Developments in Mobile Health Just-in-Time Adaptive Interventions for Addiction Science

Applying the Just-In-Time Adaptive Intervention Framework to the Development of Gambling Interventions

Just-In-Time Adaptive Interventions (JITAIs) are emerging "push" mHealth interventions that provide the right type, timing, and amount of support to address the dynamically-changing needs for each individual. Although JITAIs are well-suited to the delivery of interventions for the addictions, few are available to support gambling behaviour change. We therefore developed GamblingLess: In-The-Moment and Gambling Habit Hacker, two smartphone-delivered JITAIs that differ with respect to their target populations, theoretical underpinnings, and decision rules. We aim to describe the decisions, methods, and tools we used to design these two treatments, with a view to providing guidance to addiction researchers who wish to develop JITAIs in the future. Specifically, we describe how we applied a comprehensive, organising scientific framework to define the problem, define just-in-time in the context of the identified problem, and formulate the adaptation strategies. While JITAIs appear to be a promising design in addiction intervention science, we describe several key challenges that arose during development, particularly in relation to applying micro-randomised trials to their evaluation, and offer recommendations for future research. Issues including evaluation considerations, integrating on-demand intervention content, intervention optimisation, combining active and passive assessments, incorporating human facilitation, adding cost-effectiveness evaluations, and redevelopment as transdiagnostic interventions are discussed.

The future of psychological treatments: The Marburg Declaration

Although psychological treatments are broadly recognized as evidence-based interventions for various mental disorders, challenges remain. For example, a substantial proportion of patients receiving such treatments do not fully recover, and many obstacles hinder the dissemination, implementation, and training of psychological treatments. These problems require those in our field to rethink some of our basic models of mental disorders and their treatments, and question how research and practice in clinical psychology should progress. To answer these questions, a group of experts of clinical psychology convened at a Think-Tank in Marburg, Germany, in August 2022 to review the evidence and analyze barriers for current and future developments. After this event, an overview of the current state-of-the-art was drafted and suggestions for improvements and specific recommendations for research and practice were integrated. Recommendations arising from our meeting cover further improving psychological interventions through translational approaches, improving clinical research methodology, bridging the gap between more nomothetic (group-oriented) studies and idiographic (person-centered) decisions, using network approaches in addition to selecting single mechanisms to embrace the complexity of clinical reality, making use of scalable digital options for assessments and interventions, improving the training and education of future psychotherapists, and accepting the societal responsibilities that clinical psychology has in improving national and global health care. The objective of the Marburg Declaration is to stimulate a significant change regarding our understanding of mental disorders and their treatments, with the aim to trigger a new era of evidence-based psychological interventions.

Interpretations and experiences of subjective effects for alcohol alone and when combined with cannabis: A mixed-methods approach

Subjective effects generally describe the feelings one has when consuming substances. There are several tools available for measuring alcohol-related subjective effects but there are reasons to believe that effects are interpreted differently across participants. The assessment of alcohol-related subjective effects is further complicated by the fact that many people use other substances with alcohol, including cannabis. The present study used a mixed-methods approach to evaluate interpretations of 21 subjective effects used in common assessments among a college student sample (N = 99; primarily White [79%], Hispanic [60%] women [74%], 72% of which reported lifetime couse of alcohol and cannabis). We sought to (a) estimate the prevalence of each effect and the amount of alcohol/number of drinks (and, for those with simultaneous use, amount of cannabis/number of hits) required to experience each effect and (b) evaluate how participants interpreted each effect that they had ever experienced when drinking (for our sample who had used only alcohol) or when simultaneously using alcohol and cannabis (for our sample who had reported simultaneous use). Across both samples, we found that several effects were far more common than others and participants had varied interpretations of each subjective effect. Further, qualitative results demonstrated that participants interpreted some subjective effects in a way that differed from the original intention of the measure. Results suggest a degree of measurement error when using common subjective effects assessment tools. Findings lay the groundwork for standardized measures of subjective effects for simultaneous use and have implications for future real-world assessment and intervention work. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Classifying Self-Reported Rheumatoid Arthritis Flares Using Daily Patient-Generated Data From a Smartphone App: Exploratory Analysis Applying Machine Learning Approaches

BACKGROUND

The ability to predict rheumatoid arthritis (RA) flares between clinic visits based on real-time, longitudinal patient-generated data could potentially allow for timely interventions to avoid disease worsening.

OBJECTIVE

This exploratory study aims to investigate the feasibility of using machine learning methods to classify self-reported RA flares based on a small data set of daily symptom data collected on a smartphone app.

METHODS

Daily symptoms and weekly flares reported on the Remote Monitoring of Rheumatoid Arthritis (REMORA) smartphone app from 20 patients with RA over 3 months were used. Predictors were several summary features of the daily symptom scores (eg, pain and fatigue) collected in the week leading up to the flare question. We fitted 3 binary classifiers: logistic regression with and without elastic net regularization, a random forest, and naive Bayes. Performance was evaluated according to the area under the curve (AUC) of the receiver operating characteristic curve. For the best-performing model, we considered sensitivity and specificity for different thresholds in order to illustrate different ways in which the predictive model could behave in a clinical setting.

RESULTS

The data comprised an average of 60.6 daily reports and 10.5 weekly reports per participant. Participants reported a median of 2 (IQR 0.75-4.25) flares each over a median follow-up time of 81 (IQR 79-82) days. AUCs were broadly similar between models, but logistic regression with elastic net regularization had the highest AUC of 0.82. At a cutoff requiring specificity to be 0.80, the corresponding sensitivity to detect flares was 0.60 for this model. The positive predictive value (PPV) in this population was 53%, and the negative predictive value (NPV) was 85%. Given the prevalence of flares, the best PPV achieved meant only around 2 of every 3 positive predictions were correct (PPV 0.65). By prioritizing a higher NPV, the model correctly predicted over 9 in every 10 non-flare weeks, but the accuracy of predicted flares fell to only 1 in 2 being correct (NPV and PPV of 0.92 and 0.51, respectively).

CONCLUSIONS

Predicting self-reported flares based on daily symptom scorings in the preceding week using machine learning methods was feasible. The observed predictive accuracy might improve as we obtain more data, and these exploratory results need to be validated in an external cohort. In the future, analysis of frequently collected patient-generated data may allow us to predict flares before they unfold, opening opportunities for just-in-time adaptative interventions. Depending on the nature and implication of an intervention, different cutoff values for an intervention decision need to be considered, as well as the level of predictive certainty required.

Innovation in the Delivery of Behavioral Health Services

Several factors motivate the need for innovation to improve the delivery of behavioral health services, including increased rates of mental health and substance use disorders, limited access to services, inconsistent use of evidence-based practices, and persistent racial and ethnic disparities. This narrative review identifies promising innovations that address these challenges, assesses empirical evidence for the effectiveness of these innovations and the extent to which they have been adopted and implemented, and suggests next steps for research. We review five categories of innovations: organizational models, including a range of novel locations for providing services and new ways of organizing services within and across sites; information and communication technologies; workforce; treatment technologies; and policy and regulatory changes. We conclude by discussing the need to strengthen and accelerate the contributions of implementation science to close the gap between the launch of innovative behavioral health services and their widespread use.

