X N Na1, Z Zhu1, Y Y Chen1, D P Wang2, H J Wang2, Y Song2, X C Ma1, P Y Wang1, A P Liu1. 1. Department of Social Medicine and Health Education, Peking University School of Public Health, Beijing 100191, China. 2. Center for Disease Control and Prevention of Wuhai City, Wuhai 016000, Inner Mongolia, China.
Abstract
OBJECTIVE: To explore associations of distribution of time spent in physical activity (PA) and sedentary behavior (SB) with obesity with taking account that time is finite during the day of adult residents in Wuhai City. METHODS: A cross-sectional study was undertaken in Wuhai City, and we carried out a sampling of local residents aged 18-79 by using multiple stratified cluster sampling method. Data about social demographic characteristics, time spent in PA and SB, diet intake, controlling situation of chronic disease and other covariates were obtained by qualified investigators for face-to-face questionnaire survey. Data about height, weight, and waist circumstance, were obtained by doctors in a secondary hospital or above for body measurements. The statistical method used in our study was known as compositional data analysis, which had been used to process compositional data in many fields. Liner regression analysis with compositional data was used to synthetically analyze the associations of distribution of time spent in PA and SB with obesity,and to investigate the effect of re-allocating time from one behavior to another one whilst the remaining one was kept stable. RESULTS: The investigation revealed the special advantage of compositional data analysis in processing time-use data. The result of liner regression analysis with the compositional data showed that after controlling the potential confounding factors, the associations of distribution of time spent in PA and SB was significantly associated with body mass index (BMI, P<0.001) and the negative natural logarithm of waist to height ratio (-lnWHtR, P<0.001). Among them, in professional population, the proportion of time spent in moderate-to-vigorous physical activity (MVPA) was negatively correlated with -lnWHtR (β=-0.008, P=0.022), while the proportion of time spent in SB was positively correlated with BMI and -lnWHtR (β=0.117, P=0.003; β=0.007, P=0.005). However, in nonprofessional population, the proportion of time spent in MVPA was only negatively correlated with BMI (β=-0.079, P=0.041). Nevertheless, the proportion of time spent in low-intensity physical activity (LIPA) was not significantly associated with BMI and -lnWHtR in both professional and nonprofessional population. In addition, the effects of MVPA replacing another behavior and of MVPA being displaced by another behavior were not symmetrical, and 10 minutes of MVPA replacing LIPA or SB had a greater influence on intervention and prevention of obesity than 10 minutes MVPA being replaced by LIPA or SB. CONCLUSION: The research has resulted in a solution of the associations of the distribution of time spent in PA, SB with health risk. Our results suggest that public health messages should target the health effects of the distribution of time of PA and SB synergistically in developing PA guidelines and health management practice, rather than simply increasing or decreasing the absolute time of PA or SB, so that we can provide scientific suggestions to make people get a profounder healthy effect.
OBJECTIVE: To explore associations of distribution of time spent in physical activity (PA) and sedentary behavior (SB) with obesity with taking account that time is finite during the day of adult residents in Wuhai City. METHODS: A cross-sectional study was undertaken in Wuhai City, and we carried out a sampling of local residents aged 18-79 by using multiple stratified cluster sampling method. Data about social demographic characteristics, time spent in PA and SB, diet intake, controlling situation of chronic disease and other covariates were obtained by qualified investigators for face-to-face questionnaire survey. Data about height, weight, and waist circumstance, were obtained by doctors in a secondary hospital or above for body measurements. The statistical method used in our study was known as compositional data analysis, which had been used to process compositional data in many fields. Liner regression analysis with compositional data was used to synthetically analyze the associations of distribution of time spent in PA and SB with obesity,and to investigate the effect of re-allocating time from one behavior to another one whilst the remaining one was kept stable. RESULTS: The investigation revealed the special advantage of compositional data analysis in processing time-use data. The result of liner regression analysis with the compositional data showed that after controlling the potential confounding factors, the associations of distribution of time spent in PA and SB was significantly associated with body mass index (BMI, P<0.001) and the negative natural logarithm of waist to height ratio (-lnWHtR, P<0.001). Among them, in professional population, the proportion of time spent in moderate-to-vigorous physical activity (MVPA) was negatively correlated with -lnWHtR (β=-0.008, P=0.022), while the proportion of time spent in SB was positively correlated with BMI and -lnWHtR (β=0.117, P=0.003; β=0.007, P=0.005). However, in nonprofessional population, the proportion of time spent in MVPA was only negatively correlated with BMI (β=-0.079, P=0.041). Nevertheless, the proportion of time spent in low-intensity physical activity (LIPA) was not significantly associated with BMI and -lnWHtR in both professional and nonprofessional population. In addition, the effects of MVPA replacing another behavior and of MVPA being displaced by another behavior were not symmetrical, and 10 minutes of MVPA replacing LIPA or SB had a greater influence on intervention and prevention of obesity than 10 minutes MVPA being replaced by LIPA or SB. CONCLUSION: The research has resulted in a solution of the associations of the distribution of time spent in PA, SB with health risk. Our results suggest that public health messages should target the health effects of the distribution of time of PA and SB synergistically in developing PA guidelines and health management practice, rather than simply increasing or decreasing the absolute time of PA or SB, so that we can provide scientific suggestions to make people get a profounder healthy effect.
Entities:
Keywords:
Compositional data analysis; Obesity; Physical activity; Sedentary behavior
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