Many individuals with alcohol use disorder (AUD) prefer a goal of moderation, because they do not see their drinking as causing severe enough consequences to merit abstinence. Given that individuals attempting to moderate will continue to put themselves in contexts where drinking occurs, understanding how distinct external alcohol cues prompt craving is important for implementing the optimal treatments for individuals with AUD. Using data from a randomized controlled trial of stepped care brief interventions for AUD, this study explored the relationship between drinking contexts and craving in individuals attempting to moderate their drinking using ecological momentary assessment (EMA). At baseline, participants were asked to prospectively identify drinking contexts that were particularly likely to elicit intense craving and heavy drinking, called highly valued drinking contexts (HVCs). During EMA, participants were asked to report three times a day (morning, afternoon, evening) on their non-mutually exclusive contexts and their level of craving. Using multilevel modeling, all drinking contexts were tested as concurrent predictors of craving across the 84 days of the study. Next, AUD severity was tested as a moderator of HVC on craving. Results demonstrated that being in an HVC corresponded to greater reports of any craving and intensity of craving, over and above the influences of several other contextual factors (e.g., negative affect and already drinking). AUD severity significantly moderated HVC's impact on any craving, such that greater AUD severity potentiated HVC's already high odds of any craving. Implications for treatments for individuals with AUD are discussed. Public Health SignificanceAlcohol use disorder (AUD) is a highly prevalent disorder, experienced around the globe. Craving, one of the hallmarks of AUD, is highly associated with continued use and/or relapse to use. This study provides important information about context eliciting craving that is useful to both those suffering with and treating AUD, so as to provide greater opportunities for reduction of harm and successful recovery.

Background The classic Marshmallow Test, where children were offered a choice between one small but immediate reward (eg, one marshmallow) or a larger reward (eg, two marshmallows) if they waited for a period of time, instigated a wealth of research on the relationships among impulsive responding, self-regulation, and clinical and life outcomes. Impulsivity is a hallmark feature of self-regulation failures that lead to poor health decisions and outcomes, making understanding and treating impulsivity one of the most important constructs to tackle in building a culture of health. Despite a large literature base, impulsivity measurement remains difficult due to the multidimensional nature of the construct and limited methods of assessment in daily life. Mobile devices and the rise of mobile health (mHealth) have changed our ability to assess and intervene with individuals remotely, providing an avenue for ambulatory diagnostic testing and interventions. Longitudinal studies with mobile devices can further help to understand impulsive behaviors and variation in state impulsivity in daily life. Objective The aim of this study was to develop and validate an impulsivity mHealth diagnostics and monitoring app called Digital Marshmallow Test (DMT) using both the Apple and Android platforms for widespread dissemination to researchers, clinicians, and the general public. Methods The DMT app was developed using Apple’s ResearchKit (iOS) and Android’s ResearchStack open source frameworks for developing health research study apps. The DMT app consists of three main modules: self-report, ecological momentary assessment, and active behavioral and cognitive tasks. We conducted a study with a 21-day assessment period (N=116 participants) to validate the novel measures of the DMT app. Results We used a semantic differential scale to develop self-report trait and momentary state measures of impulsivity as part of the DMT app. We identified three state factors (inefficient, thrill seeking, and intentional) that correlated highly with established measures of impulsivity. We further leveraged momentary semantic differential questions to examine intraindividual variability, the effect of daily life, and the contextual effect of mood on state impulsivity and daily impulsive behaviors. Our results indicated validation of the self-report sematic differential and related results, and of the mobile behavioral tasks, including the Balloon Analogue Risk Task and Go-No-Go task, with relatively low validity of the mobile Delay Discounting task. We discuss the design implications of these results to mHealth research. Conclusions This study demonstrates the potential for assessing different facets of trait and state impulsivity during everyday life and in clinical settings using the DMT mobile app. The DMT app can be further used to enhance our understanding of the individual facets that underlie impulsive behaviors, as well as providing a promising avenue for digital interventions. Trial Registration ClinicalTrials.gov NCT03006653; https://www.clinicaltrials.gov/ct2/show/NCT03006653

Background Mobile health technology has demonstrated the ability of smartphone apps and sensors to collect data pertaining to patient activity, behavior, and cognition. It also offers the opportunity to understand how everyday passive mobile metrics such as battery life and screen time relate to mental health outcomes through continuous sensing. Impulsivity is an underlying factor in numerous physical and mental health problems. However, few studies have been designed to help us understand how mobile sensors and self-report data can improve our understanding of impulsive behavior. Objective The objective of this study was to explore the feasibility of using mobile sensor data to detect and monitor self-reported state impulsivity and impulsive behavior passively via a cross-platform mobile sensing application. Methods We enrolled 26 participants who were part of a larger study of impulsivity to take part in a real-world, continuous mobile sensing study over 21 days on both Apple operating system (iOS) and Android platforms. The mobile sensing system (mPulse) collected data from call logs, battery charging, and screen checking. To validate the model, we used mobile sensing features to predict common self-reported impulsivity traits, objective mobile behavioral and cognitive measures, and ecological momentary assessment (EMA) of state impulsivity and constructs related to impulsive behavior (ie, risk-taking, attention, and affect). Results Overall, the findings suggested that passive measures of mobile phone use such as call logs, battery charging, and screen checking can predict different facets of trait and state impulsivity and impulsive behavior. For impulsivity traits, the models significantly explained variance in sensation seeking, planning, and lack of perseverance traits but failed to explain motor, urgency, lack of premeditation, and attention traits. Passive sensing features from call logs, battery charging, and screen checking were particularly useful in explaining and predicting trait-based sensation seeking. On a daily level, the model successfully predicted objective behavioral measures such as present bias in delay discounting tasks, commission and omission errors in a cognitive attention task, and total gains in a risk-taking task. Our models also predicted daily EMA questions on positivity, stress, productivity, healthiness, and emotion and affect. Perhaps most intriguingly, the model failed to predict daily EMA designed to measure previous-day impulsivity using face-valid questions. Conclusions The study demonstrated the potential for developing trait and state impulsivity phenotypes and detecting impulsive behavior from everyday mobile phone sensors. Limitations of the current research and suggestions for building more precise passive sensing models are discussed. Trial Registration ClinicalTrials.gov NCT03006653; https://clinicaltrials.gov/ct2/show/NCT03006653

