On the mathematical modeling of the effect of treatment on human physical activity

Ashour, Mahmoud; Bekiroğlu, Korkut; Yang, Chih‐Hsiang; Lagoa, Constantino; Conroy, David E.; Smyth, Joshua M.; Lanza, Stephanie T.

doi:10.1109/cca.2016.7587951

Cited by 14 publications

(13 citation statements)

References 14 publications

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“…Current work is exploring if dynamical system models of behavior can be used to define dynamic concepts such as “ambitious but doable” daily step goals that take into account past behavioral patterns (e.g., previous ability to meet step goals), daily variations in individual characteristics (e.g., stress, busyness), and contextual characteristics (e.g., location, weather, busyness based on calendar) to define what an appropriate “ambitious but doable” step goal would be for a particular individual at a particular time. In a complementary study, dynamic system models have also been used to optimize the timing and content of text messages to encourage physical activity (Ashour et al, 2016). Another recent example of a health behavior change intervention that leverages dynamic systems modeling involves tailoring personalized, just-in-time coaching feedback to patients with chronic back pain (Hermens, op den Akker, Tabak, Wijsman, & Vollenbroek, 2014).…”

Section: Emerging Methods For Capitalizing On the Digital Health Rmentioning

confidence: 99%

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

et al. 2017

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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.

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Section: Emerging Methods For Capitalizing On the Digital Health Rmentioning

confidence: 99%

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

et al. 2017

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“…For the most part, contemporary behavioral theories do not account for time-varying motivational processes. The major exceptions in the health domain are recent proposals to extend behavioral theories using feedback principles from engineering; these dynamic approaches have recently been applied with sedentary behavior (2, 70). …”

Section: New Behavioral Determinants: Targeting Motivational Processesmentioning

confidence: 99%

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

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Buman

et al. 2017

Medicine &Amp; Science in Sports &Amp; Exercise

121

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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-vigorous intensity physical activity (MVPA), much less is known about how to reduce sedentary time in order 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 MVPA. As a result, novel intervention strategies that focus on sitting and lower intensity activities by leveraging the surrounding environment (e.g., workplace, school, 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.

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“…Control systems engineering is a field that focuses on decision making in systems that change over time. Control engineering is pervasive (eg, pacemakers, climate control, and robotics) but often goes unnoticed as a hidden technology [ 23 ] and to date, has only been minimally applied for use in testing and improving behavioral interventions [ 24 - 27 ]. The methods of control systems engineering are well suited to iteratively improving elements of adaptive interventions for real-world health behavior change.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2018

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BackgroundAdaptive 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.ObjectiveThe 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.OverviewWe 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.ImplicationsControl 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.

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On the mathematical modeling of the effect of treatment on human physical activity

Cited by 14 publications

References 14 publications

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

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

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

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

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