IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v19y2022i17p10679-d899206.html
   My bibliography  Save this article

Uncovering the Heterogeneity in Fitness App Use: A Latent Class Analysis of Chinese Users

Author

Listed:
  • Li Crystal Jiang

    (Department of Media and Communication, City University of Hong Kong, Hong Kong SAR, China)

  • Mengru Sun

    (College of Media and International Culture, Zhejiang University, Hangzhou 310058, China)

  • Guanxiong Huang

    (Department of Media and Communication, City University of Hong Kong, Hong Kong SAR, China)

Abstract

This study examines fitness app use patterns and their correlates among Chinese users from the perspectives of uses and gratification theory and self-determination theory. Our sample comprised 632 users of WeRun, the fitness plugin of WeChat, the largest Chinese mobile social networking app; participants completed an online survey and provided self-tracked physical activity data, which were subjected to latent class analysis. Based on the four-class latent class model (which yielded the best model fit and the most interpretable results), 30.5%, 27.5%, 24.7%, and 17.3% of the users were categorized as light users, reward-oriented users, lifestyle-oriented users, and interaction-oriented users, respectively. Moreover, class membership was associated with gender, age, education, income, life satisfaction, autonomy, and platform-based motivations. There is a significant heterogeneity in fitness app use and exercise behaviors. Platform-based motivations and autonomy are important classification factors, as users are looking for specific kinds of gratification from their use of fitness apps. Demographics and individual characteristics are also explanatory factors for class membership. The study findings suggest that fitness app designers should segment users based on motivation and gratification.

Suggested Citation

  • Li Crystal Jiang & Mengru Sun & Guanxiong Huang, 2022. "Uncovering the Heterogeneity in Fitness App Use: A Latent Class Analysis of Chinese Users," IJERPH, MDPI, vol. 19(17), pages 1-12, August.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:17:p:10679-:d:899206
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/19/17/10679/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/19/17/10679/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ray, Arghya & Dhir, Amandeep & Bala, Pradip Kumar & Kaur, Puneet, 2019. "Why do people use food delivery apps (FDA)? A uses and gratification theory perspective," Journal of Retailing and Consumer Services, Elsevier, vol. 51(C), pages 221-230.
    2. Linzer, Drew A. & Lewis, Jeffrey B., 2011. "poLCA: An R Package for Polytomous Variable Latent Class Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 42(i10).
    3. Liu, Zhengying & Kemperman, Astrid & Timmermans, Harry & Yang, Dongfeng, 2021. "Heterogeneity in physical activity participation of older adults: A latent class analysis," Journal of Transport Geography, Elsevier, vol. 92(C).
    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. Yanlong Guo & Xueqing Ma & Denghang Chen & Han Zhang, 2022. "Factors Influencing Use of Fitness Apps by Adults under Influence of COVID-19," IJERPH, MDPI, vol. 19(23), pages 1-17, November.

