Microrandomized trials: An experimental design for developing just-in-time adaptive interventions

Toward Increasing Engagement in Substance Use Data Collection: Development of the Substance Abuse Research Assistant App and Protocol for a Microrandomized Trial Using Adolescents and Emerging Adults

Background

Substance use is an alarming public health issue associated with significant morbidity and mortality. Adolescents and emerging adults are at particularly high risk because substance use typically initiates and peaks during this developmental period. Mobile health apps are a promising data collection and intervention delivery tool for substance-using youth as most teens and young adults own a mobile phone. However, engagement with data collection for most mobile health applications is low, and often, large fractions of users stop providing data after a week of use.

Objective

Substance Abuse Research Assistant (SARA) is a mobile application to increase or sustain engagement of substance data collection overtime. SARA provides a variety of engagement strategies to incentivize data collection: a virtual aquarium in the app grows with fish and aquatic resources; occasionally, funny or inspirational contents (eg, memes or text messages) are provided to generate positive emotions. We plan to assess the efficacy of SARA’s engagement strategies over time by conducting a micro-randomized trial, where the engagement strategies will be sequentially manipulated.

Methods

We aim to recruit participants (aged 14-24 years), who report any binge drinking or marijuana use in the past month. Participants are instructed to use SARA for 1 month. During this period, participants are asked to complete one survey and two active tasks every day between 6 pm and midnight. Through the survey, we assess participants’ daily mood, stress levels, loneliness, and hopefulness, while through the active tasks, we measure reaction time and spatial memory. To incentivize and support the data collection, a variety of engagement strategies are used. First, predata collection strategies include the following: (1) at 4 pm, a push notification may be issued with an inspirational message from a contemporary celebrity; or (2) at 6 pm, a push notification may be issued reminding about data collection and incentives. Second, postdata collection strategies include various rewards such as points which can be used to grow a virtual aquarium with fishes and other treasures and modest monetary rewards (up to US $12; US $1 for each 3-day streak); also, participants may receive funny or inspirational content as memes or gifs or visualizations of prior data. During the study, the participants will be randomized every day to receive different engagement strategies. In the primary analysis, we will assess whether issuing 4 pm push-notifications or memes or gifs, respectively, increases self-reporting on the current or the following day.

Results

The microrandomized trial started on August 21, 2017 and the trial ended on February 28, 2018. Seventy-three participants were recruited. Data analysis is currently underway.

Conclusions

To the best of our knowledge, SARA is the first mobile phone app that systematically manipulates engagement strategies in order to identify the best sequence of strategies that keep participants engaged in data collection. Once the optimal strategies to collect data are identified, future versions of SARA will use this data to provide just-in-time adaptive interventions to reduce substance use among youth.

Trial Registration

ClinicalTrials.gov NCT03255317; https://clinicaltrials.gov/show/NCT03255317 (Archived by WebCite at http://www.webcitation.org/70raGWV0e)

Registered Report Identifier

RR1-10.2196/9850

Optimizing Digital Integrated Care via Micro-Randomized Trials

Mobile health (mHealth) interventions are a promising tool in providing digitally mediated integrative care. They can extend care outside of the clinic by providing reminders to take medications, assisting in managing symptoms, and supporting healthy behaviors including physical activity, healthy eating, and stress management. mHealth interventions can adapt the delivery of care across time in order to optimize treatment effectiveness. Yet there exists limited empirical evidence useful to the development of adaptive mHealth interventions. This article describes a new randomized trial design, the Micro-Randomized Trial (MRT), for informing the development of mHealth interventions. We provide examples of scientific questions important to the development of an mHealth intervention, and describe how these questions can be answered using an MRT.

Predictors of positive drinking outcomes among youth receiving an alcohol brief intervention in the emergency department

BACKGROUND

Reducing underage drinking is a public health priority given increased risk for injury and other consequences. Emergency department (ED) visits offer a potential "teachable moment" to initiate interventions among youth engaged in risky drinking. Given mixed findings for alcohol brief interventions (BIs), this paper examined baseline markers of BI response in order to inform future interventions.

METHOD

We conducted secondary analyses of data from a randomized controlled trial of an alcohol BI delivered to youth in an ED. Among 475 participants (Mage = 18.6, SD = 1.4; 48.7% Female, 78.6% White/Caucasian) receiving a computer or therapist BI, we examined baseline characteristics (i.e., demographic, substance use, mood, risk/protective factors, and readiness to change) that predicted positive intervention response (i.e., BI responder) at 3-month follow-up using AUDIT-C scores (cut offs: <3 for ages 14-17; <4 for ages 18-20).

RESULTS

Mediated logistic regression analysis (controlling for demographics) showed that greater readiness to change at baseline was positively related to BI response and baseline alcohol consumption was negatively related to BI response. Having a mentor was indirectly related to BI response via baseline alcohol consumption. Baseline readiness to change and alcohol consumption mediated the association between positive peer influences and BI response, whereas readiness to change mediated the relation between depression and BI response.

CONCLUSION

Findings suggest that BI response is influenced by protective social factors, such as positive peers and mentors, and depression, via baseline alcohol severity indicators (i.e., readiness to change, consumption), thus providing clues for enhancing the content and dose of early interventions.

