Modeling engagement with a digital behavior change intervention (HeartSteps II): An exploratory system identification approach.

OBJECTIVE: Digital behavior change interventions (DBCIs) are feasibly effective tools for addressing physical activity. However, in-depth understanding of participants' long-term engagement with DBCIs remains sparse. Since the effectiveness of DBCIs to impact behavior change depends, in part, upon participant engagement, there is a need to better understand engagement as a dynamic process in response to an individual's ever-changing biological, psychological, social, and environmental context. METHODS: The year-long micro-randomized trial (MRT) HeartSteps II provides an unprecedented opportunity to investigate DBCI engagement among ethnically diverse participants. We combined data streams from wearable sensors (Fitbit Versa, i.e., walking behavior), the HeartSteps II app (i.e. page views), and ecological momentary assessments (EMAs, i.e. perceived intrinsic and extrinsic motivation) to build the idiographic models. A system identification approach and a fluid analogy model were used to conduct autoregressive with exogenous input (ARX) analyses that tested hypothesized relationships between these variables inspired by Self-Determination Theory (SDT) with DBCI engagement through time. RESULTS: Data from 11 HeartSteps II participants was used to test aspects of the hypothesized SDT dynamic model. The average age was 46.33 (SD=7.4) years, and the average steps per day at baseline was 5,507 steps (SD=6,239). The hypothesized 5-input SDT-inspired ARX model for app engagement resulted in a 31.75 % weighted RMSEA (31.50 % on validation and 31.91 % on estimation), indicating that the model predicted app page views almost 32 % better relative to the mean of the data. Among Hispanic/Latino participants, the average overall model fit across inventories of the SDT fluid analogy was 34.22 % (SD=10.53) compared to 22.39 % (SD=6.36) among non-Hispanic/Latino Whites, a difference of 11.83 %. Across individuals, the number of daily notification prompts received by the participant was positively associated with increased app page views. The weekend/weekday indicator and perceived daily busyness were also found to be key predictors of the number of daily application page views. CONCLUSIONS: This novel approach has significant implications for both personalized and adaptive DBCIs by identifying factors that foster or undermine engagement in an individual's respective context. Once identified, these factors can be tailored to promote engagement and support sustained behavior change over time.

The Digital Therapeutics Real-World Evidence Framework: An Approach for Guiding Evidence-Based Digital Therapeutics Design, Development, Testing, and Monitoring.

Digital therapeutics (DTx) are a promising way to provide safe, effective, accessible, sustainable, scalable, and equitable approaches to advance individual and population health. However, developing and deploying DTx is inherently complex in that DTx includes multiple interacting components, such as tools to support activities like medication adherence, health behavior goal-setting or self-monitoring, and algorithms that adapt the provision of these according to individual needs that may change over time. While myriad frameworks exist for different phases of DTx development, no single framework exists to guide evidence production for DTx across its full life cycle, from initial DTx development to long-term use. To fill this gap, we propose the DTx real-world evidence (RWE) framework as a pragmatic, iterative, milestone-driven approach for developing DTx. The DTx RWE framework is derived from the 4-phase development model used for behavioral interventions, but it includes key adaptations that are specific to the unique characteristics of DTx. To ensure the highest level of fidelity to the needs of users, the framework also incorporates real-world data (RWD) across the entire life cycle of DTx development and use. The DTx RWE framework is intended for any group interested in developing and deploying DTx in real-world contexts, including those in industry, health care, public health, and academia. Moreover, entities that fund research that supports the development of DTx and agencies that regulate DTx might find the DTx RWE framework useful as they endeavor to improve how DTxcan advance individual and population health.

Predicting Goal Attainment in Process-Oriented Behavioral Interventions Using a Data-Driven System Identification Approach.

Behavioral interventions (such as those developed to increase physical activity, achieve smoking cessation, or weight loss) can be represented as dynamic process systems incorporating a multitude of factors, ranging from cognitive (internal) to environmental (external) influences. This facilitates the application of system identification and control engineering methods to address questions such as: what drives individuals to improve health behaviors (such as engaging in physical activity)? In this paper, the goal is to efficiently estimate personalized, dynamic models which in turn will lead to control systems that can optimize this behavior. This problem is examined in system identification applied to the Just Walk study that aimed to increase walking behavior in sedentary adults. The paper presents a Discrete Simultaneous Perturbation Stochastic Approximation (DSPSA)-based modeling of the Goal Attainment construct estimated using AutoRegressive with eXogenous inputs (ARX) models. Feature selection of participants and ARX order selection is achieved through the DSPSA algorithm, which efficiently handles computationally expensive calculations. DSPSA can search over large sets of features as well as regressor structures in an informed, principled manner to model behavioral data within reasonable computational time. DSPSA estimation highlights the large individual variability in motivating factors among participants in Just Walk, thus emphasizing the importance of a personalized approach for optimized behavioral interventions.

