Findings that interoceptive training is associated with health outcomes for women in SUD treatment are consistent with emerging neurocognitive models that link interoception to emotion regulation and to related health outcomes, providing knowledge critical to supporting and improving SUD treatment.

Background Previous research has highlighted the role of stress in substance misuse and addiction, particularly for relapse risk. Mobile health interventions that incorporate real-time monitoring of physiological markers of stress offer promise for delivering tailored interventions to individuals during high-risk states of heightened stress to prevent alcohol relapse. Before such interventions can be developed, measurements of these processes in ambulatory, real-world settings are needed. Objective This research is a proof-of-concept study to establish the feasibility of using a wearable sensor device to continuously monitor stress in an ambulatory setting. Toward that end, we first aimed to examine the quality of 2 continuously monitored physiological signals—electrodermal activity (EDA) and heart rate variability (HRV)—and show that the data follow standard quality measures according to the literature. Next, we examined the associations between the statistical features extracted from the EDA and HRV signals and self-reported outcomes. Methods Participants (N=11; female: n=10) were asked to wear an Empatica E4 wearable sensor for continuous unobtrusive physiological signal collection for up to 14 days. During the same time frame, participants responded to a daily diary study using ecological momentary assessment of self-reported stress, emotions, alcohol-related cravings, pain, and discomfort via a web-based survey, which was conducted 4 times daily. Participants also participated in structured interviews throughout the study to assess daily alcohol use and to validate self-reported and physiological stress markers. In the analysis, we first used existing artifact detection methods and physiological signal processing approaches to assess the quality of the physiological data. Next, we examined the descriptive statistics for self-reported outcomes. Finally, we investigated the associations between the features of physiological signals and self-reported outcomes. Results We determined that 87.86% (1,032,265/1,174,898) of the EDA signals were clean. A comparison of the frequency of skin conductance responses per minute with previous research confirmed that the physiological signals collected in the ambulatory setting were successful. The results also indicated that the statistical features of the EDA and HRV measures were significantly correlated with the self-reported outcomes, including the number of stressful events marked on the sensor device, positive and negative emotions, and experienced pain and discomfort. Conclusions The results demonstrated that the physiological data collected via an Empatica E4 wearable sensor device were consistent with previous literature in terms of the quality of the data and that features of these physiological signals were significantly associated with several self-reported outcomes among a sample of adults diagnosed with alcohol use disorder. These results suggest that ambulatory assessment of stress is feasible and can be used to develop tailored mobile health interventions to enhance sustained recovery from alcohol use disorder.

ObjectiveSerious mental illnesses (SMI) and alcohol use disorder (AUD) co-occurrence (SMI-AUD) is common, yet little is known about the prevalence and risk factors of cognitive impairment for this population. We used the National Institutes of Health (NIH) Toolbox to identify clinically significant cognitive impairment (CSCI), describe the cognitive profile, and investigate whether psychiatric and AUD severity measures are associated with CSCI in individuals with SMI-AUD.MethodsCSCI was defined as 2 or more fully corrected fluid subtest T scores below a set threshold based on an individual’s crystalized composite score. Psychiatric severity measures included the Structured Clinical Interview for DSM-V (SCID-5) for SMI diagnosis and the Positive and Negative Syndrome Scale. AUD severity measures included the SCID-5 for AUD symptom severity score, years of alcohol use, and urine ethyl glucuronide levels. A multivariable logistic regression was used to investigate the adjusted effects of each variable on the probability of CSCI.ResultsForty-one percent (N = 55/135) of our sample had CSCI compared with the base rate of 15% from the NIH Toolbox normative sample. Subtests measuring executive function most frequently contributed to meeting criteria for CSCI (Flanker and Dimensional Change Card Sort). A history of head injury (P = 0.033), increased AUD symptom severity score (P = 0.007) and increased negative symptom severity score (P = 0.027) were associated with CSCI.ConclusionsCognition should be considered in the treatment of people with SMI-AUD, particularly in those with history of brain injury, higher AUD symptom severity, and/or negative symptom severity.