To reduce the spread of COVID-19 transmission, government agencies in the United States (US) recommended precautionary guidelines, including wearing masks and social distancing to encourage the prevention of the disease. However, compliance with these guidelines has been inconsistent. This correlational study examined whether individual differences in risky decision-making and motivational propensities predicted compliance with COVID-19 preventative behaviors in a sample of US adults (N = 404). Participants completed an online study from September through December 2020 that included a risky choice decision-making task, temporal discounting task, and measures of appropriate mask-wearing, social distancing, and perceived risk of engaging in public activities. Linear regression results indicated that greater temporal discounting and risky decision-making were associated with less appropriate mask-wearing behavior and social distancing. Additionally, demographic factors, including personal experience with COVID-19 and financial difficulties due to COVID-19, were also associated with differences in COVID-19 preventative behaviors. Path analysis results showed that risky decision-making behavior, temporal discounting, and risk perception collectively predicted 55% of the variance in appropriate mask-wearing behavior. Individual differences in general decision-making patterns are therefore highly predictive of who complies with COVID-19 prevention guidelines.

Background Mental health apps have shown promise in improving mental health symptoms, including depressive symptoms. However, limited research has been aimed at understanding how specific app features and designs can optimize the therapeutic benefits and adherence to such mental health apps. Objective The primary purpose of this study is to investigate the effect of avatar customization on depressive symptoms and adherence to use a novel cognitive behavioral therapy (CBT)–based mental health app. The secondary aim is to examine whether specific app features, including journaling, mood tracking, and reminders, affect the usability of the mental health app. Methods College students were recruited from a university study recruitment pool website and via flyer advertisements throughout campus. A total of 94 participants completed a randomized controlled trial in which they were randomized to either customization or no customization version of the app. Customization involved personalizing a virtual avatar and a travel vehicle to one’s own preferences and use of one’s name throughout the app. Participants completed a 14-day trial using a novel CBT-based mental health app called AirHeart. Self-report scores for depressive symptoms, anxiety, and stress were measured at baseline and after the intervention. Postintervention survey measures also included usability and avatar identification questionnaires. Results Of the 94 enrolled participants, 83 (88%) completed the intervention and postintervention assessments. AirHeart app use significantly reduced symptoms of depression (P=.006) from baseline to the end of the 2-week intervention period for all participants, regardless of the customization condition. However, no differences in depressive symptoms (P=.17) or adherence (P=.80) were observed between the customization (39/83, 47%) and no customization (44/83, 53%) conditions. The frequency of journaling, usefulness of mood tracking, and helpfulness of reminders were not associated with changes in depressive symptoms or adherence (P>.05). Exploratory analyses showed that there were 3 moderate positive correlations between avatar identification and depressive symptoms (identification: r=−0.312, P=.02; connection: r=−0.305, P=.02; and lack of relatability: r=0.338, P=.01). Conclusions These results indicate that CBT mental health apps, such as AirHeart, have the potential to reduce depressive symptoms over a short intervention period. The randomized controlled trial results demonstrated that customization of app features, such as avatars, does not further reduce depressive symptoms over and above the CBT modules and standard app features, including journal, reminders, and mood tracking. However, further research elucidating the relationship between virtual avatar identification and mental health systems is needed as society becomes increasingly more digitized. These findings have potential implications for improving the optimization of mental health app designs. Trial Registration Open Science Framework t28gm; https://osf.io/t28gm

This review article presents a summary of the existing literature on well-established CBT treatments for substance use disorder. It provides an overview of the origins, procedure, and evidence for six CBT treatment models: relapse prevention (RP) and mindful-based relapse prevention (MBRP), guided self-change (GSC), community reinforcement approach (CRA), behavioral couples therapy (BCT), and personality-targeted brief interventions. Common intervention components include orienting clients towards a meaningful goal, teaching necessary skills to reduce substance use and successfully achieve the goal, and establishing plans to face potential relapses, which generally appear to produce moderate to large effects across contexts and substance-related outcomes.

To reduce the spread of COVID-19 transmission, government agencies in the United States (US) have recommended COVID prevention guidelines, including wearing masks and social distancing. However, compliance with these guidelines have been inconsistent. This study examined whether individual differences in decision-making and motivational propensities predicted compliance with COVID-19 preventative behaviors in a representative sample of US adults (N=225). Participants completed an online study in September 2020 that included a risky choice decision-making task, temporal discounting task, and measures of appropriate mask wearing, social distancing, and perceived risk of engaging in public activities. Linear regression results indicated that greater risky decision-making behavior and temporal discounting were associated with less appropriate mask-wearing behavior and social distancing. Additionally, demographic factors, including political affiliation and income level, were also associated with differences in COVID-19 preventative behaviors. Path analysis results showed that risky decision-making behavior, temporal discounting, and risk perception collectively predicted 61% of the variance in appropriate mask-wearing behavior. Individual differences in general decision-making patterns are therefore highly predictive of who complies with COVID-19 prevention guidelines.

Phishing research presents conflicting findings regarding the psychological predictors of phishing susceptibility. The present work aimed to resolve these discrepancies by utilizing a diverse online sample and email set. Participants completed a survey that included an email classification task and measured several individual differences, including phishing awareness, age, impulsivity, curiosity, and personality (the Big-5).Phishing susceptibility was measured by participants' ability to distinguish between phishing and legitimate emails. Three regression analyses were performed to predict email discrimination ability; (1) an age model, (2) a deficient self-regulation model (i.e., impulsivity, response times, and curiosity), and (3) a personality (i.e., the Big-5) model. Overall, phishing susceptibility was predicted by younger age, higher levels of impulsivity and extraversion, lower levels of openness to experience and agreeableness, and quick responses. The present work identifies several populations that are particularly vulnerable to phishing attacks and may require targeted training interventions.