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ImportanceNovel treatments for opioid use disorder (OUD) are needed to address both the ongoing opioid epidemic and long-standing barriers to existing OUD treatments that target the endogenous μ-opioid receptor (MOR) system. The goal of this review is to highlight unique clinical trial design considerations for the study of emerging treatments for OUD that address targets beyond the MOR system. In November 2019, the Analgesic, Anesthetic, and Addiction Clinical Trial Translations, Innovations, Opportunities, and Networks (ACTTION) public-private partnership with the US Food and Drug Administration sponsored a meeting to discuss the current evidence regarding potential treatments for OUD, including cannabinoids, psychedelics, sedative-hypnotics, and immunotherapeutics, such as vaccines.ObservationsConsensus recommendations are presented regarding the most critical elements of trial design for the evaluation of novel OUD treatments, such as: (1) stage of treatment that will be targeted (eg, seeking treatment, early abstinence/detoxification, long-term recovery); (2) role of treatment (adjunctive with or independent of existing OUD treatments); (3) primary outcomes informed by patient preferences that assess opioid use (including changes in patterns of use), treatment retention, and/or global functioning and quality of life; and (4) adverse events, including the potential for opioid-related relapse or overdose, especially if the patient is not simultaneously taking maintenance MOR agonist or antagonist medications.Conclusions and RelevanceApplying the recommendations provided here as well as considering input from people with lived experience in the design phase will accelerate the development, translation, and uptake of effective and safe therapeutics for individuals struggling with OUD.

Anhedonia is usually defined as partial or total loss of the capacity for pleasure. People with anhedonia in the context of major depressive disorder may have an unexpected capacity for event-related mood brightening, observable when mood is assessed dynamically (with smartphone-based ecological momentary assessment [EMA]) rather than only statically via questionnaire. We used EMA to monitor mood and pleasant events for 4 weeks in 54 people being treated with opioid agonist medication for opioid-use disorder (OUD), which is also associated with anhedonia, said to manifest especially as loss of pleasure from nondrug reward. We compared OUD patients' EMA reports with those of 47 demographically similar controls. Background positive mood was lower in OUD patients than in controls, as we hypothesized (Cohen ds = .85 to 1.32, 95% CIs [.66, 1.55]), although, contrary to our hypothesis, background negative mood was also lower (ds = .82 to .85, 95% CIs [.73, .94]). As hypothesized, instances of nondrug pleasure were as frequent in OUD patients as in controls-and were not rated much less pleasurable (d = .18, 95% CI [-.03, .35]). Event-related mood brightening occurred in both abstinent and nonabstinent OUD patients (ds = .18 to .37, CIs [-.01, .57]) and controls (ds = .04 to .60, CIs [-.17, .79]), brightening before each event began earlier for controls than OUD patients, but faded similarly postevent across groups. Our findings add to the evidence that anhedonia does not rule out reactive mood brightening, which, for people with OUD being treated on opioid agonist medication, can be elicited by nondrug activities. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

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

Objective Evidence suggests that blunted reward responsiveness may account for poor clinical outcomes in both opioid use disorder (OUD) and chronic pain. Understanding how individuals with OUD and comorbid chronic pain (OUD+CP) respond to rewards is, therefore, of clinical interest because it may reveal a potential point of behavioral intervention. Methods Patients with OUD (n = 28) and OUD+CP (n = 19) on opioid agonist treatment were compared on: 1) the Probabilistic Reward Task (an objective behavioral measure of reward response bias) and 2) ecological momentary assessment of affective responses to pleasurable events. Results Both the OUD and the OUD+CP groups evidenced an increase in reward response bias in the Probabilistic Reward Task. The rate of change in response bias across blocks was statistically significant in the OUD group (B = 0.06, standard error [SE] = 0.02, t = 3.92, P Conclusions Overall, findings across objective and subjective measures were mixed, necessitating follow-up with a larger sample. The results suggest that although there is a reward response bias in patients with OUD+CP treated with opioid agonist treatment relative to patients with OUD without CP, it is modest and does not appear to translate into patients’ responses to rewarding events as they unfold in daily life.