Designing and testing treatments for alcohol use disorder

This chapter provides a succinct overview of several recommendations for the design and analysis of treatments for AUD with a specific focus on increasing rigor and generalizability of treatment studies in order to increase the reach of AUD treatment. We recommend that researchers always register their trials in a clinical trial registry and make the protocol accessible so that the trial can be replicated in future work, follow CONSORT reporting guidelines when reporting the results of the trial, carefully describe all inclusion and exclusion criteria as well as the randomization scheme, and always use an intent to treat design with attention to analysis of missing data. In addition, we recommend that researchers pay closer attention to recruitment and engagement strategies that increase enrollment and retention of historically marginalized and understudied populations, and we end with a plea for more consideration of implementation science approaches to increase the dissemination and implementation of AUD treatment in real-world settings.

The Promises and Possibilities of Artificial Intelligence in the Delivery of Behavior Analytic Services

Artificial intelligence (AI) has begun to affect nearly every aspect of our daily lives and nearly every industry and profession. Many readers of this journal likely work in one or more areas of behavioral health. For readers who work in behavioral health and who are interested in AI, the purpose of this article is to highlight the pervasiveness of AI research being conducted around many facets of behavioral health service delivery. To do this, we first provide a brief overview of some of the areas within AI and the types of problems each area of AI attempts to solve. We then outline the prototypical client journey in behavioral healthcare beginning with diagnosis/assessment and ending with intervention withdrawal or ongoing monitoring. Next, for each stage in the client journey, we highlight several areas that parallel existing behavior analytic practice where researchers have begun to use AI, often to improve the efficiency of service delivery or to learn new things that improve the effectiveness of behavioral health services. Finally, for those whose appetite has been whet for getting involved with AI, we close by describing three roles they might consider trying out and that parallel the three main domains of behavior analysis. These three roles are an AI tool designer (akin to EAB), AI tool implementer (akin to ABA), or AI tool supporter (akin to practice).

Automated, tailored adaptive mobile messaging to reduce alcohol consumption in help-seeking adults: A randomized controlled trial

AIMS

To test differential outcomes between three 6-month text-messaging interventions to reduce at-risk drinking in help-seeking adults.

DESIGN

A three-arm single-blind randomized controlled trial with 1-, 3-, 6- and 12-month follow-ups.

SETTING

United States. A fully remote trial without human contact, with participants recruited primarily via social media outlets.

PARTICIPANTS

Seven hundred and twenty-three adults (mean = 39.9 years, standard deviation = 10.0; 62.5% female) seeking to reduce their drinking were allocated to 6 months of baseline 'tailored statically' messaging (TS; n = 240), 'tailored adaptive' messaging (TA; n = 239) or 'drink tracking' messaging (DT; n = 244).

INTERVENTIONS

TS consisted of daily text messages to reduce harmful drinking that were tailored to demographics and alcohol use. TA consisted of daily, tailored text messages that were also adapted based on goal achievement and proactive prompts. DT consisted of a weekly assessment for self-reported drinking over the past 7 days.

MEASUREMENTS

The primary outcome measure was weekly sum of standard drinks (SSD) at 6-month follow-up. Secondary outcome measures included drinks per drinking day (DDD), number of drinking days (NDD) per week and heavy drinking days (HDD) at 1-, 3-, 6- and 12-month follow-ups.

FINDINGS

At 6 months, compared with DT, TA resulted in significant SSD reductions of 16.2 (from 28.7 to 12.5) drinks [adjusted risk ratio (aRR) = 0.80, 95% confidence interval (CI) = 0.71, 0.91] using intent-to-treat analysis. TA also resulted in significant improvements in DDD (aRR = 0.84; 95% CI = 0.77-0.92) and drinking days per week (b = -0.39; 95% CI = -0.67, -0.10), but not HDD compared with DT at 6 months. TA was not significantly different from TS at any time-point, except DDD at 6 months. All groups made improvements in SSD at 12-month follow-up compared with baseline with an average reduction of 12.9 drinks per week across groups.

CONCLUSIONS

Automated tailored mobile messaging interventions are scalable solutions that can reduce weekly alcohol consumption in remote help-seeking drinkers over time.

Just-in-time adaptive ecological momentary assessment (JITA-EMA)

Interest in just-in-time adaptive interventions (JITAI) has rapidly increased in recent years. One core challenge for JITAI is the efficient and precise measurement of tailoring variables that are used to inform the timing of momentary intervention delivery. Ecological momentary assessment (EMA) is often used for this purpose, even though EMA in its traditional form was not designed specifically to facilitate momentary interventions. In this article, we introduce just-in-time adaptive EMA (JITA-EMA) as a strategy to reduce participant response burden and decrease measurement error when EMA is used as a tailoring variable in JITAI. JITA-EMA builds on computerized adaptive testing methods developed for purposes of classification (computerized classification testing, CCT), and applies them to the classification of momentary states within individuals. The goal of JITA-EMA is to administer a small and informative selection of EMA questions needed to accurately classify an individual's current state at each measurement occasion. After illustrating the basic components of JITA-EMA (adaptively choosing the initial and subsequent items to administer, adaptively stopping item administration, accommodating dynamically tailored classification cutoffs), we present two simulation studies that explored the performance of JITA-EMA, using the example of momentary fatigue states. Compared with conventional EMA item selection methods that administered a fixed set of questions at each moment, JITA-EMA yielded more accurate momentary classification with fewer questions administered. Our results suggest that JITA-EMA has the potential to enhance some approaches to mobile health interventions by facilitating efficient and precise identification of momentary states that may inform intervention tailoring.

Activity space during treatment with medication for opioid use disorder: Relationships with personality, mood, and drug use

INTRODUCTION

Activity space in people with substance use disorders (SUDs) has been assessed for theoretical reasons and for detection/prevention of relapse. In this observational study, we relate passively obtained activity space measures to mental states and behaviors relevant to the success of treatment for opioid use disorder. Our long-term goal is to use such data to assess risk in real time and to recognize when SUD patients might benefit from a just-in-time intervention.

METHODS

We used GPS data from 238 urban residents in the first 16 weeks of stabilization on medication for opioid use disorder to test preregistered hypotheses about activity space (distance traveled, number of locations, time spent moving, and psychosocial-hazard levels of neighborhoods where participants spent time) in relation to certain static variables (personality, mood propensities) and time-varying treatment-relevant behaviors such as craving and use of opioids and cocaine.

RESULTS

The most consistent findings were that 1) mobility decreased over the course of the study; 2) neuroticism was associated with overall lower mobility; 3) trait-like positive mood (averaged from momentary ratings) was associated with higher mobility; 4) participants who used cocaine more frequently had lower mobility; 5) early in treatment, participants spent less time moving (i.e., were more sedentary) on days when they were craving. Some of these findings were in the expected direction (i.e., the ones involving neuroticism and positive mood), and some were opposite to the expected direction (i.e., we expected cocaine use to be associated with higher mobility); others (e.g., changes in mobility over time or in relation to craving) involved nondirectional hypotheses.

