 28-day mobile survey protocol involved a morning report that was triggered at 7am which participants were instructed to complete as soon as they woke up. A reminder to complete this report was sent at 9am in the absence of initial response, and the report continued to be accessible all day. In addition, participants were trained to self-initiate reports during drinking events. However, analyses in the present study were limited to daily diary (morning) reports as these reports included the daily APT and were characterized by the most complete data on drinking. Participants were compensated as a function of percent compliance with daily reports (e.g., morning, 8pm check-in), and, with increasing payments each week, earned between $5 (for less than 20% compliance) to $51 (for at least 90% compliance) weekly.

Measures

Baseline Measures.

Demographics.

Demographics assessed at baseline included age, gender, year in school, race, and ethnicity.

Alcohol purchase task (APT).

A 14-item APT was administered to assess trait level relative alcohol value (Murphy & MacKillop, 2006). Participants reviewed an instructional vignette prior to completion of the APT (see Murphy & MacKillop, 2006 for detailed instructions).

Non-essential spending money.

Participants were asked to respond to an open-ended item on how much money they had available to purchase non-essential goods (e.g., clothing, movies) over the prior 30 days, excluding money budgeted for essentials (e.g., rent, groceries, school books).

Daily Measures.

Alcohol use.

Each day, participants were asked whether they consumed alcohol the prior day, and if so, to indicate the total number of standard drinks consumed.

Daily APT.

Participants reported on demand for alcohol each day, with reference to their next expected drinking event (the timing of which varied). First, participants were instructed to estimate the number of days until their next drink (0=today), followed by three items refined from prior studies (Amlung et al., 2015; Owens, Murphy, et al., 2015) and our prior work (Merrill & Aston, 2020) designed to measure demand for alcohol. For example, for a participant who expected to drink again in 3 days, instructions read: “For the next few questions, imagine the next time you drink (3 days from now) and that you are purchasing alcohol only for yourself. Remember what a standard drink is.” Intensity was assessed via the item: “If drinks were free the next time you drink, how many do you think you would have?” (response options: 0–25+ in single drink increments). Omax was assessed via the item: “The next time you drink, if you had to pay for every drink you consumed, what is the maximum total that you would spend on drinking (approximately)?” (response options: $0-$100+ in $4 increments). Breakpoint was assessed via the item: “The next time you drink, if you had to pay for every drink you consumed, what is the maximum that you would pay for a single drink?” (response options: $0-$20 in $2 increments).

Analytic Plan

Raw data from the baseline trait APT were inspected for outliers using standardized values, with a criterion of Z = 3.29 to retain maximum data. A negligible number of outliers were observed (0.01%), concluded to be legitimate high-magnitude values, and recoded as one unit above the next lowest non-outlying value (Tabachnick & Fidell, 2007). Three alcohol demand indices were observed from the baseline trait APT: intensity, Omax, and, breakpoint.

Hierarchical linear models (HLMs; day at Level 1 nested within person at Level 2) were run using HLM 7 (Raudenbush et al., 2013). Models relied on full maximum likelihood estimation, and robust standard errors were used to determine effect significance. Our primary analyses were conducted on a set of 595 days where participants planned to drink that same day and therefore reported demand for that day’s planned drinking (rather than reporting of demand for some other future drinking day). Participants actually drank on 377 of these planned drinking days; whereas no drinking occurred on the remaining 218 days. However, failure to include the days where drinking did not ultimately occur would likely bias our analysis; drinking was coded as 0 on days where despite reporting of planned drinking and associated demand, drinking did not ultimately occur.

First, to test for the presence of within-person variability in number of drinks, a fully unconditional model was run to obtain intraclass correlation coefficients (ICCs). Next, a single HLM was used to test our first aim, and examine whether any of the three daily demand indices was associated with number of drinks consumed (on days where the participant said they planned to drink). At Level 1, we controlled for day in the study (given potential changes in drinking over the course of assessment), the time the assessment of demand was submitted (as demand closer to the time of an actual drinking event may be more strongly related to actual drinking), and whether the drinking day fell on a weekend (Friday, Saturday) or weekday. At Level 2, we controlled for spending money. We also controlled at Level 2 for aggregate scores on each demand index (averaged over the course of the 28 days) to truly isolate the within-persons effect of a given day’s demand (at Level 1) on that same day’s drinking, controlling for someone’s tendency to have higher or lower levels of demand. The HLM is shown below.