AIM To assess the role of momentary pain on opioid craving and illicit opioid use among individuals receiving opioid agonist treatment. DESIGN Observational study using ecological momentary assessment. SETTING The National Institute of Drug Abuse Intramural Research Program in the United States. PARTICIPANTS Fifty-six adults who qualified for opioid agonist treatment. MEASUREMENTS Participants completed randomly prompted assessments of pain severity, stress, negative mood, opioid craving and illicit opioid use for a mean of 66 days [standard deviation (SD) = 27]. Urine samples were collected two to three times/week throughout. FINDINGS Almost 70% of participants reported moderate average pain severity in the past 24 hours at intake and 35% of participants reported chronic pain. There were no significant differences in percent of opioid-positive urine samples (P = 0.73) and average level of opioid craving during the study period (P = 0.91) among opioid agonist treatment only patients versus opioid agonist treatment patients with chronic pain. However, momentary pain severity significantly predicted concurrent opioid craving [B = 0.02, 95% confidence interval (CI) = 0.01, 0.04], over and above stress and negative mood. Momentary opioid craving, in turn, significantly predicted illicit opioid use that was assessed in the next moment [odds ratio (OR) = 1.72, 95% CI = 1.12, 2.64), while controlling for autocorrelation and the effects of pain, negative mood and stress. Momentary opioid craving significantly mediated the prospective association between momentary pain and illicit opioid use (95% CI = 0.003, 0.032). Exploratory analysis revealed that momentary pain severity also significantly moderated the momentary association between stress and opioid craving (B = 0.02, 95% CI = 0.00, 0.04), such that when momentary pain severity increased, the association between the two intensified. CONCLUSIONS Among people receiving opioid agonist treatment, momentary pain appears to be indirectly associated with illicit opioid use via momentary opioid craving.

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.

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.

Employment problems are common among people with substance use disorders (SUDs), and improving vocational functioning is an important aspect of SUD treatment. More detailed understanding of the psychosocial benefits of employment may help refine vocational interventions for people with SUDs. Here, we used ecological momentary assessment to measure possible affective improvements associated with work. Participants (n = 161) with opioid use disorder were randomized to work (job-skills training) in a contingency-management-based Therapeutic Workplace either immediately or after a waitlist delay. Throughout, participants responded via smartphone to randomly scheduled questionnaires. In linear mixed models comparing responses made at work vs. all other locations, being at work was associated with: less stress, less craving for opioids and cocaine, less negative mood, more positive mood, and more flow-like states. Some of these differences were also observed on workdays vs. non-workdays outside of work hours. These results indicate that benefits associated with work may not be restricted to being actually in the workplace; however, randomization did not reveal clear changes coinciding with the onset of work access. Overall, in contrast to work-associated negative moods measured by experience-sampling in the general population, Therapeutic Workplace participants experienced several types of affective improvements associated with work.

Affect and craving are dynamic processes that are clinically relevant in opioid use disorder (OUD) treatment, and can be quantified in terms of intra-individual variability and stability. The purpose of the present analysis was to explore associations between opioid use and variability and stability of affect and craving among individuals receiving medication treatment for OUD (MOUD). Adults (N = 224) with OUD in outpatient methadone or buprenorphine treatment completed ecological momentary assessment (EMA) prompts assessing positive affect, negative affect, opioid craving, and opioid use. Dynamic structural equation modeling (DSEM) was used to quantify person-level indices of magnitude and stability of change. Beta regression was used to examine associations between intra-individual variability and stability and proportion of opioid-use days, when controlling for overall intensity of affect and craving. Results suggested that greater magnitude of craving variability was associated with opioid use on a greater proportion of days, particularly among individuals with lower average craving. Low average positive affect was also associated with higher proportion of days of use. Individuals who experience substantial craving variability in the context of lower average craving may be particularly vulnerable to opioid use during treatment. Ongoing assessment of craving may be useful in identifying treatment needs. Examining correlates of intra-individual variability and stability in MOUD treatment remains a relevant direction for future work.

Allostatic load, an operationalization for cumulative strain on physiology from adaptation (allostasis) to stress over a lifetime, can manifest as damage to cardiovascular, neuroendocrine, and metabolic systems. The concept of allostatic load may be particularly useful in research on substance-use disorders (SUDs) because SUD researchers have sought to better understand the relationship between chronic stressors and drug use. Theoretical models hold that SUDs can be conceptualized as a spiral toward a state of persistent allostasis (i.e., allostasis so persistent as to represent homeostasis at a new, unhealthy set point). Regardless of the extent to which those models are accurate, increased allostatic load could be a mechanism by which frequent drug administration increases risk for adverse outcomes. We conducted two secondary analyses to evaluate allostatic load in the context of drug use, including alcohol use, in a locally recruited sample with a high proportion of illicit substance use (N = 752) and in a nationally representative sample from the NHANES 2009–2016. We hypothesized that after controlling for age and other potential confounds, people with longer histories of drug use would have higher allostatic-load scores. Multiple regression was used to predict allostatic load from participants' drug-use histories while controlling for known confounds. In the locally recruited sample, we found that longer lifetime use of cocaine or opioids was related to increased allostatic load. In NHANES 2009–2016, we found few or no such associations. Lengthy histories of problematic non-medical substance use may facilitate more rapid increases in allostatic load than aging alone, and, together with findings from previous investigations, this finding suggests increased risk for chronic disease.

