Is informal practice associated with outcomes in loving-kindness and compassion training? Evidence from pre-post and daily diary assessments

We investigated whether informal meditation practice (i.e., self-reported application of meditative techniques outside a period of formal meditation) was associated with outcomes in smartphone-based loving-kindness and compassion training. Meditation-naïve participants (n = 351) with clinically elevated symptoms completed measures of psychological distress, loneliness, empathy, and prosociality at baseline and following a two-week intervention. Informal practice, psychological distress, and loneliness were also assessed daily. Steeper increases in informal practice had small associations with pre-post improvements in distress (r = -.18, p = .008) and loneliness (r = -.19, p = .009) but not empathy or prosociality. Using a currently recommended approach for establishing cross-lagged effects in longitudinal data (latent curve model with structured residuals), higher current-day informal practice was associated with decreased next-day distress with a very small effect size (βs = -.06 to -.04, p = .018) but not decreased next-day loneliness. No cross-lagged associations emerged from distress or loneliness to informal practice. Findings suggest that further investigation into a potential causal role of informal practice is warranted. Future studies experimentally manipulating informal practice are needed.

An Adaptive Behavioral Intervention for Weight Loss Management: A Randomized Clinical Trial

Importance

Lifestyle interventions for weight loss are difficult to implement in clinical practice. Self-managed mobile health implementations without or with added support after unsuccessful weight loss attempts could offer effective population-level obesity management.

Objective

To test whether a wireless feedback system (WFS) yields noninferior weight loss vs WFS plus telephone coaching and whether participants who do not respond to initial treatment achieve greater weight loss with more vs less vigorous step-up interventions.

Design, Setting, and Participants

In this noninferiority randomized trial, 400 adults aged 18 to 60 years with a body mass index of 27 to 45 were randomized in a 1:1 ratio to undergo 3 months of treatment initially with WFS or WFS plus coaching at a US academic medical center between June 2017 and March 2021. Participants attaining suboptimal weight loss were rerandomized to undergo modest or vigorous step-up intervention.

Interventions

The WFS included a Wi-Fi activity tracker and scale transmitting data to a smartphone app to provide daily feedback on progress in lifestyle change and weight loss, and WFS plus coaching added 12 weekly 10- to 15-minute supportive coaching calls delivered by bachelor's degree-level health promotionists viewing participants' self-monitoring data on a dashboard; step-up interventions included supportive messaging via mobile device screen notifications (app-based screen alerts) without or with coaching or powdered meal replacement. Participants and staff were unblinded and outcome assessors were blinded to treatment randomization.

Main Outcomes and Measures

The primary outcome was the between-group difference in 6-month weight change, with the noninferiority margin defined as a difference in weight change of -2.5 kg; secondary outcomes included between-group differences for all participants in weight change at 3 and 12 months and between-group 6-month weight change difference among nonresponders exposed to modest vs vigorous step-up interventions.

Results

Among 400 participants (mean [SD] age, 40.5 [11.2] years; 305 [76.3%] women; 81 participants were Black and 266 were White; mean [SD] body mass index, 34.4 [4.3]) randomized to undergo WFS (n = 199) vs WFS plus coaching (n = 201), outcome data were available for 342 participants (85.5%) at 6 months. Six-month weight loss was -2.8 kg (95% CI, -3.5 to -2.0) for the WFS group and -4.8 kg (95% CI, -5.5 to -4.1) for participants in the WFS plus coaching group (difference in weight change, -2.0 kg [90% CI, -2.9 to -1.1]; P < .001); the 90% CI included the noninferiority margin of -2.5 kg. Weight change differences were comparable at 3 and 12 months and, among nonresponders, at 6 months, with no difference by step-up therapy.

Conclusions and Relevance

A wireless feedback system (Wi-Fi activity tracker and scale with smartphone app to provide daily feedback) was not noninferior to the same system with added coaching. Continued efforts are needed to identify strategies for weight loss management and to accurately select interventions for different individuals to achieve weight loss goals.

Trial Registration

ClinicalTrials.gov Identifier: NCT02997943.

Examining Links Between Distinct Affective States and Tobacco Lapse During a Cessation Attempt Among African Americans: A Cohort Study

BACKGROUND

Affect states are posited to play a pivotal role in addiction-related processes, including tobacco lapse (i.e., smoking during a quit attempt), and distinct affective states (e.g., joy vs. happiness) may differentially influence lapse likelihood. However, few studies have examined the influence of distinct affective states on tobacco lapse.

PURPOSE

This study examines the influence of 23 distinct affect states on tobacco lapse among a sample of tobacco users attempting to quit.

METHODS

Participants were 220 adults who identified as African American (50% female, ages 18-74). Ecological momentary assessment was used to assess affect and lapse in real-time. Between and within-person associations testing links between distinct affect states and lapse were examined with multilevel modeling for binary outcomes.

RESULTS

After adjusting for previous time's lapse and for all other positive or negative affect items, results suggested that at the between-person level, joy was associated with lower odds of lapse, and at the within-person level, attentiveness was associated with lower odds of lapse. Results also suggested that at the between-person level, guilt and nervous were associated with higher odds of lapse, and at the within-person level, shame was associated with higher odds of lapse.

CONCLUSIONS

The present study uses real-time, real-world data to demonstrate the role of distinct positive and negative affects on momentary tobacco lapse. This work helps elucidate specific affective experiences that facilitate or hinder the ability to abstain from tobacco use during a quit attempt.

Optimizing an adaptive digital oral health intervention for promoting oral self-care behaviors: Micro-randomized trial protocol

Dental disease continues to be one of the most prevalent chronic diseases in the United States. Although oral self-care behaviors (OSCB), involving systematic twice-a-day tooth brushing, can prevent dental disease, this basic behavior is not sufficiently practiced. Recent advances in digital technology offer tremendous potential for promoting OSCB by delivering Just-In-Time Adaptive Interventions (JITAIs)- interventions that leverage dynamic information about the person's state and context to effectively prompt them to engage in a desired behavior in real-time, real-world settings. However, limited research attention has been given to systematically investigating how to best prompt individuals to engage in OSCB in daily life, and under what conditions prompting would be most beneficial. This paper describes the protocol for a Micro-Randomized Trial (MRT) to inform the development of a JITAI for promoting ideal OSCB, namely, brushing twice daily, for two minutes each time, in all four dental quadrants (i.e., 2x2x4). Sensors within an electric toothbrush (eBrush) will be used to track OSCB and a matching mobile app (Oralytics) will deliver on-demand feedback and educational information. The MRT will micro-randomize participants twice daily (morning and evening) to either (a) a prompt (push notification) containing one of several theoretically grounded engagement strategies or (b) no prompt. The goal is to investigate whether, what type of, and under what conditions prompting increases engagement in ideal OSCB. The results will build the empirical foundation necessary to develop an optimized JITAI that will be evaluated relative to a suitable control in a future randomized controlled trial.

Design of experiments with sequential randomizations on multiple timescales: the hybrid experimental design

Psychological interventions, especially those leveraging mobile and wireless technologies, often include multiple components that are delivered and adapted on multiple timescales (e.g., coaching sessions adapted monthly based on clinical progress, combined with motivational messages from a mobile device adapted daily based on the person's daily emotional state). The hybrid experimental design (HED) is a new experimental approach that enables researchers to answer scientific questions about the construction of psychological interventions in which components are delivered and adapted on different timescales. These designs involve sequential randomizations of study participants to intervention components, each at an appropriate timescale (e.g., monthly randomization to different intensities of coaching sessions and daily randomization to different forms of motivational messages). The goal of the current manuscript is twofold. The first is to highlight the flexibility of the HED by conceptualizing this experimental approach as a special form of a factorial design in which different factors are introduced at multiple timescales. We also discuss how the structure of the HED can vary depending on the scientific question(s) motivating the study. The second goal is to explain how data from various types of HEDs can be analyzed to answer a variety of scientific questions about the development of multicomponent psychological interventions. For illustration, we use a completed HED to inform the development of a technology-based weight loss intervention that integrates components that are delivered and adapted on multiple timescales.

Intervention Optimization: A Paradigm Shift and Its Potential Implications for Clinical Psychology

To build a coherent knowledge base about what psychological intervention strategies work, develop interventions that have positive societal impact, and maintain and increase this impact over time, it is necessary to replace the classical treatment package research paradigm. The multiphase optimization strategy (MOST) is an alternative paradigm that integrates ideas from behavioral science, engineering, implementation science, economics, and decision science. MOST enables optimization of interventions to strategically balance effectiveness, affordability, scalability, and efficiency. In this review we provide an overview of MOST, discuss several experimental designs that can be used in intervention optimization, consider how the investigator can use experimental results to select components for inclusion in the optimized intervention, discuss the application of MOST in implementation science, and list future issues in this rapidly evolving field. We highlight the feasibility of adopting this new research paradigm as well as its potential to hasten the progress of psychological intervention science. Expected final online publication date for the Annual Review of Clinical Psychology, Volume 20 is May 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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

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

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.

