Using engineering control principles to inform the design of adaptive interventions: A conceptual introduction

Rivera, Daniel E.; Pew, Michael D.; Collins, Linda M.

doi:10.1016/j.drugalcdep.2006.10.020

Cited by 134 publications

(138 citation statements)

References 26 publications

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“…The accumulation of these types of findings has enormous potential to facilitate behavior change within the context of everyday life through JITAIs. This work also points to the need to examine time-varying reciprocal relationships between motivation and behavior through dynamical system modeling or other novel quantitative approaches, as there may be feedback loops linking changes in behavior with subsequent changes in motivations, which may in turn lead to further changes in behavior in the context of everyday life [33].…”

Section: Future Applicationsmentioning

confidence: 99%

Momentary assessment of physical activity intention-behavior coupling in adults

Maher

Rhodes

Dzubur

et al. 2017

Behav. Med. Pract. Policy Res.

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Research attempting to elucidate physical activity (PA) intention-behavior relations has focused on differences in long-term behavior forecasting between people. However, regular PA requires a repeated performance on a daily or within-daily basis. An empirical case study application is presented using intensive longitudinal data from a study of PA in adults to (a) describe the extent to which shortterm intention-behavior coupling occurs and (b) explore time-varying predictors of intention formation and shortterm intention-behavior coupling. Adults (n = 116) participated in three 4-day waves of ecological momentary assessment (EMA). Each day, participants received EMA questionnaires assessing short-term PA intentions and wore accelerometers to assess whether they engaged in ≥10 min of moderate-to-vigorous physical activity (MVPA) in the 3-hour period after each EMA prompt. Concurrent affective states and contexts were also assessed through EMA. Participants reported having short-term intentions to engage in PA in 41% of EMA prompts. However, participants only engaged in ≥10 min of MVPA following 16% of the prompts that short-term PA intentions were reported indicating an intention-behavior gap of 84%. Odds of intentions followed by PA were greater on occasions when individuals reported higher levels of positive affect than was typical for them. This study is the first to take an EMA approach to describe short-term intentionbehavior coupling in adults. Results suggest that adults have difficulty translating intentions into behavior at the momentary level, more so than over longer timescales, and that positive affect may be a key to successfully translating intentions into behavior. Keywords Intention-behavior gap, Exercise, Planned behavior, MotivationIntentions to engage in physical activity (PA) are posited as a proximal determinant of behavior in many popular theories of motivation [1,2]. Yet, the predictive power of intentions to explain PA behavior is often limited in empirical studies [3,4]. Previous research has almost exclusively focused on identifying time-invariant predictors of why some people have stronger intentions than others (i.e., intention formation) or are better able to follow-though with their intentions than others (intention-behavioral coupling) [5,6]. Studies in this area often focus on long-term intentions such as intentions to engage in regular PA over time spans up to several months. As a result, it is unclear what time-varying factors predict intentions to be active and intention-behavior coupling over shorter time frames such as minutes and hours, which may be important for understanding why individuals fail to engage in PA that was intended just a few hours earlier. This article describes methods for using intensive longitudinal data (IDL) collected through ecological momentary assessment (EMA) as well as accelerometery to investigate short-term intentions to be active and intention-behavior coupling in adults.Even though individuals may form intentions to be active, evidence ...

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Section: Future Applicationsmentioning

confidence: 99%

Momentary assessment of physical activity intention-behavior coupling in adults

Maher

Rhodes

Dzubur

et al. 2017

Behav. Med. Pract. Policy Res.

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“…Many researchers have argued that our research tools have become limited, particularly in the domain of behavioral health interventions [1][2][3][4][5][6][7][8][9]. The reasons for their arguments vary, but include an overreliance on randomized controlled trials, the slow pace and high cost of such trials, and the lack of attention to individual differences.…”

Section: Generality Optimization Research Designs Singlecase Designsmentioning

confidence: 99%

“…The logic of the component analyses described here is similar to new methods derived from an engineering framework [2,9,71]. During the initial stages of intervention development, researchers use factorial designs to allocate participants to different combinations of treatment components.…”

Section: Parametric Analysismentioning

confidence: 99%

Optimizing behavioral health interventions with single-case designs: from development to dissemination

Dallery

Raiff

2014

Behav. Med. Pract. Policy Res.

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Over the past 70 years, single-case design (SCD) research has evolved to include a broad array of methodological and analytic advances. In this article, we describe some of these advances and discuss how SCDs can be used to optimize behavioral health interventions. Specifically, we discuss how parametric analysis, component analysis, and systematic replications can be used to optimize interventions. We also describe how SCDs can address other features of optimization, which include establishing generality and enabling personalized behavioral medicine. Throughout, we highlight how SCDs can be used during both the development and dissemination stages of behavioral health interventions.

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“…Further, engineering concepts such as dynamical modeling have been applied to the modeling and controlling of behavior [11]. Examples of studies presenting some special issues that arise in the application of such approaches to behavioral research appear in [9], [10].…”

Section: A Previous Workmentioning

confidence: 99%

A robust MPC approach to the design of behavioural treatments

Bekiroğlu

Lagoa

Murphy

et al. 2013

52nd IEEE Conference on Decision and Control

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Abstract-The objective of this paper is to provide some initial results on the application of control tools to the problem treatment design. Human behavior and reaction to treatment is complex and dependent on many unmeasurable external stimuli. Therefore, to the best of our knowledge, it cannot be described by simple models. Hence, one of the main messages in this paper is that, to design a treatment (controller) one cannot rely on exact models. More precisely, to be able to design effective treatments, we propose to use "simple" uncertain affine models whose response "covers" the most probable subject responses. So, we propose a simple model that contains two different types of uncertainties: one aimed at uncertainty of the dynamics and another aimed at approximating external perturbations that patients face in their daily life. With this model at hand, we design a robust model predictive controller, where one relies on the special structure of the uncertainty to develop efficient optimization algorithms. I. INTRODUCTIONAlthough, the use of control algorithms in treatment context is not new there is still limited use of the concept of feedback in the design of treatments especially in the area of behavioral sciences. In this paper, we present a preliminary attempt to apply some well-known concepts from robust controller design to the development of efficient and robust intervention for behavioural treatments.In the design of treatments in a behavioral context, one usually starts with data from several patients collected in a study. From this, one determines an approximate behavioral model to be used in the design of the control law. The first one is both variability in behavior and the way patients respond to treatment in study data leads to high inaccuracy of the models obtained. Moreover, in a practical setting a patient's behavior is always affected by external perturbations from events in daily life. All this leads to a large amount of uncertainty in the model and, hence, the need for feedback and, especially, robustness. Therefore, given the fact that one can increase the robustness of the overall treatment by using a control approach, the interest in using algorithms from control theory in a treatment context is growing. Moreover, the increasing use of portable devices enables the collection of information and delivery of the treatment in a timely manner and the possibility of automating treatments.

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Using engineering control principles to inform the design of adaptive interventions: A conceptual introduction

Cited by 134 publications

References 26 publications

Momentary assessment of physical activity intention-behavior coupling in adults

Momentary assessment of physical activity intention-behavior coupling in adults

Optimizing behavioral health interventions with single-case designs: from development to dissemination

A robust MPC approach to the design of behavioural treatments

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