Impact of personalized alcohol intake and cognitive feedback on alcohol use behavior in hazardous drinkers: A quasi-randomized trial.

BACKGROUND: Worldwide, alcohol use is a major contributor to the burden of disease and mortality. A sizeable literature suggests that brief web-based interventions that incorporate personalized normative and/or health consequences feedback are effective at reducing alcohol intake. The relative efficacy of an intervention that also includes individualized feedback about brain health has not been examined, nor has the utility of integrating a smartphone app component. METHOD: Participants (N = 436, Mage = 21.27) completed baseline protocols (n = 178 recorded alcohol use via an app for 14 days) and were then assigned to one of three feedback conditions using randomized block allocation with stratification based on the total number of standard drinks consumed. Control participants received no feedback; Alcohol Intake Feedback (Alc) participants received personalized information about their alcohol use; Alcohol Intake plus Cognitive Feedback (AlcCog) participants received personalized details about alcohol use plus individualized brain-health information related to impulsivity. The impact of feedback on alcohol consumption behavior was examined as a function of feedback condition and hazardous/non-harmful drinking status (as defined by the World Health Organization) at an 8-week follow-up. RESULTS: Hazardous drinkers in both the Alc and AlcCog conditions reduced their alcohol intake by 31% to 50% more than those in the Control condition. Reductions were not related to whether participants completed web- plus app-based components or web-only components of the intervention. There was no change in the alcohol intake of non-harmful drinkers. CONCLUSIONS: This proof-of-concept study showed that hazardous drinkers respond well to brief electronic interventions that incorporate personalized normative and/or health consequences feedback. Further research is required to determine how best to make impulsivity-related brain-health consequences of drinking manifest and how to maximize the potential of smartphones apps.

Chronic cannabis use and error awareness: The effect on learning from errors.

BACKGROUND: Cannabis is the third most commonly used drug worldwide, with studies suggesting a deleterious effect on some aspects of performance monitoring. It is unknown, however, whether diminished error awareness influences adaptive behaviour in cannabis users. Therefore, this study examined the effect of error awareness on learning from errors in cannabis users. METHODS: Thirty-six chronic cannabis users (Mage = 23.81 years; female, 36%) and 34 controls (Mage = 21.53 years; female, 76%) completed a Go/No-Go task that allowed participants to learn from errors and adapt their behaviour. Multilevel models were specified to determine whether the effect of error awareness on learning from errors differs between cannabis users and controls, and whether cannabis-use measures predict error correction while accounting for error awareness. RESULTS: While error awareness and correction rates did not differ between the groups, there was a significant effect of age of use onset on error correction in cannabis users. Further, the effect of error awareness was dependent on age of onset, and cannabis use-related frequency and harm. That is, cannabis users reporting an earlier age of regular use or scoring higher on the cannabis use index were less likely to perform correctly following an aware error. CONCLUSION: It appears overall cannabis use might not be tightly coupled to behavioural indices of performance monitoring. There is evidence, however, that aspects of cannabis use predict impairments in learning from errors that may be associated with treatment outcomes.

Evaluating untimed and timed abridged versions of Raven's Advanced Progressive Matrices.

INTRODUCTION: Raven's Advanced Progressive Matrices (APM) are frequently utilized in clinical and experimental settings to index intellectual capacity. As the APM is a relatively long assessment, abridged versions of the test have been proposed. The psychometric properties of an untimed 12-item APM have received some consideration in the literature, but validity explorations have been limited. Moreover, both reliability and validity of a timed 12-item APM have not previously been examined. METHOD: We considered the psychometric properties of untimed (Study 1; N = 608; Mage = 27.89, SD = 11.68) and timed (Study 2; N = 479; Mage = 20.93, SD = 3.12) versions of a brief online 12-item form of the APM. RESULTS: Confirmatory factor analyses established both versions of the tests are unidimensional. Item response theory analyses revealed that, in each case, the 12 items are characterized by distinct differences in difficulty, discrimination, and guessing. Differential item functioning showed few male/female or native English/non-native English performance differences. Test-retest reliability was .65 (Study 1) to .69 (Study 2). Both tests had medium-to-large correlations with the Wechsler Abbreviated Scale of Intelligence (2nd ed.) Perceptual Reasoning Index (r = .50, Study 1; r = .56, Study 2) and Full-Scale IQ (r = .34, Study 1; r = .41, Study 2). CONCLUSION: In sum, results suggest both untimed and timed online versions of the brief APM are psychometrically sound. As test duration was found to be highly variable for the untimed version, the timed form might be a more suitable choice when it is likely to form part of a longer battery of tests. Nonetheless, classical test and item response theory analyses, plus validity considerations, suggest the untimed version might be the superior abridged form.

