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dc.contributorUniversitat Ramon Llull. Facultat de Ciències de la Salut Blanquerna
dc.contributorUniversitat Ramon Llull. Facultat de Psicologia, Ciències de l’Educació i de l’Esport Blanquerna
dc.contributor.authorJossa Bastidas, Oscar
dc.contributor.authorZahia, Sofia
dc.contributor.authorFuente-Vidal, Andrea
dc.contributor.authorSánchez Férez, Néstor
dc.contributor.authorRoda Noguera, Oriol
dc.contributor.authorGarcia-Zapirain, Begonya
dc.contributor.authorMontane Mogas, Joel
dc.date.accessioned2022-11-22T17:06:32Z
dc.date.accessioned2023-07-12T12:05:01Z
dc.date.available2022-11-22T17:06:32Z
dc.date.available2023-07-12T12:05:01Z
dc.date.created2021-08-11
dc.date.issued2021-10-14
dc.identifier.urihttp://hdl.handle.net/20.500.14342/720
dc.description.abstractThe 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.eng
dc.format.extent32 p.cat
dc.language.isoengcat
dc.publisherMDPIcat
dc.relation.ispartofInternational Journal of Environmental Research and Public Health, 2021, 18, 10769cat
dc.rightsAttribution 4.0 International
dc.rights© L'autor/a
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.sourceRECERCAT (Dipòsit de la Recerca de Catalunya)
dc.subject.otherAplicacions mòbilscat
dc.subject.otherExercicicat
dc.subject.otherEntrenament (Esport)cat
dc.titlePredicting physical exercise adherence in fitness Apps using a deep learning approachcat
dc.typeinfo:eu-repo/semantics/articlecat
dc.typeinfo:eu-repo/semantics/publishedVersioncat
dc.rights.accessLevelinfo:eu-repo/semantics/openAccess
dc.embargo.termscapcat
dc.subject.udc628
dc.subject.udc79
dc.identifier.doihttps://doi.org/10.3390/ijerph182010769cat


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Attribution 4.0 International
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