Sleep problems are commonly reported during opioid agonist treatment (OAT) for opioid use disorders. Inpatient studies have found both sleep disturbances and improved sleep during OAT. Illicit opioids can also disrupt sleep, but it is unclear how they affect sleep in outpatients receiving OAT. Therefore, we used electronic diary entries and actigraphy to measure sleep duration and timing in opioid-dependent participants (n = 37) treated with methadone (n = 15) or buprenorphine (n = 22). For 16 weeks, participants were assigned to attend our clinic under different operating hours in a crossover design: Early hours (07:00–09:00) vs. Late hours (12:00–13:00) for 4 weeks each in randomized order, followed for all participants by our Standard clinic hours (07:00–11:30) for 8 weeks. Throughout, participants made daily electronic diary self-reports of their sleep upon waking; they also wore a wrist actigraph for 6 nights in each of the three clinic-hour conditions. Drug use was assessed by thrice-weekly urinalysis. In linear mixed models controlling for other sleep-relevant factors, sleep duration and timing differed by drug use and by clinic hours. Compared to when non-using, participants slept less, went to bed later, and woke later when using illicit opioids and/or both illicit opioids and cocaine. Participants slept less and woke earlier when assigned to the Early hours. These findings highlight the role OAT clinic schedules can play in structuring the sleep/wake cycles of OAT patients and clarify some of the circumstances under which OAT patients experience sleep disruption in daily life.

Background Treatment with opioid agonists is effective for opioid use disorder, but early discontinuation of treatment is a major obstacle to success. Intensive longitudinal methods — which take many repeated measurements over time, usually in the field— have provided unique insight into the effects of stress, mood and craving on drug use while people are being treated; these methods might also be useful for studying the processes that lead people to drop out of treatment. Methods Ecological momentary assessment (EMA) was conducted for up to 17 weeks by obtaining multiple electronic diary entries per day from 238 participants being treated with methadone or buprenorphine-naloxone. Survival analysis was used to study two outcomes: dropping out of treatment and noncompliance with EMA self-report requirements. Self-reports of stress, craving, and mood were used as time-varying predictors. Demographic and psychosocial variables measured with the Addiction Severity Index at the start of treatment were used as time-invariant predictors. Results Dropping out of treatment was more likely in participants with more reported hassles (a measure of stress), higher levels of cocaine craving, lower levels of positive mood, a recent history of emotional abuse, a recent history of being bothered frequently by psychological problems, and with buprenorphine rather than methadone as their medication. In contrast, study noncompliance was not significantly associated with any of the variables analyzed. Conclusions Assessment of stress, craving and mood during treatment might identify people who are at greater risk of dropping out, and therapeutic interventions targeting these processes might increase retention.

As digital technology increasingly informs clinical trials, novel ways to collect study data in the natural field setting have the potential to enhance the richness of research data. Cocaine use in clinical trials is usually collected via self-report and/or urine drug screen results, both of which have limitations. This article examines the feasibility of developing a wrist-worn device that can detect sufficient physiological data (i.e., heart rate and heart rate variability) to detect cocaine use. This study aimed to develop a wrist-worn device that can be used in the natural field setting among people who use cocaine to collect reliable data (determined by data yield, device wearability, and data quality) that is less obtrusive than chest-based devices used in prior research. The study also aimed to further develop a cocaine use detection algorithm used in previous research with an electrocardiogram on a chestband by adapting it to a photoplethysmography sensor on the wrist-worn device which is more prone to motion artifacts. Results indicate that wrist-based heart rate data collection is feasible and can provide higher data yield than chest-based sensors, as wrist-based devices were also more comfortable and affected participants' daily lives less often than chest-based sensors. When properly worn, wrist-based sensors produced similar quality of heart rate and heart rate variability features to chest-based sensors and matched their performance in automated detection of cocaine use events.Clinical Trial Registration:www.ClinicalTrials.gov, identifier: NCT02915341.

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.

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.

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 minutes 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.