Tutorial for Using Control Systems Engineering to Optimize Adaptive Mobile Health Interventions

BACKGROUND

Adaptive behavioral interventions are individualized interventions that vary support based on a person's evolving needs. Digital technologies enable these adaptive interventions to function at scale. Adaptive interventions show great promise for producing better results compared with static interventions related to health outcomes. Our central thesis is that adaptive interventions are more likely to succeed at helping individuals meet and maintain behavioral targets if its elements can be iteratively improved via data-driven testing (ie, optimization). Control systems engineering is a discipline focused on decision making in systems that change over time and has a wealth of methods that could be useful for optimizing adaptive interventions.

OBJECTIVE

The purpose of this paper was to provide an introductory tutorial on when and what to do when using control systems engineering for designing and optimizing adaptive mobile health (mHealth) behavioral interventions.

OVERVIEW

We start with a review of the need for optimization, building on the multiphase optimization strategy (MOST). We then provide an overview of control systems engineering, followed by attributes of problems that are well matched to control engineering. Key steps in the development and optimization of an adaptive intervention from a control engineering perspective are then summarized, with a focus on why, what, and when to do subtasks in each step.

IMPLICATIONS

Control engineering offers exciting opportunities for optimizing individualization and adaptation elements of adaptive interventions. Arguably, the time is now for control systems engineers and behavioral and health scientists to partner to advance interventions that can be individualized, adaptive, and scalable. This tutorial should aid in creating the bridge between these communities.

Clonidine Increases the Likelihood That Abstinence Can Withstand Unstructured Time in Buprenorphine-maintained Outpatients

OBJECTIVE

In a clinical trial examining daily clonidine as an adjunct to buprenorphine treatment for opioid dependence, we found that clonidine increased opioid abstinence and decoupled stress from craving. From a personalized-medicine perspective, the next step is to identify people for whom clonidine would be beneficial. To that end, using data from the same clinical trial, we examined the associations of daily-life activities with treatment success.

METHODS

Outpatients (N = 118) received clonidine (0.3 mg/d) or placebo during 18 weeks of buprenorphine treatment. Participants carried a smartphone that randomly prompted them 4 times per day to report their moods and activities. Using generalized linear mixed models, we assessed the likelihoods of different types of daily activity as a function of clonidine versus placebo, days of longest continuous opioid abstinence, and their interaction.

RESULTS

Participants in the buprenorphine-only (buprenorphine plus placebo) control group who engaged in more responsibilities (work and child/elder care) had longer streaks of abstinence, whereas those who engaged in more unstructured-time activities had shorter streaks of abstinence. Conversely, for participants in the buprenorphine-plus-clonidine group, longer streaks of abstinence were associated with higher frequencies of activities associated with "unstructured" time.

CONCLUSIONS

The study replicates findings that engaging in responsibilities is related to positive treatment outcomes in standard opioid agonist therapy. The pattern of results also suggests that clonidine helped participants engage in unstructured-time activities with less risk of craving or use than they might otherwise have had.

Understanding Mechanisms of Change for Brief Alcohol Interventions Among Youth: Examination of Within-Session Interactions

OBJECTIVE

Alcohol brief interventions (BIs) for youth are efficacious, but effect sizes found have been modest. This article presents secondary data analyses from a randomized controlled trial of motivational interviewing-based (MI) alcohol BIs among youth in an emergency department, examining within-session predictors of alcohol outcomes at 3 months among those receiving BIs.

METHOD

Risky drinkers (ages 14-20) received either a computer BI (n = 277) or therapist BI (n = 278). Within-session measures were obtained via computer metrics (therapist and computer BIs) and coding of audiotapes (therapist BI only). Parallel structural equation models examined direct and indirect effects of BI components on 3-month alcohol consumption and consequences, with posttest precursors of behavior change (e.g., importance, readiness, and intentions) as potential mediators. Components examined included reasons to avoid or reduce drinking, strengths, strategies for change, benefits of change, and leisure activities (computer BI only), and MI global ratings and MI-adherent behaviors (therapist BI only).

RESULTS

For both BIs, greater number of strategies directly predicted greater posttest scores, with posttest scores mediating the relationship between strategies and alcohol consumption at 3 months. Greater number of strengths directly predicted fewer alcohol-related consequences at 3 months for both BIs; however, strengths also indirectly predicted greater consumption, mediated by posttest scores. Greater number of reasons to avoid or reduce drinking directly predicted greater alcohol consumption for the therapist BI only.

CONCLUSIONS

These findings suggest ways to optimize the content and efficacy of alcohol BIs. Reviews of strategies such as refusal skills, protective behavioral strategies, and coping with negative affect may be particularly salient for reducing alcohol misuse.

