IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v14y2017i11p1346-d117699.html
   My bibliography  Save this article

Predicting Physical Activity and Healthy Nutrition Behaviors Using Social Cognitive Theory: Cross-Sectional Survey among Undergraduate Students in Chongqing, China

Author

Listed:
  • Xianglong Xu

    (School of Public Health and Management, Chongqing Medical University, Chongqing 400016, China
    Research Center for Medicine and Social Development, Chongqing Medical University, Chongqing 400016, China
    The Innovation Center for Social Risk Governance in Health, Chongqing Medical University, Chongqing 400016, China
    These authors contributed equally to this work.)

  • Yang Pu

    (School of Public Health and Management, Chongqing Medical University, Chongqing 400016, China
    Research Center for Medicine and Social Development, Chongqing Medical University, Chongqing 400016, China
    The Innovation Center for Social Risk Governance in Health, Chongqing Medical University, Chongqing 400016, China
    These authors contributed equally to this work.)

  • Manoj Sharma

    (Department of Behavioral and Environmental Health, Jackson State University, Jackson, MS 39213, USA)

  • Yunshuang Rao

    (School of Public Health and Management, Chongqing Medical University, Chongqing 400016, China
    School of Nursing, Chongqing Medical University, Chongqing 400016, China)

  • Yilin Cai

    (School of Public Health and Management, Chongqing Medical University, Chongqing 400016, China
    Research Center for Medicine and Social Development, Chongqing Medical University, Chongqing 400016, China
    The Innovation Center for Social Risk Governance in Health, Chongqing Medical University, Chongqing 400016, China)

  • Yong Zhao

    (School of Public Health and Management, Chongqing Medical University, Chongqing 400016, China
    Research Center for Medicine and Social Development, Chongqing Medical University, Chongqing 400016, China
    The Innovation Center for Social Risk Governance in Health, Chongqing Medical University, Chongqing 400016, China)

Abstract

(1) Background: Generally suggested public health measures to reduce obesity were to limit television (TV) viewing, enhance daily physical activities, enable the consumption of fruit and vegetables, and reduce sugar-sweetened beverage intake. This study analyzed the extent to which selected social cognitive theory constructs can predict these behaviors among Chinese undergraduate students. (2) Methods: This cross-sectional study included 1976 undergraduate students from six universities in Chongqing, China. A self-administered five-point Likert common physical activity and nutrition behavior scale based on social cognitive theory was utilized. (3) Results: This study included 687 (34.77%) males and 1289 (65.23%) females. A total of 60.14% of the students engaged in exercise for less than 30 min per day. Approximately 16.5% of the participants spent at least 4 h watching TV and sitting in front of a computer daily. Approximately 79% of the participants consumed less than five cups of fruit and vegetables daily. Undergraduate students who had high self-efficacy scores had more leisure time physical activities. Those who have high expectation scores had considerable time watching TV and sitting in front of a computer. Undergraduate students who had high expectation and self-efficacy scores had substantially low consumption of sugar-sweetened beverages. Those who had high self-efficacy scores consumed considerable amounts of fruit and vegetables. Furthermore, the type of university, BMI group, gender, age, lack of siblings, and grade level were associated with the aforementioned four behaviors. (4) Conclusion: Physical inactivity and unhealthy nutrition behaviors are common among undergraduate students. This study used social cognitive theory to provide several implications for limiting the TV viewing, enhancing daily physical activities, consuming fruit and vegetables, and reducing sugar-sweetened beverage intake among undergraduate students.