Adherence monitoring is a vital component of clinical efficacy trials, as the regularity of medication consumption affects both efficacy and adverse effect profiles. Pill-counts do not confirm consumption, and invasive plasma assessments can only assist post hoc assessments. We previously reported on the pharmacokinetics of a potential adherence marker to noninvasively monitor dosage consumption during a trial without breaking a blind. We reported that consumption cessation of subtherapeutic 15 mg acetazolamide (ACZ) doses showed a predictable urinary excretion decay that was quantifiable for an extended period. The current study describes the clinical implementation of 15 mg ACZ doses as an adherence marker excipient in distinct cohorts taking ACZ for different "adherence" durations. We confirm that ACZ output did not change (accumulate) during 18-20 days of adherence, and developed and assessed urinary cutoffs as nonadherence indicators. We demonstrate that whereas an absolute concentration cutoff (989 ng/mL) lacked sensitivity, a creatinine normalized equivalent (1,376 ng/mg ACZ) was highly accurate at detecting nonadherence. We also demonstrate that during nonadherent phases of three trials, creatinine-normalized urinary ACZ elimination was reproducible within and across trials with low variability. Excretion was first order, with a decay half-life averaging ~ 2.0 days. Further, excretion remained quantifiable for 14 days, providing a long period during which the date of last consumption might be determined. We conclude that inclusion of 15 mg ACZ as a dosage form adherence marker excipient, provides a reliable and sensitive mechanism to confirm medication consumption and detect nonadherence during clinical efficacy trials.

BACKGROUND: Preliminary evidence suggested that the PPARγ agonist pioglitazone reduces opioid-withdrawal symptoms, possibly by inhibiting increases in proinflammatory cytokines. METHODS: A randomized, placebo-controlled clinical trial was conducted utilizing two different study designs (entirely outpatient, and a combination of inpatient and outpatient) to evaluate the safety and efficacy of pioglitazone as an adjunct medication for people with opioid physical dependence undergoing a buprenorphine taper. Participants were stabilized on buprenorphine/naloxone (sublingual, up to 16/4 mg/day), then randomized to receive oral pioglitazone (up to 45 mg/day) or placebo before, during, and after buprenorphine taper. Outcome measures included the Subjective Opiate Withdrawal Scale (SOWS) and Clinical Opiate Withdrawal Scale, use of rescue medications to alleviate opioid withdrawal symptoms, and opioid-positive urine specimens. Cerebrospinal fluid (CSF) and plasma were collected during the taper in a subset of participants for measurement of proinflammatory cytokines. RESULTS: The clinical trial was prematurely terminated due to slow enrollment; 40 participants per group were required for adequate statistical power to test study hypotheses. Twenty-four participants enrolled; 17 received at least one dose of study medication (6 pioglitazone, 11 placebo). SOWS scores were higher in the pioglitazone arm than in the placebo arm after adjusting for use of rescue medications; participants in the pioglitazone arm needed more rescue medications than the placebo arm during the post-taper phase. SOWS scores were positively correlated with monocyte chemoattractant protein-1 (MCP-1) in CSF (r = 0.70, p = 0.038) and plasma (r = 0.77, p = 0.015). Participants having higher levels of plasma MCP-1 reported higher SOWS, most notably after the buprenorphine taper ended. CONCLUSIONS: Results from this study provide no evidence that pioglitazone reduces opioid withdrawal symptoms during buprenorphine taper. High correlations between MCP-1 and opioid withdrawal symptoms support a role of proinflammatory processes in opioid withdrawal. TRIAL REGISTRATION: clinicaltrials.gov identifier: NCT01517165

Whereas substance-use researchers have long combined self-report with objective measures of behavior and physiology inside the laboratory, developments in mobile/wearable electronic technology are increasingly allowing for the collection of both subjective and objective information in participants’ daily lives. For self-report, ecological momentary assessment (EMA), as implemented on contemporary smartphones or personal digital assistants, can provide researchers with near-real-time information on participants’ behavior and mood in their natural environments. Data from portable/wearable electronic sensors measuring participants’ internal and external environments can be combined with EMA (e.g., by timestamps recorded on questionnaires) to provide objective information useful in determining the momentary context of behavior and mood and/or validating participants’ self-reports. Here, we review three objective ambulatory monitoring techniques that have have been combined with EMA, with a focus on detecting drug use and/or measuring the behavioral or physiological correlates of mental events (i.e., emotions, cognitions): (1) collection and processing of biological samples in the field to measure drug use or participants’ physiological activity (e.g, hypothalamic-pituitary-adrenal axis activity); (2) global positioning system (GPS) location information to link environmental characteristics (disorder/disadvantage, retail drug outlets) to drug use and affect; (3) ambulatory electronic physiological monitoring (e.g., electrocardiography) to detect drug use and mental events, as advances in machine learning algorithms make it possible to distinguish target changes from confounds (e.g., physical activity). Finally, we consider several other mobile/wearable technologies that hold promise to be combined with EMA, as well as potential challenges faced by researchers working with multiple mobile/wearable technologies simultaneously in the field.

