Health Outcomes by Neighborhood (HON): Effects of Neighborhood, Social Instability, and Health Factors on 12-Month Trajectories of Substance-Use Disorder Symptoms.

BACKGROUND: Previous studies have shown that environment and health can influence drug use trajectories and the effects of substance use disorder (SUD) treatments. We hypothesized that trajectories of drug use-related problems, based on changes in DSM-5 symptoms, would vary by type(s) of drugs used, health factors, and neighborhood characteristics. METHODS: We assessed mental and physical health, stress, social instability, neighborhood characteristics (disorderliness and home value), and DSM-5 symptom counts at two study visits, 12 months apart, in a community sample (baseline N = 735) in Baltimore, MD. Three prominent categories of drug-use trajectory were identified with K-means cluster analysis of symptom counts: Persistent (4 or more symptoms at both visits or at Visit 2), Improved (decrease from 4 or more symptoms at Visit 1 to 3 or fewer symptoms at Visit 2), and Low-Stable (3 or fewer symptoms at both visits). Baseline health and neighborhood measures were tested as predictors of trajectory in mediation and moderation models. RESULTS: Among people with current opioid- and/or stimulant-use, odds of an Improved trajectory were (1) decreased with neighborhood disorder and social instability, or (2) increased with home value and social instability. Odds of a Low-Stable trajectory were decreased by social instability and stress but increased in those who were older or self-identified as white. CONCLUSIONS: Trajectories of drug use-related problems are influenced by sociodemographic variables, neighborhood factors, and health. Assessing DSM-5 symptom counts as an outcome measure may be valuable in monitoring or predicting long-term trajectories and treatment effectiveness.

Clinical Trial Design Challenges and Opportunities for Emerging Treatments for Opioid Use Disorder: A Review.

Importance: Novel 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. Observations: Consensus 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 Relevance: Applying 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.

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).

Intra-individual variability and stability of affect and craving among individuals receiving medication treatment for opioid use disorder.

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.

Being at work improves stress, craving, and mood for people with opioid use disorder: Ecological momentary assessment during a randomized trial of experimental employment in a contingency-management-based therapeutic workplace.

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.

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.

I feel good? Anhedonia might not mean "without pleasure" for people treated for opioid use disorder.

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).

Exploring the Relationship Between Substance Use and Allostatic Load in a Treatment/Research Cohort and in a US Probability Sample (NHANES 2009-2016).

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.

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.

An Examination of the Feasibility of Detecting Cocaine Use Using Smartwatches.

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.

Reward Responsiveness in Patients with Opioid Use Disorder on Opioid Agonist Treatment: Role of Comorbid Chronic Pain.

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 < 0.001, 95% confidence interval [CI]: 0.03 to 0.09) but not in the OUD+CP group (B = 0.03, SE = 0.02, t = 1.90, P = 0.07, 95% CI: -0.002 to 0.07). However, groups did not significantly differ in the rate of change in response bias across blocks (B = 0.03, SE = 0.02, t = 1.21, P = 0.23, 95% CI: -0.02 to 0.07). Groups did not significantly differ on state measures of reward responsiveness (P's ≥0.50). 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.

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.

Quality of life during a randomized trial of a therapeutic-workplace intervention for opioid use disorder: Web-based mobile assessments reveal effects of drug abstinence and access to paid work.

Background: Employment and improved quality of life (QOL) are, separately, valued outcomes of substance use disorder (SUD) treatment. It is also important to understand QOL changes caused by employment itself; therefore, we assessed QOL during a randomized trial of a contingency-management-based Therapeutic Workplace for people with opioid use disorder. Methods: For 12 weeks, participants (n = 61) responded to QOL questionnaires in a mobile web app accessed with study-issued smartphones. At enrollment, participants were randomized to work in the Therapeutic Workplace immediately (immediate work group, IWG) or after a 3-week waitlist delay (delayed work group, DWG). Once both groups could work, wage-resetting contingencies were introduced for their opiate- and cocaine-urinalysis. Data were analyzed by (1) access to work with and without contingencies and (2) overall urinalysis-verified opiate- and cocaine-abstinence. Results: DWG and/or IWG reported improvements in several QOL areas (sleep, transportation, recreation); however, they also reported increased money-related difficulties and less time spent with friends/family. These changes did not coincide with DWG's work access, but some (more sleep, money-related difficulties) coincided with the urinalysis contingencies. Greater opiate- and/or cocaine-abstinence was also associated with several improvements: sleep, paying bills, time spent with friends/family, and exercising. Surprisingly, intermediate cocaine abstinence was associated with reductions in work-capacity satisfaction and recreation. Conclusions: Participants reported complex QOL differences during their experimental employment and associated with drug abstinence. Future work should help participants address issues that may be relevant to employment generally (e.g., time with friends/family) or contingency management specifically (e.g., money-related issues for non-abstinent participants).

Craving mediates the association between momentary pain and illicit opioid use during treatment for opioid-use disorder: an ecological momentary assessment study.

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.

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.

Subtherapeutic Acetazolamide Doses as a Noninvasive Method for Assessing Medication Adherence.

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.

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.

Multimodal assessment of sleep in men and women during treatment for opioid use disorder.

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.