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

Oscar Jossa-Bastidas, Sofia Zahia, Andrea Fuente-Vidal, Néstor Sánchez Férez, Oriol Roda Noguera, Joel Montane and Begonya Garcia-Zapirain
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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
Begonya Garcia-Zapirain: eVIDA Research Group, University of Deusto, 48007 Bilbao, Spain

IJERPH, 2021, vol. 18, issue 20, 1-32

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.

Keywords: deep learning; regression; adherence; mHealth; eHealth; fitness app; physical activity (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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