SMART Binary: New Sample Size Planning Resources for SMART Studies with Binary Outcome Measurements.

Sequential Multiple-Assignment Randomized Trials (SMARTs) play an increasingly important role in psychological and behavioral health research. This experimental approach enables researchers to answer scientific questions about how to sequence and match interventions to the unique, changing needs of individuals. A variety of sample size planning resources for SMART studies have been developed, enabling researchers to plan SMARTs for addressing different types of scientific questions. However, relatively limited attention has been given to planning SMARTs with binary (dichotomous) outcomes, which often require higher sample sizes relative to continuous outcomes. Existing resources for estimating sample size requirements for SMARTs with binary outcomes do not consider the potential to improve power by including a baseline measurement and/or multiple repeated outcome measurements. The current paper addresses this issue by providing sample size planning simulation procedures and approximate formulas for two-wave repeated measures binary outcomes (i.e., two measurement times for the outcome variable, before and after intervention delivery). The simulation results agree well with the formulas. We also discuss how to use simulations to calculate power for studies with more than two outcome measurement occasions. Results show that having at least one repeated measurement of the outcome can substantially improve power under certain conditions.

Feasibility, usability, and acceptability of MobileCoach-Teen: A smartphone app-based preventative intervention for risky adolescent drinking behavior.

BACKGROUND: Older adolescence (ages 15-18) is a critical period for experimentation with substance use, especially alcohol. Adolescent drinking poses hazards to physical and mental health, amplifies risk associated with other activities typically initiated during this life stage (e.g., driving, sexual activity), and is associated with adverse outcomes in adolescence and adulthood. Existing preventative interventions are expensive and have questionable long-term efficacy. Digital interventions may represent an accessible and personalized approach to providing preventative intervention content to youth. METHODS: This study recruited 29 adolescents aged 16-18 (M = 17.24, SD = 0.74) for a pilot feasibility trial of the MobileCoach-Teen (MC-Teen) smartphone app-based intervention. The study team randomized participants to receive either the alcohol intervention (MC-Teen) or attention control pseudo-intervention (MC-Fit). MC-Teen participants received 12 weeks of content adapted from a prior Swiss-based trial of a preventative alcohol intervention. Participants provided qualitative and quantitative feedback at baseline, via six biweekly surveys during and post-intervention. RESULTS: Both groups rated the application as easy to download (M = 4.31, SD = 0.93; 5-point Likert). All participants completed the baseline survey in less than the estimated time of 10 min (M = 7:42, SD = 2:15) and rated the survey as easy to complete (M = 4.69, SD = 0.60; 5-point Likert). MC-Teen participants favorably assessed application user experience, message user experience, and digital working alliance with application. Qualitative themes included a desire for increased rate/amount and diversity of content, greater representation via coach options, user interface/user experience improvements, and additional features. CONCLUSION: The MC-Teen intervention is feasible and acceptable based on a pilot feasibility trial with a sample of U.S. adolescents.

Understanding temporal changes and seasonal variations in glycemic trends using wearable data.

Seasonal variations in glycemic trends remain largely unstudied despite the growing prevalence of diabetes. To address this gap, our objective is to investigate temporal changes in glycemic trends by analyzing intensively sampled blood glucose data from 137 patients (ages 2 to 76, primarily type 1 diabetes) over the course of 9 months to 4.5 years. From over 91,000 days of continuous glucose monitor data, we found that glycemic control decreases significantly around the holidays, with the largest decline observed on New Year's Day among the patients with already poor glycemic control (i.e., <55% time in the target range). We also observed seasonal variations in glycemic trends, with patients having worse glycemic control in the months of November to February (i.e., mid-fall and winter, in the United States), and better control in the months of April to August (i.e., mid-spring and summer). These insights are critical to inform targeted interventions that can improve diabetes outcomes.

DiaTrend: A dataset from advanced diabetes technology to enable development of novel analytic solutions.

Objective digital data is scarce yet needed in many domains to enable research that can transform the standard of healthcare. While data from consumer-grade wearables and smartphones is more accessible, there is critical need for similar data from clinical-grade devices used by patients with a diagnosed condition. The prevalence of wearable medical devices in the diabetes domain sets the stage for unique research and development within this field and beyond. However, the scarcity of open-source datasets presents a major barrier to progress. To facilitate broader research on diabetes-relevant problems and accelerate development of robust computational solutions, we provide the DiaTrend dataset. The DiaTrend dataset is composed of intensive longitudinal data from wearable medical devices, including a total of 27,561 days of continuous glucose monitor data and 8,220 days of insulin pump data from 54 patients with diabetes. This dataset is useful for developing novel analytic solutions that can reduce the disease burden for people living with diabetes and increase knowledge on chronic condition management in outpatient settings.

