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

A systematic review on eHealth technology personalization approaches

Despite the widespread use of personalization of eHealth technologies, there is a lack of comprehensive understanding regarding its application. This systematic review aims to bridge this gap by identifying and clustering different personalization approaches based on the type of variables used for user segmentation and the adaptations to the eHealth technology and examining the role of computational methods in the literature. From the 412 included reports, we identified 13 clusters of personalization approaches, such as behavior + channeling and environment + recommendations. Within these clusters, 10 computational methods were utilized to match segments with technology adaptations, such as classification-based methods and reinforcement learning. Several gaps were identified in the literature, such as the limited exploration of technology-related variables, the limited focus on user interaction reminders, and a frequent reliance on a single type of variable for personalization. Future research should explore leveraging technology-specific features to attain individualistic segmentation approaches.

First semester college experiences: Associations with substance use and mental health

Introduction

Substance use (SU) and mental health (MH) concerns often occur as students transition from high school to college and may be associated with first semester experiences.

Methods

Incoming first-year college students at a U.S., predominantly white, midwestern university were recruited for an intervention study. Participants reported on substance use, mental health, and college experiences at the end of their first semester of college (n = 781; 62.9 % female, mean age = 18.06 [SD = 0.24]; 77.2 % non-Hispanic white; 84.6 % heterosexual). Data for these current analyses were cross-sectional.

Results

Identifying as Black, Indigenous, or another Person of Color, as a sexual minority, or as female was associated with more challenging first semester experiences. Social experiences that represent more social engagement were associated with greater substance use. College experiences reflecting an easier transition were associated with fewer mental health concerns. Alcohol use moderated the relation between academic challenges and mental health with stronger associations found at greater levels of alcohol use.

Conclusions

First semester experiences were related to student mental health and substance use, suggesting the importance of early, targeted support for students to adjust to campus life.

A systematic review of binge drinking interventions and bias assessment among college students and young adults in high-income countries

Alcohol is the number one substance used by young people and people of college age. Binge drinking (BD) in this age group is considered one of the most important global health issues, as much harm accrues from it and even lives are lost. This study aimed to review the interventions to curb BD or encourage responsible drinking among college students and young adults. MEDLINE (PubMed), ERIC and APA PsycINFO were searched. The selected articles were published in English and had to evaluate a BD reduction program through a randomized control trial (RCT) among college students or young adults between the ages of 17-24 years. The exclusion criteria included research not published in English, systematic review articles, qualitative studies, designs other than RCTs and discussion articles on college students drinking with no findings. The three reviewers independently screened and extracted the data using the PRISMA guidelines. The overall quality of the studies was assessed. Then, 10 of the 12 interventions studied were found to be successful in reducing BD among college students, though the effect sizes were small to medium. A minority of the studies used behavior change theories. Effective interventions for reducing BD among college students and young adults should include robust behavior change theories, longer follow-up time and the operationalization of multiple outcomes. Process evaluation is needed to be conducted in these studies.

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.

Examining engagement effects in an adaptive preventive intervention for college student drinking

OBJECTIVE

This study determined the characteristics of engagement and whether engagement in an adaptive preventive intervention (API) was associated with reduced binge drinking and alcohol-related consequences.

METHOD

Incoming students were recruited for a sequential multiple assignment randomized trial (SMART; N = 891, 62.4% female, 76.8% non-Hispanic White) with an assessment-only control group. The API occurred during the first semester of college, with outcomes assessed at the end of the semester. The API involved two stages. Stage 1 included universal intervention components (personalized normative feedback [PNF] and self-monitoring). Stage 2 bridged heavy drinkers to access additional resources. We estimated the effect of engagement in Stage 1 only and in the whole API (Stages 1 and 2) among the intervention group, and the effect of the API versus control had all students assigned an API engaged, on alcohol-related outcomes.

RESULTS

Precollege binge drinking, intention to pledge a fraternity/sorority, and higher conformity motives were most associated with lower odds of Stage 1 engagement. Action (readiness to change) and PNF engagement were associated with Stage 2 engagement. API engagement was associated with significant reductions in alcohol-related consequences among heavy drinkers. Compared to the control, we estimated the API would reduce the relative increase in alcohol-related consequences from baseline to follow-up by 25%, had all API students engaged.