This study examined how contextual areas defined and operationalized differently may lead to different exposure estimates. Substance users’ exposures to environmental stress (in terms of two variab...

BACKGROUND: In a recent clinical trial (NCT00295308), we demonstrated that clonidine decreased the association between opioid craving and moderate levels of stress and affect in patients receiving buprenorphine-based opioid agonist therapy. OBJECTIVES: To examine the relationship between illicit opioid use and craving and affect during the evaluation of clonidine as an adjunct medication in buprenorphine treatment for opioid use disorder. Secondarily, to examine whether those relationships are driven by within- or between-participant factors. METHODS: This was a secondary data analysis from our original trial. Participants (N = 108, female: n = 23, male n =85) receiving buprenorphine were randomized to receive adjunct clonidine or placebo. Participants used portable electronic devices to rate stress, mood, and craving via ecological momentary assessment (EMA) four times randomly each day. To associate the EMA data with illicit opioid use, each EMA report was linked to participants’ next urine drug screen (thrice weekly). We used generalized linear mixed models to examine the interaction between treatment group and illicit opioid use, as well as to decompose the analysis into within- and between-participant effects. RESULTS: Craving for opioids and cocaine was increased when participants were using illicit opioids; this effect was greater in the clonidine group. For affect, mood was poorer during periods preceding opioid-positive urines than opioid-negative urines for clonidine-treated participants, whereas there was no difference for placebo participants. CONCLUSION. This secondary analysis provides evidence that illicit opioid use was linked to stronger negative affect and craving in participants maintained on clonidine in opioid agonist therapy.

In addiction, risk factors for craving and use include stress and drug-related cues. Stress and cues have additive or more-than-additive effects on drug seeking in laboratory animals, but, surprisingly, seem to compete with one another (ie, exert less-than-additive effects) in human laboratory studies of craving. We sought heretofore elusive evidence that human drug users could show additive (or more-than-additive) effects of stress and cues on craving, using ecological momentary assessment (EMA). Outpatients (N=182) maintained on daily buprenorphine or methadone provided self-reports of stress, craving, mood, and behavior on electronic diaries for up to 16 weeks. In three randomly prompted entries (RPs) per day, participants reported the severity of stress and craving and whether they had seen or been offered opioids, cocaine, cannabis, methamphetamine, alcohol, or tobacco. In random-effects models controlling for between-person differences, we tested effects of momentary drug-cue exposure and stress (and their interaction) on momentary ratings of cocaine and heroin craving. For cocaine craving, the Stress × Cue interaction term had a positive mean effect across participants (M=0.019; CL95 0.001-0.036), denoting a more-than-additive effect. For heroin, the mean was not significantly greater than 0, but the confidence interval was predominantly positive (M=0.019; CL95 -0.007-0.044), suggesting at least an additive effect. Heterogeneity was substantial; qualitatively, the Stress × Cue effect appeared additive for most participants, more than additive for a sizeable minority, and competitive in very few. In the field, unlike in human laboratory studies to date, craving for cocaine and heroin is greater with the combination of drug cues and stress than with either alone. For a substantial minority of users, the combined effect may be more than additive.

OBJECTIVE In a clinical trial examining daily clonidine as an adjunct to buprenorphine treatment for opioid dependence, we found that clonidine increased opioid abstinence and decoupled stress from craving. From a personalized-medicine perspective, the next step is to identify people for whom clonidine would be beneficial. To that end, using data from the same clinical trial, we examined the associations of daily-life activities with treatment success. METHODS Outpatients (N = 118) received clonidine (0.3 mg/d) or placebo during 18 weeks of buprenorphine treatment. Participants carried a smartphone that randomly prompted them 4 times per day to report their moods and activities. Using generalized linear mixed models, we assessed the likelihoods of different types of daily activity as a function of clonidine versus placebo, days of longest continuous opioid abstinence, and their interaction. RESULTS Participants in the buprenorphine-only (buprenorphine plus placebo) control group who engaged in more responsibilities (work and child/elder care) had longer streaks of abstinence, whereas those who engaged in more unstructured-time activities had shorter streaks of abstinence. Conversely, for participants in the buprenorphine-plus-clonidine group, longer streaks of abstinence were associated with higher frequencies of activities associated with "unstructured" time. CONCLUSIONS The study replicates findings that engaging in responsibilities is related to positive treatment outcomes in standard opioid agonist therapy. The pattern of results also suggests that clonidine helped participants engage in unstructured-time activities with less risk of craving or use than they might otherwise have had.

