Kathleen Alice Garrison - US grants

PROJECT SUMMARY Smoking is the leading cause of preventable death in the US. Effective smoking cessation interventions are available but underutilized. Smoking cessation interventions delivered by smartphone apps are a promising tool for helping smokers quit. Delivery of treatments via smartphone apps may maximize the likelihood of use by smokers and the potential impact on smoking behavior. However, currently available smartphone apps for smoking cessation have not exploited their unique potential advantages to aid quitting. Notably, no available apps utilize wearable technologies; all current apps require users to self-report their smoking; and no apps deliver treatment automatically contingent upon smoking. Therefore, this pilot trial will test the feasibility of using a smartband to detect and track smoking and deliver brief smoking cessation interventions by smartphone app in real time. The interventions to be delivered will be brief mindfulness exercises that have been previously shown to reduce craving and smoking. This trial uses SmokeBeat, a novel mobile technology platform that uses multimodal data from wristband sensors to monitor and detect smoking, notify smokers about their smoking in real time and deliver real time interventions triggered by detected smoking episodes. SmokeBeat also applies machine learning to smoking tracking data to identify individual smoking patterns and deliver real time interventions targeted at predicted smoking episodes. This trial tests a three-step intervention to reduce smoking, in which smokers first become aware of their smoking and triggers by tracking smoking; then gain a clear recognition of the actual effects of smoking by ?mindful smoking?; and finally learn to work mindfully with cravings rather than smoke. Briefly, daily smokers (N=200, ?5 cig/day) will wear a smartband to detect and notify them of smoking for 21 days and obtain individual smoking profiles; detected smoking will then trigger a ?mindful smoking? exercise for the next 7 days leading up to their quit date at 30 days; after which another mindfulness exercise (?RAIN?: recognize, accept, investigate and note cravings rather than smoke) will be delivered prior to each predicted smoking episode according to their individual smoking profile for 30 days post-quit. Aim 1 will be to determine treatment fidelity. Fidelity measures will be: (1) percent of smoking episodes correctly detected; (2) percent of ?mindful smoking? exercises correctly triggered by smoking; and (3) users? real time ratings of how timely ?RAIN? was delivered to predicted smoking episodes. Aim 2 will be to determine adherence to treatment. Adherence measures will be: (1) percent of time spent wearing the smartband; (2) percent of smoking notifications answered; (3) percent of ecological momentary assessment (EMA) ratings (e.g., timeliness and others) answered; and (4) percent of mindfulness exercises completed. Aim 3 will be to determine the acceptability of treatment. Acceptability measures will be: (1) average helpfulness ratings after each mindfulness exercise; (2) feedback on user experience surveys. Overall: this project tests a highly innovative technology-based mindfulness intervention for smoking cessation.

