Combined ecological momentary assessment and global positioning system tracking to assess smoking behavior: a proof of concept study.

OBJECTIVE: Ecological momentary assessment (EMA) methods have provided a rich assessment of the contextual factors associated with a wide range of behaviors including alcohol use, eating, physical activity, and smoking. Despite this rich database, this information has not been linked to specific locations in space. Such location information, which can now be easily acquired from global positioning system (GPS) tracking devices, could provide unique information regarding the space-time distribution of behaviors and new insights into their determinants. In a proof of concept study, we assessed the acceptability and feasibility of acquiring and combining EMA and GPS data from adult smokers with attention deficit hyperactivity disorder (ADHD). METHODS: Participants were adults with ADHD who were enrolled in a larger EMA study on smoking and psychiatric symptoms. Among those enrolled in the latter study who were approached to participate (N = 11), 10 consented, provided daily EMA entries, and carried a GPS device with them during a 7-day assessment period to assess aspects of their smoking behavior. RESULTS: The majority of those eligible to participate were willing to carry a GPS device and signed the consent (10 out of 11, 91%). Of the 10 who consented, 7 participants provided EMA entries and carried the GPS device with them daily for at least 70% of the sampling period. Data are presented on the spatial distribution of smoking episodes and ADHD symptoms on a subset of the sample to demonstrate applications of GPS data. CONCLUSIONS: We conclude by discussing how EMA and GPS might be used to study the ecology of smoking and make recommendations for future research and analysis.

Smoking withdrawal symptoms are more severe among smokers with ADHD and independent of ADHD symptom change: results from a 12-day contingency-managed abstinence trial.

INTRODUCTION: Smokers with attention deficit hyperactivity disorder (ADHD) have greater difficulty quitting than those without ADHD, but preliminary data (McClernon, Kollins, Lutz, Fitzgerald, Murray, Redman, et al., 2008) suggest equivalent severity of withdrawal symptoms following brief abstinence. The objective of this study was to characterize the differential effects of intermediate term smoking abstinence on self-reported withdrawal and ADHD symptoms in adult smokers with and without ADHD. METHODS: Forty adult (50% female), nontreatment seeking moderate-to-heavy smokers with and without ADHD were enrolled in a 12-day quit study in which monetary incentives were provided for maintaining biologically verified abstinence. Self-reported withdrawal, mood, and ADHD symptoms were measured pre- and post-quitting. RESULTS: ADHD and controls did not vary on smoking or demographic variables. Significant Group × Session interactions were observed across a broad range of withdrawal symptoms and were generally characterized by greater withdrawal severity among ADHD smokers, particularly during the first 5 days of abstinence. In addition, Group × Sex × Session interactions were observed for craving, somatic symptoms, negative affect, and habit withdrawal; these interactions were driven by greater withdrawal severity among females with ADHD. Group × Session interactions were not observed for ADHD symptom scales. CONCLUSIONS: The results of this study suggest that smokers with ADHD, and ADHD females in particular, experience greater withdrawal severity during early abstinence-independent of effects on ADHD symptoms. Whereas additional research is needed to pinpoint mechanisms, our findings suggest that smoking cessation interventions targeted at smokers with ADHD should address their more severe withdrawal symptoms following quitting.

Identifying Smoking Environments From Images of Daily Life With Deep Learning.