Thinking Health-related Behaviors in a Climate Change Context: A Narrative Review.

BACKGROUND: Human activities have changed the environment so profoundly over the past two centuries that human-induced climate change is now posing serious health-related threats to current and future generations. Rapid action from all scientific fields, including behavioral medicine, is needed to contribute to both mitigation of, and adaption to, climate change. PURPOSE: This article aims to identify potential bi-directional associations between climate change impacts and health-related behaviors, as well as a set of key actions for the behavioral medicine community. METHODS: We synthesized the existing literature about (i) the impacts of rising temperatures, extreme weather events, air pollution, and rising sea level on individual behaviors (e.g., eating behaviors, physical activity, sleep, substance use, and preventive care) as well as the structural factors related to these behaviors (e.g., the food system); and (ii) the concurrent positive and negative roles that health-related behaviors can play in mitigation and adaptation to climate change. RESULTS: Based on this literature review, we propose a first conceptual model of climate change and health-related behavior feedback loops. Key actions are proposed, with particular consideration for health equity implications of future behavioral interventions. Actions to bridge the fields of behavioral medicine and climate sciences are also discussed. CONCLUSIONS: We contend that climate change is among the most urgent issues facing all scientists and should become a central priority for the behavioral medicine community.

A remotely delivered, peer-led intervention to improve physical activity and quality of life in younger breast cancer survivors.

Younger breast cancer survivors (YBCS) consistently report poorer quality of life (QOL) than older survivors. Increasing physical activity (PA) may improve QOL, but this has been understudied in YBCS. This single arm pilot study evaluated the feasibility and acceptability of a 3-month, peer-delivered, remote intervention to increase PA and improve QOL in YBCS. Data were collected from October 2019 - July 2020. Participants (n = 34, 43.1 ± 5.5 years old, 46 ± 34.4 months post-diagnosis, BMI = 30.2 ± 7.4 kg/m2) completed six video sessions with a trained peer mentor; self-monitored PA with a Fitbit activity tracker; and interacted with a private Fitbit Community for social support. At baseline, 3-and 6-months, participants completed QOL questionnaires and PA was measured through accelerometer (moderate-to-vigorous PA [MVPA]) and self-report (strength and flexibility). A parallel mixed-methods approach (qualitative interviews and quantitative satisfaction survey at 3-months) explored intervention feasibility and acceptability. One-way repeated-measures ANOVAs examined impacts on PA and QOL at 3-and 6-months. The intervention was feasible as evidenced by efficient recruitment, high retention, and adherence to intervention components. Remote delivery, working with a peer mentor, and using Fitbit tools were highly acceptable. From baseline to 3-months, participants increased time spent in objectively measured MVPA, strength, and flexibility exercises, and reported meaningful improvements to body image, fatigue, anxiety, and emotional support. A fully remote, peer-to-peer intervention is an acceptable and promising strategy to increase PA and improve QOL in YBCS. Refinements to the intervention and its delivery should be further assessed in future studies, toward the goal of disseminating an evidence-based, scalable intervention to the growing number of YBCS.Trial registration Prospectively registered as NCT04064892.

Lessons Learned: Beta-Testing the Digital Health Checklist for Researchers Prompts a Call to Action by Behavioral Scientists.

Digital technologies offer unique opportunities for health research. For example, Twitter posts can support public health surveillance to identify outbreaks (eg, influenza and COVID-19), and a wearable fitness tracker can provide real-time data collection to assess the effectiveness of a behavior change intervention. With these opportunities, it is necessary to consider the potential risks and benefits to research participants when using digital tools or strategies. Researchers need to be involved in the risk assessment process, as many tools in the marketplace (eg, wellness apps, fitness sensors) are underregulated. However, there is little guidance to assist researchers and institutional review boards in their evaluation of digital tools for research purposes. To address this gap, the Digital Health Checklist for Researchers (DHC-R) was developed as a decision support tool. A participatory research approach involving a group of behavioral scientists was used to inform DHC-R development. Scientists beta-tested the checklist by retrospectively evaluating the technologies they had chosen for use in their research. This paper describes the lessons learned because of their involvement in the beta-testing process and concludes with recommendations for how the DHC-R could be useful for a variety of digital health stakeholders. Recommendations focus on future research and policy development to support research ethics, including the development of best practices to advance safe and responsible digital health research.

