I am a chemical engineer whose research career has spanned the study of control engineering concepts in diverse application settings, from chemical process control to supply chain management to (more recently), adaptive interventions for the prevention and treatment of drug abuse. Adaptive interventions systematically individualize therapy through the use of decision rules that act on measurements of tailoring variables over time. I seek a K25 Mentored Quantitative Research Career Development award for the purpose of establishing myself as an independent researcher in the field. Control systems are used in engineering applications as a means to transform the behavior of a system over time from undesirable conditions to desirable ones; my work to date has established that adaptive interventions represent a form of feedback control in the context of behavioral health. Consequently, drawing from ideas in control engineering has the potential to significantly inform the analysis, design, and implementation of these interventions, leading to improved adherence, better management of limited resources, a reduction of negative effects, and overall more effective interventions. My research activities as part of this award, under the mentorship of Linda Collins (Penn State) and Susan Murphy (Michigan), and in collaboration with scientists affiliated with the Prevention Research Center at Arizona State (led by Irwin Sandier) and the Center for Continuum of Care in the Addictions at Penn (led by James McKay), will expand upon conceptual connections between adaptive interventions and control engineering principles by developing realistic simulation testbeds involving the prevention and treatment of multiple co-occurring disorders associated with substance use, HIV/AIDS, and mental health. The simulations will be used to better understand how to effectively integrate decision rules in a clinical context, and will serve as a basis to extend to problems in drug abuse prevention and treatment two significant engineering disciplines that form an important part of my expertise: modeling of phenomena associated with drug abuse using system identification methods, and optimized decision policies for multi- component interventions based on the concept of Model Predictive Control. The opportunity afforded by this award for significant interaction with prevention scientists and leaders in the field will insure that the outcomes of this research remain grounded in reality and have practical significance. ? ? ?

National Institute of Health (NIH)
National Institute on Drug Abuse (NIDA)
Mentored Quantitative Research Career Development Award (K25)
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Human Development Research Subcommittee (NIDA)
Program Officer
Jenkins, Richard A
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Arizona State University-Tempe Campus
Engineering (All Types)
Schools of Engineering
United States
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Riley, William T; Martin, Cesar A; Rivera, Daniel E et al. (2016) Development of a dynamic computational model of social cognitive theory. Transl Behav Med 6:483-495
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; Rivera, Daniel E; Piper, Megan E et al. (2014) A Hybrid Model Predictive Control Strategy for Optimizing a Smoking Cessation Intervention. Proc Am Control Conf 2014:2389-2394
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
Pina, Armando A; Holly, Lindsay E; Zerr, Argero A et al. (2014) A personalized and control systems engineering conceptual approach to target childhood anxiety in the contexts of cultural diversity. J Clin Child Adolesc Psychol 43:442-53
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

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