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Machine learning classification of consumption habits of creatine supplements in gym goers

2025Journal article Open access
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datacite.subject.fosCiências Médicas
datacite.subject.sdg03:Saúde de Qualidade
dc.contributor.authorMagalhães, Patrícia C.
dc.contributor.authorEncarnação, Samuel Gonçalves
dc.contributor.authorForte, Pedro
dc.contributor.authorTeixeira, José Eduardo
dc.contributor.authorMonteiro, A. M.
dc.contributor.authorBarbosa, Tiago M.
dc.contributor.authorPereira, Ana Maria Geraldes Rodrigues
dc.contributor.authorSchneider, André C
dc.date.accessioned2025-05-08T15:00:23Z
dc.date.available2025-05-08T15:00:23Z
dc.date.issued2025
dc.description.abstractThe aim is to identify usage patterns and the main factors that influence creatine supplementation, providing a basis for future educational interventions and recommendations for safe and effective use. The study was applied to gym goers in Bragança, where a QR code for a survey was released. 158 people participated, 65 non-consumers of creatine supplementation (37.34% men; 22.78% women) and 95 consumers (15.19% men; 24.68% women). Five machine learning algorithms were implemented to classify creatine consumption in gym goers: Logistic Regression, Gradient Boosting Classifier, Ada Boost Classifier, Xgboost Classifier. K-folds cross-validation was implemented to validate the machine learning performance. There was an increased proportion of females with considered themselves not sufficiently informed about the creatine effects/side effects (22.2%) in comparison to males (8.47%), p=0.03. The AdaBoost classifier exposed the best overall performance (86%) in classifying overuse of creatine in gym goers based on their Smoke habits (r = 0.33), grams of creatine used per day (r = 0.50) and lack information about the side effects of creatine intake (r = -0.33). The K-folds method validates the results with very good performance (86%). In conclusion, the five machine learning methods employed well characterized the overuse of creatine in gym goers based on smoke habits, grams of creatine per day, and lack information about the side effects of creatine intake.por
dc.identifier.citationMagalhães, Patrícia C.; Encarnação, Samuel Gonçalves; Schneider, André C.; Miguel Forte, Pedro; Teixeira, José Eduardo, Monteiro, A.M.; Barbosa, Tiago M. ; Pereira, Ana Maria Geraldes Rodrigues (2025). Machine learning classification of consumption habits of creatine supplements in gym goers. RBNE-Revista Brasileira De Nutricao Esportiva. ISSN 1981-9927. 19:114, p. 1-13
dc.identifier.urihttp://hdl.handle.net/10198/34459
dc.language.isoeng
dc.peerreviewedyes
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectCreatine supplementation
dc.subjectGyms
dc.subjectCharacteristics
dc.titleMachine learning classification of consumption habits of creatine supplements in gym goerspor
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage13
oaire.citation.issue114
oaire.citation.startPage1
oaire.citation.titleRevista Brasileira de Nutrição Esportiva
oaire.citation.volume18
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameForte
person.familyNameTeixeira
person.familyNameMonteiro
person.familyNameBarbosa
person.familyNamePereira
person.givenNamePedro
person.givenNameJosé Eduardo
person.givenNameAntónio M.
person.givenNameTiago M.
person.givenNameAna Maria Geraldes Rodrigues
person.identifier.ciencia-id351B-B16B-79C7
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person.identifier.ciencia-idC41C-6CCD-A1F0
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person.identifier.ciencia-id261B-FE1F-4B06
person.identifier.orcid0000-0003-0184-6780
person.identifier.orcid0000-0003-4612-3623
person.identifier.orcid0000-0003-4467-1722
person.identifier.orcid0000-0001-7071-2116
person.identifier.orcid0000-0002-8747-254X
person.identifier.scopus-author-id10044856400
person.identifier.scopus-author-id57199275973
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