Increasing the reach of evidence-based interventions for weight management and diabetes prevention among Medicaid patients: study protocol for a pilot Sequential Multiple Assignment Randomised Trial

INTRODUCTION

Over 40% of US adults meet criteria for obesity, a major risk factor for chronic disease. Obesity disproportionately impacts populations that have been historically marginalised (eg, low socioeconomic status, rural, some racial/ethnic minority groups). Evidence-based interventions (EBIs) for weight management exist but reach less than 3% of eligible individuals. The aims of this pilot randomised controlled trial are to evaluate feasibility and acceptability of dissemination strategies designed to increase reach of EBIs for weight management.

METHODS AND ANALYSIS

This study is a two-phase, Sequential Multiple Assignment Randomized Trial, conducted with 200 Medicaid patients. In phase 1, patients will be individually randomised to single text message (TM1) or multiple text messages (TM+). Phase 2 is based on treatment response. Patients who enrol in the EBI within 12 weeks of exposure to phase 1 (ie, responders) receive no further interventions. Patients in TM1 who do not enrol in the EBI within 12 weeks of exposure (ie, TM1 non-responders) will be randomised to either TM1-Continued (ie, no further TM) or TM1 & MAPS (ie, no further TM, up to 2 Motivation And Problem Solving (MAPS) navigation calls) over the next 12 weeks. Patients in TM+ who do not enrol in the EBI (ie, TM+ non-responders) will be randomised to either TM+Continued (ie, monthly text messages) or TM+ & MAPS (ie, monthly text messages, plus up to 2 MAPS calls) over the next 12 weeks. Descriptive statistics will be used to characterise feasibility (eg, proportion of patients eligible, contacted and enrolled in the trial) and acceptability (eg, participant opt-out, participant engagement with dissemination strategies, EBI reach (ie, the proportion of participants who enrol in EBI), adherence, effectiveness).

ETHICS AND DISSEMINATION

Study protocol was approved by the University of Utah Institutional Review Board (#00139694). Results will be disseminated through study partners and peer-reviewed publications.

TRIAL REGISTRATION NUMBER

clinicaltrials.gov; NCT05666323.

Multiple imputation of missing data in multilevel ecological momentary assessments: an example using smoking cessation study data

Advances in digital technology have greatly increased the ease of collecting intensive longitudinal data (ILD) such as ecological momentary assessments (EMAs) in studies of behavior changes. Such data are typically multilevel (e.g., with repeated measures nested within individuals), and are inevitably characterized by some degrees of missingness. Previous studies have validated the utility of multiple imputation as a way to handle missing observations in ILD when the imputation model is properly specified to reflect time dependencies. In this study, we illustrate the importance of proper accommodation of multilevel ILD structures in performing multiple imputations, and compare the performance of a multilevel multiple imputation (multilevel MI) approach relative to other approaches that do not account for such structures in a Monte Carlo simulation study. Empirical EMA data from a tobacco cessation study are used to demonstrate the utility of the multilevel MI approach, and the implications of separating participant- and study-initiated EMAs in evaluating individuals' affective dynamics and urge.

Integrating Intensive Longitudinal Data (ILD) to Inform the Development of Dynamic Theories of Behavior Change and Intervention Design: a Case Study of Scientific and Practical Considerations

The increasing sophistication of mobile and sensing technology has enabled the collection of intensive longitudinal data (ILD) concerning dynamic changes in an individual's state and context. ILD can be used to develop dynamic theories of behavior change which, in turn, can be used to provide a conceptual framework for the development of just-in-time adaptive interventions (JITAIs) that leverage advances in mobile and sensing technology to determine when and how to intervene. As such, JITAIs hold tremendous potential in addressing major public health concerns such as cigarette smoking, which can recur and arise unexpectedly. In tandem, a growing number of studies have utilized multiple methods to collect data on a particular dynamic construct of interest from the same individual. This approach holds promise in providing investigators with a significantly more detailed view of how a behavior change processes unfold within the same individual than ever before. However, nuanced challenges relating to coarse data, noisy data, and incoherence among data sources are introduced. In this manuscript, we use a mobile health (mHealth) study on smokers motivated to quit (Break Free; R01MD010362) to illustrate these challenges. Practical approaches to integrate multiple data sources are discussed within the greater scientific context of developing dynamic theories of behavior change and JITAIs.

Using the multiphase optimization strategy to adapt cognitive processing therapy (CPT MOST): study protocol for a randomized controlled factorial experiment

BACKGROUND

Approximately ten percent of US military veterans suffer from posttraumatic stress disorder (PTSD). Cognitive processing therapy (CPT) is a highly effective, evidence-based, first-line treatment for PTSD that has been widely adopted by the Department of Veterans Affairs (VA). CPT consists of discrete therapeutic components delivered across 12 sessions, but most veterans (up to 70%) never reach completion, and those who discontinue therapy receive only four sessions on average. Unfortunately, veterans who drop out prematurely may never receive the most effective components of CPT. Thus, there is an urgent need to use empirical approaches to identify the most effective components of CPT so CPT can be adapted into a briefer format.

METHODS

The multiphase optimization strategy (MOST) is an innovative, engineering-inspired framework that uses an optimization trial to assess the performance of individual intervention components within a multicomponent intervention such as CPT. Here we use a fractional factorial optimization trial to identify and retain the most effective intervention components to form a refined, abbreviated CPT intervention package. Specifically, we used a 16-condition fractional factorial experiment with 270 veterans (N = 270) at three VA Medical Centers to test the effectiveness of each of the five CPT components and each two-way interaction between components. This factorial design will identify which CPT components contribute meaningfully to a reduction in PTSD symptoms, as measured by PTSD symptom reduction on the Clinician-Administered PTSD Scale for DSM-5, across 6 months of follow-up. It will also identify mediators and moderators of component effectiveness.

DISCUSSION

There is an urgent need to adapt CPT into a briefer format using empirical approaches to identify its most effective components. A brief format of CPT may reduce attrition and improve efficiency, enabling providers to treat more patients with PTSD. The refined intervention package will be evaluated in a future large-scale, fully-powered effectiveness trial. Pending demonstration of effectiveness, the refined intervention can be disseminated through the VA CPT training program.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05220137. Registration date: January 21, 2022.

Motivational Enhancement Interventions to Increase Pre-Exposure Prophylaxis Use in Sexual Minority Men Who Use Stimulants: Protocol for a Pilot Sequential Multiple Assignment Randomized Trial

BACKGROUND

Although pre-exposure prophylaxis (PrEP) could substantially mitigate HIV risk, sexual minority men who use stimulants commonly experience difficulties with engaging in PrEP clinical services. Motivational interviewing (MI) and contingency management (CM) reduce substance use and condomless anal sex (CAS) in this population, but these motivational enhancement interventions require modifications to promote engagement along the PrEP care continuum.

OBJECTIVE

PrEP Readiness Interventions for Supporting Motivation (PRISM) is a pilot sequential multiple assignment randomized trial testing the feasibility, acceptability, and preliminary effectiveness of distinct combinations of telehealth MI and CM in 70 cisgender sexual minority men who use stimulants that are not currently taking PrEP.

METHODS

A national sample was recruited via social networking applications to complete a baseline assessment and mail-in HIV testing. Those with nonreactive HIV results were randomized to receive either (1) a 2-session MI intervention focusing on PrEP use (session 1) and concomitant stimulant use or CAS (session 2) or (2) a CM intervention with financial incentives for documented evidence of PrEP clinical evaluation by a medical provider (US $50) and filling a PrEP prescription (US $50). At the 3-month follow-up assessment, participants who reported they had not filled a prescription for PrEP were randomized a second time to either (1) switch to a second-stage intervention (ie, MI+CM or CM+MI) or (2) continue with assessments only. Outcomes for both responders and nonresponders were reassessed at a 6-month follow-up. The primary outcome is documented evidence of filling a PrEP prescription over 6 months. Self-reported secondary outcomes include PrEP clinical evaluation by a medical provider, stimulant use, and CAS. Qualitative exit interviews were conducted with a subgroup of responders and nonresponders to characterize their experiences with the MI and CM interventions.

RESULTS

Implementation of PRISM underscores challenges in reaching sexual minority men who use stimulants to optimize HIV prevention efforts. Approximately 1 in 10 (104/1060) eligible participants have enrolled. Of the 104 who enrolled, 87 (84%) completed mail-in HIV testing. We delivered 5 preliminary HIV-positive results, including posttest counseling with referrals to confirmatory testing.

CONCLUSIONS

Lessons learned from PRISM underscore the central importance of a flexible, participant-centered approach to support the engagement of sexual minority men who use stimulants. Leveraging telehealth platforms to deliver motivational enhancement interventions also expanded their reach and potential public health impact with this high-priority population. Further research is needed to determine the effectiveness of telehealth MI and CM for supporting PrEP use in sexual minority men who use stimulants.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04205487; https://clinicaltrials.gov/study/NCT04205487.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/48459.