Patient Engagement in a Multimodal Digital Phenotyping Study of Opioid Use Disorder.

BACKGROUND: Multiple digital data sources can capture moment-to-moment information to advance a robust understanding of opioid use disorder (OUD) behavior, ultimately creating a digital phenotype for each patient. This information can lead to individualized interventions to improve treatment for OUD. OBJECTIVE: The aim is to examine patient engagement with multiple digital phenotyping methods among patients receiving buprenorphine medication for OUD. METHODS: The study enrolled 65 patients receiving buprenorphine for OUD between June 2020 and January 2021 from 4 addiction medicine programs in an integrated health care delivery system in Northern California. Ecological momentary assessment (EMA), sensor data, and social media data were collected by smartphone, smartwatch, and social media platforms over a 12-week period. Primary engagement outcomes were meeting measures of minimum phone carry (≥8 hours per day) and watch wear (≥18 hours per day) criteria, EMA response rates, social media consent rate, and data sparsity. Descriptive analyses, bivariate, and trend tests were performed. RESULTS: The participants' average age was 37 years, 47% of them were female, and 71% of them were White. On average, participants met phone carrying criteria on 94% of study days, met watch wearing criteria on 74% of days, and wore the watch to sleep on 77% of days. The mean EMA response rate was 70%, declining from 83% to 56% from week 1 to week 12. Among participants with social media accounts, 88% of them consented to providing data; of them, 55% of Facebook, 54% of Instagram, and 57% of Twitter participants provided data. The amount of social media data available varied widely across participants. No differences by age, sex, race, or ethnicity were observed for any outcomes. CONCLUSIONS: To our knowledge, this is the first study to capture these 3 digital data sources in this clinical population. Our findings demonstrate that patients receiving buprenorphine treatment for OUD had generally high engagement with multiple digital phenotyping data sources, but this was more limited for the social media data. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.3389/fpsyt.2022.871916.

End User Participation in the Development of an Ecological Momentary Intervention to Improve Coping With Cannabis Cravings: Formative Study.

BACKGROUND: Cannabis misuse in young adults is a major public health concern. An important predictor of continued use is cannabis craving. Due to the time-varying nature of cravings, brief momentary interventions delivered while cravings are elevated may improve the use of strategies to cope with cravings and reduce cannabis use. OBJECTIVE: The goal of this manuscript is to describe a formative study to develop coping strategy messages for use in a subsequent intervention. METHODS: Young adults (aged 19-25 years; n=20) who reported using cannabis >10 of the past 30 days recruited via social media participated in this formative study. Participants rated an initial set of 15 mindfulness and 15 distraction coping strategies on a scale from 1 to 4 (very low degree to very high degree) for clarity, usefulness, and tone. They also provided comments about the content. RESULTS: Participants found the initial distraction messages slightly clearer than mindfulness (mean 3.5, SD 0.4 and mean 3.4, SD 0.4, respectively), both were comparable in tone (mean 3.2, SD 0.5 and mean 3.2, SD 0.4, respectively), and mindfulness messages were more useful than distraction (mean 3.0, SD 0.5 and mean 2.8, SD 0.6, respectively). Of the 30 messages, 29 received a rating of very low or low (<2) on any domain by >3 participants or received a comment suggesting a change. We revised all these messages based on this feedback, and the participants rated the revised messages approximately 2 weeks later. Participants earned US $10 for completing the first and US $20 for the second survey. The ratings improved on usefulness (especially the distraction items) with very little change in clarity and tone. The top 10 messages of each coping type (mindfulness and distraction) were identified by overall average rating (collapsed across all 3 dimensions: all rated >3.0). The final items were comparable in clarity (distraction mean 3.6, SD 0.4; mindfulness mean 3.6, SD 0.4), tone (distraction mean 3.4, SD 0.4; mindfulness mean 3.4, SD 0.4), and usefulness (distraction mean 3.1, SD 0.5; mindfulness mean 3.2, SD 0.5). CONCLUSIONS: The inclusion of end users in the formative process of developing these messages was valuable and resulted in improvements to the content of the messages. The majority of the messages were changed in some way including the removal of potentially triggering language. These messages were subsequently used in an ecological momentary intervention.

