Preprint: Alcohol's effects during uncertain and uncontrollable stressors in the laboratory

Alcohol’s effects on reactivity to stressors depend on the nature of the stressor and the reactivity being assessed. Research identifying characteristics of stressors that modulate reactivity and clarifies the neurobehavioral, cognitive, and affective components of this reactivity may help prevent, reduce or treat the negative impacts of acute and chronic alcohol use with implications for other psychopathology involving maladaptive reactivity to stressors. We used a novel, multi-measure, cued electric shock stressor paradigm in a greater university community sample of adult recreational drinkers to test how alcohol (N=64), compared to No-alcohol (N=64), affects reactivity to stressors that vary in both their perceived certainty and controllability. Preregistered analyses suggested alcohol significantly dampened subjective anxiety (self-report) and defensive reactivity (startle potentiation) more during uncertain than during certain stressors regardless of controllability, suggesting that stressor uncertainty —but not uncontrollability— may be sufficient to enhance alcohol’s stress reactivity dampening and thus negative reinforcement potential

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Stimuli files use

Analysis

Analysis script

Prospective Prediction of Lapses in Opioid Use Disorder: Protocol for a Personal Sensing Study

Successful long-term recovery from Opioid Use Disorder requires continuous lapse risk monitoring and appropriately using and adapting recovery-supportive behaviors as lapse risk changes. Available treatments often fail to support long-term recovery by failing to account for the dynamic nature of long-term recovery. This protocol paper describes research that aims to develop a highly contextualized lapse risk prediction model that forecasts the ongoing probability of lapse. Participants will be 480 American adults in their first year of recovery from Opioid Use Disorder. Participants will report lapses and provide data relevant to lapse risk for a year with a digital therapeutic smartphone app, through both self-report and passive personal sensing methods (e.g., cellular communications, geolocation). The lapse risk prediction model will be developed using contemporary rigorous machine learning methods that optimize prediction in new data. The model this project will develop could support long-term recovery from Opioid Use Disorder, for example, by enabling just-in-time interventions within digital therapeutics. This project is funded by the National Institute on Drug Abuse with a funding period from August 2019 to June 2024. Full enrollment began in September 2021