Engineering Person-Specific Behavioral Interventions to Promote Physical Activity.

Physical activity is dynamic, complex, and often regulated idiosyncratically. In this article, we review how techniques used in control systems engineering are being applied to refine physical activity theory and interventions. We hypothesize that person-specific adaptive behavioral interventions grounded in system identification and model predictive control will lead to greater physical activity than more generic, conventional intervention approaches.

Precision Health: The Role of the Social and Behavioral Sciences in Advancing the Vision.

BACKGROUND: In 2015, Collins and Varmus articulated a vision for precision medicine emphasizing molecular characterization of illness to identify actionable biomarkers to support individualized treatment. Researchers have argued for a broader conceptualization, precision health. Precision health is an ambitious conceptualization of health, which includes dynamic linkages between research and practice as well as medicine, population health, and public health. The goal is a unified approach to match a full range of promotion, prevention, diagnostic, and treatment interventions to fundamental and actionable determinants of health; to not just address symptoms, but to directly target genetic, biological, environmental, and social and behavioral determinants of health. PURPOSE: The purpose of this paper is to elucidate the role of social and behavioral sciences within precision health. MAIN BODY: Recent technologies, research frameworks, and methods are enabling new approaches to measure, intervene, and conduct social and behavioral science research. These approaches support three opportunities in precision health that the social and behavioral sciences could colead including: (a) developing interventions that continuously "tune" to each person's evolving needs; (b) enhancing and accelerating links between research and practice; and (c) studying mechanisms of change in real-world contexts. There are three challenges for precision health: (a) methods of knowledge organization and curation; (b) ethical conduct of research; and (c) equitable implementation of precision health. CONCLUSIONS: Precision health requires active coleadership from social and behavioral scientists. Prior work and evidence firmly demonstrate why the social and behavioral sciences should colead with regard to three opportunity and three challenge areas.

Modelling multiple health behavior change with network analyses: results from a one-year study conducted among overweight and obese adults.

This study examined the between-person associations of seven health behaviors in adults with obesity participating in a weight loss intervention, as well as the covariations between these behaviors within-individuals across the intervention. The present study included data from a 12-month weight loss trial (N = 278). Seven health behaviors (physical activity, sedentary behavior, sleep duration, and consumption of fruits, vegetables, total fat and added sugar) were measured at baseline, 6- and 12-months. Between- and within-participants network analyses were conducted to examine how these behaviors were associated through the 12-month intervention and covaried across months. At the between-participants level, associations were found within the different diet behaviors and between total fat and sedentary behaviors. At the within-participants level, covariations were found between sedentary and diet behaviors, and within diet behaviors. Findings suggest that successful multiple health behaviors change interventions among adults with obesity will need to (1) simultaneously target sedentary and diet behaviors; and (2) prevent potential compensatory behaviors in the diet domain.

Development of a Control-Oriented Model of Social Cognitive Theory for Optimized mHealth Behavioral Interventions.

Mobile health (mHealth) technologies are contributing to the increasing relevance of control engineering principles in understanding and improving health behaviors, such as physical activity. Social Cognitive Theory (SCT), one of the most influential theories of health behavior, has been used as the conceptual basis for behavioral interventions for smoking cessation, weight management, and other health-related outcomes. This paper presents a control-oriented dynamical systems model of SCT based on fluid analogies that can be used in system identification and control design problems relevant to the design and analysis of intensively adaptive interventions. Following model development, a series of simulation scenarios illustrating the basic workings of the model are presented. The model's usefulness is demonstrated in the solution of two important practical problems: 1) semiphysical model estimation from data gathered in a physical activity intervention (the MILES study) and 2) as a means for discerning the range of "ambitious but doable" daily step goals in a closed-loop behavioral intervention aimed at sedentary adults. The model is the basis for ongoing experimental validation efforts, and should encourage additional research in applying control engineering technologies to the social and behavioral sciences.

Why we need a small data paradigm.

