Stress Allostasis in Substance Use Disorders: Promise, Progress, and Emerging Priorities in Clinical Research.

Clinicians and researchers alike have long believed that stressors play a pivotal etiologic role in risk, maintenance, and/or relapse of alcohol and other substance use disorders (SUDs). Numerous seminal and contemporary theories on SUD etiology posit that stressors may motivate drug use and that individuals who use drugs chronically may display altered responses to stressors. We use foundational basic stress biology research as a lens through which to evaluate critically the available evidence to support these key stress-SUD theses in humans. Additionally, we examine the field's success to date in targeting stressors and stress allostasis in treatments for SUDs. We conclude with our recommendations for how best to advance our understanding of the relationship between stressors and drug use, and we discuss clinical implications for treatment development.

Machine learning models for temporally precise lapse prediction in alcohol use disorder.

We developed three machine learning models that predict hour-by-hour probabilities of a future lapse back to alcohol use with increasing temporal precision (i.e., lapses in the next week, next day, and next hour). Model features were based on raw scores and longitudinal change in theoretically implicated risk factors collected through ecological momentary assessment. Participants (N = 151, 51% male, Mage = 41, 87% White, 97% non-Hispanic) in early recovery (1-8 weeks of abstinence) from alcohol use disorder provided 4 × daily ecological momentary assessment for up to 3 months. We used grouped, nested cross-validation to select the best models and evaluate the performance of those best models. Models yielded median areas under the receiver operating curves of 0.89, 0.90, and 0.93 in the 30 held-out test sets for week-, day-, and hour-level models, respectively. Some feature categories consistently emerged as being globally important to lapse prediction across our week-, day-, and hour-level models (i.e., past use, future self-efficacy). However, most of the more punctate, time-varying constructs (e.g., craving, past stressful events, arousal) appear to have a greater impact within the next-hour prediction model. This research represents an important step toward the development of a smart (machine learning guided) sensing system that can both identify periods of peak lapse risk and recommend specific supports to address factors contributing to this risk. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

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

Alcohol's effects on reactivity to stressors depends 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), effects 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.

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

BACKGROUND: Successful long-term recovery from opioid use disorder (OUD) requires continuous lapse risk monitoring and appropriate use and adaptation of 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. OBJECTIVE: The aim of this protocol paper is to describe research that aims to develop a highly contextualized lapse risk prediction model that forecasts the ongoing probability of lapse. METHODS: The participants will include 480 US adults in their first year of recovery from OUD. 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 (eg, cellular communications and geolocation). The lapse risk prediction model will be developed using contemporary rigorous machine learning methods that optimize prediction in new data. RESULTS: The National Institute of Drug Abuse funded this project (R01DA047315) on July 18, 2019 with a funding period from August 1, 2019 to June 30, 2024. The University of Wisconsin-Madison Health Sciences Institutional Review Board approved this project on July 9, 2019. Pilot enrollment began on April 16, 2021. Full enrollment began in September 2021. CONCLUSIONS: The model that will be developed in this project could support long-term recovery from OUD-for example, by enabling just-in-time interventions within digital therapeutics. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/29563.