Smartband-based smoking detection and real-time brief mindfulness intervention: findings from a feasibility clinical trial.

BACKGROUND: Smartbands can be used to detect cigarette smoking and deliver real time smoking interventions. Brief mindfulness interventions have been found to reduce smoking. OBJECTIVE: This single arm feasibility trial used a smartband to detect smoking and deliver brief mindfulness exercises. METHODS: Daily smokers who were motivated to reduce their smoking wore a smartband for 60 days. For 21 days, the smartband monitored, detected and notified the user of smoking in real time. After 21 days, a 'mindful smoking' exercise was triggered by detected smoking. After 28 days, a 'RAIN' (recognize, allow, investigate, nonidentify) exercise was delivered to predicted smoking. Participants received mindfulness exercises by text message and online mindfulness training. Feasibility measures included treatment fidelity, adherence and acceptability. RESULTS: Participants (N=155) were 54% female, 76% white non-Hispanic, and treatment starters (n=115) were analyzed. Treatment fidelity cutoffs were met, including for detecting smoking and delivering mindfulness exercises. Adherence was mixed, including moderate smartband use and low completion of mindfulness exercises. Acceptability was mixed, including high helpfulness ratings and mixed user experiences data. Retention of treatment starters was high (81.9%). CONCLUSIONS: Findings demonstrate the feasibility of using a smartband to track smoking and deliver quit smoking interventions contingent on smoking.

Behavioral Precursors of Nicotine Product Use Trajectories Among Youth.

INTRODUCTION: Preventing nicotine use among youth is a public health priority. Nicotine use emerges from complex relationships between numerous factors. This project used network analysis to model behavioral precursors of nicotine use (knowledge, attitudes, perceptions, intentions) among youth who had never used nicotine and determine which predicted future trajectories of use across multiple nicotine products. METHODS: Data were from the Population Assessment of Tobacco and Health study (2013-2018; analyzed 2023-2024), youth ages 12-17, who reported never using nicotine at Wave 1. Network structure was determined for behavioral precursors at Wave 1 and central network nodes were identified (N=5,087). Central nodes were then tested as predictors of trajectories of use across multiple nicotine products during Waves 1-4 (N=3,851). RESULTS: Central nodes of the Wave 1 network were harm perception, expectancy that tobacco would calm anger/reduce stress, and intention to try. Those with lower intent to try at Wave 1 had lower odds of being in an Experimentation or Use class versus a Nonuse class during Waves 1-4 (p<0.0001). Those with more accurate harm perception had lower odds of being in an Experimentation versus Nonuse class (p=0.004). Those with positive expectancies had higher odds of being in an Experimentation versus Nonuse or Use class (p=0.04, 0.02). CONCLUSIONS: Findings suggest a network model of behavioral precursors of nicotine use that can be tested, including central nodes that predicted trajectories of use across multiple nicotine products, and therefore may be priority intervention targets.

Network state dynamics underpin craving in a transdiagnostic population.

Emerging fMRI brain dynamic methods present a unique opportunity to capture how brain region interactions across time give rise to evolving affective and motivational states. As the unfolding experience and regulation of affective states affect psychopathology and well-being, it is important to elucidate their underlying time-varying brain responses. Here, we developed a novel framework to identify network states specific to an affective state of interest and examine how their instantaneous engagement contributed to its experience. This framework investigated network state dynamics underlying craving, a clinically meaningful and changeable state. In a transdiagnostic sample of healthy controls and individuals diagnosed with or at risk for craving-related disorders (N=252), we utilized connectome-based predictive modeling (CPM) to identify craving-predictive edges. An edge-centric timeseries approach was leveraged to quantify the instantaneous engagement of the craving-positive and craving-negative networks during independent scan runs. Individuals with higher craving persisted longer in a craving-positive network state while dwelling less in a craving-negative network state. We replicated the latter results externally in an independent group of healthy controls and individuals with alcohol use disorder exposed to different stimuli during the scan (N=173). The associations between craving and network state dynamics can still be consistently observed even when craving-predictive edges were instead identified in the replication dataset. These robust findings suggest that variations in craving-specific network state recruitment underpin individual differences in craving. Our framework additionally presents a new avenue to explore how the moment-to-moment engagement of behaviorally meaningful network states supports our changing affective experiences.

