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Description
Control engineering offers a systematic and efficient approach to optimizing the effectiveness of individually tailored treatment and prevention policies, also known as adaptive or ``just-in-time'' behavioral interventions. These types of interventions represent promising strategies for addressing many significant public health concerns. This dissertation explores the development of decision algorithms for adaptive sequential behavioral interventions using dynamical systems modeling, control engineering principles and formal optimization methods. A novel gestational weight gain (GWG) intervention involving multiple intervention components and featuring a pre-defined, clinically relevant set of sequence rules serves as an excellent example of a sequential behavioral intervention; it is examined in detail in this research.
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.
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.
ContributorsDong, Yuwen (Author) / Rivera, Daniel E (Thesis advisor) / Dai, Lenore (Committee member) / Forzani, Erica (Committee member) / Rege, Kaushal (Committee member) / Si, Jennie (Committee member) / Arizona State University (Publisher)
Created2014
Description
No studies have evaluated the impact of tracking resting energy expenditure (REE) and modifiable health behaviors on gestational weight gain (GWG). In this controlled trial, pregnant women aged >18 years (X=29.8±4.9 years) with a gestational age (GA) <17 weeks were randomized to Breezing™ (N=16) or control (N=12) for 13 weeks. The Breezing™ group used a real-time metabolism tracker to obtain REE. Anthropometrics, diet, and sleep data were collected every 2 weeks. Rate of GWG was calculated as weight gain divided by total duration. Early (GA weeks 14-21), late (GA weeks 21-28), and overall (GA week 14-28) changes in macronutrients, sleep, and GWG were calculated. Mediation models were constructed using SPSS PROCESS macro using a bootstrap estimation approach with 10,000 samples. The majority of women were non-Hispanic Caucasian (78.6%). A total of 35.7% (n=10), 35.7% (n=10), and 28.6% (n=8) were normal weight, overweight, and obese, respectively, with 83.3% (n=10) and 87.5% (n=14) of the Control and Breezing™ groups gaining above IOM GWG recommendations. At baseline, macronutrient consumption did not differ. Overall (Breezing™ vs. Control; M diff=-349.08±150.77, 95% CI: -660.26 to -37.90, p=0.029) and late (M diff=-379.90±143.89, 95% CI:-676.87 to -82.93, p=0.014) changes in energy consumption significantly differed between the groups. Overall (M diff=-22.45±11.03, 95% CI: -45.20 to 0.31, p=0.053), late (M diff=-23.16±11.23, 95% CI: -46.33 to 0.01, p=0.05), and early (M diff=20.3±10.19, 95% CI: -0.74 to 41.34, p=0.058) changes in protein differed by group. Nocturnal total sleep time differed by study group (Breezing vs. Control; M diff=-32.75, 95% CI: -68.34 to 2.84, p=0.069). There was a 11.5% increase in total REE throughout the study. Early changes in REE (72±211 kcals) were relatively small while late changes (128±294 kcals) nearly doubled. Interestingly, early changes in REE demonstrated a moderate, positive correlation with rates of GWG later in pregnancy (r=0.528, p=0.052), suggesting that REE assessment early in pregnancy may help predict changes in GWG. Changes in macronutrients did not mediate the relationship between the intervention and GWG, nor did sleep mediate relationships between dietary intake and GWG. Future research evaluating REE and dietary composition throughout pregnancy may provide insight for appropriate GWG recommendations.
ContributorsVander Wyst, Kiley Bernhard (Author) / Whisner, Corrie M (Thesis advisor) / Reifsnider, Elizabeth G. (Committee member) / Petrov, Megan E (Committee member) / Buman, Matthew (Committee member) / Shaibi, Gabriel Q (Committee member) / Arizona State University (Publisher)
Created2019