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- All Subjects: Social cognitive theory
- Creators: Bryce, Crystal I
Description
Behavioral health problems such as physical inactivity are among the main causes of mortality around the world. Mobile and wireless health (mHealth) interventions offer the opportunity for applying control engineering concepts in behavioral change settings. Social Cognitive Theory (SCT) is among the most influential theories of health behavior and has been used as the conceptual basis of many behavioral interventions. This dissertation examines adaptive behavioral interventions for physical inactivity problems based on SCT using system identification and control engineering principles. First, a dynamical model of SCT using fluid analogies is developed. The model is used throughout the dissertation to evaluate system identification approaches and to develop control strategies based on Hybrid Model Predictive Control (HMPC). An initial system identification informative experiment is designed to obtain basic insights about the system. Based on the informative experimental results, a second optimized experiment is developed as the solution of a formal constrained optimization problem. The concept of Identification Test Monitoring (ITM) is developed for determining experimental duration and adjustments to the input signals in real time. ITM relies on deterministic signals, such as multisines, and uncertainty regions resulting from frequency domain transfer function estimation that is performed during experimental execution. ITM is motivated by practical considerations in behavioral interventions; however, a generalized approach is presented for broad-based multivariable application settings such as process control. Stopping criteria for the experimental test utilizing ITM are developed using both open-loop and robust control considerations.
A closed-loop intensively adaptive intervention for physical activity is proposed relying on a controller formulation based on HMPC. The discrete and logical features of HMPC naturally address the categorical nature of the intervention components that include behavioral goals and reward points. The intervention incorporates online controller reconfiguration to manage the transition between the behavioral initiation and maintenance training stages. Simulation results are presented to illustrate the performance of the system using a model for a hypothetical participant under realistic conditions that include uncertainty. The contributions of this dissertation can ultimately impact novel applications of cyberphysical system in medical applications.
A closed-loop intensively adaptive intervention for physical activity is proposed relying on a controller formulation based on HMPC. The discrete and logical features of HMPC naturally address the categorical nature of the intervention components that include behavioral goals and reward points. The intervention incorporates online controller reconfiguration to manage the transition between the behavioral initiation and maintenance training stages. Simulation results are presented to illustrate the performance of the system using a model for a hypothetical participant under realistic conditions that include uncertainty. The contributions of this dissertation can ultimately impact novel applications of cyberphysical system in medical applications.
ContributorsMartín Moreno, César Antonio (Author) / Rivera, Daniel E (Thesis advisor) / Hekler, Eric B. (Committee member) / Peet, Matthew M (Committee member) / Tsakalis, Konstantinos S (Committee member) / Arizona State University (Publisher)
Created2016
Description
School and educational psychologists have a shared imperative to understand the complex inter-play of a student’s home life and perceived self-efficacy. Self-efficacy is the central facet of Bandura’s social cognitive theory (SCT, 1986, 1997). The current study improved upon the extant literature by exploring how home life in Arizona, Arkansas, California, and Oklahoma impacts the self-efficacy for self-regulated learning of mid-to-late adolescents. Although it is difficult to identify how specific aspects of life (including home life) matter for particular areas of functioning, the present study explored self-efficacy for self-regulated learning through the lens of three scales of the Late Adolescence version of the Home Observation for Measurement of the Environment Inventory (LA-HOME) (Caldwell & Bradley, 2016). The LA-HOME documents actions, objects, events and conditions connected with the home environment of children ages 16 to 20, who are still residing at home with parents or guardians (Caldwell & Bradley, 2016). This paper addresses the following research question: How are various aspects of the home life of mid-to-late adolescents, namely (1) modeling and encouragement of maturity, (2) family companionship and investment in adolescent, and (3) warmth, acceptance, and responsiveness, associated with self-efficacy for self-regulated learning? The sample of 333 adolescents is quite diverse demographically; it includes variations in family composition, race/ethnicity, household SES, language spoken in the home, and geography (rural, urban, suburban). The study utilizes a sub-sample of adolescents from the larger study who were 15 to 19 years of age (N = 333). Descriptive statistics, means, and standard deviations are reported for continuous variables, frequencies are reported for categorical variables, and correlations are presented. A hierarchical regression model was estimated in two steps. The first step included the complete set of control variables (household income, ethnicity, gender, and adolescent general health and depressive symptoms), and the second step included the set of three home life indicators. The hierarchical regression model had good fit. Study assets and limitations, as well as alternate theories for consideration and directions for future research, are discussed.
ContributorsGreen, Shannon Noelle (Author) / Bradley, Robert H (Thesis advisor) / Abry, Tashia (Committee member) / Bryce, Crystal I (Committee member) / Arizona State University (Publisher)
Created2018