IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v18y2021i8p3966-d533104.html
   My bibliography  Save this article

Does Physical Activity Predict Obesity—A Machine Learning and Statistical Method-Based Analysis

Author

Listed:
  • Xiaolu Cheng

    (Department of Health Administration and Policy, George Mason University, Fairfax, VA 22030, USA)

  • Shuo-yu Lin

    (Department of Health Administration and Policy, George Mason University, Fairfax, VA 22030, USA)

  • Jin Liu

    (Department of Biostatistics, School of Medicine, Virginia Commonwealth University, Richmond, VA 23219, USA)

  • Shiyong Liu

    (Center for Governance Studies, Beijing Normal University at Zhuhai, Zhuhai 519087, China)

  • Jun Zhang

    (Department of Physics and Engineering, Slippery Rock University of Pennsylvania, Slippery Rock, PA 16057, USA)

  • Peng Nie

    (Department of Economics, School of Economics and Finance, Xi’an Jiaotong University, Xi’an 710049, China)

  • Bernard F. Fuemmeler

    (Department of Health Behavior and Policy, School of Medicine, Virginia Commonwealth University, Richmond, VA 23219, USA)

  • Youfa Wang

    (Global Health Institute, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an 710049, China)

  • Hong Xue

    (Department of Health Administration and Policy, George Mason University, Fairfax, VA 22030, USA)

Abstract

Background: Obesity prevalence has become one of the most prominent issues in global public health. Physical activity has been recognized as a key player in the obesity epidemic. Objectives: The objectives of this study are to (1) examine the relationship between physical activity and weight status and (2) assess the performance and predictive power of a set of popular machine learning and traditional statistical methods. Methods: National Health and Nutrition Examination Survey (NHANES, 2003 to 2006) data were used. A total of 7162 participants met our inclusion criteria (3682 males and 3480 females), with average age ranging from 48.6 (normal weight) to 52.1 years old (overweight). Eleven classifying algorithms—including logistic regression, naïve Bayes, Radial Basis Function (RBF), local k-nearest neighbors (k-NN), classification via regression (CVR), random subspace, decision table, multiobjective evolutionary fuzzy classifier, random tree, J48, and multilayer perceptron—were implemented and evaluated, and they were compared with traditional logistic regression model estimates. Results: With physical activity and basic demographic status, of all methods analyzed, the random subspace classifier algorithm achieved the highest overall accuracy and area under the receiver operating characteristic (ROC) curve (AUC). The duration of vigorous-intensity activity in one week and the duration of moderate-intensity activity in one week were important attributes. In general, most algorithms showed similar performance. Logistic regression was middle-ranking in terms of overall accuracy, sensitivity, specificity, and AUC among all methods. Conclusions: Physical activity was an important factor in predicting weight status, with gender, age, and race/ethnicity being less but still essential factors associated with weight outcomes. Tailored intervention policies and programs should target the differences rooted in these demographic factors to curb the increase in the prevalence of obesity and reduce disparities among sub-demographic populations.

Suggested Citation

  • Xiaolu Cheng & Shuo-yu Lin & Jin Liu & Shiyong Liu & Jun Zhang & Peng Nie & Bernard F. Fuemmeler & Youfa Wang & Hong Xue, 2021. "Does Physical Activity Predict Obesity—A Machine Learning and Statistical Method-Based Analysis," IJERPH, MDPI, vol. 18(8), pages 1-11, April.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:8:p:3966-:d:533104
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/18/8/3966/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/18/8/3966/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Cawley, John, 2015. "An economy of scales: A selective review of obesity's economic causes, consequences, and solutions," Journal of Health Economics, Elsevier, vol. 43(C), pages 244-268.
    2. Kirby, J.B. & Liang, L. & Chen, H.-J. & Wang, Y., 2012. "Race, place, and obesity: The complex relationships among community racial/ethnic composition, individual race/ethnicity, and obesity in the United States," American Journal of Public Health, American Public Health Association, vol. 102(8), pages 1572-1578.
    3. Goodarz Danaei & Eric L Ding & Dariush Mozaffarian & Ben Taylor & Jürgen Rehm & Christopher J L Murray & Majid Ezzati, 2009. "The Preventable Causes of Death in the United States: Comparative Risk Assessment of Dietary, Lifestyle, and Metabolic Risk Factors," PLOS Medicine, Public Library of Science, vol. 6(4), pages 1-23, 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. Dana Badau & Adela Badau & Cristian Trambitas & Dia Trambitas-Miron & Raluca Moraru & Alexandru Antoniu Stan & Bogdan Marian Oancea & Ioan Turcu & Emilia Florina Grosu & Vlad Teodor Grosu & Lucia Geor, 2021. "Differences between Active and Semi-Active Students Regarding the Parameters of Body Composition Using Bioimpedance and Magnetic Bioresonance Technologies," IJERPH, MDPI, vol. 18(15), pages 1-14, July.
    2. Elżbieta Biernat & Monika Piątkowska & Michał Rozpara, 2022. "Is the Prevalence of Low Physical Activity among Teachers Associated with Depression, Anxiety, and Stress?," IJERPH, MDPI, vol. 19(14), pages 1-12, July.