Smartphone Apps for Smoking Cessation: Systematic Framework for App Review and Analysis.

BACKGROUND: Cigarette smoking is a leading cause of preventable death, and identifying novel treatment approaches to promote smoking cessation is critical for improving public health. With the rise of digital health and mobile apps, these tools offer potential opportunities to address smoking cessation, yet the functionality of these apps and whether they offer scientifically based support for smoking cessation are unknown. OBJECTIVE: The goal of this research was to use the American Psychiatric Association app evaluation model to evaluate the top-returned apps from Android and Apple app store platforms related to smoking cessation and investigate the common app features available for end users. METHODS: We conducted a search of both Android and iOS app stores in July 2021 for apps related to the keywords "smoking," "tobacco," "smoke," and "cigarette" to evaluate apps for smoking cessation. Apps were screened for relevance, and trained raters identified and analyzed features, including accessibility (ie, cost), privacy, clinical foundation, and features of the apps, using a systematic framework of 105 objective questions from the American Psychiatric Association app evaluation model. All app rating data were deposited in mindapps, a publicly accessible database that is continuously updated every 6 months given the dynamic nature of apps available in the marketplace. We characterized apps available in July 2021 and November 2022. RESULTS: We initially identified 389 apps, excluded 161 due to irrelevance and nonfunctioning, and rated 228, including 152 available for Android platforms and 120 available for iOS platforms. Some of the top-returned apps (71/228, 31%) in 2021 were no longer functioning in 2022. Our analysis of rated apps revealed limitations in accessibility and features. While most apps (179/228, 78%) were free to download, over half had costs associated with in-app purchases or full use. Less than 65% (149/228) had a privacy policy addressing the data collected in the app. In terms of intervention features, more than 56% (128/228) of apps allowed the user to set and check in on goals, and more than 46% (106/228) of them provided psychoeducation, although few apps provided evidence-based support for smoking cessation, such as peer support or skill training, including mindfulness and deep breathing, and even fewer provided evidence-based interventions, such as acceptance and commitment therapy or cognitive behavioral therapy. Only 12 apps in 2021 and 11 in 2022 had published studies supporting the feasibility or efficacy for smoking cessation. CONCLUSIONS: Numerous smoking cessation apps were identified, but analysis revealed limitations, including high rates of irrelevant and nonfunctioning apps, high rates of turnover, and few apps providing evidence-based support for smoking cessation. Thus, it may be challenging for consumers to identify relevant, evidence-based apps to support smoking cessation in the app store, and a comprehensive evaluation system of mental health apps is critically important.

Transdiagnostic Connectome-Based Prediction of Craving.

OBJECTIVE: Craving is a central construct in the study of motivation and human behavior and is also a clinical symptom of substance and non-substance-related addictive disorders. Thus, craving represents a target for transdiagnostic modeling. METHODS: The authors applied connectome-based predictive modeling (CPM) to functional connectivity data in a large (N=274) transdiagnostic sample of individuals with and without substance use-related conditions, to predict self-reported craving. Functional connectomes derived from three guided imagery conditions of personalized appetitive, stress, and neutral-relaxing experiences were used to predict craving rated before and after each imagery condition. The generalizability of the "craving network" was tested in an independent sample using functional connectomes derived from a cue-induced craving task collected before and after fasting to predict craving rated during fasting. RESULTS: CPM successfully predicted craving, thereby identifying a transdiagnostic "craving network." Anatomical localization of model contribution suggested that the strongest predictors of craving were regions of the salience, subcortical, and default mode networks. As external validation, in an independent sample, the "craving network" predicted food craving during fasting using data from a cue-induced craving task. CONCLUSIONS: These data provide a transdiagnostic perspective to a key phenomenological feature of addictive disorders-craving-and identify a common "craving network" across individuals with and without substance use-related disorders, thereby suggesting a neural signature for craving or urge for motivated behaviors.