Causal Analysis of Self-tracked Time Series Data Using a Counterfactual Framework for N-of-1 Trials

Many of an individual's historically recorded personal measurements vary over time, thereby forming a time series (e.g., wearable-device data, self-tracked fitness or nutrition measurements, regularly monitored clinical events or chronic conditions). Statistical analyses of such n-of-1 (i.e., single-subject) observational studies (N1OSs) can be used to discover possible cause-effect relationships to then self-test in an n-of-1 randomized trial (N1RT). However, a principled way of determining how and when to interpret an N1OS association as a causal effect (e.g., as if randomization had occurred) is needed.Our goal in this paper is to help bridge the methodological gap between risk-factor discovery and N1RT testing by introducing a basic counterfactual framework for N1OS design and personalized causal analysis.We introduce and characterize what we call the average period treatment effect (APTE), i.e., the estimand of interest in an N1RT, and build an analytical framework around it that can accommodate autocorrelation and time trends in the outcome, effect carryover from previous treatment periods, and slow onset or decay of the effect. The APTE is loosely defined as a contrast (e.g., difference, ratio) of averages of potential outcomes the individual can theoretically experience under different treatment levels during a given treatment period. To illustrate the utility of our framework for APTE discovery and estimation, two common causal inference methods are specified within the N1OS context. We then apply the framework and methods to search for estimable and interpretable APTEs using six years of the author's self-tracked weight and exercise data, and report both the preliminary findings and the challenges we faced in conducting N1OS causal discovery.Causal analysis of an individual's time series data can be facilitated by an N1RT counterfactual framework. However, for inference to be valid, the veracity of certain key assumptions must be assessed critically, and the hypothesized causal models must be interpretable and meaningful.

Advancing the science and practice of precision education to enhance student outcomes

School psychology research and practice has considerable room for growth to go beyond "did an intervention work?" to "what intervention worked for whom and how did it work?" The latter question reflects a more precise understanding of intervention, and involves strategic efforts to enhance the precision of services students with academic, behavioral, emotional, or physical health problems receive to enhance the degree to which interventions are appropriately tailored to and produce benefit for individual students. The purpose of this special issue is to advance the notion and science of precision education, which is defined as an approach to research and practice that is concerned with tailoring preventive and intervention practices to individuals based on the best available evidence. This introductory article provides context for the special issue by discussing reasons why precision education is needed, providing definitions/descriptions of precision education research, and outlining opportunities to advance the science of precision education. Six empirical studies and one methodological-oriented article were compiled to provide examples of the breadth of research that falls under precision education. Although each of the article focuses on students with different needs (literacy deficits, math deficits, emotional and behavior problems, and intellectual disability), there is a common thread that binds them together, and that is each one captures the heterogeneity among students with particular problems or deficits and highlights the need to select and deliver more precise interventions to optimize student outcomes.

Action Centered Contextual Bandits

Contextual bandits have become popular as they offer a middle ground between very simple approaches based on multi-armed bandits and very complex approaches using the full power of reinforcement learning. They have demonstrated success in web applications and have a rich body of associated theoretical guarantees. Linear models are well understood theoretically and preferred by practitioners because they are not only easily interpretable but also simple to implement and debug. Furthermore, if the linear model is true, we get very strong performance guarantees. Unfortunately, in emerging applications in mobile health, the time-invariant linear model assumption is untenable. We provide an extension of the linear model for contextual bandits that has two parts: baseline reward and treatment effect. We allow the former to be complex but keep the latter simple. We argue that this model is plausible for mobile health applications. At the same time, it leads to algorithms with strong performance guarantees as in the linear model setting, while still allowing for complex nonlinear baseline modeling. Our theory is supported by experiments on data gathered in a recently concluded mobile health study.

For Whom Do Parenting Interventions to Prevent Adolescent Substance Use Work?

Adolescent substance use continues to be a significant public health problem. Parent training interventions are effective preventive strategies to reduce youth substance use. However, little is known about differences in effectiveness for youth across demographic characteristics. This review assessed the effectiveness of parent training programs at reducing adolescent substance use by participant gender, age, and race/ethnicity. Pubmed/MEDLINE, ERIC, CINAHL, and PsycINFO were searched from database origin to October 31, 2016. We included randomized controlled trials that evaluated parent training interventions; reported youth initiation or use of tobacco, alcohol, or other illicit substances; and included adolescents aged 10 to 19. Two independent reviewers extracted data. Disagreements were resolved by consensus or a third researcher. Data were synthesized using harvest plots stratified by participant demographics. A total of 1806 publications were identified and reviewed; 38 unique studies were included. Risk of bias of included studies was high. No studies targeted male teens or youth in late adolescence. Few studies targeted Asian-American, Black/African-American, or Hispanic/Latino adolescents. Overall, interventions including male and female youth and youth in early adolescence (age 10 to 14 or in 5th to 8th grade) were more beneficial than interventions including female-only or both young and older adolescents. Programs tailored to specific racial/ethnic groups, as well as programs designed for youth from multiple races/ethnic groups, were effective. Current evidence supports the benefits of offering parenting guidance to all families with adolescent children, regardless of the gender, age, or race/ethnicity of the adolescent.

Impact of telephone prompts on the adherence to an Internet-based aftercare program for women with bulimia nervosa: A secondary analysis of data from a randomized controlled trial

INTRODUCTION

Poor adherence is a common challenge in self-directed mental health interventions. Research findings indicate that telephone prompts may be useful to increase adherence.

