ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
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- All Subjects: chemical engineering
- Creators: Rivera, Daniel E
Idiographic Models of Walking Behavior for Personalized mHealth Interventions: Some Novel Approaches
modern society is responsible for a large number of diseases and high mortality rates in
the world. Adaptive behavioral interventions have been suggested as a way to promote
sustained behavioral changes to address these issues. These adaptive interventions
can be modeled as closed-loop control systems, and thus applying control systems
engineering and system identification principles to behavioral settings might provide
a novel way of improving the quality of such interventions.
Good understanding of the dynamic processes involved in behavioral experiments
is a fundamental step in order to design such interventions with control systems ideas.
In the present work, two different behavioral experiments were analyzed under the
light of system identification principles and modelled as dynamic systems.
In the first study, data gathered over the course of four days served as the basis for
ARX modeling of the relationship between psychological constructs (negative affect
and self-efficacy) and the intensity of physical activity. The identified models suggest
that this behavioral process happens with self-regulation, and that the relationship
between negative affect and self-efficacy is represented by a second order underdamped
system with negative gain, while the relationship between self-efficacy and physical
activity level is an overdamped second order system with positive gain.
In the second study, which consisted of single-bouts of intense physical activity,
the relation between a more complex set of behavioral variables was identified as a
semi-physical model, with a theoretical set of system equations derived from behavioral
theory. With a prescribed set of physical activity intensities, it was found that less fit
participants were able to get higher increases in affective state, and that self-regulation
processes are also involved in the system.
A comprehensive dynamical systems model for the GWG behavioral interventions is developed, which demonstrates how to integrate a mechanistic energy balance model with dynamical formulations of behavioral models, such as the Theory of Planned Behavior and self-regulation. Self-regulation is further improved with different advanced controller formulations. These model-based controller approaches enable the user to have significant flexibility in describing a participant's self-regulatory behavior through the tuning of controller adjustable parameters. The dynamic simulation model demonstrates proof of concept for how self-regulation and adaptive interventions influence GWG, how intra-individual and inter-individual variability play a critical role in determining intervention outcomes, and the evaluation of decision rules.
Furthermore, a novel intervention decision paradigm using Hybrid Model Predictive Control framework is developed to generate sequential decision policies in the closed-loop. Clinical considerations are systematically taken into account through a user-specified dosage sequence table corresponding to the sequence rules, constraints enforcing the adjustment of one input at a time, and a switching time strategy accounting for the difference in frequency between intervention decision points and sampling intervals. Simulation studies illustrate the potential usefulness of the intervention framework.
The final part of the dissertation presents a model scheduling strategy relying on gain-scheduling to address nonlinearities in the model, and a cascade filter design for dual-rate control system is introduced to address scenarios with variable sampling rates. These extensions are important for addressing real-life scenarios in the GWG intervention.