Activity space during treatment with medication for opioid use disorder: Relationships with personality, mood, and drug use.

Introduction: Activity space in people with substance use disorders (SUDs) has been assessed for theoretical reasons and for detection/prevention of relapse. In this observational study, we relate passively obtained activity space measures to mental states and behaviors relevant to the success of treatment for opioid use disorder. Our long-term goal is to use such data to assess risk in real time and to recognize when SUD patients might benefit from a just-in-time intervention.

Variability in intensively assessed mood: Systematic sources and factor structure in outpatients with opioid use disorder.

In intensive longitudinal studies using ecological momentary assessment, mood is typically assessed by repeatedly obtaining ratings for a large set of adjectives. Summarizing and analyzing these mood data can be problematic because the reliability and factor structure of such measures have rarely been evaluated in this context, which-unlike cross-sectional studies-captures between- and within-person processes. Our study examined how mood ratings (obtained thrice daily for 8 weeks; = 306, person moments = 39,321) systematically vary and covary in outpatients receiving medication for opioid use disorder (MOUD).

Using Machine Learning to Predict Treatment Adherence in Patients on Medication for Opioid Use Disorder.

Objective: Patients receiving medication for opioid use disorder (MOUD) may continue using nonprescribed drugs or have trouble with medication adherence, and it is difficult to predict which patients will continue to do so. In this study, we develop and validate an automated risk-modeling framework to predict opioid abstinence and medication adherence at a patient's next attended appointment and evaluate the predictive performance of machine-learning algorithms versus logistic regression.

Methods: Urine drug screen and attendance records from 40,005 appointments drawn from 2742 patients at a multilocation office-based MOUD program were used to train logistic regression, logistic ridge regression, and XGBoost models to predict a composite indicator of treatment adherence (opioid-negative and norbuprenorphine-positive urine, no evidence of urine adulteration) at next attended appointment.

Nonfatal opioid overdoses before and after Covid-19: Regional variation in rates of change.

Background: The Covid-19 pandemic and its accompanying public-health orders (PHOs) have led to (potentially countervailing) changes in various risk factors for overdose. To assess whether the net effects of these factors varied geographically, we examined regional variation in the impact of the PHOs on counts of nonfatal overdoses, which have received less attention than fatal overdoses, despite their public health significance.

Methods: Data were collected from the Overdose Detection Mapping Application Program (ODMAP), which recorded suspected overdoses between July 1, 2018 and October 25, 2020.

Trajectories of craving during medication-assisted treatment for opioid-use disorder: Subtyping for early identification of higher risk.

Aims: To examine evidence for subtypes of opioid craving trajectories during medication for opioid use disorder (MOUD), and to (a) test whether these subtypes differed on MOUD-related outcomes, and (b) determine whether nonresponders could be identified before treatment initiation.

Design, Setting, And Participants: Outpatients (n = 211) being treated with buprenorphine or methadone for up to 16 weeks. Growth mixture modeling was used to identify unobserved craving-trajectory subtypes.

Longitudinal patterns of momentary stress during outpatient opioid agonist treatment: A growth-mixture-model approach to classifying patients.

Background: We previously showed, in people starting treatment for opioid use disorder (OUD), that stress is neither necessary nor sufficient for lapses to drug use to occur, despite an association between the two. Both theoretical clarity and case-by-case prediction accuracy may require initial differentiation among patients.

Aim: To examine: (a) evidence for distinct overall trajectories of momentary stress during OUD treatment, (b) relationships between stress trajectory and treatment response, and (c) relationships between stress trajectory and momentary changes in stress and craving prior to lapses.

Beyond abstinence and relapse II: momentary relationships between stress, craving, and lapse within clusters of patients with similar patterns of drug use.

Rationale: Given that many patients being treated for opioid-use disorder continue to use drugs, identifying clusters of patients who share similar patterns of use might provide insight into the disorder, the processes that affect it, and ways that treatment can be personalized.

Objectives And Methods: We applied hierarchical clustering to identify patterns of opioid and cocaine use in 309 participants being treated with methadone or buprenorphine (in a buprenorphine-naloxone formulation) for up to 16 weeks. A smartphone app was used to assess stress and craving at three random times per day over the course of the study.

Beyond abstinence and relapse: cluster analysis of drug-use patterns during treatment as an outcome measure for clinical trials.

Rationale: Many people being treated for opioid use disorder continue to use drugs during treatment. This use occurs in patterns that rarely conform to well-defined cycles of abstinence and relapse. Systematic identification and evaluation of these patterns could enhance analysis of clinical trials and provide insight into drug use.

Finite Mixture Models with Student t Distributions: an Applied Example.

The use of finite mixture modeling (FMM) to identify unobservable or latent groupings of individuals within a population has increased rapidly in applied prevention research. However, many prevention scientists are still unaware of the statistical assumptions underlying FMM. In particular, finite mixture models (FMMs) typically assume that the observed indicator variables are normally distributed within each latent subgroup (i.

Prediction of stress and drug craving ninety minutes in the future with passively collected GPS data.

Just-in-time adaptive interventions (JITAIs), typically smartphone apps, learn to deliver therapeutic content when users need it. The challenge is to "push" content at algorithmically chosen moments without making users trigger it with effortful input. We trained a randomForest algorithm to predict heroin craving, cocaine craving, or stress (reported via smartphone app 3x/day) 90 min into the future, using 16 weeks of field data from 189 outpatients being treated for opioid-use disorder.

The social networks of smokers attempting to quit: An empirically derived and validated classification.

Social relationships play an important role in the uptake, maintenance, and cessation of smoking behavior. However, little is known about the natural co-occurrence of social network features in adult smokers' networks and how multidimensional features of the network may connect to abstinence outcomes. The current investigation examined whether qualitatively distinct subgroups defined by multiple characteristics of the social network could be empirically identified within a sample of smokers initiating a quit attempt.

HPV vaccination among a community sample of young adult women.

Objectives: Despite the high efficacy of the human papillomavirus (HPV) vaccine, uptake has been slow and little data on psychosocial barriers to vaccination exist.

Methods: A community sample of 428 women enrolled in a longitudinal study of social development in the Seattle WA metropolitan area were interviewed about HPV vaccine status, attitudes, and barriers to HPV vaccination in spring 2008 or 2009 at ∼age 22.

Results: Nineteen percent of women had initiated vaccination, 10% had completed the series, and ∼40% of unvaccinated women intended to get vaccinated.