The Feasibility and Utility of Harnessing Digital Health to Understand Clinical Trajectories in Medication Treatment for Opioid Use Disorder: D-TECT Study Design and Methodological Considerations.

Introduction: Across the U.S., the prevalence of opioid use disorder (OUD) and the rates of opioid overdoses have risen precipitously in recent years. Several effective medications for OUD (MOUD) exist and have been shown to be life-saving. A large volume of research has identified a confluence of factors that predict attrition and continued substance use during substance use disorder treatment. However, much of this literature has examined a small set of potential moderators or mediators of outcomes in MOUD treatment and may lead to over-simplified accounts of treatment non-adherence. Digital health methodologies offer great promise for capturing intensive, longitudinal ecologically-valid data from individuals in MOUD treatment to extend our understanding of factors that impact treatment engagement and outcomes. Methods: This paper describes the protocol (including the study design and methodological considerations) from a novel study supported by the National Drug Abuse Treatment Clinical Trials Network at the National Institute on Drug Abuse (NIDA). This study (D-TECT) primarily seeks to evaluate the feasibility of collecting ecological momentary assessment (EMA), smartphone and smartwatch sensor data, and social media data among patients in outpatient MOUD treatment. It secondarily seeks to examine the utility of EMA, digital sensing, and social media data (separately and compared to one another) in predicting MOUD treatment retention, opioid use events, and medication adherence [as captured in electronic health records (EHR) and EMA data]. To our knowledge, this is the first project to include all three sources of digitally derived data (EMA, digital sensing, and social media) in understanding the clinical trajectories of patients in MOUD treatment. These multiple data streams will allow us to understand the relative and combined utility of collecting digital data from these diverse data sources. The inclusion of EHR data allows us to focus on the utility of digital health data in predicting objectively measured clinical outcomes. Discussion: Results may be useful in elucidating novel relations between digital data sources and OUD treatment outcomes. It may also inform approaches to enhancing outcomes measurement in clinical trials by allowing for the assessment of dynamic interactions between individuals' daily lives and their MOUD treatment response. Clinical Trial Registration: Identifier: NCT04535583.

A Digital Health Program Targeting Physical Activity Among Adolescents With Overweight or Obesity: Open Trial.

BACKGROUND: Prior studies suggest that mobile health physical activity programs that provide only weekly or daily text-based health coaching evidence limit the efficacy in improving physical activity in adolescents with overweight or obesity. It is possible that incentives, combined with health coaching and daily feedback on goal success, may increase program efficacy; however, such programs have not yet been tested with adolescents with overweight and obesity. OBJECTIVE: This study aims to examine the feasibility and acceptability of a 12-week, incentive-based, mobile health physical activity program with text-based health coaching, goal setting, and self-monitoring for adolescents with overweight or obesity. Program adherence and changes in tracked physical activity (ie, steps and active minutes while wearing a Fitbit [Google LLC]), body mass, and body fat are assessed. METHODS: A total of 28 adolescents aged 13 to 18 years with a BMI ≥90th percentile participated in the program. Of the 28 participants, 2 (7%) were lost to follow-up; thus, data from 26 (93%) participants were used in analyses. RESULTS: Participant-reported acceptability was high, with all mean ratings of text-based coaching, Fitbit use, and the overall program being >5 on a 7-point scale. In addition, 85% (23/26) of participants reported that they would like to continue to wear the Fitbit. Program adherence was also high, as participants wore the Fitbit on 91.1% (SD 12.6%) of days on average and met their weekly goals for an average of 7 (SD 3.5) of 11 possible weeks. There were no demographic (ie, sex, age, and baseline body mass) differences in the percentage of days participants wore their Fitbit. Across the 12-week study, there were significant improvements in tracked daily active minutes (P=.006) and steps (P<.001) and significant pre- to posttest improvements in body fat percentage (P=.04). CONCLUSIONS: The pilot program improved adolescent physical activity and physical health. A larger factorial design trial with adaptive daily goals may clarify the role of each program component in driving physical activity.

Corrigendum: Elena+ Care for COVID-19, a Pandemic Lifestyle Care Intervention: Intervention Design and Study Protocol.

[This corrects the article DOI: 10.3389/fpubh.2021.625640.].