Ecological Momentary Assessments and Passive Sensing in the Prediction of Short-Term Suicidal Ideation in Young Adults

Importance

Advancements in technology, including mobile-based ecological momentary assessments (EMAs) and passive sensing, have immense potential to identify short-term suicide risk. However, the extent to which EMA and passive data, particularly in combination, have utility in detecting short-term risk in everyday life remains poorly understood.

Objective

To examine whether and what combinations of self-reported EMA and sensor-based assessments identify next-day suicidal ideation.

Design, Setting, and Participants

In this intensive longitudinal prognostic study, participants completed EMAs 4 times daily and wore a sensor wristband (Fitbit Charge 3) for 8 weeks. Multilevel machine learning methods, including penalized generalized estimating equations and classification and regression trees (CARTs) with repeated 5-fold cross-validation, were used to optimize prediction of next-day suicidal ideation based on time-varying features from EMAs (affective, cognitive, behavioral risk factors) and sensor data (sleep, activity, heart rate). Young adult patients who visited an emergency department with recent suicidal ideation and/or suicide attempt were recruited. Identified via electronic health record screening, eligible individuals were contacted remotely to complete enrollment procedures. Participants (aged 18 to 25 years) completed 14 708 EMA observations (64.4% adherence) and wore a sensor wristband approximately half the time (55.6% adherence). Data were collected between June 2020 and July 2021. Statistical analysis was performed from January to March 2023.

Main Outcomes and Measures

The outcome was presence of next-day suicidal ideation.

Results

Among 102 enrolled participants, 83 (81.4%) were female; 6 (5.9%) were Asian, 5 (4.9%) were Black or African American, 9 (8.8%) were more than 1 race, and 76 (74.5%) were White; mean (SD) age was 20.9 (2.1) years. The best-performing model incorporated features from EMAs and showed good predictive accuracy (mean [SE] cross-validated area under the receiver operating characteristic curve [AUC], 0.84 [0.02]), whereas the model that incorporated features from sensor data alone showed poor prediction (mean [SE] cross-validated AUC, 0.56 [0.02]). Sensor-based features did not improve prediction when combined with EMAs. Suicidal ideation-related features were the strongest predictors of next-day ideation. When suicidal ideation features were excluded, an alternative EMA model had acceptable predictive accuracy (mean [SE] cross-validated AUC, 0.76 [0.02]). Both EMA models included features at different timescales reflecting within-day, end-of-day, and time-varying cumulative effects.

Conclusions and Relevance

In this prognostic study, self-reported risk factors showed utility in identifying near-term suicidal thoughts. Best-performing models required self-reported information, derived from EMAs, whereas sensor-based data had negligible predictive accuracy. These results may have implications for developing decision algorithms identifying near-term suicidal thoughts to guide risk monitoring and intervention delivery in everyday life.

Sample size estimation for comparing dynamic treatment regimens in a SMART: A Monte Carlo-based approach and case study with longitudinal overdispersed count outcomes

Dynamic treatment regimens (DTRs), also known as treatment algorithms or adaptive interventions, play an increasingly important role in many health domains. DTRs are motivated to address the unique and changing needs of individuals by delivering the type of treatment needed, when needed, while minimizing unnecessary treatment. Practically, a DTR is a sequence of decision rules that specify, for each of several points in time, how available information about the individual's status and progress should be used in practice to decide which treatment (e.g. type or intensity) to deliver. The sequential multiple assignment randomized trial (SMART) is an experimental design widely used to empirically inform the development of DTRs. Sample size planning resources for SMARTs have been developed for continuous, binary, and survival outcomes. However, an important gap exists in sample size estimation methodology for SMARTs with longitudinal count outcomes. Furthermore, in many health domains, count data are overdispersed-having variance greater than their mean. We propose a Monte Carlo-based approach to sample size estimation applicable to many types of longitudinal outcomes and provide a case study with longitudinal overdispersed count outcomes. A SMART for engaging alcohol and cocaine-dependent patients in treatment is used as motivation.

A multi-site trial of an electronic health integrated physical activity promotion intervention in breast and endometrial cancers survivors: MyActivity study protocol

Despite the known benefits of moderate-to-vigorous physical activity (MVPA) for breast and endometrial cancer survivors, most are insufficiently active, interventions response is heterogeneous, and MVPA programming integration into cancer care is limited. A stepped care approach, in which the least resource-intensive intervention is delivered first and additional components are added based on individual response, is one strategy to enhance uptake of physical activity programming. However, the most effective intervention augmentation strategies are unknown. In this singly randomized trial of post-treatment, inactive breast and endometrial cancer survivors (n = 323), participants receive a minimal intervention including a Fitbit linked with their clinic's patient portal and, in turn, the electronic health record (EHR) with weekly feedback delivered via the portal. MVPA progress summaries are sent to participants' oncology team via the EHR. MVPA adherence is evaluated at 4, 8, 12, 16 and 20 weeks; non-responders (those meeting ≤80% of the MVPA goal over previous 4 weeks) at each timepoint are randomized once for the remainder of the 24-week intervention to one of two "step-up" conditions: (1) online gym or (2) coaching calls, while responders continue with the minimal Fitbit+EHR intervention. The primary outcome is ActiGraph-measured MVPA at 24 and 48 weeks. Secondary outcomes include symptom burden and functional performance at 24 and 48 weeks. This trial will inform development of an effective, scalable, and tailored intervention for survivors by identifying non-responders and providing them with the intervention augmentations necessary to increase MVPA and improve health outcomes. Clinical Trials Registration # NCT04262180.

Reward Design For An Online Reinforcement Learning Algorithm Supporting Oral Self-Care

While dental disease is largely preventable, professional advice on optimal oral hygiene practices is often forgotten or abandoned by patients. Therefore patients may benefit from timely and personalized encouragement to engage in oral self-care behaviors. In this paper, we develop an online reinforcement learning (RL) algorithm for use in optimizing the delivery of mobile-based prompts to encourage oral hygiene behaviors. One of the main challenges in developing such an algorithm is ensuring that the algorithm considers the impact of current actions on the effectiveness of future actions (i.e., delayed effects), especially when the algorithm has been designed to run stably and autonomously in a constrained, real-world setting characterized by highly noisy, sparse data. We address this challenge by designing a quality reward that maximizes the desired health outcome (i.e., high-quality brushing) while minimizing user burden. We also highlight a procedure for optimizing the hyperparameters of the reward by building a simulation environment test bed and evaluating candidates using the test bed. The RL algorithm discussed in this paper will be deployed in Oralytics. To the best of our knowledge, Oralytics is the first mobile health study utilizing an RL algorithm designed to prevent dental disease by optimizing the delivery of motivational messages supporting oral self-care behaviors.

Testing adaptive interventions to improve PTSD treatment outcomes in Federally Qualified Health Centers: Protocol for a randomized clinical trial

BACKGROUND

Posttraumatic stress disorder (PTSD) disproportionately affects low-income individuals and is untreated in 70% of those affected. One third of low-income Americans are treated in Federally Qualified Health Centers (FQHCs), which do not have the capacity to provide all patients with first-line treatments such as Prolonged Exposure (PE). To address this problem, FQHCs could use low-intensity interventions (e.g., Clinician-Supported PTSD Coach: CS PTSD Coach) and medium-intensity interventions (e.g., PE for Primary Care: PE-PC) to treat PTSD with fewer resources. However, some patients will still require high-intensity treatments (e.g., full-length PE) for sustained clinical benefit. Thus, there is a critical need to develop stepped-care models for PTSD in FQHCs.

METHOD

We are conducting a Sequential, Multiple Assignment, Randomized Trial (SMART) with 430 adults with PTSD in FQHCs. Participants are initially randomized to CS PTSD Coach or PE-PC. After four sessions, early responders step down to lower frequency interaction within their assigned initial treatment strategy. Slow responders are re-randomized to either continue their initial treatment strategy or step up to Full PE for an additional eight weeks. The specific aims are to test the effectiveness of initiating treatment with PE-PC versus CS PTSD Coach in reducing PTSD symptoms and to test the effectiveness of second-stage strategies (continue versus step-up to Full PE) for slow responders.

CONCLUSIONS

This project will provide critical evidence to inform the development of an effective stepped-care model for PTSD. Testing scalable, sustainable sequences of PTSD treatments delivered in low-resource community health centers will improve clinical practice for PTSD.

Predicting short-term suicidal thoughts in adolescents using machine learning: developing decision tools to identify daily level risk after hospitalization

BACKGROUND

Mobile technology offers unique opportunities for monitoring short-term suicide risk in daily life. In this study of suicidal adolescent inpatients, theoretically informed risk factors were assessed daily following discharge to predict near-term suicidal ideation and inform decision algorithms for identifying elevations in daily level risk, with implications for real-time suicide-focused interventions.

