Machine learning classification of consumption habits of creatine supplements in gym goers
The 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 pe...
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| Format: | Article |
| Language: | Portuguese |
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Instituto Brasileiro de Pesquisa e Ensino em Fisiologia do Exercício
2025-03-01
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| Series: | Revista Brasileira de Nutrição Esportiva |
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| Online Access: | https://www.rbne.com.br/index.php/rbne/article/view/2379 |
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| author | Patrícia C. Magalhães Samuel Encarnação Andre C. Schneider Pedro Forte José Teixeira Antonio Miguel Monteiro Tiago M. Barbosa Ana M. Pereira |
| author_facet | Patrícia C. Magalhães Samuel Encarnação Andre C. Schneider Pedro Forte José Teixeira Antonio Miguel Monteiro Tiago M. Barbosa Ana M. Pereira |
| author_sort | Patrícia C. Magalhães |
| collection | DOAJ |
| description | The 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. |
| format | Article |
| id | doaj-art-cafcba0d6e5149b0a48cd2717f311a85 |
| institution | OA Journals |
| issn | 1981-9927 |
| language | Portuguese |
| publishDate | 2025-03-01 |
| publisher | Instituto Brasileiro de Pesquisa e Ensino em Fisiologia do Exercício |
| record_format | Article |
| series | Revista Brasileira de Nutrição Esportiva |
| spelling | doaj-art-cafcba0d6e5149b0a48cd2717f311a852025-08-20T02:04:26ZporInstituto Brasileiro de Pesquisa e Ensino em Fisiologia do ExercícioRevista Brasileira de Nutrição Esportiva1981-99272025-03-01191141132379Machine learning classification of consumption habits of creatine supplements in gym goersPatrícia C. Magalhães0Samuel Encarnação1Andre C. Schneider2Pedro Forte3José Teixeira4Antonio Miguel Monteiro5Tiago M. Barbosa6Ana M. Pereira7Instituto Politécnico de Bragança, 5300-253 Bragança, Portugal.Department of Sports Sciences, Instituto Politécnico de Bragança, 5300-253 Bragança, Portugal; Department of Physical Education, Sport and Human Movement, Universidad Autónoma de Madrid (UAM), Ciudad Universitaria de Cantoblanco, 28049 Madrid, Spain; CI-ISCE, Instituto Superior de Ciências Educativas do Douro (ISCE Douro), 4560-547 Penafiel, Portugal; Research Centre for Active Living and Wellbeing (Livewell), Instituto Politécnico de Bragança, Portugal.Department of Sports Sciences, Instituto Politécnico de Bragança, 5300-253 Bragança, Portugal.Department of Sports Sciences, Instituto Politécnico de Bragança, 5300-253 Bragança, Portugal; CI-ISCE, Instituto Superior de Ciências Educativas do Douro (ISCE Douro), 4560-547 Penafiel, Portugal; Research Centre for Active Living and Wellbeing (Livewell), Instituto Politécnico de Bragança, Portugal.Department of Sports Sciences, Instituto Politécnico de Bragança, 5300-253 Bragança, Portugal; Research Centre for Active Living and Wellbeing (Livewell), Instituto Politécnico de Bragança, Portugal; Department of Sports Sciences, Polytechnic Institute of Guarda, 6300-559 Guarda, Portugal; SPRINT, Sport Physical activity and health Research & Inovation Center, Guarda, Portugal; Research Center in Sports Sciences, Health Sciences & Human Development (CIDESD), 5001-801 Vila Real, Portugal.Department of Sports Sciences, Instituto Politécnico de Bragança, 5300-253 Bragança, Portugal; Research Centre for Active Living and Wellbeing (Livewell), Instituto Politécnico de Bragança, Portugal.Department of Sports Sciences, Instituto Politécnico de Bragança, 5300-253 Bragança, Portugal; Research Centre for Active Living and Wellbeing (Livewell), Instituto Politécnico de Bragança, Portugal.Research Centre for Active Living and Wellbeing (Livewell), Instituto Politécnico de Bragança, Portugal.The 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.https://www.rbne.com.br/index.php/rbne/article/view/2379creatine supplementationgymscharacteristicsadults |
| spellingShingle | Patrícia C. Magalhães Samuel Encarnação Andre C. Schneider Pedro Forte José Teixeira Antonio Miguel Monteiro Tiago M. Barbosa Ana M. Pereira Machine learning classification of consumption habits of creatine supplements in gym goers Revista Brasileira de Nutrição Esportiva creatine supplementation gyms characteristics adults |
| title | Machine learning classification of consumption habits of creatine supplements in gym goers |
| title_full | Machine learning classification of consumption habits of creatine supplements in gym goers |
| title_fullStr | Machine learning classification of consumption habits of creatine supplements in gym goers |
| title_full_unstemmed | Machine learning classification of consumption habits of creatine supplements in gym goers |
| title_short | Machine learning classification of consumption habits of creatine supplements in gym goers |
| title_sort | machine learning classification of consumption habits of creatine supplements in gym goers |
| topic | creatine supplementation gyms characteristics adults |
| url | https://www.rbne.com.br/index.php/rbne/article/view/2379 |
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