Elena+ Care for COVID-19, a Pandemic Lifestyle Care Intervention: Intervention Design and Study Protocol.

Background: The current COVID-19 coronavirus pandemic is an emergency on a global scale, with huge swathes of the population required to remain indoors for prolonged periods to tackle the virus. In this new context, individuals' health-promoting routines are under greater strain, contributing to poorer mental and physical health. Additionally, individuals are required to keep up to date with latest health guidelines about the virus, which may be confusing in an age of social-media disinformation and shifting guidelines. To tackle these factors, we developed Elena+, a smartphone-based and conversational agent (CA) delivered pandemic lifestyle care intervention. Methods: Elena+ utilizes varied intervention components to deliver a psychoeducation-focused coaching program on the topics of: COVID-19 information, physical activity, mental health (anxiety, loneliness, mental resources), sleep and diet and nutrition. Over 43 subtopics, a CA guides individuals through content and tracks progress over time, such as changes in health outcome assessments per topic, alongside user-set behavioral intentions and user-reported actual behaviors. Ratings of the usage experience, social demographics and the user profile are also captured. Elena+ is available for public download on iOS and Android devices in English, European Spanish and Latin American Spanish with future languages and launch countries planned, and no limits on planned recruitment. Panel data methods will be used to track user progress over time in subsequent analyses. The Elena+ intervention is open-source under the Apache 2 license (MobileCoach software) and the Creative Commons 4.0 license CC BY-NC-SA (intervention logic and content), allowing future collaborations; such as cultural adaptions, integration of new sensor-related features or the development of new topics. Discussion: Digital health applications offer a low-cost and scalable route to meet challenges to public health. As Elena+ was developed by an international and interdisciplinary team in a short time frame to meet the COVID-19 pandemic, empirical data are required to discern how effective such solutions can be in meeting real world, emergent health crises. Additionally, clustering Elena+ users based on characteristics and usage behaviors could help public health practitioners understand how population-level digital health interventions can reach at-risk and sub-populations.

A Digital Health Intervention (SweetGoals) for Young Adults With Type 1 Diabetes: Protocol for a Factorial Randomized Trial.

BACKGROUND: Many young adults with type 1 diabetes (T1D) struggle with the complex daily demands of adherence to their medical regimen and fail to achieve target range glycemic control. Few interventions, however, have been developed specifically for this age group. OBJECTIVE: In this randomized trial, we will provide a mobile app (SweetGoals) to all participants as a "core" intervention. The app prompts participants to upload data from their diabetes devices weekly to a device-agnostic uploader (Glooko), automatically retrieves uploaded data, assesses daily and weekly self-management goals, and generates feedback messages about goal attainment. Further, the trial will test two unique intervention components: (1) incentives to promote consistent daily adherence to goals, and (2) web health coaching to teach effective problem solving focused on personalized barriers to self-management. We will use a novel digital direct-to-patient recruitment method and intervention delivery model that transcends the clinic. METHODS: A 2x2 factorial randomized trial will be conducted with 300 young adults ages 19-25 with type 1 diabetes and (Hb)A1c ≥ 8.0%. All participants will receive the SweetGoals app that tracks and provides feedback about two adherence targets: (a) daily glucose monitoring; and (b) mealtime behaviors. Participants will be randomized to the factorial combination of incentives and health coaching. The intervention will last 6 months. The primary outcome will be reduction in A1c. Secondary outcomes include self-regulation mechanisms in longitudinal mediation models and engagement metrics as a predictor of outcomes. Participants will complete 6- and 12-month follow-up assessments. We hypothesize greater sustained A1c improvements in participants who receive coaching and who receive incentives compared to those who do not receive those components. RESULTS: Data collection is expected to be complete by February 2025. Analyses of primary and secondary outcomes are expected by December 2025. CONCLUSIONS: Successful completion of these aims will support dissemination and effectiveness studies of this intervention that seeks to improve glycemic control in this high-risk and understudied population of young adults with T1D. TRIAL REGISTRATION: ClinicalTrials.gov NCT04646473; https://clinicaltrials.gov/ct2/show/NCT04646473. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/27109.

Conversational Agents as Mediating Social Actors in Chronic Disease Management Involving Health Care Professionals, Patients, and Family Members: Multisite Single-Arm Feasibility Study.