METHODS

Adolescents (N = 78; 67.9% female) completed brief surveys texted daily for 4 weeks after discharge (n = 1621 observations). Using multi-level classification and regression trees (CARTSs) with repeated 5-fold cross-validation, we tested (a) a simple prediction model incorporating previous-day scores for each of 10 risk factors, and (b) a more complex model incorporating, for each of these factors, a time-varying person-specific mean over prior days together with deviation from that mean. Models also incorporated missingness and contextual (study week, day of the week) indicators. The outcome was the presence/absence of next-day suicidal ideation.

RESULTS

The best-performing model (cross-validated AUC = 0.86) was a complex model that included ideation duration, hopelessness, burdensomeness, and self-efficacy to refrain from suicidal action. An equivalent model that excluded ideation duration had acceptable overall performance (cross-validated AUC = 0.78). Models incorporating only previous-day scores, with and without ideation duration (cross-validated AUC of 0.82 and 0.75, respectively), showed relatively weaker performance.

CONCLUSIONS

Results suggest that specific combinations of dynamic risk factors assessed in adolescents' daily life have promising utility in predicting next-day suicidal thoughts. Findings represent an important step in the development of decision tools identifying short-term risk as well as guiding timely interventions sensitive to proximal elevations in suicide risk in daily life.

Self-relevant appeals to engage in self-monitoring of alcohol use: A microrandomized trial

OBJECTIVE

While self-monitoring can help mitigate alcohol misuse in young adults, engagement with digital self-monitoring is suboptimal. The present study investigates the utility of two types of digital prompts (reminders) to encourage young adults to self-monitor their alcohol use. These prompts leverage information that is self-relevant (i.e., represents and is valuable) to the person.

METHOD

Five hundred ninety-one college students (Mage = 18; 61% = female, 76% = White) were enrolled in an 8-week intervention study involving biweekly digital self-monitoring of their alcohol use. At baseline, participants selected an item they would like to purchase for themselves and their preferred charitable organization. Then, biweekly, participants were microrandomized to a prompt highlighting the opportunity to either (a) win their preferred item (self-interest prompt); or (b) donate to their preferred charity (prosocial prompt). Following self-monitoring completion, participants allocated reward points toward lottery drawings for their preferred item or charity.

RESULTS

The self-interest (vs. prosocial) prompt was significantly more effective in promoting proximal self-monitoring at the beginning of the study, Est = exp(.14) = 1.15; 95% confidence interval (CI) [1.01, 1.29], whereas the prosocial (vs. self-interest) prompt was significantly more effective at the end, Est = exp(-.17) = 0.84; 95% CI [0.70, 0.98]. Further, the prosocial (vs. self-interest) prompt was significantly more effective among participants who previously allocated all their reward points to drawings for their preferred item, Est = exp(-.15) = 0.86; 95% CI [.75, .97].

CONCLUSIONS

These results suggest that the advantage of prompts that appeal to a person's self-interest (vs. prosocial) motives varies over time and based on what reward options participants prioritized in previous decisions. Theoretical and practical implications for intervention design are discussed. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Socio-economic status moderates within-person associations of risk factors and smoking lapse in daily life

BACKGROUND AND AIMS

Individuals of lower socio-economic status (SES) display a higher prevalence of smoking and have more diffxiculty quitting than higher SES groups. The current study investigates whether the within-person associations of key risk (e.g. stress) and protective (self-efficacy) factors with smoking lapse varies by facets of SES.

DESIGN AND SETTING

Observational study using ecological momentary assessment to collect data for a 28-day period following a smoking quit attempt. Multi-level mixed models (i.e. generalized linear mixed models) examined cross-level interactions between lapse risk and protective factors and indicators of SES on smoking lapse.

PARTICIPANTS

A diverse sample of 330 adult US smokers who completed a larger study examining the effects of race/ethnicity and social/environmental influences on smoking cessation.

MEASUREMENTS

Risk factors included momentary urge, negative affect, stress; protective factors included positive affect, motivation, abstinence self-efficacy; SES measures: baseline measures of income and financial strain; the primary outcome was self-reported lapse.

FINDINGS

Participants provided 43 297 post-quit observations. Mixed models suggested that income and financial strain moderated the effect of some risk factors on smoking lapse. The within-person association of negative [odds ratio (OR) = 0.967, 95% CI= 0.945, 0.990, P < 0.01] and positive affect (OR = 1.023, 95% CI = 1.003, 1.044, P < 0.05) and abstinence self-efficacy (OR = 1.020, 95% CI = 1.003, 1.038, P < 0.05) on lapse varied with financial strain. The within-person association of negative affect (OR = 1.005, 95% CI = 1.002, 1.008, P < 0.01), motivation (OR = 0.995, 95% CI = 0.991, 0.999, P < 0.05) and abstinence self-efficacy (OR = 0.996, 95% CI = 0.993, 0.999, P < 0.01) on lapse varied by income. The positive association of negative affect with lapse was stronger among individuals with higher income and lower financial strain. The negative association between positive affect and abstinence self-efficacy with lapse was stronger among individuals with lower financial strain, and the negative association between motivation and abstinence self-efficacy with lapse was stronger among those with higher income. The data were insensitive to detect statistically significant moderating effects of income and financial strain on the association of urge or stress with lapse.

CONCLUSION

Some risk factors (e.g. momentary negative affect) exert a weaker influence on smoking lapse among lower compared to higher socio-economic status groups.

Randomized Controlled Trial of Motivational Enhancement Interventions to Increase Pre-Exposure Prophylaxis Use in Sexual Minority Men Who Use Stimulants

Background

Although pre-exposure prophylaxis (PrEP) could substantially mitigate HIV risk, sexual minority men (SMM) who use stimulants commonly experience difficulties with engaging in PrEP clinical services. Motivational interviewing (MI) and contingency management (CM) reduce substance use and condomless anal sex in this population, but these motivational enhancement interventions require adaptation to promote engagement along the PrEP care continuum.

Methods

PRISM is a pilot sequential multiple assignment randomized trial (SMART) testing the feasibility, acceptability, and preliminary effectiveness of distinct combinations of telehealth MI and CM in 70 cisgender SMM who use stimulants that are not currently taking PrEP. A national sample was recruited via social networking applications to complete a baseline assessment and mail-in HIV testing. Those with non-reactive HIV results are randomized to receive either: 1) a 2-session MI intervention focusing on PrEP use (session 1) and concomitant stimulant use or condomless anal sex (session 2); or 2) a CM intervention with financial incentives for documented evidence of PrEP clinical evaluation by a medical provider ($50) and filling a PrEP prescription ($50). At the 3-month follow-up assessment, participants who report they have not filled a prescription for PrEP are randomized a second time to either: 1) Switch to a second-stage intervention (i.e., MI + CM or CM + MI); or 2) Continue with assessments only. Outcomes for both responders and non-responders are reassessed at a 6-month follow-up. The primary outcome is documented evidence of filling a PrEP prescription. Self-reported, secondary outcomes include PrEP clinical evaluation by a medical provider, stimulant use, and condomless anal sex. Qualitative exit interviews are conducted with a sub-group of responders and non-responders to characterize their experiences with the MI and CM interventions.

Discussion

Implementation of this pilot SMART underscores the challenges in reaching SMM who use stimulants to optimize HIV prevention efforts such that approximately one in ten (104/1,060) eligible participants enrolled. However, 85% (70/82) of enrolled participants with non-reactive HIV results were randomized. Further research is needed to determine the effectiveness of telehealth MI and CM for supporting PrEP use in SMM who use stimulants.

Trial Registration

This protocol was registered on clinicaltrials.gov (NCT04205487) on December 19, 2019.

Time-varying model of engagement with digital self reporting: Evidence from smoking cessation longitudinal studies

Objective

Insufficient engagement is a critical barrier impacting the utility of digital interventions and mobile health assessments. As a result, engagement itself is increasingly becoming a target of studies and interventions. The purpose of this study is to investigate the dynamics of engagement in mobile health data collection by exploring whether, how, and why response to digital self-report prompts change over time in smoking cessation studies.

Method

Data from two ecological momentary assessment (EMA) studies of smoking cessation among diverse smokers attempting to quit (N = 573) with a total of 65,974 digital self-report prompts. We operationalize engagement with self-reporting in term of prompts delivered and prompt response to capture both broad and more granular engagement in self-reporting, respectively. The data were analyzed to describe trends in prompt delivered and prompt response over time. Time-varying effect modeling (TVEM) was employed to investigate the time-varying effects of response to previous prompt and the average response rate on the likelihood of current prompt response.

Results

Although prompt response rates were relatively stable over days in both studies, the proportion of participants with prompts delivered declined steadily over time in one of the studies, indicating that over time, fewer participants charged the device and kept it turned on (necessary to receive at least one prompt per day). Among those who did receive prompts, response rates were relatively stable. In both studies, there is a significant, positive and stable relationship between response to previous prompt and the likelihood of response to current prompt throughout all days of the study. The relationship between the average response rate prior to current prompt and the likelihood of responding to the current prompt was also positive, and increasing with time.

