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. METHODS: We used GPS data from 238 urban residents in the first 16 weeks of stabilization on medication for opioid use disorder to test preregistered hypotheses about activity space (distance traveled, number of locations, time spent moving, and psychosocial-hazard levels of neighborhoods where participants spent time) in relation to certain static variables (personality, mood propensities) and time-varying treatment-relevant behaviors such as craving and use of opioids and cocaine. RESULTS: The most consistent findings were that 1) mobility decreased over the course of the study; 2) neuroticism was associated with overall lower mobility; 3) trait-like positive mood (averaged from momentary ratings) was associated with higher mobility; 4) participants who used cocaine more frequently had lower mobility; 5) early in treatment, participants spent less time moving (i.e., were more sedentary) on days when they were craving. Some of these findings were in the expected direction (i.e., the ones involving neuroticism and positive mood), and some were opposite to the expected direction (i.e., we expected cocaine use to be associated with higher mobility); others (e.g., changes in mobility over time or in relation to craving) involved nondirectional hypotheses. CONCLUSIONS: Real-time information that patients actively provide is valuable for assessing their current state, but providing this information can be burdensome. The current results indicate that certain static or passively obtained data (personality variables and GPS-derived mobility information) are relevant to time-varying, treatment-relevant mental states and drug-related behavior, and therefore might be useful when incorporated into algorithms for detecting need for intervention in real time. Further research should assess how population-specific these relationships are, and how these passive measures can best be combined with low temporal-density, actively-provided data to obtain valid, reliable assessments with minimal burden.

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. RESULTS: The XGBoost model had similar accuracy and discriminative ability (accuracy, 88%; area under the receiver operating curve, 0.87) to the two logistic regression models (accuracy, 88%; area under the receiver operating curve, 0.87). The XGBoost model had nearly perfect calibration in independent validation data; the logistic and ridge regression models slightly overestimated adherence likelihood. Historical treatment adherence, attendance rate, and fentanyl-positive urine at current appointment were the strongest contributors to treatment adherence at next attended appointment. DISCUSSION: There is a need for risk prediction tools to improve delivery of MOUD. This study presents an automated and portable risk-modeling framework to predict treatment adherence at each patient's next attended appointment. The XGBoost algorithm appears to provide similar classification accuracy to logistic regression models; however, XGBoost may offer improved calibration of risk estimates compared with logistic regression.

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; n = 306, person moments = 39,321) systematically vary and covary in outpatients receiving medication for opioid use disorder (MOUD). We used generalizability theory to quantify several aspects of reliability, and multilevel confirmatory factor analysis (MCFA) to detect factor structures within and across people. Generalizability analyses showed that the largest proportion of systematic variance across mood items was at the person level, followed by the person-by-day interaction and the (comparatively small) person-by-moment interaction for items reflecting low arousal. The best-fitting MCFA model had a three-factor structure both at the between- and within-person levels: positive mood, negative mood, and low-arousal states (with low arousal considered as either a separate factor or a subfactor of negative mood). We conclude that (a) mood varied more between days than between moments and (b) low arousal may be worth scoring and reporting separately from positive and negative mood states, at least in a MOUD population. Our three-factor structure differs from prior analyses of mood; more work is needed to understand the extent to which it generalizes to other populations. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

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. We used segmented regression models to assess the impact of PHOs on nonfatal-overdose trends in Washington DC and the five geographical regions of Maryland, using a historical control time series to adjust for normative changes in overdoses that occurred around mid-March (when the PHOs were issued). RESULTS: The mean level change in nonfatal opioid overdoses immediately after mid-March was not reliably different in the Covid-19 year versus the preceding control time series for any region. However, the rate of increase in nonfatal overdose was steeper after mid-March in the Covid-19 year versus the preceding year for Maryland as a whole (B = 2.36; 95% CI, 0.65 to 4.06; p = .007) and for certain subregions. No differences were observed for Washington DC. CONCLUSIONS: The pandemic and its accompanying PHOs were associated with steeper increases in nonfatal opioid overdoses in most but not all of the regions we assessed, with a net effect that was deleterious for the Maryland region as a whole.