BACKGROUND: Successful management of chronic diseases requires a trustful collaboration between health care professionals, patients, and family members. Scalable conversational agents, designed to assist health care professionals, may play a significant role in supporting this collaboration in a scalable way by reaching out to the everyday lives of patients and their family members. However, to date, it remains unclear whether conversational agents, in such a role, would be accepted and whether they can support this multistakeholder collaboration. OBJECTIVE: With asthma in children representing a relevant target of chronic disease management, this study had the following objectives: (1) to describe the design of MAX, a conversational agent-delivered asthma intervention that supports health care professionals targeting child-parent teams in their everyday lives; and (2) to assess the (a) reach of MAX, (b) conversational agent-patient working alliance, (c) acceptance of MAX, (d) intervention completion rate, (e) cognitive and behavioral outcomes, and (f) human effort and responsiveness of health care professionals in primary and secondary care settings. METHODS: MAX was designed to increase cognitive skills (ie, knowledge about asthma) and behavioral skills (ie, inhalation technique) in 10-15-year-olds with asthma, and enables support by a health professional and a family member. To this end, three design goals guided the development: (1) to build a conversational agent-patient working alliance; (2) to offer hybrid (human- and conversational agent-supported) ubiquitous coaching; and (3) to provide an intervention with high experiential value. An interdisciplinary team of computer scientists, asthma experts, and young patients with their parents developed the intervention collaboratively. The conversational agent communicates with health care professionals via email, with patients via a mobile chat app, and with a family member via SMS text messaging. A single-arm feasibility study in primary and secondary care settings was performed to assess MAX. RESULTS: Results indicated an overall positive evaluation of MAX with respect to its reach (49.5%, 49/99 of recruited and eligible patient-family member teams participated), a strong patient-conversational agent working alliance, and high acceptance by all relevant stakeholders. Moreover, MAX led to improved cognitive and behavioral skills and an intervention completion rate of 75.5%. Family members supported the patients in 269 out of 275 (97.8%) coaching sessions. Most of the conversational turns (99.5%) were conducted between patients and the conversational agent as opposed to between patients and health care professionals, thus indicating the scalability of MAX. In addition, it took health care professionals less than 4 minutes to assess the inhalation technique and 3 days to deliver related feedback to the patients. Several suggestions for improvement were made. CONCLUSIONS: This study provides the first evidence that conversational agents, designed as mediating social actors involving health care professionals, patients, and family members, are not only accepted in such a "team player" role but also show potential to improve health-relevant outcomes in chronic disease management.

Attentional bias to diabetes cues mediates disease management improvements in a pilot randomized controlled trial for adolescents with type 1 diabetes.

For type 1 diabetes management, the role of attentional bias remains unclear. This secondary analysis examined type 1 diabetes attentional bias and adolescent type 1 diabetes management prior to and during a cognitive and behavioral intervention. Youth with type 1 diabetes and above target glycemic control were assigned to intervention or usual care control. Participants completed baseline and follow-up type 1 diabetes Stroop tasks, HbA1c tests, and blood glucose meter downloads. Intervention was associated with greater reductions in type 1 diabetes attentional bias than control, and these reductions partially mediated the effect of treatment on diabetes management behaviors. Type 1 diabetes attentional bias is a potential target to improve type 1 diabetes management.

Interventions for cannabis use disorder.

How to treat adults and adolescents with a Cannabis Use Disorder is a burgeoning research area. This article reviews the empirical literature pertaining to several psychosocial approaches (cognitive-behavior therapy, motivational enhancement, and contingency management), all of which are associated with favorable outcomes. We also review the emerging research on the use of pharmacotherapy, brief interventions and technology-delivered interventions, and conclude with an overview of future research needs.

Workshop on the Development and Evaluation of Digital Therapeutics for Health Behavior Change: Science, Methods, and Projects.

