5 research outputs found

    A consensus guide to capturing the ability to inhibit actions and impulsive behaviors in the stop-signal task

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    © Verbruggen et al. Response inhibition is essential for navigating everyday life. Its derailment is considered integral to numerous neurological and psychiatric disorders, and more generally, to a wide range of behavioral and health problems. Response-inhibition efficiency furthermore correlates with treatment outcome in some of these conditions. The stop-signal task is an essential tool to determine how quickly response inhibition is implemented. Despite its apparent simplicity, there are many features (ranging from task design to data analysis) that vary across studies in ways that can easily compromise the validity of the obtained results. Our goal is to facilitate a more accurate use of the stop-signal task. To this end, we provide 12 easy-to-implement consensus recommendations and point out the problems that can arise when they are not followed. Furthermore, we provide user-friendly open-source resources intended to inform statistical-power considerations, facilitate the correct implementation of the task, and assist in proper data analysis

    Lifetime Doctor-Diagnosed Mental Health Conditions and Current Substance Use Among Gay and Bisexual Men Living in Vancouver, Canada

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    BackgroundStudies have found that gay, bisexual, and other men who have sex with men (GBM) have higher rates of mental health conditions and substance use than heterosexual men, but are limited by issues of representativeness.ObjectivesTo determine the prevalence and correlates of mental health disorders among GBM in Metro Vancouver, Canada.MethodsFrom 2012 to 2014, the Momentum Health Study recruited GBM (≥16 years) via respondent-driven sampling (RDS) to estimate population parameters. Computer-assisted self-interviews (CASI) collected demographic, psychosocial, and behavioral information, while nurse-administered structured interviews asked about mental health diagnoses and treatment. Multivariate logistic regression using manual backward selection was used to identify covariates for any lifetime doctor diagnosed: (1) alcohol/substance use disorder and (2) any other mental health disorder.ResultsOf 719 participants, 17.4% reported a substance use disorder and 35.2% reported any other mental health disorder; 24.0% of all GBM were currently receiving treatment. A lifetime substance use disorder diagnosis was negatively associated with being a student (AOR = 0.52, 95% CI [confidence interval]: 0.27-0.99) and an annual income ≥$30,000 CAD (AOR = 0.38, 95% CI: 0.21-0.67) and positively associated with HIV-positive serostatus (AOR = 2.54, 95% CI: 1.63-3.96), recent crystal methamphetamine use (AOR = 2.73, 95% CI: 1.69-4.40) and recent heroin use (AOR = 5.59, 95% CI: 2.39-13.12). Any other lifetime mental health disorder diagnosis was negatively associated with self-identifying as Latin American (AOR = 0.25, 95% CI: 0.08-0.81), being a refugee or visa holder (AOR = 0.18, 95% CI: 0.05-0.65), and living outside Vancouver (AOR = 0.52, 95% CI: 0.33-0.82), and positively associated with abnormal anxiety symptomology scores (AOR = 3.05, 95% CI: 2.06-4.51).ConclusionsMental health conditions and substance use, which have important implications for clinical and public health practice, were highly prevalent and co-occurring

    A consensus guide to capturing the ability to inhibit actions and impulsive behaviors in the stop-signal task.

    Get PDF
    Response inhibition is essential for navigating everyday life. Its derailment is considered integral to numerous neurological and psychiatric disorders, and more generally, to a wide range of behavioral and health problems. Response-inhibition efficiency furthermore correlates with treatment outcome in some of these conditions. The stop-signal task is an essential tool to determine how quickly response inhibition is implemented. Despite its apparent simplicity, there are many features (ranging from task design to data analysis) that vary across studies in ways that can easily compromise the validity of the obtained results. Our goal is to facilitate a more accurate use of the stop-signal task. To this end, we provide 12 easy-to-implement consensus recommendations and point out the problems that can arise when they are not followed. Furthermore, we provide user-friendly open-source resources intended to inform statistical-power considerations, facilitate the correct implementation of the task, and assist in proper data analysis
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