Web-Based Independent Versus Laboratory-Based Stop-Signal Task Performance: Within-Subjects Counterbalanced Comparison Study.

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.

Addressing methodological issues in a study of impulsivity and vulnerability for transition to alcohol use disorder.

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.

Transition to substance use disorders: impulsivity for reward and learning from reward.

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.

A Smartphone App to Assess Alcohol Consumption Behavior: Development, Compliance, and Reactivity.

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.

Predictors of Adverse Alcohol Use Consequences Among Tertiary Students.

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 as compared to their nonuniversity enrolled peers. The Brief Young Adult Alcohol Consequences Questionnaire (BYAACQ) is one of several tools utilized to explore adverse alcohol-related outcomes among tertiary students. Alcohol intake behavior, 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 7.53 (SD = 5.37). 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.

Assessment of alcohol intake: Retrospective measures versus a smartphone application.

INTRODUCTION: Research investigating problem drinking often relies on retrospective measures to assess alcohol consumption behaviour. Limitations associated with such instruments can, however, distort actual consumption levels and patterns. We developed the smartphone application (app), CNLab-A, to assess alcohol intake behaviour in real-time. METHODS: Healthy individuals (N=671, M age 23.12) completed demographic questions plus the Alcohol Use Questionnaire and a 21-day Timeline Followback before using CNLab-A for 21days. The app asked participants to record alcohol consumption details in real time. We compared data reported via retrospective measures with that captured using CNLab-A. RESULTS: On average, participants submitted data on 20.27days using CNLab-A. Compared to Timeline Followback, a significantly greater percentage of drinking days (24.79% vs. 26.44%) and significantly higher total intake (20.30 vs. 24.26 standard drinks) was recorded via the app. CNLab-A captured a substantially greater number of high intake occasions at all levels from 8 or more drinks than Timeline Followback. Additionally, relative to the Alcohol Use Questionnaire, a significantly faster rate of consumption was recorded via the app. CONCLUSIONS: CNLab-A provided more nuanced information regarding quantity and pattern of alcohol intake than the retrospective measures. In particular, it revealed higher levels of drinking than retrospective reporting. This will have implications for how particular at-risk alcohol consumption patterns are identified in future and might enable a more sophisticated exploration of the causes and consequences of drinking behaviour.

Cognitive Control Over Immediate Reward in Binge Alcohol Drinkers.

BACKGROUND: Cognitive control deficits, as captured by inhibitory control measures, are indicative of increased impulsivity and are considered a marker for substance use disorder vulnerability. While individuals with alcohol use disorder (AUD) typically exhibit inhibitory control dysfunction, evidence of impaired inhibitory control among harmful drinkers, who are at increased risk of developing an AUD, is mixed. This study examined the response inhibition of binge drinkers using a task that employed neutral, as well as both immediate and delayed reward contingencies, to determine whether reward induced heightened impulsivity in this population. METHODS: Binge alcohol users (n = 42) and controls (n = 42) were administered a Monetary Incentive Control Task that required participants to successfully inhibit a prepotent motor response to both neutral and immediately rewarding stimuli in order to secure a large delayed reward. RESULTS: Binge drinkers had significantly worse response inhibition than controls irrespective of trial condition and even after controlling for differences in weekly intake. Although both binge and control participants exhibited significantly worse inhibitory control in the presence of immediate reward, the control group showed a greater reduction in inhibition accuracy compared to the binge group in reward relative to neutral conditions. Both groups demonstrated significantly enhanced control when forewarned there was an increased chance response inhibition would be required. Control participants secured the delayed reward more often than binge participants. CONCLUSIONS: 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.