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. Support Vector Machines (SVM) were trained to predict subtype membership from pretreatment data. MEASUREMENTS: Self-reported opioid craving (Ecological Momentary Assessment - EMA - three random moments per day). Participant-initiated EMA reports of drug use or higher-than-usual stress. Addiction Severity Index (ASI) pretreatment. FINDINGS: Four craving trajectories were identified: Low (73%); High and Increasing (HIC) (10.9%); Increasing and Decreasing (8.5%); and Rapidly Declining (7.6%). The HIC subgroup reported the highest use of heroin, any opiate, and cannabis during treatment. The Low Craving subgroup reported the lowest use of heroin or any opiate use, and the lowest levels of stress and drug-cue exposure during treatment. SVM models predicting HIC membership before treatment initiation had a sensitivity of 0.70, specificity of 0.78, and accuracy of 0.77. Including 3 weeks of EMA reports increased sensitivity to 0.78, specificity to 0.84, and accuracy to 0.85. CONCLUSIONS: Subgroups of MOUD patients show distinct patterns of opioid craving during treatment. Subgroups differ on critical outcomes including drug-use lapse, stress, and exposure to drug cues. Data from enrollment and early in treatment may help focus clinical attention.

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. METHODS: We used ecological momentary assessment (EMA) to collect ratings of stress and craving 3x/day for up to 16 weeks in 211 outpatients during agonist treatment for OUD. With growth mixture models, we identified trajectories of stress. We used mixed effect models to examine trajectory-group differences in the dynamics of stress and craving just before lapses to any drug use. RESULTS: We identified four trajectories of stress: Increasing (13.7 %); Moderate and Stable (23.7 %); Declining and Increasing (18 %); and Low (44.6 %). Overall drug use and opioid craving were lowest in the Low Stress group. Overall drug use was highest in the Moderate and Stable group. Alcohol use and opioid craving were highest in the Increasing Stress group. Opioid craving increased before lapse for most groups, but stress increased before lapses for only the Moderate and Stable group. CONCLUSION: There are natural groupings of participants with distinct patterns of stress severity during OUD treatment. Momentary stress/craving/lapse associations may be better characterized when these groupings are considered first.

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. RESULTS: Five basic patterns of use were identified: frequent opioid use, frequent cocaine use, frequent dual use (opioids and cocaine), sporadic use, and infrequent use. These patterns were differentially associated with medication (methadone vs. buprenorphine), race, age, drug-use history, drug-related problems prior to the study, stress-coping strategies, specific triggers of use events, and levels of cue exposure, craving, and negative mood. Craving tended to increase before use in all except those who used sporadically. Craving was sharply higher during the 90 min following moderate-to-severe stress in those with frequent use, but only moderately higher in those with infrequent or sporadic use. CONCLUSIONS: People who share similar patterns of drug-use during treatment also tend to share similarities with respect to psychological processes that surround instances of use, such as stress-induced craving. Cluster analysis combined with smartphone-based experience sampling provides an effective strategy for studying how drug use is related to personal and environmental factors.

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. OBJECTIVES: To evaluate such an approach, we analyzed patterns of opioid and cocaine use from three randomized clinical trials of contingency management in methadone-treated participants. METHODS: Sequences of drug test results were analyzed with unsupervised machine-learning techniques, including hierarchical clustering of categorical results (i.e., whether any samples were positive during each week) and K-means longitudinal clustering of quantitative results (i.e., the proportion positive each week). The sensitivity of cluster membership as an experimental outcome was assessed based on the effects of contingency management. External validation of clusters was based on drug craving and other symptoms of substance use disorder. RESULTS: In each clinical trial, we identified four clusters of use patterns, which can be described as opioid use, cocaine use, dual use (opioid and cocaine), and partial/complete abstinence. Different clustering techniques produced substantially similar classifications of individual participants, with strong above-chance agreement. Contingency management increased membership in clusters with lower levels of drug use and fewer symptoms of substance use disorder. CONCLUSIONS: Cluster analysis provides person-level output that is more interpretable and actionable than traditional outcome measures, providing a concrete answer to the question of what clinicians can tell patients about the success rates of new treatments.