METHOD

Due to poor adherence in a randomized controlled trial evaluating an Internet-based aftercare program for women with bulimia nervosa we implemented regular short telephone prompts into the study protocol halfway through the trial period. Of the 126 women in the intervention group, the first 63 women were not prompted by telephone (unprompted group) and compared with 63 women who subsequently enrolled into the study and were attempted to prompt bimonthly by a research assistant (telephone prompt group). Completed telephone calls took less than 5 min and did not include any symptom-related counseling.

RESULTS

Most of the women in the telephone prompt group (67%) were reached only once or twice during the intervention period. However, overall adherence in the telephone prompt group was significantly higher than in the unprompted group (T = - 3.015, df = 124, p = 0.003).

CONCLUSION

Our findings from this secondary analysis suggest that telephone prompts can positively affect adherence to an Internet-based aftercare intervention directed at patients with bulimia nervosa.

Encouraging Physical Activity in Patients With Diabetes: Intervention Using a Reinforcement Learning System

BACKGROUND

Regular physical activity is known to be beneficial for people with type 2 diabetes. Nevertheless, most of the people who have diabetes lead a sedentary lifestyle. Smartphones create new possibilities for helping people to adhere to their physical activity goals through continuous monitoring and communication, coupled with personalized feedback.

OBJECTIVE

The aim of this study was to help type 2 diabetes patients increase the level of their physical activity.

METHODS

We provided 27 sedentary type 2 diabetes patients with a smartphone-based pedometer and a personal plan for physical activity. Patients were sent short message service messages to encourage physical activity between once a day and once per week. Messages were personalized through a Reinforcement Learning algorithm so as to improve each participant's compliance with the activity regimen. The algorithm was compared with a static policy for sending messages and weekly reminders.

RESULTS

Our results show that participants who received messages generated by the learning algorithm increased the amount of activity and pace of walking, whereas the control group patients did not. Patients assigned to the learning algorithm group experienced a superior reduction in blood glucose levels (glycated hemoglobin [HbA1c]) compared with control policies, and longer participation caused greater reductions in blood glucose levels. The learning algorithm improved gradually in predicting which messages would lead participants to exercise.

CONCLUSIONS

Mobile phone apps coupled with a learning algorithm can improve adherence to exercise in diabetic patients. This algorithm can be used in large populations of diabetic patients to improve health and glycemic control. Our results can be expanded to other areas where computer-led health coaching of humans may have a positive impact. Summary of a part of this manuscript has been previously published as a letter in Diabetes Care, 2016.

Development of a Just-in-Time Adaptive Intervention for Smoking Cessation Among Korean American Emerging Adults

PURPOSE

Cigarette smoking is a preventable risk factor that contributes to unnecessary lung cancer burden among Korean Americans and there is limited research on effective smoking cessation strategies for this population. Smartphone-based smoking cessation apps that leverage just-in-time adaptive interventions (JITAIs) hold promise for smokers attempting to quit. However, little is known about how to develop and tailor a smoking cessation JITAI for Korean American emerging adult (KAEA) smokers.

METHOD

This paper documents the development process of MyQuit USC according to design guidelines for JITAI. Our development process builds on findings from a prior ecological momentary assessment study by using qualitative research methods. Semi-structured interviews and a focus group were conducted to inform which intervention options to offer and the decision rules that dictate their delivery.

RESULTS

Qualitative findings highlighted that (1) smoking episodes are highly context-driven and that (2) KAEA smokers believe they need personalized cessation strategies tailored to different contexts. Thus, MyQuit USC operates via decision rules that guide the delivery of personalized implementation intentions, which are contingent on dynamic factors, to be delivered "just in time" at user-scheduled, high-risk smoking situations.

CONCLUSION

Through an iterative design process, informed by quantitative and qualitative formative research, we developed a smoking cessation JITAI tailored specifically for KAEA smokers. Further testing is under way to optimize future versions of the app with the most effective intervention strategies and decision rules. MyQuit USC has the potential to provide cessation support in real-world settings, when KAEAs need them the most.

Agile science: creating useful products for behavior change in the real world

Evidence-based practice is important for behavioral interventions but there is debate on how best to support real-world behavior change. The purpose of this paper is to define products and a preliminary process for efficiently and adaptively creating and curating a knowledge base for behavior change for real-world implementation. We look to evidence-based practice suggestions and draw parallels to software development. We argue to target three products: (1) the smallest, meaningful, self-contained, and repurposable behavior change modules of an intervention; (2) “computational models” that define the interaction between modules, individuals, and context; and (3) “personalization” algorithms, which are decision rules for intervention adaptation. The “agile science” process includes a generation phase whereby contender operational definitions and constructs of the three products are created and assessed for feasibility and an evaluation phase, whereby effect size estimates/casual inferences are created. The process emphasizes early-and-often sharing. If correct, agile science could enable a more robust knowledge base for behavior change.