A Network Analysis of Cigarette Craving.

INTRODUCTION: Craving is considered a central process to addictive behavior including cigarette smoking, although the clinical utility of craving relies on how it is defined and measured. Network analysis enables examining the network structure of craving symptoms, identifying the most central symptoms of cigarette craving, and improving our understanding of craving and its measurement. AIMS AND METHODS: This study used network analysis to identify the central symptoms of self-reported cigarette craving as measured by the Craving Experience Questionnaire, which assesses both craving strength and craving frequency. Data were obtained from baseline of a randomized controlled trial of mindfulness training for smoking cessation. RESULTS: The most central symptoms in an overall cigarette craving network were the frequency of imagining its smell, imagining its taste, and intrusive thoughts. The most central symptoms of both craving frequency and craving strength sub-networks were imagining its taste, the urge to have it, and intrusive thoughts. CONCLUSIONS: The most central craving symptoms reported by individuals in treatment for cigarette smoking were from the frequency domain, demonstrating the value of assessing craving frequency along with craving strength. Central craving symptoms included multisensory imagery (taste, smell), intrusive thoughts, and urge, providing additional evidence that these symptoms may be important to consider in craving measurement and intervention. Findings provide insight into the symptoms that are central to craving, contributing to a better understanding of cigarette cravings, and suggesting potential targets for clinical interventions. IMPLICATIONS: This study used network analysis to identify central symptoms of cigarette craving. Both craving frequency and strength were assessed. The most central symptoms of cigarette craving were related to craving frequency. Central symptoms included multisensory imagery, intrusive thoughts, and urge. Central symptoms might be targeted by smoking cessation treatment.

E-cigarette Marketing Expenditures in the United States From 2016 to 2021: Targeted Media Outlets Geared Toward People Who Are at Increased Risk for Tobacco Use.

INTRODUCTION: E-cigarette advertising exposure is linked to e-cigarette initiation and use. Thus, monitoring trends in e-cigarette advertising practices is important to understand e-cigarette use patterns observed over recent years. AIMS AND METHODS: E-cigarette advertising expenditures (January 2016-July 2021; Numerator Ad Intel) for 154 U.S. market areas were harmonized with U.S. Census sociodemographic data through Nielsen zip code designations by market area. Descriptive statistics and multivariable linear regressions were used to examine trends in e-cigarette advertising expenditures across media outlets and associations between sociodemographic characteristics and e-cigarette advertising over time. RESULTS: E-cigarette advertising expenditures peaked in 2018/2019, followed by a sharp decline in 2020. Expenditures were concentrated primarily on print (58.9%), TV (20.6%), and radio (14.4%). Major print outlets were Sports Illustrated, Rolling Stone, and Star magazines. Top TV channels were AMC, Investigation Discovery, and TBS. TV advertisements were purchased commonly during popular movies and TV series (eg King of Queens, Everybody Loves Raymond, The Walking Dead). Higher expenditures were associated with U.S. market areas that had (1) a larger percentage of non-rural zip codes (radio), (2) smaller male populations (radio), and (3) larger White or Caucasian, Black or African American, American Indian or Alaska Native, Asian, and Other or Multiracial populations (radio, print, online display, and online video). CONCLUSIONS: E-cigarette companies advertised in print magazines geared toward males and youth and young adults, radio commercials focused in urban areas with smaller male populations, and nationwide TV commercials. Declines in e-cigarette advertising expenditures in 2020 demonstrate the potential impact that federal policies may have on protecting populations who are at higher risk for tobacco use from predatory advertising practices. IMPLICATIONS: E-cigarette advertising exposure is associated with the initiation and use of e-cigarettes. This study shows how e-cigarette marketing expenditures in the United States may have targeted specific consumers (eg youth and young adults) between 2016 and 2021. The precipitous drop in advertising expenditures across all outlets during early 2020 corresponds with the implementation of the Tobacco 21 federal policy, the federal enforcement policy to remove most unauthorized flavored e-cigarette cartridges from the U.S. market, preparations for FDA's premarket review of e-cigarette products, and the decision by several TV broadcast companies to stop showing e-cigarette ads. The potential impact of federal policies may have far-reaching implications for protecting populations who are at high risk for tobacco use and its health consequences.