Knowing how stress manifests in the lives of people with substance-use disorders could help inform mobile “just in time” treatment. The purpose of this paper is to examine discrete episodes of stress, as distinct from the fluctuations in background stress assessed in most EMA studies. For up to 16 weeks, outpatients on opioid-agonist treatment carried smartphones on which they initiated an entry whenever they experienced a stressful event (SE) and when randomly prompted (RP) three times daily. Participants reported the severity of stress and craving and the context of the report (location, activities, companions). Decomposition of covariance was used to separate within-person from between-person effects; r effect sizes below are within-person. Participants (158 of 182; 87%) made 1787 stress-event entries. Craving for opioids increased with stress severity (r effect = 0.50). Stress events tended to occur in social company (with acquaintances, 0.63, friends, 0.17, or on the phone, 0.41) rather than with family (spouse, −0.14; child, −0.18), and in places with more overall activity (bars, 0.32; outside, 0.28; walking, 0.28) and more likelihood of unexpected experiences (with strangers, 0.17). Being on the internet was slightly protective (−0.22). Our prior finding that being at the workplace protects against background stress in our participants was partly supported in these stressful-event data. The contexts of specific stressful events differ from those we have seen in prior studies of ongoing background stress. However, both are associated with drug craving.

BACKGROUND: Treatment with methadone is effective in reducing heroin use, HIV risk, and death; however, not all patients respond to treatment. Better outcomes may emerge with personalized treatment based on factors that influence treatment courses. OBJECTIVES: to investigate psychosocial variables contributing to treatment response, using a comprehensive definition of treatment response. METHODS: Seventy participants seeking treatment for heroin and cocaine addiction completed up to 40 weeks of daily methadone. At week 22, we administered a semi-structured interview for DSM-IV symptoms. We defined opioid treatment responders as people still enrolled at 22 weeks, not meeting past-30-day criteria for DSM-IV opioid abuse or dependence or DSM-5 opioid-use disorder, and providing ≥ 75% opioid-negative urine samples in the 30 days prior to week 22. The same criteria were applied to assess cocaine treatment response. RESULTS: Sample was 71% male, 41% White, and averaged 39.4 ± 7.9 years old. Opioid treatment response was more likely in participants who had been employed over the past 3 years (OR 8.1, 95% CI 1.2–55) and less likely in those who spent more time on hobbies (OR 0.45, 95% CI 0.23–0.88). Cocaine treatment response was more likely in participants who had a good relationship with their father (OR 5.3, 95% CI 1.2–24) and less likely if positive for hepatitis C (OR 0.15, 95% CI 0.03–0.75). CONCLUSIONS: Pretreatment characteristics differentially predict treatment response for heroin and cocaine use. Similar research in diverse patient groups may aid in the development of personalized treatment combining biologic treatment with targeted psychosocial interventions.

Background Individual trajectories of drug use and drug-related problems are highly heterogeneous. There is no standard taxonomy of these trajectories, but one could be developed by defining natural categories based on changes in symptoms of substance-use disorders over time. Methods Our study was conducted in a community sample in Baltimore, Maryland. At baseline, all participants were using opioids and/or cocaine, but none were in treatment. Drug use and symptomatology were assessed again at 12 months (N = 115). Results We defined Quitters as participants who had not used for at least 30 days at follow-up (17%). For the remaining participants, we performed longitudinal cluster analysis on DSM symptom-counts, identifying three trajectory clusters: newly or persistently Symptomatic (40%) participants, Chippers (21.5%) with few symptoms, and Converted Chippers (21.5%) with improved symptom counts. Logistic regression showed that profiles of Quitters did not resemble Chippers, but instead resembled Symptomatic participants, having high probability of disorderly home neighborhood, nonwhite race, and negative mood. Quitters tended to have two protective factors: initiating opioid-agonist treatment during the study (reffect = 0.25, CL95 0.02–0.48) and lack of polydrug use (reffect = 0.25, CL95 0.004–0.49). Converted Chippers tended to be white, with orderly home neighborhoods and less negative mood (reffects 0.24 to 0.31, CL95 0.01–0.54). Conclusions Changes in DSM symptomology provided a meaningful measure of individual trajectories. Quitters shared psychosocial characteristics with Symptomatic participants, but not with participants who continued to use with few symptoms. This suggests that Quitters abstained out of necessity, not because their problems were less severe.