    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. Charles Courtemanche & James Marton & Benjamin Ukert & Aaron Yelowitz & Daniela Zapata, 2018. "Early Effects of the Affordable Care Act on Health Care Access, Risky Health Behaviors, and Self‐Assessed Health," Southern Economic Journal, John Wiley & Sons, vol. 84(3), pages 660-691, January.
    2. Giuntella, Osea & Rieger, Matthias & Rotunno, Lorenzo, 2020. "Weight gains from trade in foods: Evidence from Mexico," Journal of International Economics, Elsevier, vol. 122(C).
    3. Valentina Alvarez-Saavedra & Pierre Levasseur & Suneha Seetahul, 2023. "The Role of Gender Inequality in the Obesity Epidemic: A Case Study from India," Journal of Development Studies, Taylor & Francis Journals, vol. 59(7), pages 980-996, July.
    4. Giorgio Brunello & Dimitris Christelis & Anna Sanz‐de‐Galdeano & Anastasia Terskaya, 2024. "Does college selectivity reduce obesity? A partial identification approach," Health Economics, John Wiley & Sons, Ltd., vol. 33(10), pages 2306-2320, October.
    5. Dusanee Kesavayuth & Prompong Shangkhum & Vasileios Zikos, 2022. "Well-Being and Physical Health: A Mediation Analysis," Journal of Happiness Studies, Springer, vol. 23(6), pages 2849-2879, August.
    6. Brian L. Rostron & Cindy M. Chang & Brittny C. Davis Lynn & Chunfeng Ren & Esther Salazar & Bridget K. Ambrose, 2022. "The contribution of smoking-attributable mortality to differences in mortality and life expectancy among US African-American and white adults, 2000–2019," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 46(31), pages 905-918.
    7. Anthony M Yezer & Stephen J Popick, 2017. "Climate Preferences, Obesity, and Unobserved Heterogeneity in Cities," The Review of Regional Studies, Southern Regional Science Association, vol. 47(3), pages 309-329, Fall.
    8. Matthew Ritchey & Stavros Tsipas & Fleetwood Loustalot & Gregory Wozniak, 2016. "Use of Pharmacy Sales Data to Assess Changes in Prescription- and Payment-Related Factors that Promote Adherence to Medications Commonly Used to Treat Hypertension, 2009 and 2014," PLOS ONE, Public Library of Science, vol. 11(7), pages 1-14, July.
    9. Pınar Mine Güneş, 2016. "The effects of teenage childbearing on long-term health in the US: a twin-fixed-effects approach," Review of Economics of the Household, Springer, vol. 14(4), pages 891-920, December.
    10. Nguyen, Trang & de Brauw, Alan & van den Berg, Marrit, 2022. "Sweet or not: Using information and cognitive dissonance to nudge children toward healthier food choices," Economics & Human Biology, Elsevier, vol. 47(C).
    11. Lawrence, Elizabeth M. & Mollborn, Stefanie & Hummer, Robert A., 2017. "Health lifestyles across the transition to adulthood: Implications for health," Social Science & Medicine, Elsevier, vol. 193(C), pages 23-32.
    12. Belot, Michèle & James, Jonathan & Nolen, Patrick, 2016. "Incentives and children's dietary choices: A field experiment in primary schools," Journal of Health Economics, Elsevier, vol. 50(C), pages 213-229.
    13. Yan Zheng & Qingsong Chang & Paul Siu Fai Yip, 2019. "Understanding the Increase in Life Expectancy in Hong Kong: Contributions of Changes in Age- and Cause-Specific Mortality," IJERPH, MDPI, vol. 16(11), pages 1-15, June.
    14. Ronchetti, Jérôme & Terriau, Anthony, 2021. "Help me quit smoking but don't make me sick! The controversial effects of electronic cigarettes on tobacco smokers," Social Science & Medicine, Elsevier, vol. 274(C).
    15. Scott A. Carson, 2021. "Omitting the Obvious: Cohort Effects in 19th and 20th Century BMI Variation," CESifo Working Paper Series 8817, CESifo.
    16. Paolo Nicola Barbieri, 2022. "Social distortion in weight perception: a decomposition of the obesity epidemic," Economia Politica: Journal of Analytical and Institutional Economics, Springer;Fondazione Edison, vol. 39(2), pages 685-713, July.
    17. Shima Hamidi, 2020. "Urban sprawl and the emergence of food deserts in the USA," Urban Studies, Urban Studies Journal Limited, vol. 57(8), pages 1660-1675, June.
    18. Zarko Kalamov, 2020. "A sales tax is better at promoting healthy diets than the fat tax and the thin subsidy," Health Economics, John Wiley & Sons, Ltd., vol. 29(3), pages 353-366, March.
    19. Dixon, Padraig & Hollingworth, William & Harrison, Sean & Davies, Neil M. & Davey Smith, George, 2020. "Mendelian Randomization analysis of the causal effect of adiposity on hospital costs," Journal of Health Economics, Elsevier, vol. 70(C).
    20. Wang, Rui & Lin, Faqin & Feng, Kuo, 2024. "Soybean overweight shock (SOS): The impact of trade liberalization in China on overweight prevalence," China Economic Review, Elsevier, vol. 87(C).

    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:18:y:2021:i:8:p:3966-:d:533104. 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.