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.

Agency
National Institute of Health (NIH)
Institute
National Institute on Drug Abuse (NIDA)
Type
Mentored Quantitative Research Career Development Award (K25)
Project #
5K25DA021173-05
Application #
8116029
Study Section
Human Development Research Subcommittee (NIDA)
Program Officer
Jenkins, Richard A
Project Start
2007-09-30
Project End
2013-08-31
Budget Start
2011-09-01
Budget End
2013-08-31
Support Year
5
Fiscal Year
2011
Total Cost
$174,869
Indirect Cost
Name
Arizona State University-Tempe Campus
Department
Engineering (All Types)
Type
Schools of Engineering
DUNS #
943360412
City
Tempe
State
AZ
Country
United States
Zip Code
85287
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
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
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
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
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; 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
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

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