A control systems engineering approach for adaptive behavioral interventions: illustration with a fibromyalgia intervention

Deshpande, Sunil; Rivera, Daniel E.; Younger, Jarred; Nandola, Naresh N.

doi:10.1007/s13142-014-0282-z

Cited by 19 publications

(11 citation statements)

References 47 publications

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“…For instance, dynamical models can be used not only for smoking cessation but also for many other health problems such as weight lost (Dong et al, 2012; Navarro-Barrientos et al, 2011), physical activity avoidance (Hekler et al, 2013), and fibromyalgia (Deshpande et al, 2014). …”

Section: Discussionmentioning

confidence: 99%

Evaluating the effect of smoking cessation treatment on a complex dynamical system

Bekiroğlu

Russell

Lagoa

et al. 2017

Drug and Alcohol Dependence

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Objective To understand the dynamic relations among tobacco withdrawal symptoms to inform the development of effective smoking cessation treatments. Dynamical system models from control engineering are introduced and utilized to evaluate complex treatment effects. We demonstrate how dynamical models can be used to examine how distinct withdrawal-related processes are related over time and how treatment influences these relations. Method Intensive longitudinal data from a randomized placebo-controlled smoking cessation trial (N=1504) are used to estimate a dynamical model of withdrawal-related processes including momentary craving, negative affect, quitting self-efficacy, and cessation fatigue for each of six treatment conditions (nicotine patch, nicotine lozenge, bupropion, patch + lozenge, bupropion + lozenge, and placebo). Results Estimation and simulation results show that (1) withdrawal measurements are interrelated over time, (2) nicotine patch + nicotine lozenge showed reduced cessation fatigue and enhanced self-efficacy in the long-term while bupropion + nicotine lozenge was more effective at reducing negative affect and craving, and (3) although nicotine patch + nicotine lozenge had a better initial effect on cessation fatigue and self-efficacy, nicotine lozenge had a stronger effect on negative affect and nicotine patch had a stronger impact on craving. Conclusions This approach can be used to provide new evidence illustrating (a) the total impact of treatment conditions (via steady state values) and (b) the total initial impact (via rate of initial change values) on smoking-related outcomes for separate treatment conditions, noting that the conditions that produce the largest change may be different than the conditions that produce the fastest change.

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Section: Discussionmentioning

confidence: 99%

Evaluating the effect of smoking cessation treatment on a complex dynamical system

Bekiroğlu

Russell

Lagoa

et al. 2017

Drug and Alcohol Dependence

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“…After obtaining experimental data, the system identification methodology guides decisions of model structure, parameter estimation, and model validation prior to dictating the usefulness of the model for controller design. For examples see Timms et al 46 and Deshpande et al; 47 experimental procedures involving pseudo-random multisine signals are currently being evaluated in a physical activity intervention based on Social Cognitive Theory. 48 …”

Section: Research Questionsmentioning

confidence: 99%

Evaluating Digital Health Interventions

Murray

Hekler

Andersson

et al. 2016

American Journal of Preventive Medicine

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602

467

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Digital health interventions (DHI) have enormous potential as scalable tools to improve health and healthcare delivery by improving effectiveness, efficiency, accessibility, safety and personalisation. Achieving these improvements requires a cumulative knowledge base to inform development and deployment of DHI. However, evaluations of DHI present special challenges. This paper aims to examine these challenges and outline an evaluation strategy in terms of the Research Questions (RQs) needed to appraise DHIs. As DHI are at the intersection of biomedical, behavioural, computing and engineering research, methods drawn from all these disciplines are required. Relevant RQs include defining the problem and the likely benefit of the DHI, which in turn requires establishing the likely reach and uptake of the intervention, the causal model describing how the intervention will achieve its intended benefit, key components and how they interact with one another, and estimating overall benefit in terms of effectiveness, cost-effectiveness and harms. While Randomised Controlled Trials (RCTs) are important for evaluation of effectiveness and cost-effectiveness, they are best undertaken only when: a) the intervention and its delivery package are stable; b) these can be implemented with high fidelity and c) there is a reasonable likelihood that the overall benefits will be clinically meaningful (improved outcomes or equivalent outcomes at less cost). Broadening the portfolio of RQs and evaluation methods will help with developing the necessary knowledge base to inform decisions on policy, practice and research.

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“…Control system engineering principles have been applied to diverse behavioral intervention settings [7]. In order to design optimal interventions through the use of effective controllers, it is necessary to obtain an adequate model that properly represents the dynamical nature of the system.…”

Section: Introductionmentioning

confidence: 99%

A system identification approach for improving behavioral interventions based on Social Cognitive Theory

Martín

Deshpande

Hekler

et al. 2015

2015 American Control Conference (ACC)

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Mobile and wireless health (mHealth) interventions offer the opportunity for applying control engineering and system identification concepts in behavioral change settings. Social Cognitive Theory provides a recognized theoretical framework that can be applied to explain changes in behavior over time. Based on earlier work describing a dynamical model of this theory, a semi-physical system identification approach is developed in this paper for interventions associated with improving physical activity. An initial informative experiment that relies on prior knowledge from similar interventions is first designed to obtain basic insights regarding the dynamics of the system. Based on these results a second, optimized experiment is developed which solves a constrained optimization problem to find the intervention component profiles needed to mirror a desired behavioral pattern and to provide sufficient information that allows a more precise estimation of the parameters. A simulation study is presented to illustrate the design procedure.

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A control systems engineering approach for adaptive behavioral interventions: illustration with a fibromyalgia intervention

Cited by 19 publications

References 47 publications

Evaluating the effect of smoking cessation treatment on a complex dynamical system

Evaluating the effect of smoking cessation treatment on a complex dynamical system

Evaluating Digital Health Interventions

A system identification approach for improving behavioral interventions based on Social Cognitive Theory

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