BACKGROUND Impulsivity is a hallmark feature of self-regulation failures that lead to poor health decisions and outcomes, making understanding and treating impulsivity one of the most important constructs to tackle in building a culture of health. Despite a large literature base, impulsivity measurement remains difficult due to the multi-dimensional nature of the construct and limited methods of assessment. Mobile devices and the rise of mobile health changed our ability to assess and intervene with individuals remotely, providing an avenue for ambulatory diagnostic testing and interventions. OBJECTIVE Develop and validate a mobile health (mHealth) diagnostic and monitoring app of impulsivity called the Digital Marshmallow Test (DMT) using both the Apple and Android platforms for widespread dissemination to researchers, clinicians, and the general public. METHODS Digital Marshmallow Test (DMT) was developed based on Apple's ResearchKit (iOS) and ResearchStack (Android) open-source frameworks for developing health research study apps. DMT app consists of three main modules: basic and smart self-report, ecological momentary assessments, and active behavioral and cognitive tasks. We conducted a 21-days study (N=116) to validate the novel measures of the DMT app. RESULTS We used semantic differential scale to develop a self-report trait and state measures of impulsivity as part of the DMT app. We identify three factors (inefficient, thrill-seeking, and intentional) that correlated highly with established measures of impulsivity traits. We further leveraged momentary semantic differential questions to examine intra-individual variability, the effect of daily life, and the contextual effect of mood on state impulsivity and related constructs. We also validated novel mobile versions of behavioral and cognitive tasks. Our results indicate high validity of the mBART task (Balloon Analogue Risk Task), moderate validity of the mGNG task (Go-no-go), and low validity of the mDD task (Delayed Discounting). We discuss the design implications of these results to mobile health research. CONCLUSIONS The study demonstrated the potential for assessing trait and state impulsivity during everyday life using the Digital Marshmallow Test (DMT) mobile app. DMT app can be further used to enhance our understanding of impulsivity and related constructs as well as to provide a promising avenue for digital interventions. CLINICALTRIAL ClinicalTrials.gov NCT03006653

Digital health technologies (DHTs) are coming of age in improving the assessment, treatment, and continuing care of individuals struggling with problem substance use and impulsive behavior. While many of these methods are still being tested and continue to evolve, DHTs constitute a powerful new way of enhancing traditional treatment models and offer the opportunity to meet individuals in their natural environments to combat reductions in self-regulation with just-in-time tailored interventions. This chapter provides readers with a general overview of DHTs in the context of the treatment of substance use disorders, detailing a brief history of computer-based interventions for substance use, the core mechanisms of mobile and wireless technologies, a review of how specific technology components (e.g., video chat, text messaging) can be used to assess and treat substance use disorders and self-regulation failures, and potential concerns surrounding the integration of DHTs into addiction care.

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BACKGROUND Mobile health technology has demonstrated the ability of smartphone apps and sensors to collect data pertaining to patient activity, behavior, and cognition. It also offers the opportunity to understand how everyday passive mobile metrics such as battery life and screen time relate to mental health outcomes through continuous sensing. Impulsivity is an underlying factor in numerous physical and mental health problems. However, few studies have been designed to help us understand how mobile sensors and self-report data can improve our understanding of impulsive behavior. OBJECTIVE The objective of this study was to explore the feasibility of using mobile sensor data to detect and monitor self-reported state impulsivity and impulsive behavior passively via a cross-platform mobile sensing application. METHODS We enrolled 26 participants who were part of a larger study of impulsivity to take part in a real-world, continuous mobile sensing study over 21 days on both Apple operating system (iOS) and Android platforms. The mobile sensing system (mPulse) collected data from call logs, battery charging, and screen checking. To validate the model, we used mobile sensing features to predict common self-reported impulsivity traits, objective mobile behavioral and cognitive measures, and ecological momentary assessment (EMA) of state impulsivity and constructs related to impulsive behavior (ie, risk-taking, attention, and affect). RESULTS Overall, the findings suggested that passive measures of mobile phone use such as call logs, battery charging, and screen checking can predict different facets of trait and state impulsivity and impulsive behavior. For impulsivity traits, the models significantly explained variance in sensation seeking, planning, and lack of perseverance traits but failed to explain motor, urgency, lack of premeditation, and attention traits. Passive sensing features from call logs, battery charging, and screen checking were particularly useful in explaining and predicting trait-based sensation seeking. On a daily level, the model successfully predicted objective behavioral measures such as present bias in delay discounting tasks, commission and omission errors in a cognitive attention task, and total gains in a risk-taking task. Our models also predicted daily EMA questions on positivity, stress, productivity, healthiness, and emotion and affect. Perhaps most intriguingly, the model failed to predict daily EMA designed to measure previous-day impulsivity using face-valid questions. CONCLUSIONS The study demonstrated the potential for developing trait and state impulsivity phenotypes and detecting impulsive behavior from everyday mobile phone sensors. Limitations of the current research and suggestions for building more precise passive sensing models are discussed. CLINICALTRIAL ClinicalTrials.gov NCT03006653; https://clinicaltrials.gov/ct2/show/NCT03006653