Researchers recently have begun using Mechanical Turk (MTurk), an online crowdsourcing platform, to recruit addiction populations. However, whether the data obtained from substance users and gamblers on MTurk are reliable and valid is unknown. Herein, we assessed the internal and retest reliability of and concurrent and convergent validity of data obtained from addiction populations on MTurk. Current drinkers (N = 208), cannabis users (N = 200), and gamblers (N = 200) residing in the United States completed measures of alcohol, cannabis, and gambling severity, psychological constructs (e.g., impulsivity) related to addictions, overt and subtle measures of valid responding, and motivations for completing MTurk studies. Of the original sample, 88-92% of participants who provided informed consent for recontact completed a reassessment 1 week later. The internal consistency of the addiction severity measures ranged from α = .75 to .93. The stability over 1 week ranged from κ = .57 to .70 for categorical classification, and intraclass correlation coefficient (ICC) = .71 to .86 for continuous measures. The addiction measures were significantly correlated with each other and with other constructs related to addictive behaviors. Overall, 80-85% of participants provided valid responses. They reported attending and answering questions honestly, with financial motives being the most frequently endorsed motivation. After invalid responses were excluded, results remained the same for alcohol and gambling, but significant differences emerged for the cannabis sample. The results suggest that the self-report data obtained from alcohol and gambling populations are of high quality, however, caution is warranted with cannabis populations. MTurk shows promise as a recruitment tool for some addictive behaviors. (PsycINFO Database Record

Behavioral addictions such as gambling, video games, sex, and shopping share many clinical features with substance use addictions including etiology, course, and neurobiology. Yet, the treatment of behavioral and substance use addictions tends to be separated. However, we argue that a more effective and efficient treatment approach is to conceptualize behavioral and substance use addictions as different expressions of a common underlying disorder and, in treatment, to address the underlying mechanisms common to both. To this end, the article presents a developing transdiagnostic treatment model of addictions that targets underlying similarities between behavioral and substance use addictions, called the component model of addiction treatment (CMAT). The CMAT is transdiagnostic in that it can be used in the treatment of both behavioral and substance use addictions. It is pragmatic in that it targets component vulnerabilities, which are enduring, yet malleable, individual psychological, cognitive, and neurobiological characteristics that are common to all addictive disorders and have been demonstrated to be modifiable. A working model of CMAT is presented, including proposed component vulnerabilities: lack of motivation, urgency, maladaptive expectancies, deficits in self-control, deficits in social support, and compulsivity, as well as their potential intervention possibilities. Future directions and potential implications of the CMAT are discussed.

Social casino games (i.e., free-to-play online gambling games) are enjoyed by millions of players worldwide on a daily basis. Despite being free to play, social casino games share many similarities to traditional casino games. As such, concerns have been raised as to whether social casino games influences the migration to online gambling among people who have not engaged in such activity (see Griffiths in World Online Gambl 9:12–13, 2010). To date, however, no empirical research has assessed this possibility. Thus, the purpose of the present research was to assess the extent to which social casino gamers migrate to online gambling and potential predictors (time spent on social casino games, skill building, enhancement and micro-transactions) of such migration. To this end, social casino gamers who never gambled online (N = 409) completed a questionnaire battery assessing our variables of interest and were re-contacted 6-months later to see if they had engaged in online gambling during the intervening months. Approximately 26 % of social casino gamers reported having migrated to online gambling. Importantly, engagement in micro-transactions was the only unique predictor of migration from social casino gaming to online gambling. The implications for the potential harms associated with social casino gaming are discussed.

In the current study, we assessed whether undergraduate electronic gaming machine (EGM) gamblers would be more apt to set a time limit and spend less time gambling when asked to consider setting an explicit time limit prior to their gambling session. To this end, participants (N = 43) were randomly assigned to a time limit pop-up condition or control condition, both of which involved gambling on an EGM in a virtual reality (VR) casino. In the time limit pop-up condition, participants were asked (via pop-up message) to consider setting a time limit on play and entering that limit in an available text box prior to commencing play. In the no time limit pop-up condition, participants engaged in play immediately upon accessing the EGM in the VR casino (i.e. they were not exposed to a time limit pop-up message). As predicted, participants who were explicitly asked to consider setting a time limit on their EGM play were significantly more likely to do so and spent less time gambling than those who were not given such instructions. The results provide preliminary support for the contention that setting a time limit on EGM play is an effective responsible gambling strategy.