This study will develop methods to enhance the conduct of research in the area of behavioral intervention development and evaluation. Behavioral interventions aim to prevent and treat disease by using a strategy that relies on reducing unhealthful behaviors and promoting healthful behaviors. These interventions play an increasingly prominent role in a wide variety of areas of public health importance, including drug abuse, HIV/AIDS, cancer, mental health, diabetes, obesity, cardiovascular health, and aging. The standard treatment/control randomized clinical trial (RCT) provides a principled methodological framework for establishing whether behavioral interventions work. The proposed research will develop a corresponding principled methodological framework for building interventions that have been optimized so that they are operating at peak efficacy (impact under ideal conditions), effectiveness (impact in real-world conditions) and efficiency (impact in relation to use of resources). The interdisciplinary research team includes a behavioral scientist and an engineer as PI's, statisticians, and a distinguished panel of eight behavioral intervention scientists from different public health areas. The proposed framework for optimizing behavioral interventions is based on methods widely used in engineering. This research will adapt these methods for use in behavioral interventions. The methods involve expressing behavioral interventions as detailed dynamical models. Dynamical models are well suited to behavioral interventions, which are typically complex multivariate multi-level time-varying processes. After a dynamical model of a behavioral intervention has been expressed, it can then used as part of established engineering procedures to optimize the intervention. This project has three Specific Aims. The first is to work with each member of the panel of behavioral intervention scientists to express an intervention as a detailed dynamical system model, and then to apply engineering optimization methods, such as Internal Model Control and Model Predictive Control, to each one. The second Specific Aim is to develop, document, and disseminate a computer program that behavioral scientists can use to model behavioral interventions as dynamical systems and apply optimization techniques to them. The third Specific Aim is to lay the groundwork for further adaptation of engineering optimization approaches for use in behavioral science. This part of the project will focus on system identification and multi-level optimization. Benefits of the proposed research will extend to any area of public health that employs behavioral interventions for prevention and treatment of disease, because it will result in behavioral interventions that are more efficacious, effective, and efficient at reducing morbidity and mortality. The proposed work will lead directly to improved behavioral interventions for prevention and treatment of disease. Any area of public health that employs behavioral interventions will benefit from the resulting increase in intervention efficacy, effectiveness and efficiency and corresponding reduction in morbidity and mortality.
| Dong, Yuwen; Deshpande, Sunil; Rivera, Daniel E et al. (2014) Hybrid Model Predictive Control for Sequential Decision Policies in Adaptive Behavioral Interventions. Proc Am Control Conf 2014:4198-4203 |
| Riley, William T; Martin, Cesar A; Rivera, Daniel E (2014) The importance of behavior theory in control system modeling of physical activity sensor data. Conf Proc IEEE Eng Med Biol Soc 2014:6880-3 |
| Deshpande, Sunil; Rivera, Daniel E; Younger, Jarred W et al. (2014) A control systems engineering approach for adaptive behavioral interventions: illustration with a fibromyalgia intervention. Transl Behav Med 4:275-89 |
| Trail, Jessica B; Collins, Linda M; Rivera, Daniel E et al. (2014) Functional data analysis for dynamical system identification of behavioral processes. Psychol Methods 19:175-87 |
| Timms, Kevin P; Martin, Cesar A; Rivera, Daniel E et al. (2014) Leveraging intensive longitudinal data to better understand health behaviors. Conf Proc IEEE Eng Med Biol Soc 2014:6888-91 |
| Deshpande, Sunil; Nandola, Naresh N; Rivera, Daniel E et al. (2014) Optimized Treatment of Fibromyalgia Using System Identification and Hybrid Model Predictive Control. Control Eng Pract 33:161-173 |
| Timms, Kevin P; Rivera, Daniel E; Collins, Linda M et al. (2014) Continuous-Time System Identification of a Smoking Cessation Intervention. Int J Control 87:1423-1437 |
| Timms, Kevin P; Rivera, Daniel E; Collins, Linda M et al. (2014) A dynamical systems approach to understanding self-regulation in smoking cessation behavior change. Nicotine Tob Res 16 Suppl 2:S159-68 |
| Savage, Jennifer S; Downs, Danielle Symons; Dong, Yuwen et al. (2014) Control systems engineering for optimizing a prenatal weight gain intervention to regulate infant birth weight. Am J Public Health 104:1247-54 |
| Nandola, Naresh N; Rivera, Daniel E (2013) An Improved Formulation of Hybrid Model Predictive Control With Application to Production-Inventory Systems. IEEE Trans Control Syst Technol 21: |
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