Assessing Physical Activity Levels among Chinese College Students by BMI, HR, and Multi-Sensor Activity Monitors

Dansong Liu, Xiaojuan Li, Qi Han, Bo Zhang, Xin Wei, Shuang Li, Xuemei Sui () and Qirong Wang ()
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Dansong Liu: School of Physical Education, Hubei University of Technology, Wuhan 430068, China
Xiaojuan Li: School of Physical Education, Hubei University, Wuhan 430062, China
Qi Han: Sports Nutrition Center, National Institute of Sports Medicine, Beijing 100029, China
Bo Zhang: School of Physical Education, Hubei University of Technology, Wuhan 430068, China
Xin Wei: School of Physical Education, Hubei University of Technology, Wuhan 430068, China
Shuang Li: College of Physical Education, Guangzhou University, Guangzhou 510006, China
Xuemei Sui: Department of Exercise Science, Arnold School of Public Health, University of South Carolina, Columbia, SC 29208, USA
Qirong Wang: Sports Nutrition Center, National Institute of Sports Medicine, Beijing 100029, China

IJERPH, 2023, vol. 20, issue 6, 1-12

Abstract: We investigated the use of multi-sensor physical activity monitors, body mass index (BMI), and heart rate (HR) to measure energy expenditure (EE) of various physical activity levels among Chinese collegiate students, compared with portable indirect calorimetry. Methods: In a laboratory experiment, 100 college students, 18–25 years old, wore the SenseWear Pro3 Armband™ (SWA; BodyMedia, Inc., Pittsburg, PA, USA) and performed 7 different physical activities. EE was measured by indirect calorimetry, while body motion and accelerations were measured with an SWA accelerometer. Special attention was paid to the analysis of unidirectional and three-directional accelerometer output. Results: Seven physical activities were recorded and distinguished by SWA, and different physical activities demonstrated different data features. The mean values of acceleration ACz (longitudinal accel point, axis Z) and VM (vector magnitude) were significantly different ( p = 0.000, p < 0.05) for different physical activities, whereas no significant difference was found in one single physical activity with varied speeds ( p = 0.9486, p > 0.05). When all physical activities were included in a correlation regression analysis, a strong linear correlation between the EE and accelerometer reporting value was found. According to the correlation analysis, sex, BMI, HR, ACz, and VM were independent variables, and the EE algorithm model demonstrated a high correlation coefficient R 2 value of 0.7. Conclusions: The predictive energy consumption model of physical activity based on multi-sensor physical activity monitors, BMI, and HR demonstrated high accuracy and can be applied to daily physical activity monitoring among Chinese collegiate students.

Keywords: energy expenditure monitoring; algorithm model; physical activity monitors (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2023
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