SARA: A Mobile App to Engage Users in Health Data Collection

Despite the recent progress in sensor technologies, many relevant health data can be only captured with manual input (e.g., food intake, stress appraisal, subjective emotion, substance use). A common problem of manual logging is that users often disengage within a short time because of high burden. In this work, we propose SARA, a novel app to engage users with ongoing tracking using timely rewards thereby reinforcing users for data input. SARA is developed for adolescents and emerging adults at risk for substance abuse. The rewards in SARA are designed to be developmentally and culturally appropriate to the target demographic and are theoretically grounded in the behavioral science literature. In this paper, we describe SARA and its rewards to increase data collection. We also briefly discuss future plans to evaluate SARA and develop just in time adaptive interventions for engagement and behavior change.

Targeting Reductions in Sitting Time to Increase Physical Activity and Improve Health

: New evidence suggests that reductions in sedentary behavior may increase physical activity and improve health. These findings point to new behavioral targets for intervention and new ways to think about intervening to increase overall physical activity in the population. This report provides a knowledge update reflecting the rapid accumulation of new evidence related to sedentary behavior and health among adults. Recent observational studies suggest that leveraging the time-inverse relationship between sedentary and active behaviors by replacing sitting with standing, light- or moderate-intensity activity can have important health benefits, particularly among less active adults. Clinical studies are providing evidence of the probable physiologic mechanisms underlying these associations, as well as insights into the cardiometabolic impact of breaking up and reducing sedentary behavior. In contrast to the well-established behavioral theories that guide the development and dissemination of evidence-based interventions to increase moderate- to vigorous-intensity physical activity, much less is known about how to reduce sedentary time to increase daily activities. It has become clear that the environmental, social, and individual level determinants for sedentary time are distinct from those linked to the adoption and maintenance of moderate- to vigorous-intensity physical activity. As a result, novel intervention strategies that focus on sitting and lower-intensity activities by leveraging the surrounding environment (e.g., workplace, school, and home) as well as individual-level cues and habits of sedentary behavior are being tested to increase the potency of interventions designed to increase overall physical activity. Herein we summarize the solutions-oriented research across the behavioral research framework, with a focus on highlighting areas of synergy across disciplines and identifying gaps for future research.

Ecological momentary interventions for depression and anxiety

Ecological momentary interventions (EMIs) are becoming more popular and more powerful resources for the treatment and prevention of depression and anxiety due to advances in technological capacity and analytic sophistication. Previous work has demonstrated that EMIs can be effective at reducing symptoms of depression and anxiety as well as related outcomes of stress and at increasing positive psychological functioning. In this review, we highlight the differences between EMIs and other forms of treatment due to the nature of EMIs to be deeply integrated into the fabric of people's day-to-day lives. EMIs require unique considerations in their design, deployment, and evaluation. Furthermore, given that EMIs have been advanced by changes in technologies and that the use of behavioral intervention technologies for mental health has been increasing, we discuss how technologies and analytics might usher in a new era of EMIs. Future EMIs might reduce user burden and increase intervention personalization and sophistication by leveraging digital sensors and advances in natural language processing and machine learning. Thus, although current EMIs are effective, the EMIs of the future might be more engaging, responsive, and adaptable to different people and different contexts.

Breast cancer survivors' preferences for technology-supported exercise interventions

PURPOSE

The purpose of this study was to explore breast cancer survivors' interest in and preferences for technology-supported exercise interventions.

METHODS

Post-treatment survivors [n = 279; M _age = 60.7 (SD = 9.7)] completed a battery of online questionnaires in August 2015. Descriptive statistics were calculated for all data. Logistic regression analyses were conducted to examine relationships between survivors' interest in a technology-supported exercise interventions and demographic, disease, and behavioral factors. These same factors were examined in relation to perceived effectiveness of such interventions using multiple regression analyses.

RESULTS

About half (53.4%) of survivors self-reported meeting public health recommendations for physical activity. Fewer than half reported using an exercise or diet mobile app (41.2%) or owning an activity tracker (40.5%). The majority were interested in receiving remotely delivered exercise counseling (84.6%), participating in a remotely delivered exercise intervention (79.5%), and using an exercise app or website (68%). Survivors reported that the most helpful technology-supported intervention components would be an activity tracker (89.5%), personalized feedback (81.2%), and feedback on how exercise is influencing mood, fatigue, etc. (73.6%). Components rated as least helpful were social networking integration (31.2%), group competitions (33.9%), and ability to see others' progress (35.1%).

CONCLUSIONS

Preferences for technology-supported exercise interventions varied among breast cancer survivors. Nonetheless, data indicate that technology-supported interventions may be feasible and acceptable. Engaging stakeholders may be important in developing and testing potential intervention components.

Toward Usable Evidence: Optimizing Knowledge Accumulation in HCI Research on Health Behavior Change

Over the last ten years, HCI researchers have introduced a range of novel ways to support health behavior change, from glanceable displays to sophisticated game dynamics. Yet, this research has not had as much impact as its originality warrants. A key reason for this is that common forms of evaluation used in HCI make it difficult to effectively accumulate-and use-knowledge across research projects. This paper proposes a strategy for HCI research on behavior change that retains the field's focus on novel technical contributions while enabling accumulation of evidence that can increase impact of individual research projects both in HCI and the broader behavior-change science. The core of this strategy is an emphasis on the discovery of causal effects of individual components of behavior-change technologies and the precise ways in which those effects vary with individual differences, design choices, and contexts in which those technologies are used.