Conclusion

Our study highlights the importance of integrating various indicators to measure engagement in digital self-reporting. Both average response rate and response to previous prompt were highly predictive of response to the next prompt across days in the study. Dynamic patterns of engagement in digital self-reporting can inform the design of new strategies to promote and optimize engagement in digital interventions and mobile health studies.

The ILHBN: challenges, opportunities, and solutions from harmonizing data under heterogeneous study designs, target populations, and measurement protocols

The ILHBN is funded by the National Institutes of Health to collaboratively study the interactive dynamics of behavior, health, and the environment using Intensive Longitudinal Data (ILD) to (a) understand and intervene on behavior and health and (b) develop new analytic methods to innovate behavioral theories and interventions. The heterogenous study designs, populations, and measurement protocols adopted by the seven studies within the ILHBN created practical challenges, but also unprecedented opportunities to capitalize on data harmonization to provide comparable views of data from different studies, enhance the quality and utility of expensive and hard-won ILD, and amplify scientific yield. The purpose of this article is to provide a brief report of the challenges, opportunities, and solutions from some of the ILHBN's cross-study data harmonization efforts. We review the process through which harmonization challenges and opportunities motivated the development of tools and collection of metadata within the ILHBN. A variety of strategies have been adopted within the ILHBN to facilitate harmonization of ecological momentary assessment, location, accelerometer, and participant engagement data while preserving theory-driven heterogeneity and data privacy considerations. Several tools have been developed by the ILHBN to resolve challenges in integrating ILD across multiple data streams and time scales both within and across studies. Harmonization of distinct longitudinal measures, measurement tools, and sampling rates across studies is challenging, but also opens up new opportunities to address cross-cutting scientific themes of interest.

Supervisor Undermining, Social Isolation and Subordinates' Problematic Drinking: The Role of Depression and Perceived Drinking Norms

Findings regarding the mechanism underlying the impact of supervisor incivility on subordinate alcohol misuse remain equivocal. Specifically, some studies indicate that stress mediates the impact of supervisor incivility on subordinate alcohol misuse, while others, find no evidence for such an effect, suggesting the need to investigate other mechanisms. Extending Conservation of Resource (COR) theory and employing a longitudinal study design, this study examines two alternative mechanisms grounded on social isolation. The first suggests drinking as a resource-mobilizing response, with social isolation eliciting the perception of more permissive injunctive drinking norms, thus facilitating problematic drinking. The second suggests problematic drinking as a mode of coping with a negative emotional state elicited by social isolation, namely depression. Findings indicate that supervisor undermining's association with subsequent subordinate problematic drinking is serially mediated by social isolation and depression, with no support found for the first mechanism. Implications for research, practice and policy are discussed.

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

BACKGROUND

Cannabis misuse in young adults is a major public health concern. An important predictor of continued use is cannabis craving. Due to the time-varying nature of cravings, brief momentary interventions delivered while cravings are elevated may improve the use of strategies to cope with cravings and reduce cannabis use.

OBJECTIVE

The goal of this manuscript is to describe a formative study to develop coping strategy messages for use in a subsequent intervention.

METHODS

Young adults (aged 19-25 years; n=20) who reported using cannabis >10 of the past 30 days recruited via social media participated in this formative study. Participants rated an initial set of 15 mindfulness and 15 distraction coping strategies on a scale from 1 to 4 (very low degree to very high degree) for clarity, usefulness, and tone. They also provided comments about the content.

RESULTS

Participants found the initial distraction messages slightly clearer than mindfulness (mean 3.5, SD 0.4 and mean 3.4, SD 0.4, respectively), both were comparable in tone (mean 3.2, SD 0.5 and mean 3.2, SD 0.4, respectively), and mindfulness messages were more useful than distraction (mean 3.0, SD 0.5 and mean 2.8, SD 0.6, respectively). Of the 30 messages, 29 received a rating of very low or low (<2) on any domain by >3 participants or received a comment suggesting a change. We revised all these messages based on this feedback, and the participants rated the revised messages approximately 2 weeks later. Participants earned US $10 for completing the first and US $20 for the second survey. The ratings improved on usefulness (especially the distraction items) with very little change in clarity and tone. The top 10 messages of each coping type (mindfulness and distraction) were identified by overall average rating (collapsed across all 3 dimensions: all rated >3.0). The final items were comparable in clarity (distraction mean 3.6, SD 0.4; mindfulness mean 3.6, SD 0.4), tone (distraction mean 3.4, SD 0.4; mindfulness mean 3.4, SD 0.4), and usefulness (distraction mean 3.1, SD 0.5; mindfulness mean 3.2, SD 0.5).

CONCLUSIONS

The inclusion of end users in the formative process of developing these messages was valuable and resulted in improvements to the content of the messages. The majority of the messages were changed in some way including the removal of potentially triggering language. These messages were subsequently used in an ecological momentary intervention.

The microrandomized trial for developing digital interventions: Experimental design and data analysis considerations

Just-in-time adaptive interventions (JITAIs) are time-varying adaptive interventions that use frequent opportunities for the intervention to be adapted-weekly, daily, or even many times a day. The microrandomized trial (MRT) has emerged for use in informing the construction of JITAIs. MRTs can be used to address research questions about whether and under what circumstances JITAI components are effective, with the ultimate objective of developing effective and efficient JITAI. The purpose of this article is to clarify why, when, and how to use MRTs; to highlight elements that must be considered when designing and implementing an MRT; and to review primary and secondary analyses methods for MRTs. We briefly review key elements of JITAIs and discuss a variety of considerations that go into planning and designing an MRT. We provide a definition of causal excursion effects suitable for use in primary and secondary analyses of MRT data to inform JITAI development. We review the weighted and centered least-squares (WCLS) estimator which provides consistent causal excursion effect estimators from MRT data. We describe how the WCLS estimator along with associated test statistics can be obtained using standard statistical software such as R (R Core Team, 2019). Throughout we illustrate the MRT design and analyses using the HeartSteps MRT, for developing a JITAI to increase physical activity among sedentary individuals. We supplement the HeartSteps MRT with two other MRTs, SARA and BariFit, each of which highlights different research questions that can be addressed using the MRT and experimental design considerations that might arise. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Engagement in digital interventions

The notion of "engagement," which plays an important role in various domains of psychology, is gaining increased currency as a concept that is critical to the success of digital interventions. However, engagement remains an ill-defined construct, with different fields generating their own domain-specific definitions. Moreover, given that digital interactions in real-world settings are characterized by multiple demands and choice alternatives competing for an individual's effort and attention, they involve fast and often impulsive decision-making. Prior research seeking to uncover the mechanisms underlying engagement has nonetheless focused mainly on psychological factors and social influences and neglected to account for the role of neural mechanisms that shape individual choices. This article aims to integrate theories and empirical evidence across multiple domains to define engagement and discuss opportunities and challenges to promote effective engagement in digital interventions. We also propose the affect-integration-motivation and attention-context-translation (AIM-ACT) framework, which is based on a neurophysiological account of engagement, to shed new light on how in-the-moment engagement unfolds in response to a digital stimulus. Building on this framework, we provide recommendations for designing strategies to promote engagement in digital interventions and highlight directions for future research. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Utilizing daily mood diaries and wearable sensor data to predict depression and suicidal ideation among medical interns

BACKGROUND

Intensive longitudinal methods (ILMs) for collecting self-report (e.g., daily diaries, ecological momentary assessment) and passive data from smartphones and wearable sensors provide promising avenues for improved prediction of depression and suicidal ideation (SI). However, few studies have utilized ILMs to predict outcomes for at-risk, non-clinical populations in real-world settings.

METHODS

Medical interns (N = 2881; 57 % female; 58 % White) were recruited from over 300 US residency programs. Interns completed a pre-internship assessment of depression, were given Fitbit wearable devices, and provided daily mood ratings (scale: 1-10) via mobile application during the study period. Three-step hierarchical logistic regressions were used to predict depression and SI at the end of the first quarter utilizing pre-internship predictors in step 1, Fitbit sleep/step features in step 2, and daily diary mood features in step 3.

RESULTS

Passively collected Fitbit features related to sleep and steps had negligible predictive validity for depression, and no incremental predictive validity for SI. However, mean-level and variability in mood scores derived from daily diaries were significant independent predictors of depression and SI, and significantly improved model accuracy.

LIMITATIONS

Work schedules for interns may result in sleep and activity patterns that differ from typical associations with depression or SI. The SI measure did not capture intent or severity.