In rodents, antagonism of receptors for corticotropin-releasing factor (CRF) blocks stress-induced reinstatement of drug or palatable food seeking. To test anticraving properties of the CRF1 antagonist pexacerfont in humans. We studied stress-induced eating in people scoring high on dietary restraint (food preoccupation and chronic unsuccessful dieting) with body-mass index (BMI) >22. In a double-blind, between-groups trial, 31 “restrained” eaters were stabilized on either pexacerfont (300 mg/day for 7 days, then 100 mg/day for 21 days) or placebo. On day 15, they underwent a math-test stressor; during three subsequent visits, they heard personalized craving-induction scripts. In each session, stress-induced food consumption and craving were assessed in a bogus taste test and on visual analog scales. We used digital video to monitor daily ingestion of study capsules and nightly rating of food problems/preoccupation on the Yale Food Addiction Scale (YFAS). The study was stopped early due to an administrative interpretation of US federal law, unrelated to safety or outcome. The bogus taste tests suggested some protective effect of pexacerfont against eating after a laboratory stressor (r effect = 0.30, 95 % CL = −0.12, 0.63, Bayes factor 11.30). Similarly, nightly YFAS ratings were lower with pexacerfont than placebo (r effect = 0.39, CI 0.03, 0.66), but this effect should be interpreted with caution because it was present from the first night of pill ingestion, despite pexacerfont’s slow pharmacokinetics. The findings may support further investigation of the anticraving properties of CRF1 antagonists, especially for food.

Background Sleep disturbance is common in patients with opioid use disorder (OUD) receiving medication for addiction treatment. Differences between patients on the two primary agonist medications—methadone and buprenorphine—are not well understood. Methods In patients receiving either methadone or buprenorphine treatment for OUD, we examined sleep continuity and architecture using ambulatory monitoring to gather both an objective measure (daily sleep EEG; M = 5.76 days, SD = 1.46) and a subjective measure (daily sleep diary; M = 54.10 days, SD = 25.10) of sleep. Results Patients treated with buprenorphine versus methadone did not differ on any measure of sleep continuity or architecture. Women had longer EEG-derived total sleep time than men (d = -0.68, 95 % CI -1.32 to -0.09), along with lower %N2 (d = 0.94, 95 % CI 0.34–1.64) and greater %N3 (d = -0.94, 95 % CI -1.61 to -0.32). Self-reported sleep differed from EEG-derived estimates: wake after sleep onset was greater by EEG than by diary (d = 2.58, 95 % CI 1.74–3.63), and total sleep time and sleep efficiency were lower by EEG than by diary (d for sleep time = 2.93, 95 % CI 2.06–4.14; d for efficiency = 1.69, 95 % CI 0.98–2.49). Conclusions Patients treated with buprenorphine or methadone did not substantively differ in ambulatory measures of sleep. With both medications, there was a discrepancy between objective and subjective sleep measures. Further confirmatory evidence would inform the development of sleep-related recommendations for OUD patients undergoing agonist treatment.

Background and aims Stress can be validly assessed “live” or by a summary evaluation of the very recent past. Using smartphone-based ecological momentary assessment (EMA) combined with end-of-day (EOD) entries, we assessed the association between daily hassles, stressful events and use of opioids and cocaine, in opioid- and cocaine-using men and women. Methods For up to 16 weeks, 161 outpatients in opioid-agonist treatment who reported cigarette smoking carried smartphones on which they reported stressful events (SEs) and drug use (DU) and completed an EOD questionnaire to report hassles encountered throughout the day, current perceived stress, cigarettes/day, and current mood. We compared EOD responses on days with and without SE and DU reports and on days when thrice-weekly urine drug screens indicated opioid or cocaine use or abstinence. Results Participants (N = 161) made 11,544 EOD entries; EMA SEs were reported on 861 (7.5%) days, and DUs on 1685 (14.6%) days. The most frequently reported hassles in EOD entries were “not enough money” (31.4% of daily reports) and maintaining abstinence (18.7%). Total EOD hassles showed small but statistically significant associations [odds ratios (95% CIs)] with EMA SEs [1.09 (1.06–1.13)], DUs [1.08 (1.06–1.10)], and urine-positive opioid [1.06 (1.04–1.09)] and cocaine [1.03 (1.00–1.06)] results. Men and women had similar rates (mean/day (SD)) of hassles: men 2.25 (3.55); women 2.55 (3.76) (F1,159 = 0.53, p = 0.47). Conclusions Daily hassles, reported at the end of the day, are associated with both same-day stressful events and drug use. Monitoring hassles and devising specific coping strategies might be useful therapeutic targets.