Advancing psychological therapies for chronic pain

There is a strong tradition of therapy development and evaluation in the field of psychological interventions for chronic pain. However, despite this research production, the effects of treatments remain uncertain, and treatment development has stalled. This review summarises the current evidence but focusses on promising areas for improvement. Advancing psychological therapies for chronic pain will come from a radical re-imagining of the content, delivery, place, and control of therapy. The next generation of therapeutic interventions will also need alternative methods of measurement and evaluation, and options are discussed.

Assessing Time-Varying Causal Effect Moderation in Mobile Health

In mobile health interventions aimed at behavior change and maintenance, treatments are provided in real time to manage current or impending high risk situations or promote healthy behaviors in near real time. Currently there is great scientific interest in developing data analysis approaches to guide the development of mobile interventions. In particular data from mobile health studies might be used to examine effect moderators-individual characteristics, time-varying context or past treatment response that moderate the effect of current treatment on a subsequent response. This paper introduces a formal definition for moderated effects in terms of potential outcomes, a definition that is particularly suited to mobile interventions, where treatment occasions are numerous, individuals are not always available for treatment, and potential moderators might be influenced by past treatment. Methods for estimating moderated effects are developed and compared. The proposed approach is illustrated using BASICS-Mobile, a smartphone-based intervention designed to curb heavy drinking and smoking among college students.

Issues for eHealth in Psychiatry: Results of an Expert Survey

BACKGROUND

Technology has changed the landscape in which psychiatry operates. Effective, evidence-based treatments for mental health care are now available at the fingertips of anyone with Internet access. However, technological solutions for mental health are not necessarily sought by consumers nor recommended by clinicians.

OBJECTIVE

The objectives of this study are to identify and discuss the barriers to introducing eHealth technology-supported interventions within mental health.

METHODS

An interactive polling tool was used to ask "In this brave new world, what are the key issues that need to be addressed to improve mental health (using technology)?" Respondents were the multidisciplinary attendees of the "Humans and Machines: A Quest for Better Mental Health" conference, held in Sydney, Australia, in 2016. Responses were categorized into 10 key issues using team-based qualitative analysis.

RESULTS

A total of 155 responses to the question were received from 66 audience members. Responses were categorized into 10 issues and ordered by importance: access to care, integration and collaboration, education and awareness, mental health stigma, data privacy, trust, understanding and assessment of mental health, government and policy, optimal design, and engagement. In this paper, each of the 10 issues are outlined, and potential solutions are discussed. Many of the issues were interrelated, having implications for other key areas identified.

CONCLUSIONS

As many of the issues identified directly related to barriers to care, priority should be given to addressing these issues that are common across mental health delivery. Despite new challenges raised by technology, technology-supported mental health interventions represent a tremendous opportunity to address in a timely way these major concerns and improve the receipt of effective, evidence-based therapy by those in need.

Lessons from Practice: Designing Tools to Facilitate Individualized Support for Quitting Smoking

Many health care providers, with a variety of trainings, counsel clients on quitting smoking on a day-to-day basis. In their clinical practice, they draw from and adapt guidelines and research-based strategies to fit individual client situations and challenges. Designers of technologies to support quitting smoking can learn from these real world practices to create tools that better adapt to individual differences. We present findings from interviews with 28 providers with diverse experiences in smoking cessation counselling. Through analysis of their individualization strategies, challenges, and perceptions of technology, we find that providers: (1) individualize context appropriate coping strategies by involving clients in brainstorming, (2) emphasize the need to support nicotine withdrawal in clients, (3) mitigate social triggers and mediate social support for clients, and (4) need to navigate dependencies with other providers for managing medications and comorbid health conditions of clients. With this empirical understanding, we extend the discussion on the design of technology to support quitting smoking, highlight current barriers to individualization, and suggest future opportunities to address these barriers.

Advancing Models and Theories for Digital Behavior Change Interventions

To be suitable for informing digital behavior change interventions, theories and models of behavior change need to capture individual variation and changes over time. The aim of this paper is to provide recommendations for development of models and theories that are informed by, and can inform, digital behavior change interventions based on discussions by international experts, including behavioral, computer, and health scientists and engineers. The proposed framework stipulates the use of a state-space representation to define when, where, for whom, and in what state for that person, an intervention will produce a targeted effect. The "state" is that of the individual based on multiple variables that define the "space" when a mechanism of action may produce the effect. A state-space representation can be used to help guide theorizing and identify crossdisciplinary methodologic strategies for improving measurement, experimental design, and analysis that can feasibly match the complexity of real-world behavior change via digital behavior change interventions.