CONCLUSIONS

Real-time information that patients actively provide is valuable for assessing their current state, but providing this information can be burdensome. The current results indicate that certain static or passively obtained data (personality variables and GPS-derived mobility information) are relevant to time-varying, treatment-relevant mental states and drug-related behavior, and therefore might be useful when incorporated into algorithms for detecting need for intervention in real time. Further research should assess how population-specific these relationships are, and how these passive measures can best be combined with low temporal-density, actively-provided data to obtain valid, reliable assessments with minimal burden.

Developing Message Strategies to Engage Racial and Ethnic Minority Groups in Digital Oral Self-Care Interventions: Participatory Co-Design Approach

BACKGROUND

The prevention of oral health diseases is a key public health issue and a major challenge for racial and ethnic minority groups, who often face barriers in accessing dental care. Daily toothbrushing is an important self-care behavior necessary for sustaining good oral health, yet engagement in regular brushing remains a challenge. Identifying strategies to promote engagement in regular oral self-care behaviors among populations at risk of poor oral health is critical.

OBJECTIVE

The formative research described here focused on creating messages for a digital oral self-care intervention targeting a racially and ethnically diverse population. Theoretically grounded strategies (reciprocity, reciprocity-by-proxy, and curiosity) were used to promote engagement in 3 aspects: oral self-care behaviors, an oral care smartphone app, and digital messages. A web-based participatory co-design approach was used to develop messages that are resource efficient, appealing, and novel; this approach involved dental experts, individuals from the general population, and individuals from the target population-dental patients from predominantly low-income racial and ethnic minority groups. Given that many individuals from racially and ethnically diverse populations face anonymity and confidentiality concerns when participating in research, we used an approach to message development that aimed to mitigate these concerns.

METHODS

Messages were initially developed with feedback from dental experts and Amazon Mechanical Turk workers. Dental patients were then recruited for 2 facilitator-mediated group webinar sessions held over Zoom (Zoom Video Communications; session 1: n=13; session 2: n=7), in which they provided both quantitative ratings and qualitative feedback on the messages. Participants interacted with the facilitator through Zoom polls and a chat window that was anonymous to other participants. Participants did not directly interact with each other, and the facilitator mediated sessions by verbally asking for message feedback and sharing key suggestions with the group for additional feedback. This approach plausibly enhanced participant anonymity and confidentiality during the sessions.

RESULTS

Participants rated messages highly in terms of liking (overall rating: mean 2.63, SD 0.58; reciprocity: mean 2.65, SD 0.52; reciprocity-by-proxy: mean 2.58, SD 0.53; curiosity involving interactive oral health questions and answers: mean 2.45, SD 0.69; curiosity involving tailored brushing feedback: mean 2.77, SD 0.48) on a scale ranging from 1 (do not like it) to 3 (like it). Qualitative feedback indicated that the participants preferred messages that were straightforward, enthusiastic, conversational, relatable, and authentic.

CONCLUSIONS

This formative research has the potential to guide the design of messages for future digital health behavioral interventions targeting individuals from diverse racial and ethnic populations. Insights emphasize the importance of identifying key stimuli and tasks that require engagement, gathering multiple perspectives during message development, and using new approaches for collecting both quantitative and qualitative data while mitigating anonymity and confidentiality concerns.

Digital Adaptive Behavioral Interventions to Improve HIV Prevention and Care: Innovations in Intervention Approach and Experimental Design

PURPOSE OF REVIEW

Recent advances in digital technologies can be leveraged to adapt HIV prevention and treatment services to the rapidly changing needs of individuals in everyday life. However, to fully take advantage of these technologies, it is critical to effectively integrate them with human-delivered components. Here, we introduce a new experimental approach for optimizing the integration and adaptation of digital and human-delivered behavioral intervention components for HIV prevention and treatment.

RECENT FINDINGS

Typically, human-delivered components can be adapted on a relatively slow timescale (e.g., every few months or weeks), while digital components can be adapted much faster (e.g., every few days or hours). Thus, the systematic integration of these components requires an experimental approach that involves sequential randomizations on multiple timescales. Selecting an experimental approach should be motivated by the type of adaptive intervention investigators would like to develop, and the scientific questions they have about its construction.

Social stressors, emotional responses, and NSSI urges and behaviors in daily life

BACKGROUND

The defective self model of nonsuicidal self-injury (NSSI) theorizes that individuals who are highly self-critical are more likely to choose NSSI to regulate emotions. This model indirectly suggests that individuals who engage in NSSI may experience more self-conscious emotions in response to negative social feedback, increasing risk for near-term NSSI. This study examined (1) whether individuals with a history of NSSI (vs. without) display greater self-conscious and negative emotional reactions to daily social stressors, and more problematic features of these daily social stressors, and (2) whether greater-than-usual negative emotional reactions and social stressor features predict NSSI urges and behaviors in daily life.

METHODS

Participants were 134 female college students with recent, recurrent NSSI (n = 77) or no NSSI history (n = 57). Participants completed baseline measures of socioemotional functioning and a two-week daily diary protocol.

RESULTS

The NSSI (vs. no NSSI) group reported significantly greater self-conscious and negative emotional reactions to daily social stressors, and social stressors characterized by greater dysfunction. In the NSSI group, experiencing social stressors characterized by greater distress than one's average during the daily diary period was associated with same-day NSSI urges and behavior, greater confusion than one's average predicted same-day NSSI urges, and greater conflict than one's average predicted same-day NSSI behavior. Greater self-conscious and negative emotional reactions to these stressors than one's average predicted same-day NSSI urges and behavior.

LIMITATIONS

Limitations include reliance on self-report, a once-daily assessment, and lack of generalizability to other samples.

CONCLUSIONS

Interpersonal conflict and increased self-conscious emotions pose vulnerability for NSSI. Prevention and intervention efforts would benefit from including a focus on interpersonal functioning.

A Call to Action on Assessing and Mitigating Bias in Artificial Intelligence Applications for Mental Health

Advances in computer science and data-analytic methods are driving a new era in mental health research and application. Artificial intelligence (AI) technologies hold the potential to enhance the assessment, diagnosis, and treatment of people experiencing mental health problems and to increase the reach and impact of mental health care. However, AI applications will not mitigate mental health disparities if they are built from historical data that reflect underlying social biases and inequities. AI models biased against sensitive classes could reinforce and even perpetuate existing inequities if these models create legacies that differentially impact who is diagnosed and treated, and how effectively. The current article reviews the health-equity implications of applying AI to mental health problems, outlines state-of-the-art methods for assessing and mitigating algorithmic bias, and presents a call to action to guide the development of fair-aware AI in psychological science.

Acceptability of Personal Sensing Among People With Alcohol Use Disorder: Observational Study

BACKGROUND

Personal sensing may improve digital therapeutics for mental health care by facilitating early screening, symptom monitoring, risk prediction, and personalized adaptive interventions. However, further development and the use of personal sensing requires a better understanding of its acceptability to people targeted for these applications.