Models for Zero-Inflated and Overdispersed Correlated Count Data: An Application to Cigarette Use.

INTRODUCTION: Count outcomes in tobacco research are often analyzed with the Poisson distribution. However, they often exhibit features such as overdispersion (variance larger than expected) and zero inflation (extra zeros) that violate model assumptions. Furthermore, longitudinal studies have repeated measures that generate correlated counts. Failure to account for overdispersion, zero inflation, and correlation can yield incorrect statistical inferences. Thus, it is important to familiarize researchers with proper models for such data. AIMS AND METHODS: Poisson and Negative Binomial models with correlated random effects with and without zero inflation are presented. The illustrative data comes from a study comparing a mindfulness training app (Craving to Quit [C2Q], n = 60) with a control app (experience sampling-only app, n = 66) on smoking frequency at 1, 3, and 6 months. Predictors include app, time, the app-by-time interaction, and baseline smoking. Each model is evaluated in terms of accounting for zero inflation, overdispersion, and correlation in the data. Emphasis is placed on evaluating model fit, subject-specific interpretation of effects, and choosing an appropriate model. RESULTS: The hurdle Poisson model provided the best fit to the data. Smoking abstinence rates were 33%, 32%, and 28% at 1, 3, and 6 months, respectively, with variance larger than expected by a factor >7 at each follow-up. Individuals on C2Q were less likely to achieve abstinence across time but likely to smoke fewer cigarettes if smoking. CONCLUSIONS: The models presented are specifically suited for analyzing correlated count outcomes and account for zero inflation and overdispersion. We provide guidance to researchers on the use of these models to better inform nicotine and tobacco research. IMPLICATIONS: In tobacco research, count outcomes are often measured repeatedly on the same subject and thus correlated. Such outcomes often have many zeros and exhibit large variances relative to the mean. Analyzing such data require models specifically suited for correlated counts. The presented models and guidelines could improve the rigor of the analysis of correlated count data and thus increase the impact of studies in nicotine and tobacco research using such outcomes.

Smartphone Apps Targeting Youth Tobacco Use Prevention and Cessation: An Assessment of Credibility and Quality.

Purpose of Review: The goals of this study were to identify smartphone apps targeting youth tobacco use prevention and/or cessation discussed in the academic literature and/or available in the Apple App Store and to review and rate the credibility of the apps. We took a multiphase approach in a non-systematic review that involved conducting parallel literature and App Store searches, screening the returned literature and apps for inclusion, characterizing the studies and apps, and evaluating app quality using a standardized rating scale. Recent Findings: The negative consequences of youth tobacco use initiation are profound and far-reaching. Half of the youth who use nicotine want to quit, but quit rates are low. The integration of smartphone apps shows promise in complementing and enhancing evidence-based youth tobacco prevention and treatment methods. Summary: Consistent with prior reviews, we identified a disconnect between apps that are readily accessible and those that have an evidence base, and many popular apps received low quality scores. Findings suggest a need for better integration between evidence-based and popular, available apps targeting youth tobacco use.

A Network Investigation of Core Mindfulness Processes.

