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.

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.

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.

Using mobile location data in biomedical research while preserving privacy.

Location data are becoming easier to obtain and are now bundled with other metadata in a variety of biomedical research applications. At the same time, the level of sophistication required to protect patient privacy is also increasing. In this article, we provide guidance for institutional review boards (IRBs) to make informed decisions about privacy protections in protocols involving location data. We provide an overview of some of the major categories of technical algorithms and medical-legal tools at the disposal of investigators, as well as the shortcomings of each. Although there is no "one size fits all" approach to privacy protection, this article attempts to describe a set of practical considerations that can be used by investigators, journal editors, and IRBs.

Finding Significant Stress Episodes in a Discontinuous Time Series of Rapidly Varying Mobile Sensor Data.

Management of daily stress can be greatly improved by delivering sensor-triggered just-in-time interventions (JITIs) on mobile devices. The success of such JITIs critically depends on being able to mine the time series of noisy sensor data to find the most opportune moments. In this paper, we propose a time series pattern mining method to detect significant stress episodes in a time series of discontinuous and rapidly varying stress data. We apply our model to 4 weeks of physiological, GPS, and activity data collected from 38 users in their natural environment to discover patterns of stress in real-life. We find that the duration of a prior stress episode predicts the duration of the next stress episode and stress in mornings and evenings is lower than during the day. We then analyze the relationship between stress and objectively rated disorder in the surrounding neighborhood and develop a model to predict stressful episodes.

Continuous in-the-field measurement of heart rate: Correlates of drug use, craving, stress, and mood in polydrug users.

BACKGROUND: Ambulatory physiological monitoring could clarify antecedents and consequences of drug use and could contribute to a sensor-triggered mobile intervention that automatically detects behaviorally risky situations. Our goal was to show that such monitoring is feasible and can produce meaningful data. METHODS: We assessed heart rate (HR) with AutoSense, a suite of biosensors that wirelessly transmits data to a smartphone, for up to 4 weeks in 40 polydrug users in opioid-agonist maintenance as they went about their daily lives. Participants also self-reported drug use, mood, and activities on electronic diaries. We compared HR with self-report using multilevel modeling (SAS Proc Mixed). RESULTS: Compliance with AutoSense was good; the data yield from the wireless electrocardiographs was 85.7%. HR was higher when participants reported cocaine use than when they reported heroin use (F(2,9)=250.3, p<.0001) and was also higher as a function of the dose of cocaine reported (F(1,8)=207.7, p<.0001). HR was higher when participants reported craving heroin (F(1,16)=230.9, p<.0001) or cocaine (F(1,14)=157.2, p<.0001) than when they reported of not craving. HR was lower (p<.05) in randomly prompted entries in which participants reported feeling relaxed, feeling happy, or watching TV, and was higher when they reported feeling stressed, being hassled, or walking. CONCLUSIONS: High-yield, high-quality heart-rate data can be obtained from drug users in their natural environment as they go about their daily lives, and the resultant data robustly reflect episodes of cocaine and heroin use and other mental and behavioral events of interest.

Real-time tracking of neighborhood surroundings and mood in urban drug misusers: application of a new method to study behavior in its geographical context.

BACKGROUND: Maladaptive behaviors may be more fully understood and efficiently prevented by ambulatory tools that assess people's ongoing experience in the context of their environment. METHODS: To demonstrate new field-deployable methods for assessing mood and behavior as a function of neighborhood surroundings (geographical momentary assessment; GMA), we collected time-stamped GPS data and ecological momentary assessment (EMA) ratings of mood, stress, and drug craving over 16 weeks at randomly prompted times during the waking hours of opioid-dependent polydrug users receiving methadone maintenance. Locations of EMA entries and participants' travel tracks calculated for the 12 before each EMA entry were mapped. Associations between subjective ratings and objective environmental ratings were evaluated at the whole neighborhood and 12-h track levels. RESULTS: Participants (N=27) were compliant with GMA data collection; 3711 randomly prompted EMA entries were matched to specific locations. At the neighborhood level, physical disorder was negatively correlated with negative mood, stress, and heroin and cocaine craving (ps<.0001-.0335); drug activity was negatively correlated with stress, heroin and cocaine craving (ps .0009-.0134). Similar relationships were found for the environments around respondents' tracks in the 12h preceding EMA entries. CONCLUSIONS: The results support the feasibility of GMA. The relationships between neighborhood characteristics and participants' reports were counterintuitive and counter-hypothesized, and challenge some assumptions about how ostensibly stressful environments are associated with lived experience and how such environments ultimately impair health. GMA methodology may have applications for development of individual- or neighborhood-level interventions.