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

A review of factors influencing drinking behaviours in young Australian adults using a behavioural framework approach

ISSUES

Young adults (aged 18-24 years) are more likely to binge drink than any other age group in Australia. These behaviours expose young adults to possible adverse events, with potential for acute harms.

APPROACH

A behavioural framework (capability, opportunity, motivation-behaviour [COM-B model]) was used to explore factors that influence the drinking behaviours of young Australian adults. Potential studies were identified by searching four online data bases. Content pertaining to factors moderating young adults' alcohol consumption behaviours (either increasing or decreasing alcohol consumption) in an Australian population was extracted. Factors were then categorised/mapped into the six sub-components of the COM-B model. A narrative synthesis/discussion was subsequently undertaken.

KEY FINDINGS

Factors increasing or reducing alcohol consumption behaviours were identified across all components of the COM-B model. Overall, alcohol consumption behaviours appear strongly influenced by physical and social opportunities, and young adults have reflective and automatic motivations (i.e., habitual processes, emotional responding and analytical decisional making that directs behaviour) to consume alcohol with purpose and/or reason.

IMPLICATIONS AND CONCLUSION

The use of a behavioural framework (e.g., the COM-B model) facilitates an integrated understanding of factors influencing alcohol consumption behaviours. Future harm minimisation strategies need to consider the interrelated, contemporary factors underpinning a young adult's decision to consume alcohol within the context of modern Australian society.

Value-based decision-making predicts alcohol use and related problems in young men

BACKGROUND

Alcohol consumption is a leading cause of morbidity and mortality worldwide, disproportionately affecting young men. Heavy episodic drinking is particularly prevalent among men, with this behavior peaking between the ages of 20 and 24.

AIMS

We sought to identify dimensions of decision-making in men that would predict the development of hazardous alcohol use through emerging adulthood.

METHODS

This prospective observational study profiled value-based decision-making in 198 healthy men at age 18 and assessed their alcohol involvement annually until age 24. Latent growth curve modeling estimated individual variability in trajectories of alcohol involvement and regressed this variability on five choice dimensions.

RESULTS

Low loss aversion predicted sustained heavy episodic drinking from age 18 to 24. Both high delay discounting and risk-seeking for gains independently predicted a considerably higher cumulative alcohol use during these 6 years, with high delay discounting indicating escalating consumption from age 21. Risk-seeking for gains additionally predicted meeting more criteria for Alcohol Use Disorder in these 6 years. Risk-seeking for losses was not significantly related to alcohol outcomes. Choice preferences were largely independent of each other but were correlated with choice consistency, with low consistency predicting heavy episodic drinking from age 18 to 24 beyond these associations.

CONCLUSIONS

The predictive effects collectively suggest that overvaluing immediate and probabilistic incentives, rather than underestimating harm, drives hazardous drinking in young men. The differential relations of choice preferences and consistency to alcohol involvement through emerging adulthood provide distinct cognitive-behavioral patterns that warrant consideration in the development of harm reduction interventions.

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.