Objective: Mindfulness has been associated with improved psychological well-being and health, although outcomes from mindfulness-based interventions are mixed. One challenge is a limited understanding about which specific processes are core to mindfulness. Network analysis offers a method to characterize the core processes of mindfulness. Methods: This study used network analysis to identify which processes are central to mindfulness (have the strongest connectivity with other mindfulness processes) as measured by the Five Facets Mindfulness Questionnaire- Short Form, analyzed at the item-level. Data were obtained from baseline of a randomized clinical trial of smartphone app-based mindfulness training for smoking cessation. Results: The most central processes in the mindfulness network included, "I think some of my emotions are bad or inappropriate and I shouldn't feel them," an aspect of Nonjudgment/acceptance; as well as "I can easily put my beliefs, opinions, and expectations into words," and "It's hard for me to find the words to describe what I'm thinking," aspects of Describing. Conclusions: Findings help to clarify which processes are to mindfulness, contributing to a better understanding of the definition of mindfulness, and suggest factors that may be promising to target in mindfulness-based interventions. Future research should examine if mindfulness-based interventions may be improved by targeting these core mindfulness processes.

Effects of Smoking Status and State on Intrinsic Connectivity.

BACKGROUND: Smoking behavior during the first 24 hours of a quit attempt is a significant predictor of longer-term abstinence, yet little is known about the neurobiology of early tobacco abstinence. Specifically, the effects of acute tobacco deprivation and reinstatement on brain function-particularly at the level of large-scale network dynamics and assessed across the entire brain-remain incompletely understood. To address this gap, this study used a mixed within- and between-subjects design to assess the effects of smoking status (yes/no smoker) and state (deprived vs. satiated) on whole-brain patterns of intrinsic connectivity. METHODS: Participants included 42 tobacco smokers who underwent resting-state functional magnetic resonance imaging following overnight abstinence (deprived state) and following smoking reinstatement (satiated state, randomized order across participants). Sixty healthy control nonsmokers underwent a single resting-state scan using the same acquisition parameters. Functional connectivity data were analyzed using both a canonical network-of-interest approach and a whole-brain, data-driven approach, i.e., intrinsic connectivity distribution. RESULTS: Network-of-interest-based analyses indicated decreased functional connectivity within frontoparietal and salience networks among smokers relative to nonsmokers as well as effects of smoking state on default mode connectivity. In addition, intrinsic connectivity distribution analyses identified novel between-group differences in subcortical-cerebellar and corticocerebellar networks that were largely smoking state dependent. CONCLUSIONS: These data demonstrate the importance of considering smoking state and the utility of using both theory- and data-driven analysis approaches. These data provide much-needed insight into the functional neurobiology of early abstinence, which may be used in the development of novel treatments.

Systematic review of structural and functional neuroimaging studies of cannabis use in adolescence and emerging adulthood: evidence from 90 studies and 9441 participants.

Cannabis use peaks in adolescence, and adolescents may be more vulnerable to the neural effects of cannabis and cannabis-related harms due to ongoing brain development during this period. In light of ongoing cannabis policy changes, increased availability, reduced perceptions of harm, heightened interest in medicinal applications of cannabis, and drastic increases in cannabis potency, it is essential to establish an understanding of cannabis effects on the developing adolescent brain. This systematic review aims to: (1) synthesize extant literature on functional and structural neural alterations associated with cannabis use during adolescence and emerging adulthood; (2) identify gaps in the literature that critically impede our ability to accurately assess the effect of cannabis on adolescent brain function and development; and (3) provide recommendations for future research to bridge these gaps and elucidate the mechanisms underlying cannabis-related harms in adolescence and emerging adulthood, with the long-term goal of facilitating the development of improved prevention, early intervention, and treatment approaches targeting adolescent cannabis users (CU). Based on a systematic search of Medline and PsycInfo and other non-systematic sources, we identified 90 studies including 9441 adolescents and emerging adults (n = 3924 CU, n = 5517 non-CU), which provide preliminary evidence for functional and structural alterations in frontoparietal, frontolimbic, frontostriatal, and cerebellar regions among adolescent cannabis users. Larger, more rigorous studies are essential to reconcile divergent results, assess potential moderators of cannabis effects on the developing brain, disentangle risk factors for use from consequences of exposure, and elucidate the extent to which cannabis effects are reversible with abstinence. Guidelines for conducting this work are provided.

Smartband-Based Automatic Smoking Detection and Real-time Mindfulness Intervention: Protocol for a Feasibility Trial.

