Browsing by Subject "Functional data analysis"
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Item Coactivation in sedentary and active older adults during maximal power and submaximal power tasks : activity-related differences(2010-05) Newstead, Ann Hamilton; Jensen, Jody L.; Abraham, Lawrence D.; Dingwell, Jonathan B.; Korff, Thomas; Shewokis, Patricia A.; Spirduso, Waneen W.As adults age, they lose the ability to produce maximal power and speed of movement. Success in daily living is often dependent upon power and speed. Thus these age-related decrements in performance can reduce physical independence and quality of life. An active lifestyle in older adulthood is associated with more successful aging. The purpose of this research program was to define the link between habitual activity and performance, specifically in regard to activities requiring power and speed. The hypothesis was that active older adults, compared to sedentary older adults, would be characterized by greater power production in maximal- and submaximal-effort tasks. Grouping older adults by activity level, coactivation was associated with activity level. Functional tasks are performed with a range of power requirements. Coactivation was used to distinguish groups in a maximal power task (Study 1) and submaximal power tasks (Study 2). In Study 1, the young adults demonstrated a greater maximal power than the older adults. While maximal power was not different between the older active and sedentary groups, the groups did differ on how they created maximal power. The active older adults produced a greater coactivation in the lower leg muscles compared to the older sedentary adults. In Study 2, the active older adults responded to different speeds during a submaximal power task with greater coactivation in the muscles of the lower leg at slow speeds compared with the sedentary older adults. Both older adults groups increased coactivation in the thigh muscles at high speeds. The sedentary older adults responded to speed with increased coactivation in the lower leg at fast speeds. The active older adults increased proximal thigh coactivation, EMG index, at the fastest speed compared with the sedentary older adults. Both older adult groups showed muscle activation adaptation to the change in task demands. The results of this dissertation increase our understanding about the link between physical activity and performance. Age-related differences in coactivation were observed during both maximal and submaximal tasks. Activity-related differences were observed suggesting the active older adults have a greater capability to adjust muscle activity to meet the challenges of community living.Item Statistical Approaches to Analyzing Energy Expenditure Data Among Zucker Diabetic Fatty Rats.(2014-01-07) Kim, HyunkyoungObesity is widely becoming a worldwide epidemic and often results from a combination of a sedentary lifestyle, inadequate food intake, and genetic predisposition. It is often of interest to scientists studying this epidemic to assess how much physical activity the study participants partake in or the amount of energy expenditure expended within a given time period. Energy expenditure is often used for this purpose where the study participants are subjected to devices which measure the amount of energy expended frequently within a specified time period. For example, in studying obesity among Zucker diabetic fatty (ZDF) rats, an animal model often used for studying obesity and the onset of diabetes, energy expenditure can be assessed by the use of an Oxymas instrument (an open circuit calorimeter; Columbus Instruments, Ohio, USA), a device which measures various components of energy expenditure every five to ten minutes. The resulting data are often of the functional longitudinal form and several statistical techniques can be employed to analyze such data. In this paper, we apply various statistical approaches to analyze the energy expenditure data from the ZDF rats; we compare statistical models based on linear mixed effects models and functional mixed effects models with smoothing splines. We find that in our current analyses, the use of the mixed effects models with a quadratic term for the time of observation following a summary of the data from minutes to hours and a log transformation to achieve approximate normality perform adequately well in assessing the effects of the treatment on the energy expenditure variables. We also find that the functional mixed effects model with a quadratic spline can be used as an effective option for analyzing the data after summarizing the data per hour without applying any transformation techniques. We therefore recommend first summarizing the energy expenditure per hour to reduce the noise associated with the frequency of the data collection and using either linear mixed effects models with polynomial terms for time or functional mixed effects model with smoothing splines to analyze the data collected repeatedly over a 24-hour period, when a curve linear relationship is suspected between time and the various energy expenditure variables.