OBJECTIVE

We aimed to assess the acceptability of active and passive personal sensing methods in a sample of people with moderate to severe alcohol use disorder using both behavioral and self-report measures. This sample was recruited as part of a larger grant-funded project to develop a machine learning algorithm to predict lapses.

METHODS

Participants (N=154; n=77, 50% female; mean age 41, SD 11.9 years; n=134, 87% White and n=150, 97% non-Hispanic) in early recovery (1-8 weeks of abstinence) were recruited to participate in a 3-month longitudinal study. Participants were modestly compensated for engaging with active (eg, ecological momentary assessment [EMA], audio check-in, and sleep quality) and passive (eg, geolocation, cellular communication logs, and SMS text message content) sensing methods that were selected to tap into constructs from the Relapse Prevention model by Marlatt. We assessed 3 behavioral indicators of acceptability: participants' choices about their participation in the study at various stages in the procedure, their choice to opt in to provide data for each sensing method, and their adherence to a subset of the active methods (EMA and audio check-in). We also assessed 3 self-report measures of acceptability (interference, dislike, and willingness to use for 1 year) for each method.

RESULTS

Of the 192 eligible individuals screened, 191 consented to personal sensing. Most of these individuals (169/191, 88.5%) also returned 1 week later to formally enroll, and 154 participated through the first month follow-up visit. All participants in our analysis sample opted in to provide data for EMA, sleep quality, geolocation, and cellular communication logs. Out of 154 participants, 1 (0.6%) did not provide SMS text message content and 3 (1.9%) did not provide any audio check-ins. The average adherence rate for the 4 times daily EMA was .80. The adherence rate for the daily audio check-in was .54. Aggregate participant ratings indicated that all personal sensing methods were significantly more acceptable (all P<.001) compared with neutral across subjective measures of interference, dislike, and willingness to use for 1 year. Participants did not significantly differ in their dislike of active methods compared with passive methods (P=.23). However, participants reported a higher willingness to use passive (vs active) methods for 1 year (P=.04).

CONCLUSIONS

These results suggest that active and passive sensing methods are acceptable for people with alcohol use disorder over a longer period than has previously been assessed. Important individual differences were observed across people and methods, indicating opportunities for future improvement.

Use of geofencing interventions in population health research: a scoping review

OBJECTIVES

Technological advancements that use global positioning system (GPS), such as geofencing, provide the opportunity to examine place-based context in population health research. This review aimed to systematically identify, assess and synthesise the existing evidence on geofencing intervention design, acceptability, feasibility and/or impact.

DESIGN

Scoping review, using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guidance for reporting.

DATA SOURCES

PubMed, CINAHL, EMBASE, Web of Science, Cochrane and PsycINFO for articles in English published up to 31 December 2021.

ELIGIBILITY CRITERIA

Articles were included if geofencing was used as a mechanism for intervention delivery.

EXCLUSION CRITERIA

(1) a component or combination of GPS, geographical information system or ecological momentary assessment was used without delivery of an intervention; (2) did not include a health or health-related outcome from the geofencing intervention; or (3) was not a peer-reviewed study.

DATA EXTRACTION AND SYNTHESIS

Several researchers independently reviewed all abstracts and full-text articles for final inclusion.

RESULTS

A total of 2171 articles were found; after exclusions, nine studies were included in the review. The majority were published in 5 years preceding the search (89%). Geofences in most studies (n=5) were fixed and programmed in the mobile application carried by participants without their input. Mechanisms of geofencing interventions were classified as direct or indirect, with five studies (56%) using direct interventions. There were several different health outcomes (from smoking to problematic alcohol use) across the five studies that used a direct geofencing intervention.

CONCLUSIONS

This scoping review found geofencing to be an emerging technology that is an acceptable and feasible intervention applied to several different populations and health outcomes. Future studies should specify the rationale for the locations that are geofenced and user input. Moreover, attention to mechanisms of actions will enable scientists to understand not only whether geofencing is an appropriate and effective intervention but why it works to achieve the outcomes observed.

Associations between alcohol demand, delayed reward discounting, and high-intensity drinking in a diverse emerging adult sample

The high-intensity drinking threshold (HID; 8+/10+ drinks for women/men) is more strongly associated with significant alcohol-related health consequences than the more common heavy episodic drinking threshold (HED; 4+/5+ drinks for women/men). Behavioral economic measures of alcohol reward value (demand) and delayed reward discounting (DRD) have shown associations with other alcohol-related risk behaviors and may contribute to efforts to identify individuals who are at risk for HID from the larger subgroup of at-risk drinkers who engage in HED. Logistic regression analyses tested if alcohol demand and DRD were associated with HID in a sample of 477 emerging adults who reported recent heavy drinking. Receiver operating curve (ROC) analyses were conducted to test these variables' ability to classify HID group membership and to select an optimal cutoff score. In logistic regression analyses controlling for typical weekly drinking, demographics, and other variables associated with HID, participants reporting higher demand intensity (amount of alcohol purchased when price is zero; Adjusted Odds Ratio (AOR) = 20.27, 95% CI [5.71, 71.91]) and lower demand elasticity (sensitivity of alcohol consumption to increases in cost; AOR = .29, 95% CI [.11, 72]) were more likely to report HID relative to HED. Omax (maximum alcohol expenditure) and DRD were associated with HID in bivariate, but not in multivariate models. The ROC analysis provided support for an intensity cutoff of 7.5 or higher. These findings suggest that brief alcohol demand curve measures, in particular demand intensity and elasticity, may have utility in identifying individuals who are at risk for HID. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Digital Biomarker Applications Across the Spectrum of Opioid Use Disorder

Opioid use disorder (OUD) is one of the most pressing public health problems of the past decade, with over eighty thousand overdose related deaths in 2021 alone. Digital technologies to measure and respond to disease states encompass both on- and off-body sensors. Such devices can be used to detect and monitor end-user physiologic or behavioral measurements (i.e. digital biomarkers) that correlate with events of interest, health, or pathology. Recent work has demonstrated the potential of digital biomarkers to be used as a tools in the prevention, risk mitigation, and treatment of opioid use disorder (OUD). Multiple physiologic adaptations occur over the course of opioid use, and represent potential targets for digital biomarker based monitoring strategies. This review explores the current evidence (and potential) for digital biomarkers monitoring across the spectrum of opioid use. Technologies to detect opioid administration, withdrawal, hyperalgesia and overdose will be reviewed. Driven by empirically derived algorithms, these technologies have important implications for supporting the safe prescribing of opioids, reducing harm in active opioid users, and supporting those in recovery from OUD.