BACKGROUND: Smoking is the leading cause of preventable death in the United States. Smoking cessation interventions delivered by smartphone apps are a promising tool for helping smokers quit. However, currently available smartphone apps for smoking cessation have not exploited their unique potential advantages to aid quitting. Notably, few to no available apps use wearable technologies, most apps require users to self-report their smoking, and few to no apps deliver treatment automatically contingent upon smoking. OBJECTIVE: This pilot trial tests the feasibility of using a smartband and smartphone to monitor and detect smoking and deliver brief mindfulness interventions in real time to reduce smoking. METHODS: Daily smokers (N=100, ≥5 cigarettes per day) wear a smartband for 60 days to monitor and detect smoking, notify them about their smoking events in real time, and deliver real-time brief mindfulness exercises triggered by detected smoking events or targeted at predicted smoking events. Smokers set a quit date at 30 days. A three-step intervention to reduce smoking is tested. First, participants wear a smartband to monitor and detect smoking, and notify them of smoking events in real time to bring awareness to smoking and triggers for 21 days. Next, a "mindful smoking" exercise is triggered by detected smoking events to bring a clear recognition of the actual effects of smoking for 7 days. Finally, after their quit date, a "RAIN" (recognize, allow, investigate, nonidentification) exercise is delivered to predicted smoking events (based on the initial 3 weeks of tracking smoking data) to help smokers learn to work mindfully with cravings rather than smoke for 30 days. The primary outcomes are feasibility measures of treatment fidelity, adherence, and acceptability. The secondary outcomes are smoking rates at end of treatment. RESULTS: Recruitment for this trial started in May 2021 and will continue until November 2021 or until enrollment is completed. Data monitoring and management are ongoing for enrolled participants. The final 60-day end of treatment data is anticipated in January 2022. We expect that all trial results will be available in April 2022. CONCLUSIONS: Findings will provide data and information on the feasibility of using a smartband and smartphone to monitor and detect smoking and deliver real-time brief mindfulness interventions, and whether the intervention warrants additional testing for smoking cessation. TRIAL REGISTRATION: ClinicalTrials.gov NCT03995225; https://clinicaltrials.gov/ct2/show/NCT03995225. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/32521.

Awareness, affect, and craving during smoking cessation: An experience sampling study.

OBJECTIVE: Mindfulness has received attention in smoking cessation research, yet the mechanisms by which mindfulness may promote smoking cessation are not well understood. Mindfulness training may help individuals increase awareness and respond skillfully to processes that contribute to smoking, such as affective states and craving. This study used experience sampling (ES) to test how awareness was related to craving, positive and negative affect and smoking, in the moment, among smokers in treatment for smoking cessation. METHOD: Participants (N = 228) were part of a clinical trial evaluating Craving to Quit, a smartphone app for mindfulness training for smoking cessation, compared to an app delivering only ES. All participants were asked to complete 22 days of ES, with up to 6 ES surveys per day, measuring awareness, craving, positive and negative affect and smoking. Data were analyzed using multilevel linear modeling. RESULTS: Both at the within and between-person level, higher awareness was associated with higher positive affect, lower craving and lower negative affect. Lower within-person craving was associated with lower smoking. Within-person awareness, positive and negative affect were not significantly associated with smoking. At the between-person level, higher awareness and higher positive affect, and lower negative affect and lower craving were associated with lower smoking. CONCLUSIONS: Awareness of current experience was related to key psychological variables linked to behavior change in smoking cessation, namely positive and negative affect and craving, among smokers trying to quit. Future studies should test whether learning to increase awareness, such as through mindfulness training, may benefit smokers in treatment. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Drinking and responses to antidrinking messages among young adults: An fMRI study.