Level-1 Model

Drinksti=π0i+π1i*(Studydayti)+π2i*(Weekendti)+π3i*(Surveysubmittimeti)+π4i*(Daily Intensityti)+π5i*(Daily Omaxti)+π6i*(Daily Breakpointti)+eti

Level-2 Model

π0i=β00+β01*(Spendingi)+β02*(AggregatedailyIntensityi)+β04*(AggregatedailyOmaxi)+β05*(AggregatedailyBreakpointi)+r0i

Next, three separate HLM models were run (one for each index) predicting drinks consumed on planned drinking days. to test whether daily indices of alcohol demand (aggregated across time) would better predict number of drinks consumed than their corresponding baseline trait demand metrics. In these models, to obtain a more direct comparison, we first entered both sets of demand variables (baseline, daily aggregate) into the same portion of the model (Level 2), requiring that we aggregate the daily measures. We controlled for the same covariates as noted above. An example model testing the relative effects of baseline and daily intensity is shown below.

Level-1 Model

Drinksti=π0i+π1i*(Studydayti)+π2i*(Weekendti)+π3i*(Surveysubmittimeti)+eti

Level-2 Model

π0i=β00+β01*(Spendingi)+β02*(Baselineintensityi)+β03*(Aggregatedailyintensity)+r0iπ1i=β10π2i=β20π3i=β30

Parallel models were run for Omax and breakpoint. We also ran a set of models with only one or the other Level 2 predictor of interest (e.g., baseline intensity or aggregate daily intensity), to assess significance of each when not accounting for the other. In all models, Level 1 continuous variables were person-centered, and Level 2 continuous variables were sample mean centered. Intercepts were specified as random effects. Random effects of slopes were tested, but non-significant, so all were fixed for parsimony.

Results

Descriptives

Sample demographics and descriptive characteristics are displayed in Table 1. Both state and trait alcohol demand indices (i.e., intensity, Omax, and breakpoint) were examined for skewness and kurtosis and were all normally distributed. Moreover, alcohol consumption decreased in tandem with increasing price. Trait Pmax and elasticity are reported for descriptive purposes in Table 1. Elasticity was derived using the exponentiated demand equation (Koffarnus et al., 2015).

Table 1.

Demographics, drinking behavior, and alcohol demand in a sample of underage (18–20) college men and women (N=92)

Variables Assessed at Baseline Mean (SD) or n (%)
Age 18.68 (0.66)
Year in School
 First year 73 (80.2%)
 Second year 14 (15.4%)
 Third or fourth year 4 (4.4%)
Female 46 (50.0%)
Hispanic/Latino 14 (15.4%)
Race (check all that apply)
 White 68 (73.9%)
 Black or African American 6 (6.5%)
 Asian 19 (20.7%)
 Native American or Native Alaskan 1 (1.1%)
 Native Hawaiian or other Pacific Islander 1 (1.1%)
 Other 5 (5.4%)
Multiracial 13 (14.3%)
Alcohol Use (past 30 days)
 Drinks per week 10.54 (6.41), range 0–33
 Drinking days per week 2.31 (0.94), range 0–6
Baseline Alcohol Purchase Task
 Intensity 6.55 (2.67), range 2.00–15.00
 Omax 16.14 (8.77), range 2.00–49.00
 Breakpoint 7.89 (1.66), range 1.50–9.00
 Pmax 5.00 (2.10), range 1.00–9.00
 Elasticity 0.01 (0.01), range 0.00–0.07
Aggregated Data Reported over 28 Daily Assessments
 Daily Intensity 5.50 (2.51), range 1.23–15.17
 Daily Omax 10.99 (6.83), range 0–32
 Daily Breakpoint 3.92 (2.77), range 0–15.14
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Note: Intensity = consumption at zero cost, Omax = peak expenditure for alcohol, Breakpoint = cost at which consumption is suppressed to zero, Pmax = price at maximum expenditure, Elasticity = the degree to which consumption declines with increasing price; Aggregated data from 28 days of assessment represent 2541 (out of 2576) daily assessment points

Across the 28-day assessment period, the degree of missing data was negligible, as surveys were completed on 2541 (out of 2576 possible; 99%) days. Timing of morning survey submission ranged from 7:01 in the morning to 10:44 at night (average time: 10:39 am). Across the set of 595 days where drinking was planned, the ICC for number of drinks consumed was 0.20, indicating that 20% of the variance in this outcome was attributed to between-person differences, while 80% was attributed to within-person differences.

