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The Evolutionary Trends of Health Behaviors in Chinese Elderly and the Influencing Factors of These Trends: 2005–2014

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  • Yan Feng

    (School of Business, Guizhou Minzu University, Guiyang 550025, China)

  • Erpeng Liu

    (Centre for Social Security Studies, Wuhan University, Wuhan 430072, China)

  • Zhang Yue

    (Institute for Social Policy Research, Zhongnan University of Economics and Law, Wuhan 430073, China)

  • Qilin Zhang

    (Centre for Social Security Studies, Wuhan University, Wuhan 430072, China)

  • Tiankuo Han

    (Centre for Social Security Studies, Wuhan University, Wuhan 430072, China)

Abstract

As China is now facing the severe challenge of rapid population ageing, the health behaviors in Chinese elderly people are of great significance for realizing the goal of “Healthy Ageing” and the construction of a “Healthy China”. Little is known about the evolutionary trends of health behaviors in the Chinese elderly and about the factors influencing these trends; thus, the purposes of this paper are: (1) To describe the classes and evolutionary trends of health behaviors in the Chinese elderly; and (2) to explore the factors that influence the changes in the health behaviors in the elderly in China. Latent class analysis (LCA) is applied in this study to analyze the classes of health behaviors in the Chinese elderly. Growth mixture modelling (GMM) is employed to describe the evolutionary trends of the health behaviors in elderly people in China. In addition, the Bivariate analysis model is adopted to identify the influencing factors of the evolution of health behaviors. The data were derived from the Chinese Longitudinal Healthy Longevity Survey (CLHLS) from 2005 to 2014. The results reveal that the health behaviors in the Chinese elderly can be grouped into five classes: Negative, relatively negative, fair, relatively positive, and positive. Approximately 77.2% of the health behaviors in the Chinese elderly have the characteristics of “modified”, with a positive tendency. Moreover, approximately 22.8% of the health behaviors in Chinese elderly people have the characteristics of “non-modified”, with a negative tendency or remaining unchanged. The evolution of the health behaviors in the elderly in China is more affected by economic factors such as timely medical treatment during childhood, pension, occupations before the age of 60 and family income, as well as by self-rated health (SRH) and demographic characteristics such as household registration, age, and education level. Hence, various possible interventions should be made to improve the health behaviors in elderly people.

Suggested Citation

  • Yan Feng & Erpeng Liu & Zhang Yue & Qilin Zhang & Tiankuo Han, 2019. "The Evolutionary Trends of Health Behaviors in Chinese Elderly and the Influencing Factors of These Trends: 2005–2014," IJERPH, MDPI, vol. 16(10), pages 1-17, May.
  • Handle: RePEc:gam:jijerp:v:16:y:2019:i:10:p:1687-:d:230987
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    1. Hang Fu & Da Feng & Shangfeng Tang & Zhifei He & Yuanxi Xiang & Tailai Wu & Ruoxi Wang & Tian Shao & Chunyan Liu & Piaopiao Shao & Zhanchun Feng, 2017. "Prevalence of Tobacco Smoking and Determinants of Success in Quitting Smoking among Patients with Chronic Diseases: A Cross-Sectional Study in Rural Western China," IJERPH, MDPI, vol. 14(2), pages 1-10, February.
    2. Fubaihui Wang & Qingkai Zhen & Kaigang Li & Xu Wen, 2018. "Association of socioeconomic status and health-related behavior with elderly health in China," PLOS ONE, Public Library of Science, vol. 13(9), pages 1-14, September.
    3. Zhifei He & Zhaohui Cheng & Tian Shao & Chunyan Liu & Piaopiao Shao & Ghose Bishwajit & Da Feng & Zhanchun Feng, 2016. "Factors Influencing Health Knowledge and Behaviors among the Elderly in Rural China," IJERPH, MDPI, vol. 13(10), pages 1-16, September.
    4. Yong-Bing Liu & Liu Liu & Yan-Fei Li & Yan-Li Chen, 2015. "Relationship between Health Literacy, Health-Related Behaviors and Health Status: A Survey of Elderly Chinese," IJERPH, MDPI, vol. 12(8), pages 1-12, August.
    5. Elizabeth S. Garrett & Scott L. Zeger, 2000. "Latent Class Model Diagnosis," Biometrics, The International Biometric Society, vol. 56(4), pages 1055-1067, December.
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    1. Qilin Zhang & Yanli Wu & Tiankuo Han & Erpeng Liu, 2019. "Changes in Cognitive Function and Risk Factors for Cognitive Impairment of the Elderly in China: 2005–2014," IJERPH, MDPI, vol. 16(16), pages 1-13, August.

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