Young adults consume most of their alcohol by binge drinking, and more than one-third report binge drinking in the past month. Some will transition out of excessive drinking, while others will maintain or increase alcohol use into adulthood. Public health campaigns depicting negative consequences of drinking have shown some efficacy at reducing this behavior. However, substance use in dependent individuals is governed in part by automatic or habitual responses to drug cues rather than the consequences. This study used functional magnetic resonance imaging to measure neural responses to drinking cues and drinking cues paired with antidrinking messages among young adults who binge drink (N = 30). This study also explored responses to smoking cues and antismoking messages. Neural responses were also compared between drinking/smoking and neutral cues. Self-reported drinking and smoking were collected at baseline, postscan, and 1 month. Results indicate that activity in the ventral striatum-implicated in reward processing-was lower for drinking cues paired with antidrinking messages than drinking cues. This difference was less pronounced in young adults who reported greater baseline past month drinking quantity. Past month drinking quantity decreased from baseline to 1 month. Further, young adults who showed higher activity during antidrinking messages in the medial prefrontal cortex-implicated in processing message self-relevance- reported a greater decrease in past month drinking frequency from baseline to 1 month. Findings may help to identify young adults who are at risk for continued heavy drinking in adulthood and inform interventions aimed to reduce drinking and reward in young adults.

Qualitative Evaluations of mHealth Interventions: Current Gaps and Future Directions.

Psycho-social factors are often addressed in behavioral health studies. While the purpose of many mHealth interventions is to facilitate behavior change, the focus is more prominently on the functionality and usability of the technology and less on the psycho-social factors that contribute to behavior change. Here we aim to identify the extent to which mHealth interventions for patient self- management address psychological factors. By understanding users' motivations, facilitators, and mindsets, we can better tailor mHealth interventions to promote behavior change.

Review of Popularity and Quality Standards of Opioid-Related Smartphone Apps.

PURPOSE OF REVIEW: Opioid misuse, addiction, and related harm is a global crisis that affects public health and social and economic welfare. Many of the strategies being used to combat the opioid crisis could benefit from improved access and dissemination, such as that afforded by smartphone apps. The goal of this study was to characterize the purpose, audience, quality and popularity of opioid-related smartphone apps. Using web scraping, available information from 619 opioid-related apps (e.g., popularity metrics) was downloaded from Google Play, and 59 apps met criteria for review. The apps were additionally coded for quality by two raters using an 8-item screener for the American Psychiatric Association App Evaluation Model. FINDINGS: Sixty one percent of apps targeted patients, 29% providers, 8% the general community, and 2% healthcare trainees. Regarding app purpose, 49% addressed treatment, 27% prevention, and 24% overdose. Only one app met all criteria on the screener for quality, and there was no association between a total score calculated for the screener and measures of app popularity (e.g., star ratings; R2=0.10, p=0.19). SUMMARY: Opioid-related apps available for consumers addressed key stakeholders (patients, providers, community) and were consistent with strategies to address the opioid crisis (prevention, treatment, overdose). However, there was little evidence that available opioid-related apps meet basic quality standards, and no relationship was found between app quality and popularity. This review was conducted at the level of consumer decision-making (i.e., the app store), where only a handful of opioid-related apps met quality standards enough to warrant a more detailed evaluation of the app before recommendation for use. Because smartphone apps could be a critical tool to increase access to and utilization of opioid prevention, treatment, and recovery services, further development and testing is sorely needed.

Craving to Quit: A Randomized Controlled Trial of Smartphone App-Based Mindfulness Training for Smoking Cessation.

