IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v17y2020i14p5235-d387100.html
   My bibliography  Save this article

Relationships among Leisure Physical Activity, Sedentary Lifestyle, Physical Fitness, and Happiness in Adults 65 Years or Older in Taiwan

Author

Listed:
  • Yi-Tien Lin

    (Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City 242, Taiwan)

  • Mingchih Chen

    (Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City 242, Taiwan
    Artificial Intelligence Development Center, Fu Jen Catholic University, New Taipei City 242, Taiwan)

  • Chien-Chang Ho

    (Department of Physical Education, Fu Jen Catholic University, New Taipei City 242, Taiwan
    Research and Development Center for Physical Education, Health, and Information Technology, Fu Jen Catholic University, New Taipei City 242, Taiwan)

  • Tian-Shyug Lee

    (Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City 242, Taiwan
    Artificial Intelligence Development Center, Fu Jen Catholic University, New Taipei City 242, Taiwan)

Abstract

The purpose of this study is to understand the relationship among leisure physical activity, sedentary lifestyle, physical fitness, and happiness in healthy elderly adults aged over 65 years old in Taiwan. Data were recruited from the National Physical Fitness Survey in Taiwan, which was proposed in the Project on the Establishment of Physical Fitness Testing Stations by the Sports Administration of the Ministry of Education. Participants were recruited from fitness testing stations set up in 22 counties and cities from October 2015 to May 2016. A total of 20,111 healthy older adults aged 65–102 years were recruited as research participants. The fitness testing procedure was described to all participants, who were provided with a standardized structured questionnaire. Participants’ data included sex, city or county of residence, living status (living together with others or living alone), education level, and income. Physical fitness testing was conducted in accordance with The Fitness Guide for Older Adults published by the Sports Administration of the Ministry of Education. The testing involved cardiorespiratory endurance, muscle strength, muscle endurance, flexibility, balance, and body composition. The t -test was used to evaluate the differences between continuous and grade variables under the two classification variables of sex, city or county of residence, and living status. We used the MARS (multivariate adaptive regression splines) model to analyze the effects of physical fitness variables and leisure physical activity variables on happiness. Among healthy elderly adults, sex, age, living status, body mass index, and leisure physical activity habits proved to be related to happiness. Aerobic endurance (2-min step test), muscular strength and endurance (30-s arm curl and 30-s chair stand tests), flexibility (back stretch and chair sit-and-reach tests), and balance ability (8-foot up-and-go tests and one-leg stance with eyes open tests) were found to be related to happiness. The results of this study indicate that increased physical activity and intensity, as well as physical fitness performance, are associated with improved happiness.