BACKGROUND: There is great interest in and excitement about the concept of personalized or precision medicine and, in particular, advancing this vision via various 'big data' efforts. While these methods are necessary, they are insufficient to achieve the full personalized medicine promise. A rigorous, complementary 'small data' paradigm that can function both autonomously from and in collaboration with big data is also needed. By 'small data' we build on Estrin's formulation and refer to the rigorous use of data by and for a specific N-of-1 unit (i.e., a single person, clinic, hospital, healthcare system, community, city, etc.) to facilitate improved individual-level description, prediction and, ultimately, control for that specific unit. MAIN BODY: The purpose of this piece is to articulate why a small data paradigm is needed and is valuable in itself, and to provide initial directions for future work that can advance study designs and data analytic techniques for a small data approach to precision health. Scientifically, the central value of a small data approach is that it can uniquely manage complex, dynamic, multi-causal, idiosyncratically manifesting phenomena, such as chronic diseases, in comparison to big data. Beyond this, a small data approach better aligns the goals of science and practice, which can result in more rapid agile learning with less data. There is also, feasibly, a unique pathway towards transportable knowledge from a small data approach, which is complementary to a big data approach. Future work should (1) further refine appropriate methods for a small data approach; (2) advance strategies for better integrating a small data approach into real-world practices; and (3) advance ways of actively integrating the strengths and limitations from both small and big data approaches into a unified scientific knowledge base that is linked via a robust science of causality. CONCLUSION: Small data is valuable in its own right. That said, small and big data paradigms can and should be combined via a foundational science of causality. With these approaches combined, the vision of precision health can be achieved.

Efficacy of Contextually Tailored Suggestions for Physical Activity: A Micro-randomized Optimization Trial of HeartSteps.

BACKGROUND: HeartSteps is an mHealth intervention that encourages regular walking via activity suggestions tailored to the individuals' current context. PURPOSE: We conducted a micro-randomized trial (MRT) to evaluate the efficacy of HeartSteps' activity suggestions to optimize the intervention. METHODS: We conducted a 6-week MRT with 44 adults. Contextually tailored suggestions could be delivered up to five times per day at user-selected times. At each of these five times, for each participant on each day of the study, HeartSteps randomized whether to provide an activity suggestion, and, if so, whether to provide a walking or an antisedentary suggestion. We used a centered and weighted least squares method to analyze the effect of suggestions on the 30-min step count following suggestion randomization. RESULTS: Averaging over study days and types of activity suggestions, delivering a suggestion versus no suggestion increased the 30-min step count by 14% (p = .06), 35 additional steps over the 253-step average. The effect was not evenly distributed in time. Providing any type of suggestion versus no suggestion initially increased the step count by 66% (167 steps; p < .01), but this effect diminished over time. Averaging over study days, delivering a walking suggestion versus no suggestion increased the average step count by 24% (59 steps; p = .02). This increase was greater at the start of study (107% or 271 additional steps; p < .01), but decreased over time. Antisedentary suggestions had no detectable effect on the 30-min step count. CONCLUSION: Contextually tailored walking suggestions are a promising way of initiating bouts of walking throughout the day. CLINICAL TRIAL INFORMATION: This study was registered on ClinicalTrials.gov number NCT03225521.

The history and future of digital health in the field of behavioral medicine.

Since its earliest days, the field of behavioral medicine has leveraged technology to increase the reach and effectiveness of its interventions. Here, we highlight key areas of opportunity and recommend next steps to further advance intervention development, evaluation, and commercialization with a focus on three technologies: mobile applications (apps), social media, and wearable devices. Ultimately, we argue that future of digital health behavioral science research lies in finding ways to advance more robust academic-industry partnerships. These include academics consciously working towards preparing and training the work force of the twenty first century for digital health, actively working towards advancing methods that can balance the needs for efficiency in industry with the desire for rigor and reproducibility in academia, and the need to advance common practices and procedures that support more ethical practices for promoting healthy behavior.

Modeling individual differences: A case study of the application of system identification for personalizing a physical activity intervention.

BACKGROUND: Control systems engineering methods, particularly, system identification (system ID), offer an idiographic (i.e., person-specific) approach to develop dynamic models of physical activity (PA) that can be used to personalize interventions in a systematic, scalable way. The purpose of this work is to: (1) apply system ID to develop individual dynamical models of PA (steps/day measured using Fitbit Zip) in the context of a goal setting and positive reinforcement intervention informed by Social Cognitive Theory; and (2) compare insights on potential tailoring variables (i.e., predictors expected to influence steps and thus moderate the suggested step goal and points for goal achievement) selected using the idiographic models to those selected via a nomothetic (i.e., aggregated across individuals) approach. METHOD: A personalized goal setting and positive reinforcement intervention was deployed for 14 weeks. Baseline PA measured in weeks 1-2 was used to inform personalized daily step goals delivered in weeks 3-14. Goals and expected reward points (granted upon goal achievement) were pseudo-randomly assigned using techniques from system ID, with goals ranging from their baseline median steps/day up to 2.5× baseline median steps/day, and points ranging from 100 to 500 (i.e., $0.20-$1.00). Participants completed a series of daily self-report measures. Auto Regressive with eXogenous Input (ARX) modeling and multilevel modeling (MLM) were used as the idiographic and nomothetic approaches, respectively. RESULTS: Participants (N = 20, mean age = 47.25 ±â€¯6.16 years, 90% female) were insufficiently active, overweight (mean BMI = 33.79 ±â€¯6.82 kg/m2) adults. Results from ARX modeling suggest that individuals differ in the factors (e.g., perceived stress, weekday/weekend) that influence their observed steps/day. In contrast, the nomothetic model from MLM suggested that goals and weekday/weekend were the key variables that were predictive of steps. Assuming the ARX models are more personalized, the obtained nomothetic model would have led to the identification of the same predictors for 5 of the 20 participants, suggesting a mismatch of plausible tailoring variables to use for 75% of the sample. CONCLUSION: The idiographic approach revealed person-specific predictors beyond traditional MLM analyses and unpacked the inherent complexity of PA; namely that people are different and context matters. System ID provides a feasible approach to develop personalized dynamical models of PA and inform person-specific tailoring variable selection for use in adaptive behavioral interventions.