From Computation to Clinic

Theory-driven and data-driven computational approaches to psychiatry have enormous potential for elucidating mechanism of disease and providing translational linkages between basic science findings and the clinic. These approaches have already demonstrated utility in providing clinically relevant understanding, primarily via back translation from clinic to computation, revealing how specific disorders or symptoms map onto specific computational processes. Nonetheless, forward translation, from computation to clinic, remains rare. In addition, consensus regarding specific barriers to forward translation-and on the best strategies to overcome these barriers-is limited. This perspective review brings together expert basic and computationally trained researchers and clinicians to 1) identify challenges specific to preclinical model systems and clinical translation of computational models of cognition and affect, and 2) discuss practical approaches to overcoming these challenges. In doing so, we highlight recent evidence for the ability of computational approaches to predict treatment responses in psychiatric disorders and discuss considerations for maximizing the clinical relevance of such models (e.g., via longitudinal testing) and the likelihood of stakeholder adoption (e.g., via cost-effectiveness analyses).

How Notifications Affect Engagement With a Behavior Change App: Results From a Micro-Randomized Trial

BACKGROUND

Drink Less is a behavior change app to help higher-risk drinkers in the United Kingdom reduce their alcohol consumption. The app includes a daily notification asking users to "Please complete your drinks and mood diary," yet we did not understand the causal effect of the notification on engagement nor how to improve this component of Drink Less. We developed a new bank of 30 new messages to increase users' reflective motivation to engage with Drink Less. This study aimed to determine how standard and new notifications affect engagement.

OBJECTIVE

Our objective was to estimate the causal effect of the notification on near-term engagement, to explore whether this effect changed over time, and to create an evidence base to further inform the optimization of the notification policy.

METHODS

We conducted a micro-randomized trial (MRT) with 2 additional parallel arms. Inclusion criteria were Drink Less users who consented to participate in the trial, self-reported a baseline Alcohol Use Disorders Identification Test score of ≥8, resided in the United Kingdom, were aged ≥18 years, and reported interest in drinking less alcohol. Our MRT randomized 350 new users to test whether receiving a notification, compared with receiving no notification, increased the probability of opening the app in the subsequent hour, over the first 30 days since downloading Drink Less. Each day at 8 PM, users were randomized with a 30% probability of receiving the standard message, a 30% probability of receiving a new message, or a 40% probability of receiving no message. We additionally explored time to disengagement, with the allocation of 60% of eligible users randomized to the MRT (n=350) and 40% of eligible users randomized in equal number to the 2 parallel arms, either receiving the no notification policy (n=98) or the standard notification policy (n=121). Ancillary analyses explored effect moderation by recent states of habituation and engagement.

RESULTS

Receiving a notification, compared with not receiving a notification, increased the probability of opening the app in the next hour by 3.5-fold (95% CI 2.91-4.25). Both types of messages were similarly effective. The effect of the notification did not change significantly over time. A user being in a state of already engaged lowered the new notification effect by 0.80 (95% CI 0.55-1.16), although not significantly. Across the 3 arms, time to disengagement was not significantly different.

CONCLUSIONS

We found a strong near-term effect of engagement on the notification, but no overall difference in time to disengagement between users receiving the standard fixed notification, no notification at all, or the random sequence of notifications within the MRT. The strong near-term effect of the notification presents an opportunity to target notifications to increase "in-the-moment" engagement. Further optimization is required to improve the long-term engagement.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.2196/18690.

The Craving-Manager smartphone app designed to diagnose substance use/addictive disorders, and manage craving and individual predictors of relapse: a study protocol for a multicenter randomized controlled trial

Background

The rate of individuals with addiction who are currently treated are low, and this can be explained by barriers such as stigma, desire to cope alone, and difficulty to access treatment. These barriers could be overcome by mobile technologies. EMI (Ecological Momentary Intervention) is a treatment procedure characterized by the delivery of interventions (messages on smartphones) to people in their daily lives. EMI presents opportunities for treatments to be available to people during times and in situations when they are most needed. Craving is a strong predictor of relapse and a key target for addiction treatment. Studies using Ecological Momentary Assessment (EMA) method have revealed that, in daily life, person-specific cues could precipitate craving, that in turn, is associated with a higher probability to report substance use and relapse in the following hours. Assessment and management of these specific situations in daily life could help to decrease addictive use and avoid relapse. The Craving-Manager smartphone app has been designed to diagnose addictive disorders, and assess and manage craving as well as individual predictors of use/relapse. It delivers specific and individualized interventions (counseling messages) composed of evidence-based addiction treatments approaches (cognitive behavioral therapy and mindfulness). The Craving-Manager app can be used for any addiction (substance or behavior). The objective of this protocol is to evaluate the efficacy of the Craving-Manager app in decreasing use (of primary substance(s)/addictive behavior(s)) over 4 weeks, among individuals on a waiting list for outpatient addiction treatment.

Methods/design

This multicenter double-blind randomized controlled trial (RCT) will compare two parallel groups: experimental group (full interventional version of the app, 4 weeks, EMA + EMI), versus control group (restricted version of the app, 4 weeks, only EMA). Two hundred and seventy-four participants will be recruited in 6 addiction treatment centers in France.

Discussion

This RCT will provide indication on how the Craving-Manager app will reduce addictive use (e.g., better craving management, better stimulus control) in both substance and behavioral addictions. If its efficacy is confirmed, the app could offer the possibility of an easy to use and personalized intervention accessible to the greatest number of individuals with addiction.

Clinical Trial Registration

ClinicalTrials.gov: NCT04732676.

Addictions treatment mechanisms of change science and implementation science: A critical review

This manuscript aims to contribute to the next phase of mechanisms of behavior change (MOBC) science on alcohol or other drug use. Specifically, we encourage the transition from a basic science orientation (i.e., knowledge generation) to a translational science orientation (i.e., knowledge application or Translational MOBC Science). To inform that transition, we examine MOBC science and implementation science and consider how these two research areas can intersect to capitalize on the goals, strengths, and key methodologies of each. First, we define MOBC science and implementation science and offer a brief historical rationale for these two areas of clinical research. Second, we summarize similarities in rationale and discuss two scenarios where one draws from the other-MOBC science on implementation strategy outcomes and implementation science on MOBC. We then focus on the latter scenario, and briefly review the MOBC knowledge base to consider its readiness for knowledge translation. Finally, we provide a series of research recommendations to facilitate the translation of MOBC science. These recommendations include: (1) identifying and targeting MOBC that are well suited for implementation, (2) use of MOBC research results to inform broader health behavior change theory, and (3) triangulation of a more diverse set of research methodologies to build a translational MOBC knowledge base. Ultimately, it is important for gains borne from MOBC science to affect direct patient care, while basic MOBC research continues to be developed and refined over time. Potential implications of these developments include greater clinical significance for MOBC science, an efficient feedback loop between clinical research methodologies, a multi-level approach to understanding behavioral change, and reduced or eliminated siloes between MOBC science and implementation science.

