Improving Energy Expenditure Estimation for Physical Activity

Abstract

The purpose of this study was to validate the Intelligent Device for Energy Expenditure and Activity (IDEEA) for estimation of energy expenditure during a variety of activities. An additional aim was to improve the accuracy of the estimation of energy expenditure of physical activity based on second-by-second information of type, onset, and duration of activity. Methods: This study included two tests: a mask calorimetry test with 27 subjects [age ϭ 33.7 Ϯ 13.8 (mean Ϯ SD) yr; BMI ϭ 24.8 Ϯ 4.8 kg•m Ϫ2 ] and a respiratory chamber calorimetry test with 10 subjects (age ϭ 32.9 Ϯ 12.4 yr; BMI ϭ 26.1 Ϯ 5.6 kg•m Ϫ2). In the mask test, the subjects performed activities (sitting, standing, lying down, level treadmill walking, and running at different speeds) for 50-min durations. For the chamber test, subjects lived in the metabolic chamber for 23 h and performed three exercise sessions to compensate for the confined environment. Results: The results showed significant correlations (P Ͻ 0.0001) between energy expenditure estimated by IDEEA and energy expenditure measured by the calorimeters with an accuracy Ͼ 95%. After corrections for the decrease in sleeping metabolic rate, the estimation accuracy for the chamber test was increased by 1-96.2%, whereas the estimation accuracy for nighttime activity was significantly improved by 4-99%. Conclusion: IDEEA provides a suitable method for estimating the energy expenditure of physical activity. It provides both instantaneous and cumulative estimates of energy expenditure over a given period.

Multivariate adaptive regression splines models for the prediction of energy expenditure in children and adolescents

“…However, the mathematical modeling of the HR and accelerometer counts (AC) to predict EE was limited to simple linear regression or branched equation modeling that do not take into account the complex, dynamic, and nonlinear relationships between EE, HR, and AC over time. Nonlinear methods, such as artificial neural network (ANN), have been used both for classification of activity mode and prediction of EE (16,26,27). A probabilistic ANN was used to extract information about the type, duration, and intensity of activity from the IDEEA multiple sensor system, which, together with energy cost of activities, was used to predict EE (26,27).…”

“…Nonlinear methods, such as artificial neural network (ANN), have been used both for classification of activity mode and prediction of EE (16,26,27). A probabilistic ANN was used to extract information about the type, duration, and intensity of activity from the IDEEA multiple sensor system, which, together with energy cost of activities, was used to predict EE (26,27). Raw signals from a biaxial accelerometer and subject characteristics were used to develop an ANN model for the prediction of minute-by-minute EE in 102 young adults (16).…”

Relationship of body anthropometry with cardiovascular risk factors in a random community sample in Phoenix, Kwazulu-Natal

“…Worldwide, it is well established that males are more likely to smoke than females, and older males (age [30][31][32][33][34][35][36][37][38][39][40][41][42][43][44][45][46][47][48][49] are more likely to use tobacco products than younger males (11). Of interest, smoking prevalence is increasing among men and women in SubSaharan Africa (11).…”

“…This is evident in current studies which show an increase in the prevalence of obesity in the young as a consequence of the reduced physical activity. Physical activity is the most important determinant of energy expenditure (48).…”

2009 Marshall Urist Young Investigator Award: How Often Do Patients with High-Flex Total Knee Arthroplasty Use High Flexion?

“…The device can analyze gait, speed, distance, power, work, and energy expenditure. Investigations into the IDEEA 1 system's accuracy show it is accurate for measuring energy expenditure, postures and limb movements, and speed of walking and running [58,59,64,65]. The original IDEEA 1 system was modified for this experiment by adding two electrogoniometers.…”