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.e., within-class normality). These assumptions are rarely met in applied psychological and prevention research, and violating these assumptions when fitting a FMM can lead to the identification of spurious subgroups and/or biased parameter estimates. Although new methods have been developed that relax the within-class normality assumption when fitting a FMM, prevention scientists continue to rely on FMM methods that assume within-class normality. The purpose of the current article is to introduce prevention researchers to a FMM method for heavy-tailed data: FMM with Student t distributions. We begin by reviewing the distributional assumptions that underlie FMM and the limitations of FMM with normal distributions. Next, we introduce FMM with Student t distributions, and show, step by step, the analytic and substantive results of fitting a FMM with normal and Student t distributions to data from a smoking-cessation trial. Finally, we extend the results of the applied example to draw conclusions about the use of FMM with Student t distributions in applied settings and to provide guidelines for researchers who wish to use these methods in their own research.

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. We used only one form of continuous input (along with person-level demographic data), collected passively: an indicator of environmental exposures along the past 5 h of movement, as assessed by GPS. Our models achieved excellent overall accuracy-as high as 0.93 by the end of 16 weeks of tailoring-but this was driven mostly by correct predictions of absence. For predictions of presence, "believability" (positive predictive value, PPV) usually peaked in the high 0.70s toward the end of the 16 weeks. When the prediction target was more rare, PPV was lower. Our findings complement those of other investigators who use machine learning with more broadly based "digital phenotyping" inputs to predict or detect mental and behavioral events. When target events are comparatively subtle, like stress or drug craving, accurate detection or prediction probably needs effortful input from users, not passive monitoring alone. We discuss ways in which accuracy is difficult to achieve or even assess, and warn that high overall accuracy (including high specificity) can mask the abundance of false alarms that low PPV reveals.

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. Egocentric social network data were collected from 1571 smokers (58% female, 83% white) engaged in a 3-year smoking cessation clinical trial. Using nine indicator variables reflecting both risk and protective network features, finite mixture models identified five social network subgroups: High Stress/High Contact, Large and Supportive, Socially Disconnected, Risky Friends and Low Contact, and High Contact with Smokers and Light Drinkers. External variables supported the validity of the identified subgroups and the subgroups were meaningfully associated with baseline demographic, psychiatric, and tobacco measures. The Socially Disconnected subgroup was characterized by little social interaction, low levels of stress, and low exposure to social environmental smoking cues, and had the highest probability of successful cessation at 1 week compared with all other social network subgroups. At 6 months posttreatment its members had higher abstinence rates than members of the High Stress/High Contact subgroup and the Risky Friends and Low Contact subgroup. The present study highlights the heterogeneity of smokers' social milieus and suggests that network features, especially those entailing exposure to smoking cues and contexts, heighten risk for smoking cessation failure. (PsycINFO Database Record

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. Peer approval was associated with vaccine initiation (adjusted prevalence ratio (APR) 2.1; 95% confidence interval 1.4-3.2) and intention to vaccinate (APR 1.4; 1.1-1.9). Belief the vaccine is <75% effective was associated with less initiation (APR 0.6; 0.4-0.9) or intention to vaccinate (APR 0.5; 0.4-0.7). Vaccine initiation was also less likely among cigarette smokers and illegal drug users, whereas intention to vaccinate was more common among women currently attending school or with >5 lifetime sex partners, but less common among women perceiving low susceptibility to HPV (APR 0.6; 0.5-0.9). CONCLUSIONS: HPV vaccination uptake was low in this community sample of young adult women. Increasing awareness of susceptibility to HPV and the high efficacy of the vaccine, along with peer interventions to increase acceptability, may be most effective.