Adaptive step goals and rewards: a longitudinal growth model of daily steps for a smartphone-based walking intervention.

Adaptive interventions are an emerging class of behavioral interventions that allow for individualized tailoring of intervention components over time to a person's evolving needs. The purpose of this study was to evaluate an adaptive step goal + reward intervention, grounded in Social Cognitive Theory delivered via a smartphone application (Just Walk), using a mixed modeling approach. Participants (N = 20) were overweight (mean BMI = 33.8 ± 6.82 kg/m2), sedentary adults (90% female) interested in participating in a 14-week walking intervention. All participants received a Fitbit Zip that automatically synced with Just Walk to track daily steps. Step goals and expected points were delivered through the app every morning and were designed using a pseudo-random multisine algorithm that was a function of each participant's median baseline steps. Self-report measures were also collected each morning and evening via daily surveys administered through the app. The linear mixed effects model showed that, on average, participants significantly increased their daily steps by 2650 (t = 8.25, p < 0.01) from baseline to intervention completion. A non-linear model with a quadratic time variable indicated an inflection point for increasing steps near the midpoint of the intervention and this effect was significant (t2 = -247, t = -5.01, p < 0.001). An adaptive step goal + rewards intervention using a smartphone app appears to be a feasible approach for increasing walking behavior in overweight adults. App satisfaction was high and participants enjoyed receiving variable goals each day. Future mHealth studies should consider the use of adaptive step goals + rewards in conjunction with other intervention components for increasing physical activity.

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.

Applying and advancing behavior change theories and techniques in the context of a digital health revolution: proposals for more effectively realizing untapped potential.

As more behavioral health interventions move from traditional to digital platforms, the application of evidence-based theories and techniques may be doubly advantageous. First, it can expedite digital health intervention development, improving efficacy, and increasing reach. Second, moving behavioral health interventions to digital platforms presents researchers with novel (potentially paradigm shifting) opportunities for advancing theories and techniques. In particular, the potential for technology to revolutionize theory refinement is made possible by leveraging the proliferation of "real-time" objective measurement and "big data" commonly generated and stored by digital platforms. Much more could be done to realize this potential. This paper offers proposals for better leveraging the potential advantages of digital health platforms, and reviews three of the cutting edge methods for doing so: optimization designs, dynamic systems modeling, and social network analysis.

Behavior change interventions: the potential of ontologies for advancing science and practice.

A central goal of behavioral medicine is the creation of evidence-based interventions for promoting behavior change. Scientific knowledge about behavior change could be more effectively accumulated using "ontologies." In information science, an ontology is a systematic method for articulating a "controlled vocabulary" of agreed-upon terms and their inter-relationships. It involves three core elements: (1) a controlled vocabulary specifying and defining existing classes; (2) specification of the inter-relationships between classes; and (3) codification in a computer-readable format to enable knowledge generation, organization, reuse, integration, and analysis. This paper introduces ontologies, provides a review of current efforts to create ontologies related to behavior change interventions and suggests future work. This paper was written by behavioral medicine and information science experts and was developed in partnership between the Society of Behavioral Medicine's Technology Special Interest Group (SIG) and the Theories and Techniques of Behavior Change Interventions SIG. In recent years significant progress has been made in the foundational work needed to develop ontologies of behavior change. Ontologies of behavior change could facilitate a transformation of behavioral science from a field in which data from different experiments are siloed into one in which data across experiments could be compared and/or integrated. This could facilitate new approaches to hypothesis generation and knowledge discovery in behavioral science.