Responses to stress and drug craving differ between men and women. Differences in the momentary experience of stress in relation to craving are less well-understood.Using ecological momentary assessment (EMA), we examined sex differences in real-time in two areas: (1) causes and contexts associated with stress, and (2) the extent to which stress and drug cues are associated with craving.Outpatients on opioid-agonist treatment (135 males, 47 females) reported stress, craving, and behavior on smartphones for 16 weeks. They initiated an entry each time they felt more stressed than usual (stress event) and made randomly prompted entries 3 times/day. In stress-event entries, they identified the causes and context (location, activity, companions), and rated stress and craving severity.The causes reported for stress events did not differ significantly by sex. Women reported arguing and being in a store more often during stress events, and men reported working more often during stress events, compared to base rates (assessed via random prompts). Women showed a greater increase in opioid craving as a function of stress (p 0.0001) and had higher stress ratings in the presence of both stress and drug cues relative to men (p 0.01). Similar effects were found for cocaine craving in men (p 0.0001).EMA methods provide evidence based on real-time activities and moods that opioid-dependent men and women experience similar contexts and causes for stress but differ in stress- and cue-induced craving. These findings support sex-based tailoring of treatment, but because not all participants conformed to the overall pattern of sex differences, any such tailoring should also consider person-level differences.

Many patients with opioid use disorder do not have successful outcomes during treatment but the underlying reasons are not well understood. An OPRD1 variant (rs678849) was previously associated with methadone and buprenorphine efficacy in African–Americans with opioid use disorder. The objective of this study was to determine if the effect of rs678849 on opioid use disorder treatment outcome could be replicated in an independent population. Participants were recruited from African–American patients who had participated in previous studies of methadone or buprenorphine treatment at the outpatient treatment research clinic of the NIDA Intramural Research Program in Baltimore, MD, USA between 2000 and 2017. Rs678849 was genotyped retrospectively, and genotypes were compared with urine drug screen results from the previous studies for opioids other than the one prescribed for treatment. Genotypes were available for 24 methadone patients and 55 buprenorphine patients. After controlling for demographics, the effect of rs678849 genotype was significant in the buprenorphine treatment group (RR = 1.69, 95% confidence interval (CI) 1.59–1.79, p = 0.021). Buprenorphine patients with the C/C genotype were more likely to have opioid-positive drug screens than individuals with the C/T or T/T genotypes, replicating the original pharmacogenetic finding. The effect of genotype was not significant in the methadone group (p = 0.087). Thus, the genotype at rs678849 is associated with buprenorphine efficacy in African–Americans being treated for opioid use disorder. This replication suggests that rs678849 genotype may be a valuable pharmacogenetic marker for deciding which opioid use disorder medication to prescribe in this population.

RATIONALE: Ecological momentary assessment (EMA) of specific events usually focuses more on antecedents and concomitants than on aftermaths. OBJECTIVES: To examine mental state both before and after discrete episodes of stress and drug use. METHODS: For up to 16 weeks, outpatients on opioid-agonist treatment carried smartphones on which they initiated entries for stressful events (SEs) or lapses to drug use (DUs), and thrice daily when randomly prompted (RPs). Participants rated their stress, opioid craving, cocaine craving, and moods. RP entries within 5 hours of an event were analyzed and compared to other RPs. RESULTS: Stress, negative mood, and craving were generally higher before and after DUs and SEs compared to background levels in participants with at least one DU (n=149) or SE (n=158). Before DUs, there were increases in negative mood, opioid craving, and cocaine craving, but not background stress. Before SEs, there were increases in background stress, opioid craving, and cocaine craving, but not negative mood. These changes were more variable after events than before. Neither DUs nor SEs were significantly related to positive mood. CONCLUSIONS: Stress increased before stressful-event entries, but was less evident before drug use. Craving increased in the hours before drug use and stressful events—and remained elevated in the hours after either event. These results suggest a stronger link between drug use and craving than between drug use and stress. Lapses to drug use did not improve mood or reduce stress, at least not at our one-hour-bin time resolution, suggesting that if such benefits exist, they are brief.