Importance: Environments associated with smoking increase a smoker's craving to smoke and may provoke lapses during a quit attempt. Identifying smoking risk environments from images of a smoker's daily life provides a basis for environment-based interventions. Objective: To apply a deep learning approach to the clinically relevant identification of smoking environments among settings that smokers encounter in daily life. Design, Setting, and Participants: In this cross-sectional study, 4902 images of smoking (n = 2457) and nonsmoking (n = 2445) locations were photographed by 169 smokers from Durham, North Carolina, and Pittsburgh, Pennsylvania, areas from 2010 to 2016. These images were used to develop a probabilistic classifier to predict the location type (smoking or nonsmoking location), thus relating objects and settings in daily environments to established smoking patterns. The classifier combines a deep convolutional neural network with an interpretable logistic regression model and was trained and evaluated via nested cross-validation with participant-wise partitions (ie, out-of-sample prediction). To contextualize model performance, images taken by 25 randomly selected participants were also classified by smoking cessation experts. As secondary validation, craving levels reported by participants when viewing unfamiliar environments were compared with the model's predictions. Data analysis was performed from September 2017 to May 2018. Main Outcomes and Measures: Classifier performance (accuracy and area under the receiver operating characteristic curve [AUC]), comparison with 4 smoking cessation experts, contribution of objects and settings to smoking environment status (standardized model coefficients), and correlation with participant-reported craving. Results: Of 169 participants, 106 (62.7%) were from Durham (53 [50.0%] female; mean [SD] age, 41.4 [12.0] years) and 63 (37.3%) were from Pittsburgh (31 [51.7%] female; mean [SD] age, 35.2 [13.8] years). A total of 4902 images were available for analysis, including 3386 from Durham (mean [SD], 31.9 [1.3] images per participant) and 1516 from Pittsburgh (mean [SD], 24.1 [0.5] images per participant). Images were evenly split between the 2 classes, with 2457 smoking images (50.1%) and 2445 nonsmoking images (49.9%). The final model discriminated smoking vs nonsmoking environments with a mean (SD) AUC of 0.840 (0.024) (accuracy [SD], 76.5% [1.6%]). A model trained only with images from Durham participants effectively classified images from Pittsburgh participants (AUC, 0.757; accuracy, 69.2%), and a model trained only with images from Pittsburgh participants effectively classified images from Durham participants (AUC, 0.821; accuracy, 75.0%), suggesting good generalizability between geographic areas. Only 1 expert's performance was a statistically significant improvement compared with the classifier (α = .05). Median self-reported craving was significantly correlated with model-predicted smoking environment status (ρ = 0.894; P = .003). Conclusions and Relevance: In this study, features of daily environments predicted smoking vs nonsmoking status consistently across participants. The findings suggest that a deep learning approach can identify environments associated with smoking, can predict the probability that any image of daily life represents a smoking environment, and can potentially trigger environment-based interventions. This work demonstrates a framework for predicting how daily environments may influence target behaviors or symptoms that may have broad applications in mental and physical health.

ADHD, Smoking Withdrawal, and Inhibitory Control: Results of a Neuroimaging Study with Methylphenidate Challenge.

Smoking withdrawal negatively impacts inhibitory control, and these effects are greater for smokers with preexisting attention problems, such as attention deficit/hyperactivity disorder (ADHD). The current study preliminarily evaluated changes in inhibitory control-related behavior and brain activation during smoking withdrawal among smokers with ADHD. Moreover, we investigated the role of catecholamine transmission in these changes by examining the effects of 40 mg methylphenidate (MPH) administration. Adult daily smokers with (n=17) and without (n=20) ADHD completed fMRI scanning under each of three conditions: (a) smoking as usual+placebo; (b) 24 h smoking abstinence+placebo and (c) 24 h smoking abstinence+MPH. Scan order was randomized and counterbalanced. Participants completed a modified Go/No-Go task to assess both sustained and transient inhibitory control. Voxelwise analysis of task-related BOLD signal revealed a significant group-by-abstinence interaction in occipital/parietal cortex during sustained inhibition, with greater abstinence-induced decreases in activation observed among ADHD smokers compared with non-ADHD smokers. Changes in behavioral performance during abstinence were associated with changes in activation in regions of occipital and parietal cortex and bilateral insula during sustained inhibition in both groups. MPH administration improved behavioral performance and increased sustained inhibitory control-related activation for both groups. During transient inhibition, MPH increased prefrontal activation for both groups and increased striatal activation only among ADHD smokers. These preliminary findings suggest that abstinence-induced changes in catecholamine transmission in visual attention areas (eg, occipital and superior parietal cortex) may be associated with inhibitory control deficits and contribute to smoking vulnerability among individuals with ADHD.