Suggested Citation

  • Yi-Tien Lin & Mingchih Chen & Chien-Chang Ho & Tian-Shyug Lee, 2020. "Relationships among Leisure Physical Activity, Sedentary Lifestyle, Physical Fitness, and Happiness in Adults 65 Years or Older in Taiwan," IJERPH, MDPI, vol. 17(14), pages 1-12, July.
  • Handle: RePEc:gam:jijerp:v:17:y:2020:i:14:p:5235-:d:387100
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/17/14/5235/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1660-4601/17/14/5235/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Lee, Tian-Shyug & Chiu, Chih-Chou & Chou, Yu-Chao & Lu, Chi-Jie, 2006. "Mining the customer credit using classification and regression tree and multivariate adaptive regression splines," Computational Statistics & Data Analysis, Elsevier, vol. 50(4), pages 1113-1130, February.
    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. Daniel W. L. Lai & Xiaoting Ou & Jiahui Jin, 2022. "A Quasi-Experimental Study on the Effect of an Outdoor Physical Activity Program on the Well-Being of Older Chinese People in Hong Kong," IJERPH, MDPI, vol. 19(15), pages 1-8, July.
    2. Mei-Fang Chen & Chun-Chin Tsai, 2022. "The Effectiveness of a Thanks, Sorry, Love, and Farewell Board Game in Older People in Taiwan: A Quasi-Experimental Study," IJERPH, MDPI, vol. 19(5), pages 1-13, March.
    3. Elizabeth Wianto & Elty Sarvia & Chien-Hsu Chen, 2021. "Authoritative Parents and Dominant Children as the Center of Communication for Sustainable Healthy Aging," IJERPH, MDPI, vol. 18(6), pages 1-18, March.
    4. Eui-Jae Kim & Hyun-Wook Kang & Seong-Man Park, 2024. "Leisure and Happiness of the Elderly: A Machine Learning Approach," Sustainability, MDPI, vol. 16(7), pages 1-18, March.
    5. Hyun-Min Choi & Chansol Hurr & Sukwon Kim, 2020. "Effects of Elastic Band Exercise on Functional Fitness and Blood Pressure Response in the Healthy Elderly," IJERPH, MDPI, vol. 17(19), pages 1-10, 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. Davidescu Adriana AnaMaria & Agafiței Marina-Diana & Strat Vasile Alecsandru & Dima Alina Mihaela, 2024. "Mapping the Landscape: A Bibliometric Analysis of Rating Agencies in the Era of Artificial Intelligence and Machine Learning," Proceedings of the International Conference on Business Excellence, Sciendo, vol. 18(1), pages 67-85.
    2. Adnan Dželihodžić & Dženana Đonko & Jasmin Kevrić, 2018. "Improved Credit Scoring Model Based on Bagging Neural Network," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 17(06), pages 1725-1741, November.
    3. Huseyin Ince & Bora Aktan, 2009. "A comparison of data mining techniques for credit scoring in banking: A managerial perspective," Journal of Business Economics and Management, Taylor & Francis Journals, vol. 10(3), pages 233-240, March.
    4. Elcin Koc & Cem Iyigun, 2014. "Restructuring forward step of MARS algorithm using a new knot selection procedure based on a mapping approach," Journal of Global Optimization, Springer, vol. 60(1), pages 79-102, September.
    5. Chen, Shiyi & Jeong, Kiho & Härdle, Wolfgang Karl, 2008. "Recurrent support vector regression for a nonlinear ARMA model with applications to forecasting financial returns," SFB 649 Discussion Papers 2008-051, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    6. Ibtissem Baklouti, 2014. "A Psychological Approach To Microfinance Credit Scoring Via A Classification And Regression Tree," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 21(4), pages 193-208, October.
    7. Evžen Kocenda & Martin Vojtek, 2011. "Default Predictors in Retail Credit Scoring: Evidence from Czech Banking Data," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 47(6), pages 80-98, November.
    8. Gianpaolo Iazzolino & Rossella Gabriele, 2016. "Energy Efficiency and Sustainable Development: An Analysis of Financial Reliability in Energy Service Companies Industry," International Journal of Energy Economics and Policy, Econjournals, vol. 6(2), pages 222-233.
    9. Lessmann, Stefan & Baesens, Bart & Seow, Hsin-Vonn & Thomas, Lyn C., 2015. "Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research," European Journal of Operational Research, Elsevier, vol. 247(1), pages 124-136.
    10. Bozağaç, Doruk & Batmaz, İnci & Oğuztüzün, Halit, 2016. "Dynamic simulation metamodeling using MARS: A case of radar simulation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 124(C), pages 69-86.
    11. Antonio Angelo Romano & Giuseppe Scandurra & Alfonso Carfora, 2016. "Estimating the Impact of Feed-in Tariff Adoption: Similarities and Divergences among Countries through a Propensity-score Matching Method," International Journal of Energy Economics and Policy, Econjournals, vol. 6(2), pages 144-151.
    12. Hu, Xiaolu & Huang, Haozhi & Pan, Zheyao & Shi, Jing, 2019. "Information asymmetry and credit rating: A quasi-natural experiment from China," Journal of Banking & Finance, Elsevier, vol. 106(C), pages 132-152.
    13. Gerunov, Anton, 2016. "Modeling Economic Choice under Radical Uncertainty: Machine Learning Approaches," MPRA Paper 69199, University Library of Munich, Germany.
    14. Soo Young Kim, 2018. "Predicting hospitality financial distress with ensemble models: the case of US hotels, restaurants, and amusement and recreation," Service Business, Springer;Pan-Pacific Business Association, vol. 12(3), pages 483-503, September.
    15. Ching-Chin Chern & Weng-U Lei & Kwei-Long Huang & Shu-Yi Chen, 2021. "A decision tree classifier for credit assessment problems in big data environments," Information Systems and e-Business Management, Springer, vol. 19(1), pages 363-386, March.
    16. Elcin Koc & Cem Iyigun & İnci Batmaz & Gerhard-Wilhelm Weber, 2014. "Efficient adaptive regression spline algorithms based on mapping approach with a case study on finance," Journal of Global Optimization, Springer, vol. 60(1), pages 103-120, September.
    17. Mostafa, Mohamed M. & Nataraajan, Rajan, 2009. "A neuro-computational intelligence analysis of the ecological footprint of nations," Computational Statistics & Data Analysis, Elsevier, vol. 53(9), pages 3516-3531, July.
    18. Reynes, Christelle & Sabatier, Robert & Molinari, Nicolas, 2006. "Choice of B-splines with free parameters in the flexible discriminant analysis context," Computational Statistics & Data Analysis, Elsevier, vol. 51(3), pages 1765-1778, December.
    19. Saeedeh Eskandari & Mahdis Amiri & Nitheshnirmal Sãdhasivam & Hamid Reza Pourghasemi, 2020. "Comparison of new individual and hybrid machine learning algorithms for modeling and mapping fire hazard: a supplementary analysis of fire hazard in different counties of Golestan Province in Iran," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 104(1), pages 305-327, October.
    20. Ayşe Özmen, 2023. "Sparse regression modeling for short- and long‐term natural gas demand prediction," Annals of Operations Research, Springer, vol. 322(2), pages 921-946, March.

    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:17:y:2020:i:14:p:5235-:d:387100. 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.