Continuous-Time Decision Transformer for Healthcare Applications

Offline reinforcement learning (RL) is a promising approach for training intelligent medical agents to learn treatment policies and assist decision making in many healthcare applications, such as scheduling clinical visits and assigning dosages for patients with chronic conditions. In this paper, we investigate the potential usefulness of Decision Transformer (Chen et al., 2021)-a new offline RL paradigm-in medical domains where decision making in continuous time is desired. As Decision Transformer only handles discrete-time (or turn-based) sequential decision making scenarios, we generalize it to Continuous-Time Decision Transformer that not only considers the past clinical measurements and treatments but also the timings of previous visits, and learns to suggest the timings of future visits as well as the treatment plan at each visit. Extensive experiments on synthetic datasets and simulators motivated by real-world medical applications demonstrate that Continuous-Time Decision Transformer is able to outperform competitors and has clinical utility in terms of improving patients' health and prolonging their survival by learning high-performance policies from logged data generated using policies of different levels of quality.

A Just-In-Time Adaptive intervention (JITAI) for smoking cessation: Feasibility and acceptability findings

Smoking cessation treatments that are easily accessible and deliver intervention content at vulnerable moments (e.g., high negative affect) have great potential to impact tobacco abstinence. The current study examined the feasibility and acceptability of a multi-component Just-In-Time Adaptive Intervention (JITAI) for smoking cessation. Daily smokers interested in quitting were consented to participate in a 6-week cessation study. Visit 1 occurred 4 days pre-quit, Visit 2 was on the quit day, Visit 3 occurred 3 days post-quit, Visit 4 was 10 days post-quit, and Visit 5 was 28 days post-quit. During the first 2 weeks (Visits 1-4), the JITAI delivered brief mindfulness/motivational strategies via smartphone in real-time based on negative affect or smoking behavior detected by wearable sensors. Participants also attended 5 in-person visits, where brief cessation counseling (Visits 1-4) and nicotine replacement therapy (Visits 2-5) were provided. Outcomes were feasibility and acceptability; biochemically-confirmed abstinence was also measured. Participants (N = 43) were 58.1 % female (AgeMean = 49.1, mean cigarettes per day = 15.4). Retention through follow-up was high (83.7 %). For participants with available data (n = 38), 24 (63 %) met the benchmark for sensor wearing, among whom 16 (67 %) completed at least 60 % of strategies. Perceived ease of wearing sensors (Mean = 5.1 out of 6) and treatment satisfaction (Mean = 3.6 out of 4) were high. Biochemically-confirmed abstinence was 34 % at Visit 4 and 21 % at Visit 5. Overall, the feasibility of this novel multi-component intervention for smoking cessation was mixed but acceptability was high. Future studies with improved technology will decrease participant burden and better detect key intervention moments.

Examining Associations Between Negative Affect and Substance Use in Treatment-Seeking Samples: A Review of Studies Using Intensive Longitudinal Methods

Purpose of Review

Understanding dynamic relationships between negative affect and substance use disorder (SUD) outcomes, including craving, may help inform adaptive and personalized interventions. Recent studies using intensive longitudinal methods were reviewed to examine relationships between negative affect and the outcomes of either craving or substance use during and following SUD treatment.

Recent Findings

Results on associations between negative affect and craving/substance use were mixed and difficult to synthesize, given methodological differences across studies. The strength and direction of these relationships varied across outcomes, subgroups, contexts, and time course.

Summary

The current literature is mixed concerning negative affect and craving/substance use associations during and following SUD treatment. Researchers should increasingly recruit diverse individuals, for example, samples of varying racial and ethnic backgrounds and those reporting co-occurring disorders and polysubstance use. Experimental, qualitative, and person-specific methods will improve our understanding of relationships between negative affect and substance-related outcomes during SUD treatment.

An Overview of Innovative Approaches to Support Timely and Agile Health Communication Research and Practice

Innovative approaches are needed to make health communication research and practice more timely, responsive, and effective in a rapidly changing information ecosystem. In this paper we provide an overview of strategies that can enhance the delivery and effectiveness of health communication campaigns and interventions, as well as research approaches that can generate useful data and insights for decisionmakers and campaign designers, thereby reducing the research-to-practice gap. The discussion focuses on the following approaches: digital segmentation and microtargeting, social media influencer campaigns, recommender systems, adaptive interventions, A/B testing, efficient message testing protocols, rapid cycle iterative message testing, megastudies, and agent-based modeling. For each method highlighted, we also outline important practical and ethical considerations for utilizing the approach in the context of health communication research and practice, including issues related to transparency, privacy, equity, and potential for harm.

Technology-mediated just-in-time adaptive interventions (JITAIs) to reduce harmful substance use: a systematic review

BACKGROUND AND AIMS

Lapse risk when trying to stop or reduce harmful substance use is idiosyncratic, dynamic and multi-factorial. Just-in-time adaptive interventions (JITAIs) aim to deliver tailored support at moments of need or opportunity. We aimed to synthesize evidence on decision points, tailoring variables, intervention options, decision rules, study designs, user engagement and effectiveness of technology-mediated JITAIs for reducing harmful substance use.

METHODS

Systematic review of empirical studies of any design with a narrative synthesis. We searched Ovid MEDLINE, Embase, PsycINFO, Web of Science, the ACM Digital Library, the IEEE Digital Library, ClinicalTrials.gov, the ISRCTN register and dblp using terms related to substance use/mHealth/JITAIs. Outcomes were user engagement and intervention effectiveness. Study quality was assessed with the mHealth Evidence Reporting and Assessment checklist.

FINDINGS

We included 17 reports of 14 unique studies, including two randomized controlled trials. JITAIs targeted alcohol (S = 7, n = 120 520), tobacco (S = 4, n = 187), cannabis (S = 2, n = 97) and a combination of alcohol and illicit substance use (S = 1, n = 63), and primarily relied on active measurement and static (i.e. time-invariant) decision rules to deliver support tailored to micro-scale changes in mood or urges. Two studies used data from prior participants and four drew upon theory to devise decision rules. Engagement with available JITAIs was moderate-to-high and evidence of effectiveness was mixed. Due to substantial heterogeneity in study designs and outcome variables assessed, no meta-analysis was performed. Many studies reported insufficient detail on JITAI infrastructure, content, development costs and data security.

CONCLUSIONS

Current implementations of just-in-time adaptive interventions (JITAIs) for reducing harmful substance use rely on active measurement and static decision rules to deliver support tailored to micro-scale changes in mood or urges. Studies on JITAI effectiveness are lacking.

GAMBLINGLESS IN-THE-MOMENT: Protocol for a micro-randomised trial of a gambling Just-In-Time Adaptive Intervention (Preprint)

Background

The presence of discrete but fluctuating precipitants, in combination with the dynamic nature of gambling episodes, calls for the development of tailored interventions delivered in real time, such as just-in-time adaptive interventions (JITAIs). JITAIs leverage mobile and wireless technologies to address dynamically changing individual needs by providing the type and amount of support required at the right time and only when needed. They have the added benefit of reaching underserved populations by providing accessible, convenient, and low-burden support. Despite these benefits, few JITAIs targeting gambling behavior are available.