The Pace of Technologic Change: Implications for Digital Health Behavior Intervention Research

This paper addresses the rapid pace of change in the technologies that support digital interventions; the complexity of the health problems they aim to address; and the adaptation of scientific methods to accommodate the volume, velocity, and variety of data and interventions possible from these technologies. Information, communication, and computing technologies are now part of every societal domain and support essentially every facet of human activity. Ubiquitous computing, a vision articulated fewer than 30 years ago, has now arrived. Simultaneously, there is a global crisis in health through the combination of lifestyle and age-related chronic disease and multiple comorbidities. Computationally intensive health behavior interventions may be one of the most powerful methods to reduce the consequences of this crisis, but new methods are needed for health research and practice, and evidence is needed to support their widespread use. The challenges are many, including a reluctance to abandon timeworn theories and models of health behavior-and health interventions more broadly-that emerged in an era of self-reported data; medical models of prevention, diagnosis, and treatment; and scientific methods grounded in sparse and expensive data. There are also many challenges inherent in demonstrating that newer approaches are, indeed, effective. Potential solutions may be found in leveraging methods of research that have been shown to be successful in other domains, particularly engineering. A more "agile science" may be needed that streamlines the methods through which elements of health interventions are shown to work or not, and to more rapidly deploy and iteratively improve those that do. There is much to do to advance the issues discussed in this paper, and the papers in this theme issue. It remains an open question whether interventions based in these new models and methods are, in fact, equally if not more efficacious as what is available currently. Economic analyses of these new approaches are needed because assumptions of net worth compared to other approaches are just that, assumptions. Human-centered design research is needed to ensure that users ultimately benefit. Finally, a translational research agenda will be needed, as the status quo will likely be resistant to change.

Technology-based interventions for weight management: current randomized controlled trial evidence and future directions

Obesity is a prevalent health care issue associated with disability, premature morality, and high costs. Behavioral weight management interventions lead to clinically significant weight losses in overweight and obese individuals; however, many individuals are not able to participate in these face-to-face treatments due to limited access, cost, and/or time constraints. Technological advances such as widespread access to the Internet, increased use of smartphones, and newer behavioral self-monitoring tools have resulted in the development of a variety of eHealth weight management programs. In the present paper, a summary of the most current literature is provided along with potential solutions to methodological challenges (e.g., high attrition, minimal participant racial/ethnic diversity, heterogeneity of technology delivery modes). Dissemination and policy implications will be highlighted as future directions for the field of eHealth weight management.

Using Intensive Longitudinal Data Collected via Mobile Phone to Detect Imminent Lapse in Smokers Undergoing a Scheduled Quit Attempt

Background

Mobile phone‒based real-time ecological momentary assessments (EMAs) have been used to record health risk behaviors, and antecedents to those behaviors, as they occur in near real time.

Objective

The objective of this study was to determine if intensive longitudinal data, collected via mobile phone, could be used to identify imminent risk for smoking lapse among socioeconomically disadvantaged smokers seeking smoking cessation treatment.

Methods

Participants were recruited into a randomized controlled smoking cessation trial at an urban safety-net hospital tobacco cessation clinic. All participants completed in-person EMAs on mobile phones provided by the study. The presence of six commonly cited lapse risk variables (ie, urge to smoke, stress, recent alcohol consumption, interaction with someone smoking, cessation motivation, and cigarette availability) collected during 2152 prompted or self-initiated postcessation EMAs was examined to determine whether the number of lapse risk factors was greater when lapse was imminent (ie, within 4 hours) than when lapse was not imminent. Various strategies were used to weight variables in efforts to improve the predictive utility of the lapse risk estimator.

Results

Participants (N=92) were mostly female (52/92, 57%), minority (65/92, 71%), 51.9 (SD 7.4) years old, and smoked 18.0 (SD 8.5) cigarettes per day. EMA data indicated significantly higher urges (P=.01), stress (P=.002), alcohol consumption (P<.001), interaction with someone smoking (P<.001), and lower cessation motivation (P=.03) within 4 hours of the first lapse compared with EMAs collected when lapse was not imminent. Further, the total number of lapse risk factors present within 4 hours of lapse (mean 2.43, SD 1.37) was significantly higher than the number of lapse risk factors present during periods when lapse was not imminent (mean 1.35, SD 1.04), P<.001. Overall, 62% (32/52) of all participants who lapsed completed at least one EMA wherein they reported ≥3 lapse risk factors within 4 hours of their first lapse. Differentially weighting lapse risk variables resulted in an improved risk estimator (weighted area=0.76 vs unweighted area=0.72, P<.004). Specifically, 80% (42/52) of all participants who lapsed had at least one EMA with a lapse risk score above the cut-off within 4 hours of their first lapse.

Conclusions

Real-time estimation of smoking lapse risk is feasible and may pave the way for development of mobile phone‒based smoking cessation treatments that automatically tailor treatment content in real time based on presence of specific lapse triggers. Interventions that identify risk for lapse and automatically deliver tailored messages or other treatment components in real time could offer effective, low cost, and highly disseminable treatments to individuals who do not have access to other more standard cessation treatments.

Beyond the Randomized Controlled Trial: A Review of Alternatives in mHealth Clinical Trial Methods

Background

Randomized controlled trials (RCTs) have long been considered the primary research study design capable of eliciting causal relationships between health interventions and consequent outcomes. However, with a prolonged duration from recruitment to publication, high-cost trial implementation, and a rigid trial protocol, RCTs are perceived as an impractical evaluation methodology for most mHealth apps.

Objective

Given the recent development of alternative evaluation methodologies and tools to automate mHealth research, we sought to determine the breadth of these methods and the extent that they were being used in clinical trials.

