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Background There are disadvantages—largely related to cost, participant burden, and missing data—associated with traditional electronic methods of assessing drinking behavior in real time. This potentially diminishes some of the advantages—namely, enhanced sample size and diversity—typically attributed to these methods. Download of smartphone apps to participants’ own phones might preserve these advantages. However, to date, few researchers have detailed the process involved in developing custom-built apps for use in the experimental arena or explored methodological concerns regarding compliance and reactivity. Objective The aim of this study was to describe the process used to guide the development of a custom-built smartphone app designed to capture alcohol intake behavior in the healthy population. Methodological issues related to compliance with and reactivity to app study protocols were examined. Specifically, we sought to investigate whether hazard and nonhazard drinkers would be equally compliant. We also explored whether reactivity in the form of a decrease in drinking or reduced responding (“yes”) to drinking behavior would emerge as a function of hazard or nonhazard group status. Methods An iterative development process that included elements typical of agile software design guided the creation of the CNLab-A app. Healthy individuals used the app to record alcohol consumption behavior each day for 21 days. Submissions were either event- or notification-contingent. We considered the size and diversity of the sample, and assessed the data for evidence of app protocol compliance and reactivity as a function of hazard and nonhazard drinker status. Results CNLab-A yielded a large and diverse sample (N=671, mean age 23.12). On average, participants submitted data on 20.27 (SD 1.88) out of 21 days (96.5%, 20.27/21). Both hazard and nonhazard drinkers were highly compliant with app protocols. There were no differences between groups in terms of number of days of app use ( P =.49) or average number of app responses ( P =.54). Linear growth analyses revealed hazardous drinkers decreased their alcohol intake by 0.80 standard drinks over the 21-day experimental period. There was no change to the drinking of nonhazard individuals. Both hazard and nonhazard drinkers showed a slight decrease in responding (“yes”) to drinking behavior over the same period. Conclusions Smartphone apps participants download to their own phones are effective and methodologically sound means of obtaining alcohol consumption information for research purposes. Although further investigation is required, such apps might, in future, allow for a more thorough examination of the antecedents and consequences of drinking behavior.

Abstract Substance dependence constitutes a profound societal burden. Although large numbers of individuals use licit or illicit substances, few transition to dependence. The specific factors influencing this transition are not well understood. Substance-dependent individuals tend to be swayed by the immediate rewards of drug taking, but are often insensitive to delayed negative consequences of their behavior. Dependence is consequently associated with impulsivity for reward and atypical learning from feedback. Behavioral impulsivity is indexed using tasks measuring spontaneous decision-making and capacity to control impulses. While evidence indicates drug taking exacerbates behavioral impulsivity for reward, animal and human studies of drug naïve populations demonstrate it might precede any drug-related problems. Research suggests dependent individuals are also more likely to learn from rewarding (relative to punishing) feedback. This may partly explain why substance-dependent individuals fail to modify their behavior in response to negative outcomes. This enhanced learning from reward may constitute a further pre-existing risk factor for substance dependence. Although impulsivity for reward and preferential learning from rewarding feedback are both underpinned by a compromised dopaminergic system, few studies have examined the relationship between these two mechanisms. The interplay of these processes may help enrich understanding of why some individuals transition to substance dependence.

Despite the variability in the literature, this study demonstrated consistent generalized impulse control deficits among binge-drinking individuals that were unrelated to reward manipulations. These findings point to mechanisms that may confer vulnerability for transition from binge drinking to AUD.