Objective

This study aims to redress this gap in service provision by developing and evaluating a theoretically informed and evidence-based JITAI for people who want to reduce their gambling. Delivered via a smartphone app, GamblingLess: In-The-Moment provides tailored cognitive-behavioral and third-wave interventions targeting cognitive processes explicated by the relapse prevention model (cravings, self-efficacy, and positive outcome expectancies). It aims to reduce gambling symptom severity (distal outcome) through short-term reductions in the likelihood of gambling episodes (primary proximal outcome) by improving craving intensity, self-efficacy, or expectancies (secondary proximal outcomes). The primary aim is to explore the degree to which the delivery of a tailored intervention at a time of cognitive vulnerability reduces the probability of a subsequent gambling episode.

Methods

GamblingLess: In-The-Moment interventions are delivered to gamblers who are in a state of receptivity (available for treatment) and report a state of cognitive vulnerability via ecological momentary assessments 3 times a day. The JITAI will tailor the type, timing, and amount of support for individual needs. Using a microrandomized trial, a form of sequential factorial design, each eligible participant will be randomized to a tailored intervention condition or no intervention control condition at each ecological momentary assessment across a 28-day period. The microrandomized trial will be supplemented by a 6-month within-group follow-up evaluation to explore long-term effects on primary (gambling symptom severity) and secondary (gambling behavior, craving severity, self-efficacy, and expectancies) outcomes and an acceptability evaluation via postintervention surveys, app use and engagement indices, and semistructured interviews. In all, 200 participants will be recruited from Australia and New Zealand.

Results

The project was funded in June 2019, with approval from the Deakin University Human Research Ethics Committee (2020-304). Stakeholder user testing revealed high acceptability scores. The trial began on March 29, 2022, and 84 participants have been recruited (as of June 24, 2022). Results are expected to be published mid-2024.

Conclusions

GamblingLess: In-The-Moment forms part of a suite of theoretically informed and evidence-based web-based and mobile gambling interventions. This trial will provide important empirical data that can be used to facilitate the JITAI’s optimization to make it a more effective, efficient, and scalable tailored intervention.

Trial Registration

Australian New Zealand Clinical Trials Registry (ANZCTR) ACTRN12622000490774; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=380757&isClinicalTrial=False

International Registered Report Identifier (IRRID)

PRR1-10.2196/38958

Gambling Habit Hacker: Protocol for a micro-randomised trial of planning interventions delivered via a Just-In-Time Adaptive Intervention for adult gamblers (Preprint)

Background

People with gambling problems frequently report repeated unsuccessful attempts to change their behavior. Although many behavior change techniques are available to individuals to reduce gambling harm, they can be challenging to implement or maintain. The provision of implementation support tailored for immediate, real-time, individualized circumstances may improve attempts at behavior change.

Objective

We aimed to develop and evaluate a Just-In-Time Adaptive Intervention (JITAI) for individuals who require support to adhere to their gambling limits. JITAI development is based on the principles of the Health Action Process Approach with delivery, in alignment with the principles of self-determination theory. The primary objective was to determine the effect of action- and coping planning compared with no intervention on the goal of subsequently adhering to gambling expenditure limits.

Methods

Gambling Habit Hacker is delivered as a JITAI providing in-the-moment support for adhering to gambling expenditure limits (primary proximal outcome). Delivered via a smartphone app, this JITAI delivers tailored behavior change techniques related to goal setting, action planning, coping planning, and self-monitoring. The Gambling Habit Hacker app will be evaluated using a 28-day microrandomized trial. Up to 200 individuals seeking support for their own gambling from Australia and New Zealand will set a gambling expenditure limit (ie, goal). They will then be asked to complete 3 time-based ecological momentary assessments (EMAs) per day over a 28-day period. EMAs will assess real-time adherence to gambling limits, strength of intention to adhere to goals, goal self-efficacy, urge self-efficacy, and being in high-risk situations. On the basis of the responses to each EMA, participants will be randomized to the control (a set of 25 self-enactable strategies containing names only and no implementation information) or intervention (self-enactable strategy implementation information with facilitated action- and coping planning) conditions. This microrandomized trial will be supplemented with a 6-month within-group follow-up that explores the long-term impact of the app on gambling expenditure (primary distal outcome) and a range of secondary outcomes, as well as an evaluation of the acceptability of the JITAI via postintervention surveys, app use and engagement indices, and semistructured interviews. This trial has been approved by the Deakin University Human Research Ethics Committee (2020-304).

Results

The intervention has been subject to expert user testing, with high acceptability scores. The results will inform a more nuanced version of the Gambling Habit Hacker app for wider use.

Conclusions

Gambling Habit Hacker is part of a suite of interventions for addictive behaviors that deliver implementation support grounded in lived experience. This study may inform the usefulness of delivering implementation intentions in real time and in real-world settings. It potentially offers people with gambling problems new support to set their gambling intentions and adhere to their limits.

Trial Registration

Australian New Zealand Clinical Trials Registry ACTRN12622000497707; www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=383568

International Registered Report Identifier (IRRID)

DERR1-10.2196/38919

Mobile phone and internet use among people who inject drugs: Implications for mobile health interventions

Background: Mobile health (mHealth) interventions have the potential to improve substance use treatment engagement and outcomes, and to reduce risk behaviors among people who inject drugs (PWID). However, there are few studies assessing mobile technology use among PWID and none have investigated continuity of mobile phone use. Methods: We surveyed 494 PWID. We used bivariate (independent-sample t- and chi-square tests) and multivariate (logistic regression) analyses to determine whether mobile phone and/or internet use differed as a function of participant- and/or injection-related characteristics. Results: Most participants (77%) had a mobile phone, with 67% having a phone that was free of charge. Participants with a phone were significantly less likely to be homeless (AOR = 0.28), to have shared syringes (AOR = 0.53), and to have reused syringes (AOR = 0.26) in the past 3 months. We observed high rates of phone and number turnover, with more than half reporting that they got a new phone (57%) and/or number (56%) at least once within the past 3 months. Most participants were familiar with using the internet (80% ever use), though participants who had ever used the internet were younger (AOR = 0.89), were less likely to be homeless (AOR = 0.38), were less likely to have shared syringes (AOR = 0.49), and were more likely to have injected methamphetamine by itself (AOR = 2.49) in the past 3 months. Conclusions: Overall, mobile technology and internet use was high among our sample of PWID. Several factors should be considered in recruiting diverse samples of PWID to minimize bias in mHealth study outcomes, including mobile phone access and protocol type (text- vs internet-based).

Forgoing plans for alcohol and cannabis use in daily life: Examining reasons for nonuse when use was planned in a predominantly white college student sample

BACKGROUND

The reasons for college students to abstain from alcohol and cannabis use on a given day can inform efforts to prevent or intervene in those behaviors. Research on reasons for alcohol nonuse remains in its nascent stages and no study to date has examined reasons for cannabis nonuse on a given day. Here we examine reasons for nonuse among college students after they planned to use alcohol and/or cannabis.

METHODS

College students (N = 341; M_age  = 19.79; 53% women; 74% White) from 3 universities completed 54 days of data collection across which approximately 50% were nonuse days. Each morning, participants indicated whether they planned to use that day; nonuse reasons were assessed the next morning, if applicable. Generalized linear mixed-effects models were used to disentangle within- and between-person effects.