CONCLUSIONS

Mobile self-reporting of daily mood improved the prediction of depression and SI during a meaningful at-risk period under naturalistic conditions. Additional research is needed to guide the development of adaptive interventions among vulnerable populations.

mRisk: Continuous Risk Estimation for Smoking Lapse from Noisy Sensor Data with Incomplete and Positive-Only Labels

Passive detection of risk factors (that may influence unhealthy or adverse behaviors) via wearable and mobile sensors has created new opportunities to improve the effectiveness of behavioral interventions. A key goal is to find opportune moments for intervention by passively detecting rising risk of an imminent adverse behavior. But, it has been difficult due to substantial noise in the data collected by sensors in the natural environment and a lack of reliable label assignment of low- and high-risk states to the continuous stream of sensor data. In this paper, we propose an event-based encoding of sensor data to reduce the effect of noises and then present an approach to efficiently model the historical influence of recent and past sensor-derived contexts on the likelihood of an adverse behavior. Next, to circumvent the lack of any confirmed negative labels (i.e., time periods with no high-risk moment), and only a few positive labels (i.e., detected adverse behavior), we propose a new loss function. We use 1,012 days of sensor and self-report data collected from 92 participants in a smoking cessation field study to train deep learning models to produce a continuous risk estimate for the likelihood of an impending smoking lapse. The risk dynamics produced by the model show that risk peaks an average of 44 minutes before a lapse. Simulations on field study data show that using our model can create intervention opportunities for 85% of lapses with 5.5 interventions per day.

Designing Reinforcement Learning Algorithms for Digital Interventions: Pre-Implementation Guidelines

Online reinforcement learning (RL) algorithms are increasingly used to personalize digital interventions in the fields of mobile health and online education. Common challenges in designing and testing an RL algorithm in these settings include ensuring the RL algorithm can learn and run stably under real-time constraints, and accounting for the complexity of the environment, e.g., a lack of accurate mechanistic models for the user dynamics. To guide how one can tackle these challenges, we extend the PCS (predictability, computability, stability) framework, a data science framework that incorporates best practices from machine learning and statistics in supervised learning to the design of RL algorithms for the digital interventions setting. Furthermore, we provide guidelines on how to design simulation environments, a crucial tool for evaluating RL candidate algorithms using the PCS framework. We show how we used the PCS framework to design an RL algorithm for Oralytics, a mobile health study aiming to improve users' tooth-brushing behaviors through the personalized delivery of intervention messages. Oralytics will go into the field in late 2022.

Hybrid Experimental Designs for Intervention Development: What, Why, and How

Advances in mobile and wireless technologies offer tremendous opportunities for extending the reach and impact of psychological interventions and for adapting interventions to the unique and changing needs of individuals. However, insufficient engagement remains a critical barrier to the effectiveness of digital interventions. Human delivery of interventions (e.g., by clinical staff) can be more engaging but potentially more expensive and burdensome. Hence, the integration of digital and human-delivered components is critical to building effective and scalable psychological interventions. Existing experimental designs can be used to answer questions either about human-delivered components that are typically sequenced and adapted at relatively slow timescales (e.g., monthly) or about digital components that are typically sequenced and adapted at much faster timescales (e.g., daily). However, these methodologies do not accommodate sequencing and adaptation of components at multiple timescales and hence cannot be used to empirically inform the joint sequencing and adaptation of human-delivered and digital components. Here, we introduce the hybrid experimental design (HED)-a new experimental approach that can be used to answer scientific questions about building psychological interventions in which human-delivered and digital components are integrated and adapted at multiple timescales. We describe the key characteristics of HEDs (i.e., what they are), explain their scientific rationale (i.e., why they are needed), and provide guidelines for their design and corresponding data analysis (i.e., how can data arising from HEDs be used to inform effective and scalable psychological interventions).

Behavior change versus stability during the college-to-work transition: Life course and the "stickiness" of alcohol misuse at career entry

To what extent and under what conditions do college graduates disengage from employment-incompatible behaviors during the college-to-work transition? Drawing from the life course perspective, we proposed a model highlighting considerable stability of employment-incompatible behaviors during initial months of organizational socialization. Our model predicted that maturing out of such behaviors, which is expected by employers and beneficial to career adjustment, would be more likely to occur given a conducive transition context. Using a large dataset tracking graduates from their last semester in college to up to approximately 1.5 years after graduation and with alcohol use as our empirical referent, we demonstrated that a pattern of high-risk drinking behavior may remain even after the transition into full-time employment. We further showed that lower levels of perceived cohort drinking norms and higher levels of mentoring were associated with a higher probability of maturing out, manifesting in a transition from a high-risk drinking profile before graduation to a moderate drinking profile after starting full-time employment. Finally, we found that maturing out was associated with lagged outcomes including lower levels of sleep problems and depression and fewer work days lost to absenteeism, thus underscoring the consequential nature of behavior profile shifts during the college-to-work transition.

Digital Prompts to Increase Engagement With the Headspace App and for Stress Regulation Among Parents: Feasibility Study

Background

Given the interrelated health of children and parents, strategies to promote stress regulation are critically important in the family context. However, the uptake of preventive mental health is limited among parents owing to competing family demands.

Objective

In this study, we aim to determine whether it is feasible and acceptable to randomize digital prompts designed to engage parents in real-time brief mindfulness activities guided by a commercially available app.

Methods

We conducted a 30-day pilot microrandomized trial among a sample of parents who used Android smartphones. Each day during a parent-specified time frame, participants had a 50% probability of receiving a prompt with a message encouraging them to engage in a mindfulness activity using a commercial app, Headspace. In the 24 hours following randomization, ecological momentary assessments and passively collected smartphone data were used to assess proximal engagement (yes or no) with the app and any mindfulness activity (with or without the app). These data were combined with baseline and exit surveys to determine feasibility and acceptability.

Results

Over 4 months, 83 interested parents were screened, 48 were eligible, 16 were enrolled, and 10 were successfully onboarded. Reasons for nonparticipation included technology barriers, privacy concerns, time constraints, or change of mind. In total, 80% (8/10) of parents who onboarded successfully completed all aspects of the intervention. While it is feasible to randomize prompt delivery, only 60% (6/10) of parents reported that the timing of prompts was helpful despite having control over the delivery window. Across the study period, we observed higher self-reported engagement with Headspace on days with prompts (31/62, 50% of days), as opposed to days without prompts (33/103, 32% of days). This pattern was consistent for most participants in this study (7/8, 87%). The time spent using the app on days with prompts (mean 566, SD 378 seconds) was descriptively higher than on days without prompts (mean 225, SD 276 seconds). App usage was highest during the first week and declined over each of the remaining 3 weeks. However, self-reported engagement in mindfulness activities without the app increased over time. Self-reported engagement with any mindfulness activity was similar on days with (40/62, 65% of days) and without (65/103, 63% of days) prompts. Participants found the Headspace app helpful (10/10, 100%) and would recommend the program to others (9/10, 90%).

Conclusions

Preliminary findings suggest that parents are receptive to using mindfulness apps to support stress management, and prompts are likely to increase engagement with the app. However, we identified several implementation challenges in the current trial, specifically a need to optimize prompt timing and frequency as a strategy to engage users in preventive digital mental health.

MCMTC: A Pragmatic Framework for Selecting an Experimental Design to Inform the Development of Digital Interventions

Advances in digital technologies have created unprecedented opportunities to deliver effective and scalable behavior change interventions. Many digital interventions include multiple components, namely several aspects of the intervention that can be differentiated for systematic investigation. Various types of experimental approaches have been developed in recent years to enable researchers to obtain the empirical evidence necessary for the development of effective multiple-component interventions. These include factorial designs, Sequential Multiple Assignment Randomized Trials (SMARTs), and Micro-Randomized Trials (MRTs). An important challenge facing researchers concerns selecting the right type of design to match their scientific questions. Here, we propose MCMTC – a pragmatic framework that can be used to guide investigators interested in developing digital interventions in deciding which experimental approach to select. This framework includes five questions that investigators are encouraged to answer in the process of selecting the most suitable design: (1) Multiple-component intervention: Is the goal to develop an intervention that includes multiple components; (2) Component selection: Are there open scientific questions about the selection of specific components for inclusion in the intervention; (3) More than a single component: Are there open scientific questions about the inclusion of more than a single component in the intervention; (4) Timing: Are there open scientific questions about the timing of component delivery, that is when to deliver specific components; and (5) Change: Are the components in question designed to address conditions that change relatively slowly (e.g., over months or weeks) or rapidly (e.g., every day, hours, minutes). Throughout we use examples of tobacco cessation digital interventions to illustrate the process of selecting a design by answering these questions. For simplicity we focus exclusively on four experimental approaches—standard two- or multi-arm randomized trials, classic factorial designs, SMARTs, and MRTs—acknowledging that the array of possible experimental approaches for developing digital interventions is not limited to these designs.

New Directions for Motivational Incentive Interventions for Smoking Cessation

Background

Motivational incentive interventions are highly effective for smoking cessation. Yet, these interventions are not widely available to people who want to quit smoking, in part, due to barriers such as administrative burden, concern about the use of extrinsic reinforcement (i.e., incentives) to improve cessation outcomes, suboptimal intervention engagement, individual burden, and up-front costs.

