IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v18y2021i20p10769-d655853.html
   My bibliography  Save this article

Predicting Physical Exercise Adherence in Fitness Apps Using a Deep Learning Approach

Author

Listed:
  • Oscar Jossa-Bastidas

    (eVIDA Research Group, University of Deusto, 48007 Bilbao, Spain)

  • Sofia Zahia

    (eVIDA Research Group, University of Deusto, 48007 Bilbao, Spain)

  • Andrea Fuente-Vidal

    (Department of Physical Activity and Sport Sciences, FPCEE Blanquerna, Ramon Llull University, 08022 Barcelona, Spain)

  • Néstor Sánchez Férez

    (Mammoth Hunters S.L., 08036 Barcelona, Spain)

  • Oriol Roda Noguera

    (Mammoth Hunters S.L., 08036 Barcelona, Spain)

  • Joel Montane

    (Department of Physical Activity and Sport Sciences, FPCEE Blanquerna, Ramon Llull University, 08022 Barcelona, Spain
    Blanquerna School of Health Sciences, Ramon Llull University, 08025 Barcelona, Spain
    Both authors contributed equally to this work.)

  • Begonya Garcia-Zapirain

    (eVIDA Research Group, University of Deusto, 48007 Bilbao, Spain
    Both authors contributed equally to this work.)

Abstract

The use of mobile fitness apps has been on the rise for the last decade and especially during the worldwide SARS-CoV-2 pandemic, which led to the closure of gyms and to reduced outdoor mobility. Fitness apps constitute a promising means for promoting more active lifestyles, although their attrition rates are remarkable and adherence to their training plans remains a challenge for developers. The aim of this project was to design an automatic classification of users into adherent and non-adherent, based on their training behavior in the first three months of app usage, for which purpose we proposed an ensemble of regression models to predict their behaviour (adherence) in the fourth month. The study was conducted using data from a total of 246 Mammoth Hunters Fitness app users. Firstly, pre-processing and clustering steps were taken in order to prepare the data and to categorize users into similar groups, taking into account the first 90 days of workout sessions. Then, an ensemble approach for regression models was used to predict user training behaviour during the fourth month, which were trained with users belonging to the same cluster. This was used to reach a conclusion regarding their adherence status, via an approach that combined affinity propagation (AP) clustering algorithm, followed by the long short-term memory (LSTM), rendering the best results (87% accuracy and 85% F1_score). This study illustrates the suggested the capacity of the system to anticipate future adherence or non-adherence, potentially opening the door to fitness app creators to pursue advanced measures aimed at reducing app attrition.

Suggested Citation

  • Oscar Jossa-Bastidas & Sofia Zahia & Andrea Fuente-Vidal & Néstor Sánchez Férez & Oriol Roda Noguera & Joel Montane & Begonya Garcia-Zapirain, 2021. "Predicting Physical Exercise Adherence in Fitness Apps Using a Deep Learning Approach," IJERPH, MDPI, vol. 18(20), pages 1-32, October.
  • Handle: RePEc:gam:jijerp:v:18:y:2021:i:20:p:10769-:d:655853
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/18/20/10769/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1660-4601/18/20/10769/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yaira Barranco-Ruiz & Emilio Villa-González, 2020. "Health-Related Physical Fitness Benefits in Sedentary Women Employees after an Exercise Intervention with Zumba Fitness ®," IJERPH, MDPI, vol. 17(8), pages 1-16, 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. Andrea Fuente-Vidal & Myriam Guerra-Balic & Oriol Roda-Noguera & Javier Jerez-Roig & Joel Montane, 2022. "Adherence to eHealth-Delivered Exercise in Adults with no Specific Health Conditions: A Scoping Review on a Conceptual Challenge," IJERPH, MDPI, vol. 19(16), pages 1-19, August.

    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. Adriana Ljubojevic & Vladimir Jakovljevic & Snezana Bijelic & Ioan Sârbu & Dragoș Ioan Tohănean & Constantin Albină & Dan Iulian Alexe, 2022. "The Effects of Zumba Fitness ® on Respiratory Function and Body Composition Parameters: An Eight-Week Intervention in Healthy Inactive Women," IJERPH, MDPI, vol. 20(1), pages 1-11, December.
    2. Antonio Jesús Casimiro-Andújar & Ricardo Martín-Moya & María Maravé-Vivas & Pedro Jesús Ruiz-Montero, 2022. "Effects of a Personalised Physical Exercise Program on University Workers Overall Well-Being: “UAL-Activa” Program," IJERPH, MDPI, vol. 19(18), pages 1-10, September.
    3. Manuel Chavarrias & Santos Villafaina & Ana Myriam Lavín-Pérez & Jorge Carlos-Vivas & Eugenio Merellano-Navarro & Jorge Pérez-Gómez, 2020. "Zumba ® , Fat Mass and Maximum Oxygen Consumption: A Systematic Review and Meta-Analysis," IJERPH, MDPI, vol. 18(1), pages 1-14, December.
    4. Yao Zhang & Beier Zhang & Liaoyan Gan & Limei Ke & Yingyao Fu & Qian Di & Xindong Ma, 2021. "Effects of Online Bodyweight High-Intensity Interval Training Intervention and Health Education on the Mental Health and Cognition of Sedentary Young Females," IJERPH, MDPI, vol. 18(1), pages 1-15, January.
    5. Yaira Barranco-Ruiz & Susana Paz-Viteri & Emilio Villa-González, 2020. "Dance Fitness Classes Improve the Health-Related Quality of Life in Sedentary Women," IJERPH, MDPI, vol. 17(11), pages 1-13, May.

    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:20:p:10769-:d:655853. 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.