Patterns of Lifestyle Behaviors and Relevant Metabolic Profiles in Chinese Adults: Latent Class Analysis from Two Independent Surveys in Urban and Rural Populations

• Main Authors: Qian Cui (Author), Ying Chen (Author), Xinhua Ye (Author), Yamei Cai (Author), Rui Qin (Author), Tao Chen (Author), Ting Yan (Author), Dahai Yu (Author)
• Format: Book
• Published: Tehran University of Medical Sciences, 2022-05-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 10.18502/ijph.v51i5.9423; 2251-6085; 2251-6093
• Keywords: Physical activity; Sedentary time; Sleep duration; Latent class analysis; Public aspects of medicine; RA1-1270
• Source: Iranian Journal of Public Health, Vol 51, Iss 5 (2022)

Background: This study was determined to describe the patterns of lifestyle behaviors and their associations with metabolic profiles among Chinese urban and rural adults. Methods: This was a cross-sectional study set in the Nanjing (5,824) and Hefei (20,269) Community Cardiovascular Risk Surveys from 2011-2013, using random cluster sampling. Questionnaires were completed via face-to-face interview, and data on lifestyle behaviors including daily night sleep duration, nap duration (if any) and sitting time, and weekly physical activity (measured using the International Physical Activity Questionnaire, in metabolic equivalents of task × minutes, and separated into walking and moderate-to-vigorous physical activity (MOVPA) according to intensity) was collected. The patterns of physical activity in Chinese urban and rural populations and the metabolic profile in each pattern were identified by the latent class analysis. Results: Six distinct clusters were determined, with the sizes ranging from 45% to 5% of the total population. For example, the most common cluster was associated with a sufficient night and nap sleep duration, a long sitting time, and above WHO recommended physical activities for both walking and MOVPA, and the smallest cluster was featured by its huge amount of MOVPA and limited amount of walking activity. Difference in proportion of each cluster was observed between the two survey sites. No obvious abnormal blood measures were seen in any cluster. Conclusion: Common lifestyle behavior clusters were described, leading to a better understanding of people's routine activities.