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Anna Kratz

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Anna Kratz, PhD, is Assistant Professor of Physical Medicine and Rehabilitation and the Center for Clinical Outcomes Development and Application (CODA) at the University of Michigan, Ann Arbor.

Dr. Kratz’s clinical research is focused on the characteristics and mechanisms of common symptoms (e.g. pain, fatigue, cognitive dysfunction) and functional outcomes in those with chronic clinical conditions. ¬†Using a combination of ambulatory measurement methods of physical activity (actigraphy), heart rate variability, galvanic skin response, and self-reported experiences, her research aims to overlay the patient’s day-to-day experience with physiological markers of stress, sleep quality, and physical activity. She utilizes a number of computational approaches, including multilevel statistical modeling, signal processing, and machine learning to analyze these data. The ultimate goal is to use insights from these data to design better clinical interventions to help patients better manage symptoms and optimize functioning and quality of life.

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Mark Peterson

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Research in my lab occurs within a multidisciplinary and translational space that promotes greater understanding of issues in public health, clinical rehabilitation, human performance, and physiology. My specific research interests have been devoted to physical activity epidemiology and behavioral interventions for the treatment/prevention of obesity and related cardiometabolic diseases, frailty, functional motor declines, and early mortality. Although predictive models based on healthy cohorts have a certain degree of generalizability, it is necessary to better understand populations at heightened risk. Our current and future research efforts are therefore directed at understanding and identifying precision strategies to prevent metabolic dysregulation and secondary musculoskeletal pathology among children and adults with neuromuscular impairments, as well as a variety of frailty syndromes. Our primary data collection occurs through typical clinical and basic laboratory studies, high throughput imaging, and remote sensing/tracking of human movement and various biomarkers. Numerous secondary, large-data analysis efforts are also incorporated for epidemiologic studies utilizing nationally-representative samples.

 

Data science applications: Connecting statistical models and disease data; design of behavioral interventions with remote sensing and messaging; understanding pediatric cardiometabolic and muscle health; sports analytics and human performance; population-based epidemiology/surveillance; secondary disease disparities among acquired and chronic frailty syndromes

Normalized strength growth percentile curves with polynomials for men (A) and women (B) from quantile regression (Peterson and Krishnan, 2015).

Normalized strength growth percentile curves with polynomials for men (A) and women (B) from quantile regression (Peterson and Krishnan, 2015).