Purpose

Technological advancements can mitigate some of these barriers. For example, mobile abstinence monitoring and digital, automated incentive delivery have the potential to lower the clinic burden associated with monitoring abstinence and administering incentives while also reducing the frequency of clinic visits. However, to fully realize the potential of digital technologies to deliver motivational incentives it is critical to develop strategies to mitigate longstanding concerns that reliance on extrinsic monetary reinforcement may hamper internal motivation for cessation, improve individual engagement with the intervention, and address scalability limitations due to the up-front cost of monetary incentives. Herein, we describe the state of digitally-delivered motivational incentives. We then build on existing principles for creating just-in-time adaptive interventions to highlight new directions in leveraging digital technology to improve the effectiveness and scalability of motivational incentive interventions.

Conclusions

Technological advancement in abstinence monitoring coupled with digital delivery of reinforcers has made the use of motivational incentives for smoking cessation increasingly feasible. We propose future directions for a new era of motivational incentive interventions that leverage technology to integrate monetary and non-monetary incentives in a way that addresses the changing needs of individuals as they unfold in real-time.

Application of community - engaged dissemination and implementation science to improve health equity

Community engagement is critical to accelerate and improve implementation of evidence-based interventions to reduce health inequities. Community-engaged dissemination and implementation research (CEDI) emphasizes engaging stakeholders (e.g., community members, practitioners, community organizations, etc.) with diverse perspectives, experience, and expertise to provide tacit community knowledge regarding the local context, priorities, needs, and assets. Importantly, CEDI can help improve health inequities through incorporating unique perspectives from communities experiencing health inequities that have historically been left out of the research process. The community-engagement process that exists in practice can be highly variable, and characteristics of the process are often underreported, making it difficult to discern how engagement of community partners was used to improve implementation. This paper describes the community-engagement process for a multilevel, pragmatic randomized trial to increase the reach and impact of evidence-based tobacco cessation treatment among Community Health Center patients; describes how engagement activities and the resulting partnership informed the development of implementation strategies and improved the research process; and presents lessons learned to inform future CEDI research.

Dynamic models of stress-smoking responses based on high-frequency sensor data

Self-reports indicate that stress increases the risk for smoking; however, intensive data from sensors can provide a more nuanced understanding of stress in the moments leading up to and following smoking events. Identifying personalized dynamical models of stress-smoking responses can improve characterizations of smoking responses following stress, but techniques used to identify these models require intensive longitudinal data. This study leveraged advances in wearable sensing technology and digital markers of stress and smoking to identify person-specific models of stress and smoking system dynamics by considering stress immediately before, during, and after smoking events. Adult smokers (n = 45) wore the AutoSense chestband (respiration-inductive plethysmograph, electrocardiogram, accelerometer) with MotionSense (accelerometers, gyroscopes) on each wrist for three days prior to a quit attempt. The odds of minute-level smoking events were regressed on minute-level stress probabilities to identify person-specific dynamic models of smoking responses to stress. Simulated pulse responses to a continuous stress episode revealed a consistent pattern of increased odds of smoking either shortly after the beginning of the simulated stress episode or with a delay, for all participants. This pattern is followed by a dramatic reduction in the probability of smoking thereafter, for about half of the participants (49%). Sensor-detected stress probabilities indicate a vulnerability for smoking that may be used as a tailoring variable for just-in-time interventions to support quit attempts.

Translating strategies for promoting engagement in mobile health: A proof-of-concept microrandomized trial

OBJECTIVE

Mobile technologies allow for accessible and cost-effective health monitoring and intervention delivery. Despite these advantages, mobile health (mHealth) engagement is often insufficient. While monetary incentives may increase engagement, they can backfire, dampening intrinsic motivations and undermining intervention scalability. Theories from psychology and behavioral economics suggest useful nonmonetary strategies for promoting engagement; however, examinations of the applicability of these strategies to mHealth engagement are lacking. This proof-of-concept study evaluates the translation of theoretically-grounded engagement strategies into mHealth, by testing their potential utility in promoting daily self-reporting.

METHOD

A microrandomized trial (MRT) was conducted with adolescents and emerging adults with past-month substance use. Participants were randomized multiple times daily to receive theoretically-grounded strategies, namely reciprocity (the delivery of inspirational quote prior to self-reporting window) and nonmonetary reinforcers (e.g., the delivery of meme/gif following self-reporting completion) to improve proximal engagement in daily mHealth self-reporting.

RESULTS

Daily self-reporting rates (62.3%; n = 68) were slightly lower than prior literature, albeit with much lower financial incentives. The utility of specific strategies was found to depend on contextual factors pertaining to the individual's receptivity and risk for disengagement. For example, the effect of reciprocity significantly varied depending on whether this strategy was employed (vs. not employed) during the weekend. The nonmonetary reinforcement strategy resulted in different outcomes when operationalized in various ways.

CONCLUSIONS

While the results support the translation of the reciprocity strategy into this mHealth setting, the translation of nonmonetary reinforcement requires further consideration prior to inclusion in a full scale MRT. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

The mobile assistance for regulating smoking (MARS) micro-randomized trial design protocol

Smoking is the leading preventable cause of death and disability in the U.S. Empirical evidence suggests that engaging in evidence-based self-regulatory strategies (e.g., behavioral substitution, mindful attention) can improve smokers' ability to resist craving and build self-regulatory skills. However, poor engagement represents a major barrier to maximizing the impact of self-regulatory strategies. This paper describes the protocol for Mobile Assistance for Regulating Smoking (MARS) - a research study designed to inform the development of a mobile health (mHealth) intervention for promoting real-time, real-world engagement in evidence-based self-regulatory strategies. The study will employ a 10-day Micro-Randomized Trial (MRT) enrolling 112 smokers attempting to quit. Utilizing a mobile smoking cessation app, the MRT will randomize each individual multiple times per day to either: (a) no intervention prompt; (b) a prompt recommending brief (low effort) cognitive and/or behavioral self-regulatory strategies; or (c) a prompt recommending more effortful cognitive or mindfulness-based strategies. Prompts will be delivered via push notifications from the MARS mobile app. The goal is to investigate whether, what type of, and under what conditions prompting the individual to engage in self-regulatory strategies increases engagement. The results will build the empirical foundation necessary to develop a mHealth intervention that effectively utilizes intensive longitudinal self-report and sensor-based assessments of emotions, context and other factors to engage an individual in the type of self-regulatory activity that would be most beneficial given their real-time, real-world circumstances. This type of mHealth intervention holds enormous potential to expand the reach and impact of smoking cessation treatments.

Sense2Stop: A micro-randomized trial using wearable sensors to optimize a just-in-time-adaptive stress management intervention for smoking relapse prevention

BACKGROUND

Relapse to smoking is commonly triggered by stress, but behavioral interventions have shown only modest efficacy in preventing stress-related relapse. Continuous digital sensing to detect states of smoking risk and intervention receptivity may make it feasible to increase treatment efficacy by adapting intervention timing.

OBJECTIVE

Aims are to investigate whether the delivery of a prompt to perform stress management behavior, as compared to no prompt, reduces the likelihood of (a) being stressed and (b) smoking in the subsequent two hours, and (c) whether current stress moderates these effects.

STUDY DESIGN

A micro-randomized trial will be implemented with 75 adult smokers who wear Autosense chest and wrist sensors and use the mCerebrum suite of smartphone apps to report and respond to ecological momentary assessment (EMA) questions about smoking and mood for 4 days before and 10 days after a quit attempt and to access a set of stress-management apps. Sensor data will be processed on the smartphone in real time using the cStress algorithm to classify minutes as probably stressed or probably not stressed. Stressed and non-stressed minutes will be micro-randomized to deliver either a prompt to perform a stress management exercise via one of the apps or no prompt (2.5-3 stress management prompts will be delivered daily). Sensor and self-report assessments of stress and smoking will be analyzed to optimize decision rules for a just-in-time adaptive intervention (JITAI) to prevent smoking relapse.

SIGNIFICANCE

Sense2Stop will be the first digital trial using wearable sensors and micro-randomization to optimize a just-in-time adaptive stress management intervention for smoking relapse prevention.

Multitiered Systems of Support, Adaptive Interventions, and SMART Designs

This article introduces the special section on adaptive interventions and sequential multiple-assignment randomized trial (SMART) research designs. In addition to describing the two accompanying articles, we discuss features of adaptive interventions (AIs) and describe the use of SMART design to optimize AIs in the context of multitiered systems of support (MTSS) and integrated MTSS. AI is a treatment delivery model that explicitly specifies how information about individuals should be used to decide which treatment to provide in practice. Principles that apply to the design of AIs may help to more clearly operationalize MTSS-based programs, improve their implementation in school settings, and increase their efficacy when used according to evidence-based decision rules. A SMART is a research design for developing and optimizing MTSS-based programs. We provide a running example of a SMART design to optimize an MTSS-aligned AI that integrates academic and behavioral interventions.