Association Between Baseline Corticothalamic-Mediated Inhibitory Control and Smoking Relapse Vulnerability.

Importance: Tobacco use disorder is associated with dysregulated neurocognitive function in the right inferior frontal gyrus (IFG)-one node in a corticothalamic inhibitory control (IC) network. Objective: To examine associations between IC neural circuitry structure and function and lapse/relapse vulnerability in 2 independent studies of adult smokers. Design, Setting, and Participants: In study 1, treatment-seeking smokers (n = 81) completed an IC task during functional magnetic resonance imaging (fMRI) before making a quit attempt and then were followed up for 10 weeks after their quit date. In study 2, a separate group of smokers (n = 26) performed the same IC task during fMRI, followed by completing a laboratory-based smoking relapse analog task. Study 1 was performed at Duke University Medical Center between 2008 and 2012; study 2 was conducted at the Medical University of South Carolina between 2013 and 2016. Main Outcomes and Measures: Associations between corticothalamic-mediated IC, gray-matter volume, and smoking lapse/relapse. Results: Of the 81 study participants in study 1 (cessation study), 45 were women (56%), with mean (SD) age, 38.4 (10.2) years. In study 1, smoking relapse was associated with less gray-matter volume (F1,74 = 28.32; familywise error P threshold = 0.03), greater IC task-related blood oxygenation level-dependent (BOLD) response in the right IFG (F1,78 = 14.87) and thalamus (F1,78 = 14.97) (P < .05), and weaker corticothalamic task-based functional connectivity (tbFC) (F1,77 = 5.87; P = .02). Of the 26 participants in study 2 (laboratory study), 15 were women (58%), with mean (SD) age, 34.9 (10.3). Similar to study 1, in study 2, greater IC-BOLD response in the right IFG (t23 = -2.49; ß = -0.47; P = .02), and weaker corticothalamic tbFC (t22 = 5.62; ß = 0.79; P < .001) were associated with smoking sooner during the smoking relapse-analog task. In both studies, corticothalamic tbFC mediated the association between IC performance and smoking outcomes. Conclusions and Relevance: In these 2 studies, baseline differences in corticothalamic circuitry function were associated with mediated IC and smoking relapse vulnerability. These findings warrant further examination of interventions for augmenting corticothalamic neurotransmission and enhancing IC during the course of tobacco use disorder treatment.

Reinforcing and subjective effects of methylphenidate in adults with and without attention deficit hyperactivity disorder (ADHD).

RATIONALE: There has been controversy over the abuse potential of methylphenidate (MPH) in the context of treatment for attention deficit hyperactivity disorder (ADHD). OBJECTIVE: The objective of this study was to compare the reinforcing and subjective effects of oral MPH in adults with and without ADHD. MATERIALS AND METHODS: Following screening, 33 adults (n = 16 with ADHD; n = 17 free from psychiatric diagnoses) completed four pairs of experimental sessions, each of which included a sampling session and a self-administration session. During sampling sessions, subjects received in randomized order 0 (placebo), 20, 40, and 60 mg MPH. During self-administration sessions, subjects completed a progressive ratio (PR) task to earn portions of the dose received on the corresponding sampling session. Subjective effects were recorded throughout all sessions. The main outcome measure for the study was the number of ratios completed on the PR task. Secondary measures included peak subjective effects and area-under-the-curve values for subjective effects. RESULTS: Compared to the control group, the ADHD group completed more ratios on the PR task. Both groups showed robust effects of methylphenidate on subjective endpoints. Main effects of group were noted on subjective effects involving concentration and arousal. CONCLUSIONS: Compared to placebo, MPH produced reinforcing effects only for the ADHD group and not for the control group. Increases in stimulant-related subjective effects in non-ADHD subjects were not associated with drug reinforcement.