Access to abstract is permanently restricted to Ball State communtiy only. / Access to thesis permanently restricted to Ball State community only / School of Physical Education, Sport, and Exercise Science

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This work takes us a step closer to realizing personalized medicine, complementing empirical and heuristic way in which clinicians typically work. This thesis presents mechanistic models of physiology. These models, given continuous signals from a patient, can be fine-tuned via parameter estimation methods so that the model's outputs match the patient's. We thus obtain a virtual patient mimicking the patient at hand. Therapeutic scenarios can then be applied and optimal diagnosis and therapy can thus be attained. As such, personalized medicine can then be achieved without resorting to costly genetics. In particular we have developed a novel comprehensive mathematical model of the cardiopulmonary system that includes cardiovascular circulation, respiratory mechanics, tissue and alveolar gas exchange, as well as short-term neural control. Validity of the model was proven by the excellent agreement with real patient data, under normo-physiological as well as hypercapnic and hypoxic conditions, taken from literature. As a concrete example, a submodel of the lung mechanics was fine-tuned using real patient data and personalized respiratory parameters (resistance, R_rs, and compliance, C_rs) were estimated continually. This allows us to compute the patient's effort (Work of Breathing), continuously and more importantly noninvasively. Finally, the use of Bayesian estimation techniques, which allow incorporation of population studies and prior information about model's parameters, was proposed in the contest of patient-specific physiological models. A Bayesian Maximum a Posteriori Probability (MAP) estimator was implemented and applied to a case-study of respiratory mechanics. Its superiority against the classical Least Squares method was proven in data-poor conditions using both simulated and real animal data. This thesis can serve as a platform for a plethora of applications for cardiopulmonary personalized medicine.

The purpose of this study was to determine the effect of a light-moderate versus hard exercise intensity on health and fitness benefits in a previously sedentary population. Twenty-six subjects, 17 male (mean age 45 + 3 yrs), 9 female (mean age 48 + 3 yrs) with at least one coronary artery disease risk factor volunteered to participate in this study. Subjects underwent laboratory testing comprising of, resting heart rate and blood pressure, body composition, blood lipid analysis and aerobic capacity (V02 ), prior to and 22-32 weeks after participating > 2 days per week in the Adult Physical Fitness Program (APFP) at Ball State University. After an initial exercise prescription subjects self selected an exercise intensity between 40-80% of their maximal heart rate range (MHRR) at which to train. Subjects were then grouped into those who trained at < 60% (light-moderate) and those who trained at > 60% (hard) of their MHRR.Those that self selected a hard training intensity did show a significantly greater decrease in diastolic blood pressure than the light-moderate intensity group. Subjects received a main training effect with a mean decrease in systolic blood pressure (123 ± 2.8 to 119 ± 2.4 mmHg), diastolic blood pressure (78 ± 2.2 to 75 ± 1.7 mmHg), and mean increases for HDL-cholesterol (49 ± 2.5 to 53 ± 2.8 mg/dL), absolute functional capacity (2.676 +.162 to 2.843 +.169 L/min) and relative functional capacity (30.2 ± 1.5 to 32.8 + 1.8 ml/kg/min). In conclusion this study demonstrated health and fitness benefits when training at least 2 days per week with greater effects when training at a hard versus light-moderate intensity with regards to diastolic blood pressure. / School of Physical Education