Adaptive intervention for prevention of adolescent suicidal behavior after hospitalization: a pilot sequential multiple assignment randomized trial

BACKGROUND

The need for effective interventions for psychiatrically hospitalized adolescents who have varying levels of postdischarge suicide risk calls for personalized approaches, such as adaptive interventions (AIs). We conducted a nonrestricted pilot Sequential, Multiple Assignment, Randomized Trial (SMART) to guide the development of an AI targeting suicide risk after hospitalization.

METHODS

Adolescent inpatients (N = 80; ages 13-17; 67.5% female) were randomized in Phase 1 to a Motivational Interview-Enhanced Safety Plan (MI-SP), delivered during hospitalization, alone or in combination with postdischarge text-based support (Texts). Two weeks after discharge, participants were re-randomized in Phase 2 to added telephone booster calls or to no calls. Mechanisms of change were assessed with daily diaries for four weeks and over a 1- and 3-month follow-up. This trial is registered with clinicaltrials.gov (identifier: NCT03838198).

RESULTS

Procedures were feasible and acceptable. Mixed effects models indicate that adolescents randomized to MI-SP + Texts (Phase 1) and those randomized to booster calls (Phase 2) experienced significant improvement in daily-level mechanisms, including safety plan use, self-efficacy to refrain from suicidal action, and coping by support seeking. Those randomized to MI-SP + Texts also reported significantly higher coping self-efficacy at 1 and 3 months. Although exploratory, results were in the expected direction for MI-SP + Texts, versus MI-SP alone, in terms of lower risk of suicide attempts (Hazard ratio = 0.30; 95% CI = 0.06, 1.48) and suicidal behavior (Hazard ratio = 0.36; 95% CI = 0.10, 1.37) three months after discharge. Moreover, augmentation with booster calls did not have an overall meaningful impact on suicide attempts (Hazard ratio = 0.65; 95% CI = 0.17, 3.05) or suicidal behavior (Hazard ratio = 0.78; 95% CI = 0.23, 2.67); however, boosters benefited most those initially assigned to MI-SP + Texts.

CONCLUSIONS

The current SMART was feasible and acceptable for the purpose of informing an AI for suicidal adolescents, warranting additional study. Findings also indicate that postdischarge text-based support offers a promising augmentation to safety planning delivered during hospitalization.

Workflow analysis for design of an electronic health record-based tobacco cessation intervention in community health centers

OBJECTIVE

Tobacco use is the leading cause of preventable morbidity and mortality in the United States. Quitlines are effective telephone-based tobacco cessation services but are underutilized. The goal of this project was to describe current clinical workflows for Quitline referral and design an optimal electronic health record (EHR)-based workflow for Ask-Advice-Connect (AAC), an evidence-based intervention to increase Quitline referrals.

MATERIALS AND METHODS

Ten Community Health Center systems (CHC), which use three different EHRs, participated in this study. Methods included: 9 group discussions with CHC leaders; 33 observations/interviews of clinical teams' workflow; surveys with 57 clinical staff; and assessment of the EHR ecosystem in each CHC. Data across these methods were integrated and coded according to the Fit between Individual, Task, Technology and Environment (FITTE) framework. The current and optimal workflow were notated using Business Process Modelling Notation. We compared the requirements of the optimal workflow with EHR capabilities.

RESULTS

Current workflows are inefficient in data collection, variable in who, how, and when tobacco cessation advice and referral are enacted, and lack communication between referring clinics and the Quitline. In the optimal workflow, medical assistants deliver a standardized AAC intervention during the visit intake. Referrals are submitted electronically, and there is bidirectional communication between the clinic and Quitline. We implemented AAC within all three EHRs; however, deviations from the optimal workflow were necessary.

CONCLUSION

Current workflows for Quitline referral are inefficient and ineffective. We propose an optimal workflow and discuss improvements in EHR capabilities that would improve the implementation of AAC.

Main outcomes of M-bridge: A sequential multiple assignment randomized trial (SMART) for developing an adaptive preventive intervention for college drinking

Objective: The goal was to develop a universal and resource-efficient adaptive preventive intervention (API) for incoming first-year students as a bridge to indicated interventions to address alcohol-related risks. The aims were to examine: (a) API versus assessment-only control, (b) the different APIs (i.e., 4 intervention sequences) embedded in the study design, and (c) moderators of intervention effects on binge drinking. Method: A sequential multiple assignment randomized trial (SMART) included two randomizations: timing (summer before vs. first semester) of universal personalized normative feedback and biweekly self-monitoring and, for heavy drinkers, bridging strategy (resource email vs. health coaching invitation). Participants (N = 891, 62.4% female, 76.8% White) were surveyed at the end of first and second semesters. The primary outcome was binge drinking frequency (4+/5+ drinks for females/males); secondary outcomes were alcohol consequences and health services utilization. Results: API (vs. control) was not significantly associated with outcomes. There were no differences between embedded APIs. Among heavy drinkers, the resource email (vs. health coach invitation) led to greater health services utilization. Moderator analyses suggested students intending to pledge into Greek life benefited more from any API (vs. control; 42% smaller increase from precollege in binge drinking frequency). Conclusions: Although overall effects were not significant, students at high risk (i.e., entering fraternities/sororities) did benefit more from the intervention. Furthermore, the resource email was effective for heavier drinkers. A technology-based strategy to deliver targeted resource-light interventions for heavy drinkers may be effective for reducing binge drinking during the transition to college. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

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

BACKGROUND

Many young adults with type 1 diabetes (T1D) struggle with the complex daily demands of adherence to their medical regimen and fail to achieve target range glycemic control. Few interventions, however, have been developed specifically for this age group.

OBJECTIVE

In this randomized trial, we will provide a mobile app (SweetGoals) to all participants as a "core" intervention. The app prompts participants to upload data from their diabetes devices weekly to a device-agnostic uploader (Glooko), automatically retrieves uploaded data, assesses daily and weekly self-management goals, and generates feedback messages about goal attainment. Further, the trial will test two unique intervention components: (1) incentives to promote consistent daily adherence to goals, and (2) web health coaching to teach effective problem solving focused on personalized barriers to self-management. We will use a novel digital direct-to-patient recruitment method and intervention delivery model that transcends the clinic.

METHODS

A 2x2 factorial randomized trial will be conducted with 300 young adults ages 19-25 with type 1 diabetes and (Hb)A_1c ≥ 8.0%. All participants will receive the SweetGoals app that tracks and provides feedback about two adherence targets: (a) daily glucose monitoring; and (b) mealtime behaviors. Participants will be randomized to the factorial combination of incentives and health coaching. The intervention will last 6 months. The primary outcome will be reduction in A_1c. Secondary outcomes include self-regulation mechanisms in longitudinal mediation models and engagement metrics as a predictor of outcomes. Participants will complete 6- and 12-month follow-up assessments. We hypothesize greater sustained A_1c improvements in participants who receive coaching and who receive incentives compared to those who do not receive those components.

RESULTS

Data collection is expected to be complete by February 2025. Analyses of primary and secondary outcomes are expected by December 2025.

CONCLUSIONS

Successful completion of these aims will support dissemination and effectiveness studies of this intervention that seeks to improve glycemic control in this high-risk and understudied population of young adults with T1D.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04646473; https://clinicaltrials.gov/ct2/show/NCT04646473.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/27109.

Developing an Adaptive Mobile Intervention to Address Risky Substance Use Among Adolescents and Emerging Adults: Usability Study

BACKGROUND

Substance use among adolescents and emerging adults continues to be an important public health problem associated with morbidity and mortality. Mobile health (mHealth) provides a promising approach to deliver just-in-time adaptive interventions (JITAIs) to prevent escalation of use and substance use-related consequences.

OBJECTIVE

This pilot study aims to describe the iterative development and initial feasibility and acceptability testing of an mHealth smartphone app, called MiSARA, designed to reduce escalation in substance use.

METHODS

We used social media advertisements to recruit youth (n=39; aged 16-24 years, who screened positive for past-month binge drinking or recreational cannabis use) with a waiver of parental consent. Participants used the MiSARA app for 30 days, with feasibility and acceptability data reported at a 1-month follow-up. We present descriptive data regarding behavior changes over time.

RESULTS

The results show that most participants (31/39, 79%) somewhat liked the app at least, with most (29/39, 74%) rating MiSARA as 3 or more stars (out of 5). Almost all participants were comfortable with self-reporting sensitive information within the app (36/39, 92%); however, most participants also desired more interactivity (27/39, 69%). In addition, participants' substance use declined over time, and those reporting using the app more often reported less substance use at the 1-month follow-up than those who reported using the app less often.

CONCLUSIONS

The findings suggest that the MiSARA app is a promising platform for JITAI delivery, with future trials needed to optimize the timing and dose of messages and determine efficacy.

Toward a Just-in-Time Adaptive Intervention to Reduce Emerging Adult Alcohol Use: Testing Approaches for Identifying When to Intervene

JITAI: Just-in-time adaptive intervention; ROC: receiver operating characteristic; AUC: area under the curve; MRT: micro-randomized trial.