CONCLUSIONS

Even partial engagement in each component of the "light-touch" API rendered benefits. Analyses suggested that had all students in the intervention group engaged, the API would significantly reduce the change in alcohol-related consequences over the first semester in college. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Modified interactive Q-learning for attenuating the impact of model misspecification with treatment effect heterogeneity

A sequential multiple assignment randomized trial, which incorporates multiple stages of randomization, is a popular approach for collecting data to inform personalized and adaptive treatments. There is an extensive literature on statistical methods to analyze data collected in sequential multiple assignment randomized trials and estimate the optimal dynamic treatment regime. Q-learning with linear regression is widely used for this purpose due to its ease of implementation. However, model misspecification is a common problem with this approach, and little attention has been given to the impact of model misspecification when treatment effects are heterogeneous across subjects. This article describes the integrative impact of two possible types of model misspecification related to treatment effect heterogeneity: omitted early-stage treatment effects in late-stage main effect model, and violated linearity assumption between pseudo-outcomes and predictors despite non-linearity arising from the optimization operation. The proposed method, aiming to deal with both types of misspecification concomitantly, builds interactive models into modified parametric Q-learning with Murphy's regret function. Simulations show that the proposed method is robust to both sources of model misspecification. The proposed method is applied to a two-stage sequential multiple assignment randomized trial with embedded tailoring aimed at reducing binge drinking in first-year college students.

An example of implementing a safety protocol in remote intervention and survey research with college students

INTRODUCTION

This article draws attention to the need for open evaluation and reporting on safety protocols in survey and intervention research. We describe a protocol for responding to those who indicate increased risk of self-harm (i.e. suicidality or potentially lethal alcohol use) as an example and report on the outcome of our procedures.

METHODS

Participants were first-year college students (n = 891) participating in an intervention trial for binge drinking. We describe the protocol, provide descriptive outcomes, and examine whether participant sex, attrition, or study intervention condition were related to endorsing items that indicated risk for suicidality or potentially lethal alcohol use.

RESULTS

Of the 891 participants, 167 (18.7%) were identified as being at risk in one or more study wave. Of those, we were able to successfully contact 100 (59.9%), 76 (45.5%) by phone, and 24 (14.4%) by email. Of those 100, 78 accepted mental health resources as a result of outreach. Participant sex, attrition, and intervention condition were not related to risk.

DISCUSSION

This article may aid other research teams in developing similar protocols. Strategies to reach an even greater proportion of high-risk participants are needed. A body of literature documenting published safety protocols in research and the associated outcomes would help to identify opportunities for improvement.

Optimizing adaptive stepped-care interventions to change adults' health behaviors: A systematic review

Chronic diseases are ubiquitous and costly in American populations. Interventions targeting health behavior change to manage chronic diseases are needed, but previous efforts have fallen short of producing meaningful change on average. Adaptive stepped-care interventions, that tailor treatment based on the needs of the individual over time, are a promising new area in health behavior change. We therefore conducted a systematic review of tests of adaptive stepped-care interventions targeting health behavior changes for adults with chronic diseases. We identified 9 completed studies and 13 research protocols testing adaptive stepped-care interventions for health behavior change. The most common health behaviors targeted were substance use, weight management, and smoking cessation. All identified studies test intermediary tailoring for treatment non-responders via sequential multiple assignment randomized trials (SMARTs) or singly randomized trials (SRTs); none test baseline tailoring. From completed studies, there were few differences between embedded adaptive interventions and minimal differences between those classified as treatment responders and non-responders. In conclusion, updates to this work will be needed as protocols identified here publish results. Future research could explore baseline tailoring variables, apply methods to additional health behaviors and target populations, test tapering interventions for treatment responders, and consider adults' context when adapting interventions.