ABSTRACT E-cigarettes are now the most common tobacco product used by youth in the US. Among the primary reasons youth report for having tried e-cigarettes are low risk perceptions and appealing flavors. Youth have poor knowledge of e-cigarette health risks, which only recently have a required warning label. At the same time, e- cigarettes are advertised in sweet and fruit flavors that increase their appeal to youth. A recent study by our team indicated that these factors also interact ?images of sweet/fruit flavors on e-cigarette advertisements distracted youth from warning labels. To better understand how these factors impact e-cigarette use by youth, there is a critical need for measures linking exposure to e-cigarette advertising and warning labels to future e- cigarette use behavior. Functional magnetic resonance imaging (fMRI) has been used to identify brain activity patterns that predict future health behavior including tobacco use beyond self-report. Multiple fMRI studies indicate that the medial prefrontal cortex (MPFC) response to cigarette warnings predicts future smoking. Other fMRI studies indicate that the nucleus accumbens (NAc) response to advertisements predicts purchasing. This project will use a similar brain-as-predictor approach with fMRI and eye tracking to link neural responses to e-cigarette advertising and warning labels to future e-cigarette use behavior in youth. Adolescents (ages 14-17, N=80) will view e-cigarette advertisements and warning labels in fMRI and complete quarterly follow-up surveys for one year. MPFC and NAc activity will be measured and tested for relationships with future e-cigarette attitudes, intentions and use. Additional fMRI control conditions will allow us to test the specific impact of different categories of warning labels and different e-cigarette flavors, and the interactions between these factors, including their impact on memory for warning labels. Aim 1 will test the hypothesis that greater MPFC activity as adolescents view e-cigarette warning labels will be related to more negative e- cigarette attitudes and intentions and lower use of e-cigarettes in the next year. Exploratory Aim 1.1 will compare MPFC response between warning labels about addictiveness versus chemical constituents. Aim 2 will test the hypothesis that greater NAc activity as adolescents view e-cigarette advertisements will be related to more positive e-cigarette attitudes and intentions and greater use of e-cigarettes in the next year. Exploratory Aim 2.1 will compare the relative value of multiple measures ?fMRI, eye tracking and surveys ?to predict future e-cigarette use in the next year. Aim 3 will replicate and extend our recent study by testing whether images of flavors on e-cigarette advertisements distract adolescents from warning labels. Overall, this project should generate critical evidence on the impact of e-cigarette advertising and warning labels on e-cigarette use behavior in youth, and inform FDA efforts to regulate e-cigarette flavors, labeling and marketing. Important information will also be generated on the relative value of multiple measures to predict future e-cigarette use.

PROJECT SUMMARY The misuse of opioids, opioid addiction and overdose are a serious national public health crisis?the opioid epidemic?that despite increased scientific, clinical and government attention, continues to grow. Methadone is a generally effective treatment for opioid use disorder, however relapse rates remain high, and risk of overdose is greatest during relapse. There is a need for improved mechanistic understanding of the factors that contribute to opioid relapse to improve our understanding of opioid use disorder and its treatment. Using connectome-based methods (i.e., functional connectivity) in functional magnetic resonance imaging (fMRI), we recently identified a large-scale brain network that predicted opioid relapse from both resting and task states. Connectome-based methods enable data-driven characterization of whole brain networks related to behavior that might be better suited to describe complex clinical phenomena (e.g., opioid relapse). Building on prior work indicating the utility of real-time fMRI neurofeedback to test brain activation patterns related to specific functions and individual abilities to regulate these functions, the proposed project will use connectome-based neurofeedback to target patterns of functional connectivity within our recently identified ?opioid abstinence network?. This information is critical to improve understanding of mechanisms of opioid relapse. Individuals on methadone will be randomized to receive either active (n=12) or sham (n=12) connectome-based neurofeedback at 3 weekly scanning sessions including feedback and transfer runs. Additional baseline and follow-up scans will include resting state and reward and cognitive task runs. Craving, negative affect and opioid use will be measured weekly and at 1-mo follow-up. Based on our pilot data, connectome-based feedback will be targeted at the opioid abstinence network and we hypothesize that increased connectivity in this network will be associated with improved clinical outcomes. Aim 1 will test the hypothesis that active feedback is associated with reduced opioid use from baseline to follow-up scans (Aim 1a) and at 1-mo follow- up (Aim 1b). Aim 2 will test the hypothesis that active feedback is associated with increased opioid abstinence network connectivity in resting state (Aim 2a) and task (reward, cognitive) state (Aim 2b) versus sham feedback, as in our pilot work. Aim 3 will test the hypothesis that active feedback is associated with greater improvements in clinical features of opioid use disorder (craving, negative affect) than sham feedback (Aim 3a) and that increased opioid abstinence network connectivity will correlate with these improvements (Aim 3b). Overall, this project tests a potentially transformative hypothesis relating large-scale brain network dynamics to outcomes in opioid use disorder, and tests a highly innovative method for real-time fMRI neurofeedback from the opioid abstinence network to improve clinical features of opioid use disorder. This project will provide unprecedented insight into the functional neurobiology of opioid relapse and more generally has the potential to transform existing real-time fMRI paradigms in addictions.