    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. Adrian O’Hagan & Arthur White, 2019. "Improved model-based clustering performance using Bayesian initialization averaging," Computational Statistics, Springer, vol. 34(1), pages 201-231, March.
    2. Jebarajakirthy, Charles & Shankar, Amit, 2021. "Impact of online convenience on mobile banking adoption intention: A moderated mediation approach," Journal of Retailing and Consumer Services, Elsevier, vol. 58(C).
    3. Nathanael Johnson & Torsten Reimer, 2023. "The Adoption and Use of Smart Assistants in Residential Homes: The Matching Hypothesis," Sustainability, MDPI, vol. 15(12), pages 1-16, June.
    4. Aline Riboli Marasca & Maurício Scopel Hoffmann & Anelise Reis Gaya & Denise Ruschel Bandeira, 2021. "Subjective Well-Being and Psychopathology Symptoms: Mental Health Profiles and their Relations with Academic Achievement in Brazilian Children," Child Indicators Research, Springer;The International Society of Child Indicators (ISCI), vol. 14(3), pages 1121-1137, June.
    5. Lisa Blaydes, 2023. "Assessing the Labor Conditions of Migrant Domestic Workers in the Arab Gulf States," ILR Review, Cornell University, ILR School, vol. 76(4), pages 724-747, August.
    6. Jindřich Špička & Zdeňka Náglová, 2022. "Consumer segmentation in the meat market - The case study of Czech Republic," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 68(2), pages 68-77.
    7. Nicholas T. Davis & Kirby Goidel & Yikai Zhao, 2021. "The Meanings of Democracy among Mass Publics," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 153(3), pages 849-921, February.
    8. Hallikainen, Heli & Luongo, Milena & Dhir, Amandeep & Laukkanen, Tommi, 2022. "Consequences of personalized product recommendations and price promotions in online grocery shopping," Journal of Retailing and Consumer Services, Elsevier, vol. 69(C).
    9. Carter, Virginia & Derudder, Ben & Henríquez, Cristián, 2021. "Assessing local governments’ perception of the potential implementation of biophilic urbanism in Chile: A latent class approach," Land Use Policy, Elsevier, vol. 101(C).
    10. Arjan S. Gosal & Janine A. McMahon & Katharine M. Bowgen & Catherine H. Hoppe & Guy Ziv, 2021. "Identifying and Mapping Groups of Protected Area Visitors by Environmental Awareness," Land, MDPI, vol. 10(6), pages 1-14, May.
    11. Assem Abu Hatab & Padmaja Ravula & Swamikannu Nedumaran & Carl-Johan Lagerkvist, 2022. "Perceptions of the impacts of urban sprawl among urban and peri-urban dwellers of Hyderabad, India: a Latent class clustering analysis," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(11), pages 12787-12812, November.
    12. Martin Eling & David Pankoke, 2016. "Costs and Benefits of Financial Regulation: An Empirical Assessment for Insurance Companies," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 41(4), pages 529-554, October.
    13. Sunil Kumar & Zakir Husain & Diganta Mukherjee, 2015. "Assessing Consistency of Consumer Confidence Data using Dynamic Latent Class Analysis," Papers 1509.01215, arXiv.org.
    14. Daniel Belanche & Luis V. Casaló & Carlos Flavián & Alfredo Pérez-Rueda, 2021. "The role of customers in the gig economy: how perceptions of working conditions and service quality influence the use and recommendation of food delivery services," Service Business, Springer;Pan-Pacific Business Association, vol. 15(1), pages 45-75, March.
    15. Lorena Charrier & Paola Berchialla & Paola Dalmasso & Alberto Borraccino & Patrizia Lemma & Franco Cavallo, 2019. "Cigarette Smoking and Multiple Health Risk Behaviors: A Latent Class Regression Model to Identify a Profile of Young Adolescents," Risk Analysis, John Wiley & Sons, vol. 39(8), pages 1771-1782, August.
    16. Yang, Yongjiang & Sasaki, Kuniaki & Cheng, Long & Tao, Sui, 2022. "Does the built environment matter for active travel among older adults: Insights from Chiba City, Japan," Journal of Transport Geography, Elsevier, vol. 101(C).
    17. Raphaela Grafiadeli & Heide Glaesmer & Birgit Wagner, 2022. "Loss-Related Characteristics and Symptoms of Depression, Prolonged Grief, and Posttraumatic Stress Following Suicide Bereavement," IJERPH, MDPI, vol. 19(16), pages 1-10, August.
    18. Daniel L. Oberski, 2016. "A Review of Latent Variable Modeling With R," Journal of Educational and Behavioral Statistics, , vol. 41(2), pages 226-233, April.
    19. Guangchao Feng, 2014. "Estimating intercoder reliability: a structural equation modeling approach," Quality & Quantity: International Journal of Methodology, Springer, vol. 48(4), pages 2355-2369, July.
    20. Giorgio Eduardo Montanari & Marco Doretti & Maria Francesca Marino, 2022. "Model-based two-way clustering of second-level units in ordinal multilevel latent Markov models," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 16(2), pages 457-485, June.

    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:19:y:2022:i:17:p:10679-:d:899206. 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.