Cigarette smoking is the most common addictive behaviour co-occurring with problem gambling. Based on classical conditioning, smoking and gambling cues may acquire conditioned stimulus properties that elicit cravings for both behaviours. This study examined cross-cue reactivity in 75 men who were regular smokers, poker players or cigarette-smoking poker players. Participants were exposed to discrete cigarette, poker and neutral cues while skin conductance and psychological urges to smoke and gamble were measured. Results showed evidence of cross-cue reactivity based on skin conductance, and subjective response to smoking cues; subjective response to gambling cues was less clear. Smoking gamblers showed greater skin conductance reactivity to cues, and stronger subjective urges to smoke to smoking and gambling cues, compared to individuals who only smoked or only gambled. This study demonstrates evidence for cross-cue reactivity between a substance and a behavioural addiction, and the results encourage further research.

The explosion of technological applications in health care has provided new avenues for clinical change to occur. Mobile health (mHealth) apps, which include clinical tools that run on smartphones, are one such demonstration of these technologies. Benefits of mHealth apps are numerous, but potential limitations include poorer understanding of material due to the lack of real-time support from providers, limited scope and depth of interventions, and absence of theoretical grounding and/or empirical support. A potential reason for these limitations is that treatment experts and app developers lack a common theory-based conceptual model to inform the objectives of mHealth interventions. We discuss how clinicians and app developers can use models of learning processes, such as Experiential Learning Theory (ELT) and Bloom's Taxonomy, to facilitate collaboration among professionals and to increase experiential learning within mHealth interventions. First, we present ELT and Bloom's Taxonomy as frameworks to aid in the development of mHealth apps for behavioral health. Next, we outline some key ingredients of experiential learning to consider during the development stages of mHealth interventions, namely concrete experience, interactivity, experimentation, scaffolding, and integration (i.e., including affective, behavioral, and cognitive elements). Within these key ingredients, we consider how such factors can enhance outcomes for mHealth apps through processes such as engagement, shaping of beliefs, skills development, generalization, and scope of learning. Public Significance StatementThis work provides guidance to mental health professionals and technology experts in the use of models of experiential learning to aid in the development of mobile mental health (mHealth) apps. The intended purpose of using these models is to improve the quality, utility, and efficacy of mHealth interventions.

Problem gamblers tend to adhere to rigid rules about the chances of winning and are resistant to counterfactual information. To promote a more accurate understanding of the odds of scratch-off ticket gambling, we created a brief debiasing intervention consisting of a digital gambling accelerator program that offers demonstrations of the long-term outcomes of gambling. Using a sample of nontreatment seeking scratch-off lottery gamblers recruited from the community (42 subclinical and 45 probable pathological gamblers), we compared the accelerator intervention to brief motivational interviewing (MI) and a control condition. Participants rated their chances of winning, urge to gamble, and readiness to change before and after the interventions. Self-reported dollar amount spent on scratch-off tickets and number of days gambled were assessed at baseline and again at 2- and 4-week follow-ups. Following the active interventions, gamblers in both conditions reported greater readiness to change than controls, and those in the accelerator condition also gave lower ratings of their chances of winning and urge to gamble. Marginal models showed participants in the accelerator condition gambled fewer days at the 2-week follow-up and spent less money at both the 2- and 4-week follow-ups compared to controls; no other between-subjects differences achieved statistical significance. Digital gambling accelerators can impact several clinically relevant domains of gambling and may be useful as stand-alone or adjunct interventions to treat gambling problems.

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