INTRODUCTION: Mindfulness training may reduce smoking rates and lessen the association between craving and smoking. This trial tested the efficacy of mindfulness training via smartphone app to reduce smoking. Experience sampling (ES) was used to measure real-time craving, smoking, and mindfulness. METHODS: A researcher-blind, parallel randomized controlled trial compared the efficacy of mobile mindfulness training with experience sampling (MMT-ES; Craving to Quit) versus experience sampling only (ES) to (1) increase 1-week point-prevalence abstinence rates at 6 months, and (2) lessen the association between craving and smoking. A modified intent-to-treat approach was used for treatment starters (MMT-ES n = 143; ES n = 182; 72% female, 81% white, age 41 ± 12 year). RESULTS: No group difference was found in smoking abstinence at 6 months (overall, 11.1%; MMT-ES, 9.8%; ES, 12.1%; χ2(1) = 0.43, p = .51). From baseline to 6 months, both groups showed a reduction in cigarettes per day (p < .0001), craving strength (p < .0001) and frequency (p < .0001), and an increase in mindfulness (p < .05). Using ES data, a craving by group interaction was observed (F(1,3785) = 3.71, p = .05) driven by a stronger positive association between craving and cigarettes per day for ES (t = 4.96, p < .0001) versus MMT-ES (t = 2.03, p = .04). Within MMT-ES, the relationship between craving and cigarettes per day decreased as treatment completion increased (F(1,104) = 4.44, p = .04). CONCLUSIONS: Although mindfulness training via smartphone app did not lead to reduced smoking rates compared with control, our findings provide preliminary evidence that mindfulness training via smartphone app may help lessen the association between craving and smoking, an effect that may be meaningful to support quitting in the longer term. IMPLICATIONS: This is the first reported full-scale randomized controlled trial of any smartphone app for smoking cessation. Findings provide preliminary evidence that smartphone app-based MMT-ES may lessen the association between craving and smoking. TRIAL REGISTRATION: Clinicaltrials.gov NCT02134509.

Effects of the Psychedelic Amphetamine MDA (3,4-Methylenedioxyamphetamine) in Healthy Volunteers.

Entactogens such as 3,4-Methylenedioxymethamphetamine (MDMA, "molly", "ecstasy") appear to have unusual, potentially therapeutic, emotional effects. Understanding their mechanisms can benefit from clinical experiments with related drugs. Yet the first known drug with such properties, 3,4-Methylenedioxyamphetamine (MDA), remains poorly studied and its pharmacokinetics in humans are unknown. We conducted a within-subjects, double-blind, placebo-controlled study of 1.4 mg/kg oral racemic MDA and compared results to those from our prior similar studies with 1.5 mg/kg oral racemic MDMA. MDA was well-tolerated by participants. MDA induced robust increases in heart rate and blood pressure and increased cortisol and prolactin to a similar degree as MDMA. MDA self-report effects shared features with MDMA as well as with classical psychedelics. MDA self-report effects lasted longer than those of MDMA, with MDA effects remaining elevated at 8 h while MDMA effects resolved by 6 h. Cmax and AUC0-∞ for MDA were 229 ± 39 (mean ± SD) and 3636 ± 958 µg/L for MDA and 92 ± 61 and 1544 ± 741 µg/L for the metabolite 4-hydroxy-3-methoxyamphetamine (HMA). There was considerable between-subject variation in MDA/HMA ratios. The similarity of MDA and MDMA pharmacokinetics suggests that the greater duration of MDA effects is due to pharmacodynamics rather than pharmacokinetics.

Mobile Applications for the Treatment of Tobacco Use and Dependence.

PURPOSE OF REVIEW: Smoking remains a leading preventable cause of premature death in the world; thus, developing effective and scalable smoking cessation interventions is crucial. This review uses the Obesity-Related Behavioral Intervention Trials (ORBIT) model for early phase development of behavioral interventions to conceptually organize the state of research of mobile applications (apps) for smoking cessation, briefly highlight their technical and theory-based components, and describe available data on efficacy and effectiveness. RECENT FINDINGS: Our review suggests that there is a need for more programmatic efforts in the development of mobile applications for smoking cessation, though it is promising that more studies are reporting early phase research such as user-centered design. We identified and described the app features used to implement smoking cessation interventions, and found that the majority of the apps studied used a limited number of mechanisms of intervention delivery, though more effort is needed to link specific app features with clinical outcomes. Similar to earlier reviews, we found that few apps have yet been tested in large well-controlled clinical trials, although progress is being made in reporting transparency with protocol papers and clinical trial registration. SUMMARY: ORBIT is an effective model to summarize and guide research on smartphone apps for smoking cessation. Continued improvements in early phase research and app design should accelerate the progress of research in mobile apps for smoking cessation.