The health care field has integrated advances into digital technology at an accelerating pace to improve health behavior, health care delivery, and cost-effectiveness of care. The realm of behavioral science has embraced this evolution of digital health, allowing for an exciting roadmap for advancing care by addressing the many challenges to the field via technological innovations. Digital therapeutics offer the potential to extend the reach of effective interventions at reduced cost and patient burden and to increase the potency of existing interventions. Intervention models have included the use of digital tools as supplements to standard care models, as tools that can replace a portion of treatment as usual, or as stand-alone tools accessed outside of care settings or direct to the consumer. To advance the potential public health impact of this promising line of research, multiple areas warrant further development and investigation. The Center for Technology and Behavioral Health (CTBH), a P30 Center of Excellence supported by the National Institute on Drug Abuse at the National Institutes of Health, is an interdisciplinary research center at Dartmouth College focused on the goal of harnessing existing and emerging technologies to effectively develop and deliver evidence-based interventions for substance use and co-occurring disorders. The CTBH launched a series of workshops to encourage and expand multidisciplinary collaborations among Dartmouth scientists and international CTBH affiliates engaged in research related to digital technology and behavioral health (eg, addiction science, behavioral health intervention, technology development, computer science and engineering, digital security, health economics, and implementation science). This paper summarizes a workshop conducted on the Development and Evaluation of Digital Therapeutics for Behavior Change, which addressed (1) principles of behavior change, (2) methods of identifying and testing the underlying mechanisms of behavior change, (3) conceptual frameworks for optimizing applications for mental health and addictive behavior, and (4) the diversity of experimental methods and designs that are essential to the successful development and testing of digital therapeutics. Examples were presented of ongoing CTBH projects focused on identifying and improving the measurement of health behavior change mechanisms and the development and evaluation of digital therapeutics. In summary, the workshop showcased the myriad research targets that will be instrumental in promoting and accelerating progress in the field of digital health and health behavior change and illustrated how the CTBH provides a model of multidisciplinary leadership and collaboration that can facilitate innovative, science-based efforts to address the health behavior challenges afflicting our communities.

The application of digital health to the assessment and treatment of substance use disorders: The past, current, and future role of the National Drug Abuse Treatment Clinical Trials Network.

The application of digital technologies to better assess, understand, and treat substance use disorders (SUDs) is a particularly promising and vibrant area of scientific research. The National Drug Abuse Treatment Clinical Trials Network (CTN), launched in 1999 by the U.S. National Institute on Drug Abuse, has supported a growing line of research that leverages digital technologies to glean new insights into SUDs and provide science-based therapeutic tools to a diverse array of persons with SUDs. This manuscript provides an overview of the breadth and impact of research conducted in the realm of digital health within the CTN. This work has included the CTN's efforts to systematically embed digital screeners for SUDs into general medical settings to impact care models across the nation. This work has also included a pivotal multi-site clinical trial conducted on the CTN platform, whose data led to the very first "prescription digital therapeutic" authorized by the U.S. Food and Drug Administration (FDA) for the treatment of SUDs. Further CTN research includes the study of telehealth to increase capacity for science-based SUD treatment in rural and under-resourced communities. In addition, the CTN has supported an assessment of the feasibility of detecting cocaine-taking behavior via smartwatch sensing. And, the CTN has supported the conduct of clinical trials entirely online (including the recruitment of national and hard-to-reach/under-served participant samples online, with remote intervention delivery and data collection). Further, the CTN is supporting innovative work focused on the use of digital health technologies and data analytics to identify digital biomarkers and understand the clinical trajectories of individuals receiving medications for opioid use disorder (OUD). This manuscript concludes by outlining the many potential future opportunities to leverage the unique national CTN research network to scale-up the science on digital health to examine optimal strategies to increase the reach of science-based SUD service delivery models both within and outside of healthcare.

Introduction to the special issue in honor of the life and work of Dr. Nancy Petry.

This special issue of Psychology of Addictive Behaviors honors the life and work of a beloved friend, colleague, and mentor, Dr. Nancy M. Petry, who died on July 17, 2018 of breast cancer. At the time of her death, Dr. Petry was the editor of this journal. We were gratified to receive many submissions for this issue, resulting in an outstanding set of 24 selected articles. These articles span the multiple areas of addiction research to which Dr. Petry made key contributions, and this introduction highlights her work in these areas. The topic areas in this issue include behavior analysis and behavior pharmacology; contingency management-randomized controlled trials; contingency management-implementation and dissemination; demographic predictors of outcomes across multiple clinical trials; reinforcer pathology and decision making; and gambling. We can only imagine what great work Dr. Petry would have contributed in the future, but the research presented in this issue makes clear that her legacy and impact will continue to grow. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

Persisting on the past: Cross-sectional and prospective associations between sunk cost propensity and cannabis use.