RESULTS

On a given nonuse day (at the within-person level), "work" and "school" were reasons associated with having no plan to use alcohol and "to feel in control" was linked to having no plan to use cannabis. "Did not want to get high" was related to forgoing plans (did not use when originally planned) for alcohol use at the within-person level. At the between-person level, "no desire" was associated with no plans for alcohol or cannabis use and "did not want to get high" was related to no plans for cannabis use. "School" and "could not get" were related to forgoing plans for alcohol and cannabis use, respectively, at the between-person level.

CONCLUSION

An examination of earlier intentions for alcohol and/or cannabis use on nonuse days yielded novel findings on the intention-behavior gap. Reasons for nonuse can inform intervention and prevention strategies (e.g., those involving social norms or just-in-time adaptive efforts) for alcohol and cannabis use on college campuses.

A Pilot Study on Approach Bias Modification in Smoking Cessation: Activating Personalized Alternative Activities for Smoking in the Context of Increased Craving

Background

The act of smoking has been associated with the automatic activation of approach biases towards smoking-related stimuli. However, previous research has produced mixed findings when smokers are trained to avoid such smoking-related stimuli through the application of Approach Bias Modification (ApBM). As such, this study aimed to test an improved ApBM (ApBM +), where smokers were trained to approach personalized alternative activities for smoking in the context of increased craving, in addition to training smoking-avoidance responses.

Methods

Sixty-seven daily smokers motivated to quit (M age = 29.27, 58.2% female) were randomly assigned to seven sessions of either ApBM + (n = 26), standard-ApBM (n = 19), or sham-ApBM (n = 22), after a brief motivational smoking intervention. Primary outcomes of approach biases for smoking and for alternative activities and secondary outcomes of smoking-related behaviors were assessed at pre-test, post-test, and 1-month follow-up.

Results

Overall, no group differences by condition were demonstrated in changing approach biases or smoking-related behaviors at post-test and 1-month follow-up. A trend level indication for differences in changes of smoking-approach biases between sham-ApBM and ApBM + for relatively heavy smokers was found at post-test. This was primarily driven by a significant increase in smoking-approach biases within the sham-ApBM condition and a trend decrease in smoking-approach biases within the ApBM + condition.

Conclusions

Our findings did not provide support for the current ApBM + concerning improved effects across the whole sample. Diverging training effects on approach biases for smoking in relatively heavy smokers warrants further research, for which we provide some suggestions.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12529-021-10033-x.

Ecological Momentary Assessment of the Relationship between Positive Outcome Expectancies and Gambling Behaviour

Relapse prevention models suggest that positive outcome expectancies can constitute situational determinants of relapse episodes that interact with other factors to determine the likelihood of relapse. The primary aims were to examine reciprocal relationships between situational positive gambling outcome expectancies and gambling behaviour and moderators of these relationships. An online survey and a 28 day Ecological Momentary Assessment (EMA) were administered to 109 past-month gamblers (84% with gambling problems). EMA measures included outcome expectancies (enjoyment/arousal, self-enhancement, money), self-efficacy, craving, negative emotional state, interpersonal conflict, social pressure, positive emotional state, financial pressures, and gambling behaviour (episodes, expenditure). Pre-EMA measures included problem gambling severity, motives, psychological distress, coping strategies, and outcome expectancies. No reciprocal relationships between EMA outcome expectancies and gambling behaviour (episodes, expenditure) were identified. Moderations predicting gambling episodes revealed: (1) cravings and problem gambling exacerbated effects of enjoyment/arousal expectancies; (2) positive emotional state and positive reframing coping exacerbated effects of self-enhancement expectancies; and (3) instrumental social support buffered effects of money expectancies. Positive outcome expectancies therefore constitute situational determinants of gambling behaviour, but only when they interact with other factors. All pre-EMA expectancies predicted problem gambling severity (OR = 1.61-3.25). Real-time interventions addressing gambling outcome expectancies tailored to vulnerable gamblers are required.

A Smartphone-Delivered Ecological Momentary Intervention for Problem Gambling (GamblingLess: Curb Your Urge): Single-Arm Acceptability and Feasibility Trial

Background

Low uptake rates of traditional gambling treatments highlight the need for innovative treatment modalities. Smartphone apps can provide unprecedented access to real-time ecological momentary interventions (EMIs) delivered in people’s everyday lives.

Objective

This study aims to examine the acceptability, feasibility, and preliminary effectiveness of GamblingLess: Curb Your Urge, the first smartphone app–delivered EMI that aims to prevent gambling episodes by reducing craving intensity in people seeking help for gambling problems.

Methods

This study was a single-arm, 5-week acceptability and feasibility trial (1-week baseline and 4-week intervention periods) involving ecological momentary assessments (EMAs) delivered 3 times daily. The EMAs measured gambling episodes, cravings, and self-efficacy. Web-based evaluations at baseline, postintervention, and 1-month follow-up measured gambling outcomes (severity, cravings, frequency, expenditure, and self-efficacy) and the intervention’s perceived helpfulness, relevance, burden, satisfaction, and impact in relation to gambling cravings.

Results

A total of 36 participants, of whom 22/36 (61%) were male and 34/36 (94%) were problem gamblers, completed the baseline measures, with 61% (22/36) completing the postintervention evaluation and 58% (21/36) completing the follow-up evaluation. The intervention was considered acceptable, as participants perceived all intervention content to be above average in helpfulness and the EMA to be highly relevant but somewhat burdensome. Participants reported that they were satisfied with the intervention and that the intervention improved their knowledge, attitudes, awareness, behavior change, intention to change, and help-seeking behavior for gambling cravings. Regarding the intervention’s feasibility, compliance rates for the EMA (51%) and EMI (15%) were low; however, the intervention was used 166 times, including 59 uses within 60 minutes of EMA completion and 107 on-demand uses. Regarding the intervention’s preliminary effectiveness, descriptive EMA data showed that, compared with the baseline period, 71% and 72% reductions in the average number of gambling episodes and craving occurrences were reported in the intervention period, respectively. In addition, clustered paired-sample two-tailed t tests revealed a significant 5.4% reduction in real-time craving intensity (P=.01) immediately after intervention use, which increased to 10.5% (P=.01), where use was recommended based on craving occurrence. At the group level, significant medium-to-large reductions were observed in mean gambling symptom severity (P=.01 and .003), cravings (P=.03 and .02), frequency (P=.01 and .004), and expenditure (P=.04 and .003) at postintervention and follow-up; moreover, increased mean gambling self-efficacy and craving self-efficacy (P=.01 and .01) were observed at postintervention and increased gambling self-efficacy (P=.04) was observed at follow-up. At the individual level, over a quarter of participants (6/22, 27% to 10/21, 48%) could be categorized as recovered or improved regarding their gambling symptom severity and cravings.

Conclusions

The results support the acceptability, feasibility, and preliminary effectiveness of this app-delivered EMI for preventing gambling episodes through craving management in people with gambling problems, which has implications for extending the reach of evidence-based treatment to moments of vulnerability in people’s everyday lives.