Methods

We conducted a review of the ClinicalTrials.gov registry to identify and examine current clinical trials involving mHealth apps and retrieved relevant trials registered between November 2014 and November 2015.

Results

Of the 137 trials identified, 71 were found to meet inclusion criteria. The majority used a randomized controlled trial design (80%, 57/71). Study designs included 36 two-group pretest-posttest control group comparisons (51%, 36/71), 16 posttest-only control group comparisons (23%, 16/71), 7 one-group pretest-posttest designs (10%, 7/71), 2 one-shot case study designs (3%, 2/71), and 2 static-group comparisons (3%, 2/71). A total of 17 trials included a qualitative component to their methodology (24%, 17/71). Complete trial data collection required 20 months on average to complete (mean 21, SD 12). For trials with a total duration of 2 years or more (31%, 22/71), the average time from recruitment to complete data collection (mean 35 months, SD 10) was 2 years longer than the average time required to collect primary data (mean 11, SD 8). Trials had a moderate sample size of 112 participants. Two trials were conducted online (3%, 2/71) and 7 trials collected data continuously (10%, 7/68). Onsite study implementation was heavily favored (97%, 69/71). Trials with four data collection points had a longer study duration than trials with two data collection points: F_4,56=3.2, P=.021, η²=0.18. Single-blinded trials had a longer data collection period compared to open trials: F_2,58=3.8, P=.028, η²=0.12. Academic sponsorship was the most common form of trial funding (73%, 52/71). Trials with academic sponsorship had a longer study duration compared to industry sponsorship: F_2,61=3.7, P=.030, η²=0.11. Combined, data collection frequency, study masking, sample size, and study sponsorship accounted for 32.6% of the variance in study duration: F_4,55=6.6, P<.01, adjusted r²=.33. Only 7 trials had been completed at the time this retrospective review was conducted (10%, 7/71).

Conclusions

mHealth evaluation methodology has not deviated from common methods, despite the need for more relevant and timely evaluations. There is a need for clinical evaluation to keep pace with the level of innovation of mHealth if it is to have meaningful impact in informing payers, providers, policy makers, and patients.

Adaptive Interventions in Child and Adolescent Mental Health

The treatment or prevention of child and adolescent mental health (CAMH) disorders often requires an individualized, sequential approach to intervention, whereby treatments (or prevention efforts) are adapted over time based on the youth's evolving status (e.g., early response, adherence). Adaptive interventions are intended to provide a replicable guide for the provision of individualized sequences of interventions in actual clinical practice. Recently, there has been great interest in the development of adaptive intervenions by investigators working in CAMH. The development of such replicable, real-world, individualized sequences of decision rules to guide the treatment or prevention of CAMH disorders represents an important "next step" in interventions research. The primary purpose of this special issue is to showcase some recent work on the science of adaptive interventions in CAMH. In this overview article, we review why individualized sequences of interventions are needed in CAMH, provide an introduction to adaptive interventions, briefly describe each of the articles included in this special issue, and describe some exciting areas of ongoing and future research. A hopeful outcome of this special issue is that it encourages other researchers in CAMH to pursue creative and significant research on adaptive interventions.

Mobile Interventions Targeting Risky Drinking Among University Students: A Review

Mobile interventions based on text messages, automated telephone programs (interactive voice response (IVR)), and smartphone apps offer a new approach targeting hazardous alcohol use in university students. This review covers seven recent studies involving college or university students that evaluated intervention efficacy in comparison to controls: four using text messages, one using IVR, and two smartphone apps. Only the study evaluating IVR reported positive results for the primary outcome. Two of the text message studies reported positive results on secondary outcomes, while the other two reported no differences in comparison to control groups. For smartphone apps, one study reported positive results on secondary outcomes, while the other showed no differences in comparison to controls for a web-based app and negative results for a native app. Further development of mobile interventions is needed for this at-risk population, both in terms of intervention content and use of robust research designs.

In vitro killing of schistosomula of Schistosoma mansoni by BCG and C. parvum-activated macrophages

Resistance to Schistosoma mansoni infection in the mouse has been induced either specifically by a primary infection with this parasite or nonspecifically by a variety of immunostimulants such as BCG. In the present study we developed an in vitro system to examine the effector mechanism of nonspecifically induced resistance. Activated macrophage monolayers obtained from BCG- or Corynebacterium parvum treated mice killed a respective mean 32 +/- 6% and 48 +/- 5% of schistosomula after 24 hr incubation. The killing of the parasites was verified by their inability to mature to adult worms upon injection into normal mice. The activated macrophage-mediated killing was related to cell:parasite ratio, and was partially lost if the macrophage monolayers were kept in cultures for 24 hr before incubation with the organism. Supernatants of macrophages cultured in the presence of schistosomula killed a mean of 51 +/- 3% of the organisms whereas those from cells cultured alone resulted in a mean killing of 25 +/- 3%. Furthermore, toxic supernatants could be generated equally well on incubation with S. mansoni schistosomula or Trichinella spiralis larvae. Our data show that activated macrophage monolayers through soluble mediators destroy a significant proportion of the multicellular parasite S. mansoni schistosomula in vitro.