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(734) 763-0692
Methodologies: Complex Samples, Longitudinal Analyses, Remote Sensing, Sample Surveys, Survival Analysis Relevant Projects: NIH, NIDILRR

Mark Peterson

Assistant Professor, Physical Medicine and Rehabilitation

Affiliation(s):

Neuroscience Graduate Program

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).