Outcome trajectory estimation for optimal dynamic treatment regimes with repeated measures

In recent sequential multiple assignment randomized trials, outcomes were assessed multiple times to evaluate longer-term impacts of the dynamic treatment regimes (DTRs). Q-learning requires a scalar response to identify the optimal DTR. Inverse probability weighting may be used to estimate the optimal outcome trajectory, but it is inefficient, susceptible to model mis-specification, and unable to characterize how treatment effects manifest over time. We propose modified Q-learning with generalized estimating equations to address these limitations and apply it to the M-bridge trial, which evaluates adaptive interventions to prevent problematic drinking among college freshmen. Simulation studies demonstrate our proposed method improves efficiency and robustness.

Adaptive interventions for alcohol misuse and violent behaviors among adolescents and emerging adults in the emergency department: A sequential multiple assignment randomized controlled trial protocol

Alcohol use and violent behaviors among youth are associated with morbidity and mortality. An emergency department (ED) visit provides an opportunity to initiate prevention efforts. Despite promising findings from our single session SafERteens brief intervention (BI), impact is limited by modest effect sizes, with data lacking on optimal boosters to enhance effects. This paper describes the protocol for a sequential, multiple assignment, randomized trial (SMART). Adolescents and emerging adults (ages 14-20) in the ED screening positive for alcohol use and violent behaviors (physical aggression) were randomly assigned to: 1) SafERteens BI + Text Messaging (TM), or 2) SafERteens BI + remote Health Coach (HC). Participants completed weekly surveys over 8 weeks after the ED visit to tailor intervention content and measure mechanisms of change. At one-month, intervention response/non-response is determined (e.g., binge drinking or violent behaviors). Responders are re-randomized to continued intervention condition (e.g., maintenance) or minimized condition (e.g., stepped down). Non-responders are re-randomized to continued condition (e.g., maintenance), or intensified condition (e.g., stepped up). Outcomes were measured at 4 and 8 months, including primary outcomes of alcohol consumption and violence, with secondary outcomes of alcohol consequences and violence consequences. Although the original goal was to enroll 700 participants, COVID-19 impacts on research diminished recruitment in this trial (enrolled n = 400). Nonetheless, the proposed SMART is highly innovative by blending real-time assessment methodologies with adaptive intervention delivery among teens with comorbid alcohol misuse and violent behaviors. Findings will inform the content and timing booster interventions to alter risk behavior trajectories. Trial Registration:ClinicalTrials.govNCT03344666. University of Michigan # HUM00109156.

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).

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).

Deeply Tailored Adaptive Interventions to Reduce College Student Drinking: a Real-World Application of Q-Learning for SMART Studies

M-bridge was a sequential multiple assignment randomized trial (SMART) that aimed to develop a resource-efficient adaptive preventive intervention (API) to reduce binge drinking in first-year college students. The main results of M-bridge suggested no difference, on average, in binge drinking between students randomized to APIs versus assessment-only control, but certain elements of the API were beneficial for at-risk subgroups. This paper extends the main results of M-bridge through an exploratory analysis using Q-learning, a novel algorithm from the computer science literature. Specifically, we sought to further tailor the two aspects of the M-bridge APIs to an individual and test whether deep tailoring offers a benefit over assessment-only control. Q-learning is a method to estimate decision rules that assign optimal treatment (i.e., to minimize binge drinking) based on student characteristics. For the first aspect of the M-bridge API (when to offer), we identified the optimal tailoring characteristic post hoc from a set of 20 candidate variables. For the second (how to bridge), we used a known effect modifier from the trial. The results of our analysis are two rules that optimize (1) the timing of universal intervention for each student based on their motives for drinking and (2) the bridging strategy to indicated interventions (i.e., among those who continue to drink heavily mid-semester) based on mid-semester binge drinking frequency. We estimate that this newly tailored API, if offered to all first-year students, would reduce binge drinking by 1 occasion per 2.5 months (95% CI: decrease of 1.45 to 0.28 occasions, p < 0.01) on average. Our analyses demonstrate a real-world implementation of Q-learning for a substantive purpose, and, if replicable in future trials, our results have practical implications for college campuses aiming to reduce student binge drinking.

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.