Suggested Citation

  • Xianglong Xu & Yang Pu & Manoj Sharma & Yunshuang Rao & Yilin Cai & Yong Zhao, 2017. "Predicting Physical Activity and Healthy Nutrition Behaviors Using Social Cognitive Theory: Cross-Sectional Survey among Undergraduate Students in Chongqing, China," IJERPH, MDPI, vol. 14(11), pages 1-13, November.
    • Handle: RePEc:gam:jijerp:v:14:y:2017:i:11:p:1346-:d:117699
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/14/11/1346/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/14/11/1346/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ng, Shu Wen & Norton, Edward C. & Popkin, Barry M., 2009. "Why have physical activity levels declined among Chinese adults? Findings from the 1991-2006 China health and nutrition surveys," Social Science & Medicine, Elsevier, vol. 68(7), pages 1305-1314, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Dandan Mo & Mi Xiang & Mengyun Luo & Yuanyuan Dong & Yue Fang & Shunxing Zhang & Zhiruo Zhang & Huigang Liang, 2019. "Using Gamification and Social Incentives to Increase Physical Activity and Related Social Cognition among Undergraduate Students in Shanghai, China," IJERPH, MDPI, vol. 16(5), pages 1-17, March.
    2. Shaohua Tan & Fengxiao Cao & Jinsu Yang, 2020. "The Study on Spatial Elements of Health-Supportive Environment in Residential Streets Promoting Residents’ Walking Trips," IJERPH, MDPI, vol. 17(14), pages 1-33, July.
    3. András Fehér & Miklós Véha & Henrietta Mónika Boros & Bence Kovács & Enikő Kontor & Zoltán Szakály, 2021. "The Relationship between Online and Offline Information-Seeking Behaviors for Healthy Nutrition," IJERPH, MDPI, vol. 18(19), pages 1-18, September.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chih-Chien Huang & Scott T. Yabiku & Stephanie L. Ayers & Jennie J. Kronenfeld, 2016. "The obesity pay gap: gender, body size, and wage inequalities—a longitudinal study of Chinese adults, 1991–2011," Journal of Population Research, Springer, vol. 33(3), pages 221-242, September.
    2. Coast, Joanna, 2018. "A history that goes hand in hand: Reflections on the development of health economics and the role played by Social Science & Medicine, 1967–2017," Social Science & Medicine, Elsevier, vol. 196(C), pages 227-232.
    3. Seuring, Till & Serneels, Pieter & Suhrcke, Marc & Bachmann, Max, 2020. "Diabetes, employment and behavioural risk factors in China: Marginal structural models versus fixed effects models," Economics & Human Biology, Elsevier, vol. 39(C).
    4. Tafreschi, Darjusch, 2015. "The income body weight gradients in the developing economy of China," Economics & Human Biology, Elsevier, vol. 16(C), pages 115-134.
    5. Xuejie Ding & Francesco C. Billari & Stuart Gietel-Basten, 2017. "Health of midlife and older adults in China: the role of regional economic development, inequality, and institutional setting," International Journal of Public Health, Springer;Swiss School of Public Health (SSPH+), vol. 62(8), pages 857-867, November.
    6. Jing You & Sangui Wang & Laurence Roope, 2014. "Multi-dimensional Intertemporal Poverty in Rural China," CSAE Working Paper Series 2014-36, Centre for the Study of African Economies, University of Oxford.
    7. Mingling Chen & Yikang Wu & Hiroto Narimatsu & Xueqing Li & Chunmei Wang & Jianyong Luo & Genming Zhao & Zhongwen Chen & Wanghong Xu, 2015. "Socioeconomic Status and Physical Activity in Chinese Adults: A Report from a Community-Based Survey in Jiaxing, China," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-12, July.
    8. Peng Xu & Junfeng Jiang, 2020. "Individual Capital Structure and Health Behaviors among Chinese Middle-Aged and Older Adults: A Cross-Sectional Analysis Using Bourdieu’s Theory of Capitals," IJERPH, MDPI, vol. 17(20), pages 1-18, October.
    9. Yi Lu & Zhonghua Gou & Yang Xiao & Chinmoy Sarkar & John Zacharias, 2018. "Do Transit-Oriented Developments (TODs) and Established Urban Neighborhoods Have Similar Walking Levels in Hong Kong?," IJERPH, MDPI, vol. 15(3), pages 1-14, March.
    10. Kapinos, Kandice A. & Yakusheva, Olga & Eisenberg, Daniel, 2014. "Obesogenic environmental influences on young adults: Evidence from college dormitory assignments," Economics & Human Biology, Elsevier, vol. 12(C), pages 98-109.
    11. Shu Ng & Edward Norton & David Guilkey & Barry Popkin, 2012. "Estimation of a dynamic model of weight," Empirical Economics, Springer, vol. 42(2), pages 413-443, April.
    12. Fong, Ted Chun Tat & Ho, Rainbow Tin Hung & Yip, Paul Siu Fai, 2019. "Effects of urbanization on metabolic syndrome via dietary intake and physical activity in Chinese adults: Multilevel mediation analysis with latent centering," Social Science & Medicine, Elsevier, vol. 234(C), pages 1-1.
    13. Van de Poel, Ellen & O'Donnell, Owen & Van Doorslaer, Eddy, 2009. "Urbanization and the spread of diseases of affluence in China," Economics & Human Biology, Elsevier, vol. 7(2), pages 200-216, July.
    14. Nie, Peng & Ding, Lanlin & Sousa-Poza, Alfonso, 2019. "Decomposing adult obesity trends in China (1991–2011)," Economics & Human Biology, Elsevier, vol. 34(C), pages 5-15.
    15. Mutsa Mutowo & Usha Gowda & John Mangwiro & Paula Lorgelly & Alice Owen & Andre Renzaho, 2015. "Prevalence of diabetes in Zimbabwe: a systematic review with meta-analysis," International Journal of Public Health, Springer;Swiss School of Public Health (SSPH+), vol. 60(1), pages 1-11, January.
    16. Xuezheng Qin & Jay Pan, 2016. "The Medical Cost Attributable to Obesity and Overweight in China: Estimation Based on Longitudinal Surveys," Health Economics, John Wiley & Sons, Ltd., vol. 25(10), pages 1291-1311, October.
    17. Yi Lu & Guibo Sun & Chinmoy Sarkar & Zhonghua Gou & Yang Xiao, 2018. "Commuting Mode Choice in a High-Density City: Do Land-Use Density and Diversity Matter in Hong Kong?," IJERPH, MDPI, vol. 15(5), pages 1-13, May.
    18. Chih-Chien Huang & Scott Yabiku & Jennie Kronenfeld, 2015. "The Effects of Household Technology on Body Mass Index among Chinese Adults," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 34(6), pages 877-899, December.
    19. Zimmer, Zachary & Wen, Ming & Kaneda, Toshiko, 2010. "A multi-level analysis of urban/rural and socioeconomic differences in functional health status transition among older Chinese," Social Science & Medicine, Elsevier, vol. 71(3), pages 559-567, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:14:y:2017:i:11:p:1346-:d:117699. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.