Prevalence of cannabis use in the United States continues to rise, and 30% of cannabis users eventually meet criteria for Cannabis Use Disorder (CUD). One response to this problem is to develop decision-making constructs that indicate vulnerability to CUD that might not be gleaned from diagnostic criteria. Unfortunately, there is limited evidence that decision-making constructs consistently relate to cannabis use. Interestingly, those who exhibit the sunk cost bias, an overgeneralized tendency to persist based on past investment, and those who use cannabis, both tend to focus on the past and perseverate more than their counterparts. Despite this overlap, no studies have assessed whether the sunk cost bias is positively associated with cannabis use. In 2 experiments with undergraduates, relations between cannabis use and the propensity to engage in the sunk cost bias were examined using negative binomial models. Experiment 1 (n = 46) evaluated the association between sunk cost bias propensity (using hypothetical costs and rewards) and frequency of cannabis use over the past 30 days. Greater sunk cost propensity was associated with more frequent cannabis use after controlling for demographics and alcohol use. In Experiment 2 (n = 103), more frequent cannabis use during a 6-week follow-up period was predicted by greater sunk cost propensity at baseline (using a real cost and reward-based task), independently and after controlling for mental health symptoms, alcohol use, and demographics. These findings provide preliminary evidence that a propensity to exhibit the sunk cost bias may be an important feature associated with cannabis use. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Working memory training and high magnitude incentives for youth cannabis use: A SMART pilot trial.

The purpose of this sequential multiple-assignment randomization treatment pilot study was to examine if (a) adding working memory training to contingency management (CM) for youth with cannabis use disorder (CUD) and (b) switching nonresponding youth to higher magnitude CM incentives boosts outcomes. In Phase 1, youth with CUD (n = 59, M age = 16, male = 71%) attending an intensive outpatient program were randomly assigned to 14 weeks of CM only or CM plus working memory training (WMT). In Week 4, a Phase 2 treatment was assigned. Those with negative urine drug tests (responders) continued in their Phase 1 treatment. Those who were drug positive (nonresponders) were randomly assigned to remain in their Phase 1 treatment or to higher magnitude CM. Zero-inflated negative binomial models comparing those assigned to CM versus CM + WMT indicated no differences in the likelihood of having ≥ 1 week of continuous abstinence or longer abstinence duration. Those assigned to WMT showed greater but nonsignificant improvements in working memory (n = 35; ß = .69, p = .06). Working memory improvements were associated with achieving any abstinence (odds ratio = 3.50, 95% CI [1.01, 12.10], p = .05). Phase 2 randomization to higher magnitude CM did not boost outcomes. Overall results suggest that WMT appears promising, but the sample size was small, attrition was high, and replication is important. Alternative strategies should continue to be explored to improve outcomes for adolescent substance use disorders, such as different approaches for nonresponders, tailoring to other baseline or response characteristics, or more robust first-line interventions. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

Greater delay discounting and cannabis coping motives are associated with more frequent cannabis use in a large sample of adult cannabis users.

BACKGROUND: Self-regulation deficits expressed through a decreased ability to value future rewards (delay discounting (DD)) and impaired emotion regulation (negative urgency (NU), cannabis coping motives (CCM), and anxiety sensitivity (AS)) relate to more frequent or problematic cannabis use. However, there is a need to better understand how self-regulation and emotion regulation constructs reflect competition between deliberative and reactive systems that drive individual differences in cannabis use patterns. Further, few studies assess frequency of cannabis use within and across days of use, which may obscure differentiation of individual differences. METHODS: In a large national sample of 2545 cannabis users, Latent Class Analysis was used to derive participant sub-classes based on two frequency indices, self-reported cannabis use days and times cannabis was used per day. Three classes emerged: Low (1-9 days/month, 1 time/day; 23 %), moderate (10-29 days/month, 2-3 times/day; 41 %), and high (30 days/month, ≥4 times/day; 36 %). Relationships among frequency classes and emotional regulation and impulsivity were assessed with a multinomial logistic regression. RESULTS: Higher frequency use was associated with greater DD (χ2 = 6.0, p = .05), greater CCM (χ2 = 73.3, p < .001), and lower cognitive AS (χ2 = 12.1, p = .002), when controlling for demographics, tobacco use, and number of cannabis administration methods. Frequency class and NU were not significantly associated. CONCLUSIONS: Identifying meaningful patterns of cannabis use may improve our understanding of individual differences that increase risk of frequent or problematic cannabis use. Excessive delay discounting and using cannabis to cope with negative affect may be relevant targets for treatments designed to reduce cannabis use.