Background Heightened behavioral impulsivity has been advocated as a preexisting risk factor for the development of alcohol use disorder (AUD). Nonetheless, studies investigating impulsivity in adolescent/young adult at‐risk drinkers—who are at increased risk of developing AUD—report mixed findings. This may be due to methodological limitations related to definitions of at‐risk drinking, the retrospective assessment of alcohol intake, and/or the relatively modest sample size of some studies. Methods Healthy individuals (N = 814, Mage = 22.50) completed online surveys and a measure of choice impulsivity. Of these, a number of participants also undertook an online measure of response inhibition (n = 627, Mage = 22.66), and a further subgroup submitted real‐time alcohol consumption information for a period of 21 days using an app (n = 543, Mage = 22.96). Differences in behavioral impulsivity were assessed as a function of various at‐risk alcohol intake categories. Hierarchical multiple regression was employed to determine whether impulsivity predicted alcohol use in the form of a continuous index comprising variables related to intake and consequences of use. Results Significantly greater impulsivity was not evident in heavy, standard binge, high binge, harmful, or hazardous alcohol drinkers as compared to controls, regardless of the criteria employed to categorize these at‐risk drinkers. Neither choice impulsivity nor reduced response inhibition significantly predicted the alcohol use index. Conclusions While results could be attributed to the online nature of this research, it is possible that more sensitive measures of behavioral impulsivity are required when assessing nondependent drinkers.

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Background:The alcohol consumption patterns of young adults are of concern.Critically, tertiary students consume greater quantities of alcohol, are at increased risk of injury/harm, and have higher rates of alcohol use disorders (AUD) as compared to their non-university enrolled peers. The Brief Young Adult Alcohol Consequences Questionnaire (BYAACQ) is one of several tools utilised to explore adverse alcoholrelated outcomes among tertiary students. Alcohol intake behaviour, assessed via retrospective summary measures, has been linked to BYAACQ score. It is unclear, however, how drinking assessed in real-time, in conjunction with variables such as age of drinking onset, might predict severity of adverse alcohol consequences as captured by the BYAACQ. Methods:The psychometric properties of the BYAACQ were explored using a large Australian sample of tertiary students (N = 893). A subsample (n = 504) provided alcohol intake information in real-time (21 days; event-and notification-contingent) via a smartphone app (CNLab-A) plus details related to age of drinking onset, drug use, parental alcohol/drug use, and anxiety/depression symptomology.Results: Average BYAACQ score was 7.23 (SD = 5.47). Classical and item response theory analyses revealed inconsistencies related to dimensionality, progressive item severity, and male/female differential item functioning. Current drinking -namely, frequency of intake and quantity per drinking occasion -plus age of drinking onset predicted BYAACQ score after controlling for age, other drug use, and depression symptomology. Conclusions:The BYAACQ is a sound tool for use with Australian samples.Information related to current drinking, age of drinking onset, and drug use is useful for predicting severity of alcohol use consequences. These markers might enable tertiary institutions to better target students who could benefit from prevention/intervention programs.

Background Considered a facet of behavioral impulsivity, response inhibition facilitates adaptive and goal-directed behavior. It is often assessed using the Stop-Signal Task (SST), which is presented on stand-alone computers under controlled laboratory conditions. Sample size may consequently be a function of cost or time and sample diversity constrained to those willing or able to attend the laboratory. Statistical power and generalizability of results might, in turn, be impacted. Such limitations may potentially be overcome via the implementation of web-based testing. Objective The aim of this study was to investigate if there were differences between variables derived from a web-based SST when it was undertaken independently—that is, outside the laboratory, on any computer, and in the absence of researchers—versus when it was performed under laboratory conditions. Methods We programmed a web-based SST in HTML and JavaScript and employed a counterbalanced design. A total of 166 individuals (mean age 19.72, SD 1.85, range 18-36 years; 146/166, 88% female) were recruited. Of them, 79 undertook the independent task prior to visiting the laboratory and 78 completed the independent task following their laboratory visit. The average time between SST testing was 3.72 (SD 2.86) days. Dependent samples and Bayesian paired samples t tests were used to examine differences between laboratory-based and independent SST variables. Correlational analyses were conducted on stop-signal reaction times (SSRT). Results After exclusions, 123 participants (mean age 19.73, SD 1.97 years) completed the SST both in the laboratory and independently. While participants were less accurate on go trials and exhibited reduced inhibitory control when undertaking the independent—compared to the laboratory-based—SST, there was a positive association between the SSRT of each condition (r=.48; P<.001; 95% CI 0.33-0.61). Conclusions Findings suggest a web-based SST, which participants undertake on any computer, at any location, and in the absence of the researcher, is a suitable measure of response inhibition.