<b><i>Introduction:</i></b> Compared to older adults, emerging adults treated for substance use disorders (SUDs) are more likely to have unfavorable outcomes. However, few studies have investigated the baseline characteristics and treatment outcomes of emerging adults in inpatient SUD treatment. <b><i>Aims:</i></b> This study investigated differences in demographic and clinical characteristics and treatment outcomes (relapse or treatment discontinuation) among emerging adult and adult inpatients. Prospective associations between baseline characteristics and unfavorable treatment outcomes were also analyzed across both patient groups. <b><i>Methods:</i></b> A prospective cohort study was conducted among inpatients (<i>n</i> = 499) at 4 SUD treatment centers in Norway. The sample included emerging adult patients aged 18–25 years (<i>n</i> = 149) and adult patients above 25 years (<i>n</i> = 350). Medical records provided data on sociodemographic variables, substance use characteristics, diagnoses, and treatment completion status. Self-reported measures, including age of onset of substance use, motivation, and mental distress, were completed within 2 weeks of admission to treatment. A telephone interview 3 months after discharge provided information about relapses. <b><i>Results:</i></b> Emerging adults had a more adverse risk profile in terms of demographic characteristics, clinical variables, and treatment outcomes. Multivariable results showed that polysubstance use and an attention deficit hyperactivity disorder (ADHD) diagnosis were the strongest predictors of unfavorable treatment outcomes for emerging adults. For older adults, only baseline mental distress was a significant predictor of unfavorable treatment outcomes. <b><i>Conclusions:</i></b> Treatment and follow-up initiatives could be better tailored for emerging adults. Identification of treatment needs among emerging adults manifesting polysubstance use and ADHD may reduce the likelihood of unfavorable treatment outcomes in this patient group.

BackgroundAddictive disorders and substance use are significant health challenges worldwide, and relapse is a core component of addictive disorders. The dynamics surrounding relapse and especially the immediate period before it occurs is only partly understood, much due to difficulties collecting reliable and sufficient data from this narrow period. Mobile sensing has been an important way to improve data quality and enhance predictive capabilities for symptom worsening within physical and mental healthcare but is less developed within substance use research. MethodologyThis scoping review aimed to reviewing the currently available research on mobile sensing of substance use and relapse in substance use disorders. The search was conducted in January 2019 using PubMed and Web of Science. ResultsSix articles were identified, all concerning subjects using alcohol. In the studies a range of mobile sensors and derived aggregated features were employed. Data collected through mobile sensing was predominantly used to make dichotomous inference on ongoing substance use or not and in some cases on the quantity of substance intake. Only one of the identified studies predicted later substance use. A range of statistical machine learning techniques was employed. ConclusionsThe research on mobile sensing in this field remains scarce. The issues requiring further attention include more research on clinical populations in naturalistic settings, use of a priori knowledge in statistical modeling, focus on prediction of substance use rather than purely identification and finally research on other substances than alcohol. Methodology Research questionsThis review aims to summarize the current state of published research on passive mobile sensing in substance use research by answering the following questions. What tools, sensors and analytical approaches are typically used? Which populations have been studied? And finally, what inferences are drawn based on mobile sensor data.

Patients with severe substance use disorders are often characterized by neurocognitive impairments and elevated mental health symptom load, typically associated with craving intensity and substance use relapse. There is a need to improve the predictive capabilities of when relapse occurs in order to improve substance use treatment. The current paper contains data from 19 patients (seven females) in a long-term inpatient substance use treatment setting over the course of several weeks, with up to three weekly data collections. We collected data from 252 sessions, ranging from 1 to 24 sessions per subject. The subjects reported craving, self-control, and mental health on each occasion. Before starting the repeated data collection, a baseline neuropsychological screening was performed. In this repeated-measures prospective study, the mixed-effects models with time-lagged predictors support a model of substance use craving and relapse being predicted by the baseline reaction time as well as the temporal changes and variability in mental health symptom load, self-control, and craving intensity with moderate to high effect sizes. This knowledge may contribute to more personalized risk assessments and treatments for this group of patients.

Symptoms of ADHD are strongly associated with alcohol use disorders, and mental health symptoms attenuate this relationship. There is limited knowledge about how specific symptoms of inattentiveness and hyperactivity/impulsivity can explain this association. We aimed to identify self-reported executive cognitive functioning and mental health and variables that may help identify subjects with an elevated risk of alcohol dependence in the general population. Data included 3917 subjects between 19 and 30 years old in the 4th Trøndelag Health Study. The Adult ADHD Self report Scale—Screener, the Hospital Anxiety and Depression Scale, and demographic variables were used as input variables. The alcohol screening instrument CAGE was used as the response variable for binary alcohol dependence risk. We used logistic regression and automated model selection to arrive at our final model that identified sex, age, inattentiveness, hyperactivity/impulsivity symptoms, and anxiety as predictors of having a CAGE score ≥2, achieving an area under the receiver operating characteristic curve of 0.692. A balanced accuracy approach indicated an optimal cut-off of 0.153 with sensitivity 0.55 and specificity 0.74. Despite attrition in the data, our findings may be important in the assessment of individual risk for alcohol dependency and when developing algorithms for risk triage in public health.