Influence of Daily Demand on Drinks Consumed

As shown in Table 2, higher intensity (relative to one’s typical intensity level) on a morning when drinking was planned that day was related to greater number of drinks consumed the same day (Level 1: B=0.44, SE=.12, p<.001). Daily Omax and breakpoint were not significantly related to number of drinks consumed the same day. Additionally, one’s average intensity across days was associated with more drinks consumed (Level 2: B=0.40, SE=.11, p<.001). Of note, there were an additional 87 days where drinking occurred even though it was not planned. We ran a sensitivity analysis in which we included these unplanned drinking days in the outcome variable, and entered 0 for all demand indices on those days. Findings were similar, such that only intensity at the daily level (B=.21, SE=.09, p=.015) and aggregated across days (B=0.34, SE=.09, p<.001), but not the other daily or aggregate demand indices, were significantly associated with number of drinks consumed later that day.

Table 2.

Influence of daily demand on drinks consumed later that day

Fixed Effect B SE t-ratio p-value
Intercept 2.37 0.48 4.930 <0.001
Level 1 Predictors
 Study Day −0.03 0.02 −1.773 0.077
 Weekend 1.67 0.26 6.401 <0.001
 Survey submit time 0.02 0.04 0.419 0.676
 Daily Intensity (free) 0.44 0.12 3.560 <0.001
 Daily Omax (total) −0.00 0.04 −0.073 0.942
 Daily Breakpoint (single) −0.02 0.08 −0.300 0.764
Level 2 Predictors
 Spending −0.00 0.00 −0.307 0.760
 Average Intensity 0.40 0.11 3.468 <.001
 Average Omax −0.00 0.06 −0.046 0.963
 Average Breakpoint 0.04 0.13 0.303 0.762
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Note: SE=standard error

Aggregated daily state demand versus baseline trait demand

For intensity, when entered together, only the aggregate daily measure (B=0.34, SE=.13, p=.011), but not the baseline trait measure (B=.07, SE=.12, p=.566), significantly predicted drinks over the 28 days. However, when each was entered individually, both the aggregate daily measure (B=.40, SE=.10, p<.001) and the baseline trait measure (B=.32, SE=.09, p<.001) were significant. For breakpoint and Omax, neither the daily aggregate nor the trait measures predicted drinks, regardless of whether both were included in the same model, or when entered individually.

Discussion

The current investigation replicated findings from previous work showing that brief alcohol demand can predict drinking behavior in the natural environment. We also extended this work by confirming that brief, daily demand items, aggregated across days, perform similarly to traditional full trait APT indices in the prediction of alcohol consumption behavior. Findings presented herein demonstrate that on days when drinking was planned, brief alcohol demand, specifically intensity, predicted number of drinks later consumed that day. Moreover, aggregated daily intensity better predicted quantity of alcohol consumption as compared to trait intensity assessed at baseline. In this regard, daily variability in demand for alcohol is an important predictor of drinking behavior, and is likely impacted by key momentary motivational influences that make alcohol more salient and rewarding.

Analogous to findings from Motschman and colleagues (2022), brief intensity was the only alcohol demand index predictive of consumption. Consistent with findings across the demand literature, intensity is considered to be the most robust demand index (Zvorsky et al., 2019) and is most consistently related to alcohol consumption quantity. Intensity is typically absent of normal constraints placed on drinking, including cost, consequences, and effort employed to attain alcohol. With respect to the current sample, intensity is likely the most relevant index for college students below the age to legally consume alcohol in the United States, as underage college students are less likely to purchase alcohol using traditional methods (Merrill & Aston, 2020). Consequently, it appears that this aggregate measure best accounts for fluctuations in desire or motivation for alcohol, particularly when drinking is planned, and may be targeted in future work as a variable on which to intervene to reduce hazardous consumption behaviors among underage drinkers.

In contrast, brief Omax and breakpoint were not significant predictors of drinking quantity. In other words, the maximum amount an individual would spend for drinks at a certain price (i.e., Omax) and the point at which drinks become too expensive to purchase and consume (i.e., breakpoint) do not appear to demonstrate meaningful momentary fluctuations tied to alcohol consumption behavior among this sample, similar to what was found by Motschman and colleagues (2022). Omax and breakpoint may be better predictors of alcohol consumption behaviors among adult samples for whom cost and consequences play a greater role in the decision to continue or cease drinking, though this is hitherto unknown.

