Browsing by Subject "Energy expenditure"
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Item Measuring the validity of self-monitoring heart rate and activity tracking wearables(2016-05) Dooley, Erin Elizabeth; Bartholomew, John B.; Jowers, EsbellePURPOSE: To examine the validity of wearable physical activity tracking devices. METHODS: Participants were 62 students. Participants wore a Polar HR chest strap, Actigraph GT3X+ Acceleremetor, Apple Watch, Fitbit Charge HR, Garmin Forerunner 225 and were connected to a metabolic cart. Participants completed a seated 10-min baseline period, 4-min stages of light, moderate and vigorous intensities, and a 10-min seated recovery. Heart rate (HR), energy expenditure (EE) and step count were examined for each bout of exercise. ANALYSIS: Two-way RM-ANOVA were performed to compare the ability of the wearable devices to accurately measure each outcome relative to the criterion. Paired-samples t-tests compared the number of steps in observed videos and those reported for Fitbit. RESULTS: For HR, Apple Watch was accurate at all stages except in light and moderate intensities, in which the device measured lower HR. The Fitbit Charge HR produced accurate results in moderate PA, but measured significantly higher HR readings at baseline and light activity and lower HR readings at vigorous intensity. The Garmin Forerunner 225 was only accurate at vigorous intensity PA and measured significantly higher HR readings at all other intensities. For EE, the Fitbit measured significantly higher EE for all stages except vigorous intensity and recovery stages. The Apple Watch and Garmin measured significantly higher EE at all PA intensities. The Fitbit measured significantly lower step count than the criterion at all PA intensities. DISCUSSION: This study provides novel findings for Apple Watch and Garmin devices and provides new information regarding Fitbit accuracy. No studies have reported accuracy of these devices to measure HR. Future studies should investigate why differences between the devices exist.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.