In the present investigation, aggregate daily alcohol demand (i.e., intensity) was a better predictor of alcohol consumption quantity than trait alcohol demand assessed at baseline when both variables were included in the same model. According to foundational work by Rachlin and colleagues (2018), modern behaviorism offers a foundation for understanding and disentangling the dynamic nature of human behavior, particularly with respect to substance use. Whether or not a behavior is likely to occur is a direct function of learning and experience. For example, influential variables such as alcohol-related consequences (both positive and negative) directly impact subsequent alcohol-related behaviors (Merrill & Aston, 2020). In the current study, aggregated daily alcohol demand likely encapsulated variables influencing behavior and decision-making on days when alcohol was consumed. This aggregate of daily alcohol demand likely integrated other influences that may be more tied to drinking level in the moment (e.g., biphasic alcohol effects, subjective intoxication, next-day responsibilities, stress), including those that facilitate dynamic changes in drinking over time (e.g., consequences), than more general alcohol demand assessed at baseline. While trait alcohol demand is assessed following an instructional set refined by the researcher (Kaplan et al., 2017), it is absent of momentary environmental influences more proximal to drinking that make alcohol more or less desirable and rewarding, in line with behavioral theories of reinforcement (Rachlin et al., 2018).

While this investigation builds on prior work and contributes meaningful data to the literature, it is not without limitations. First, our primary analysis was limited to planned drinking days, as these were days for which demand was known. Consequently, we did not have data on daily alcohol demand for days when drinking events were unplanned. As shown in our prior work (Lauher et al., 2020), unplanned drinking days were characterized by less drinking. It may be inferred that these days were likely characterized by lower alcohol demand as well, though this currently is conjecture. Consequently, more work is needed to examine how demand differentially predicts drinking when it is planned versus unplanned. Second, as demand was only assessed in the morning for the next drinking event, we were unable to replicate findings from Motchman and colleagues concerning momentary alcohol demand fluctuations and how these relate to drinking behaviors. Third, work in this area thus far has been done with exclusively (Merrill & Aston, 2020) or predominantly (Motschman, Amlung, et al., 2022) college student samples, thus its generalizability to community samples of drinkers remains an area for further study. It is possible that demand indices less predictive of drinking outcomes herein (i.e., Omax, breakpoint) serve as more robust predictors of drinking behavior, quantity, and problems within samples for whom cost is a critical component in the decision to initiate and continue drinking, and may even be implicated in violation of drinking limits and impaired control over alcohol, a critical component of development and progression to alcohol use disorder (Leeman et al., 2014). Finally, our sample was primarily White, and the extent to which findings generalize to other racial and ethnic groups cannot be determined; future work with more representative samples, and with subsample sizes that allow for group comparisons, is critical.

This study replicates and extends work suggesting that brief alcohol demand (i.e., intensity) can predict same day drinking. This work continues to promote the validity of using brief demand measures, as aggregated daily (Merrill & Aston, 2020) or momentary (Motschman, Amlung, et al., 2022) items perform similarly to, and often better than, traditional trait APT indices. Consistent with demand research across substances, intensity continues to show superiority in the prediction of critical alcohol use outcomes, and greater daily and/or momentary intensity likely indicates greater risk for continued alcohol consumption within a given episode, in addition to greater risk for hazardous drinking during subsequent episodes. As such, with the aid of brief intensity as a marker for likelihood of proximal increases in alcohol use, intervention among high-risk drinkers may be possible prior to and/or during alcohol consumption. The influence of context (e.g., location, peers), situation (e.g., pre-gaming), subjective intoxication (e.g., stimulation), and co-use with other substances (e.g., cannabis), all at the momentary level, remain critical areas for subsequent work.

Public Health Significance:

This study replicated previous work suggesting that the relative reinforcing value (i.e., demand) of alcohol assessed via a brief Alcohol Purchase Task can predict same day drinking. Elevation in brief intensity in particular may signify greater risk for elevated alcohol consumption at subsequent episodes, thus intervention among at-risk drinkers may be possible prior to drinking initiation. In this regard, the brief Alcohol Purchase Task may be a useful tool to assess the potential for hazardous drinking in settings within which longer assessments of demand are impractical.

Funding:

This study was supported by a grant from the National Institute on Alcohol Abuse and Alcoholism to Jennifer E. Merrill (K01AA022938) and training support to Elizabeth Aston (K01DA039311).

Footnotes

Declarations of Interest: none

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