[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121579-en":3,"doc-seo-121579-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121579,13056703020460,"Valentina","https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923",8,"Research & Report","Machine Learning Classification of Consumption Habits of Creatine Supplements in Gym Goers - Report","The study identifies usage patterns and key factors influencing creatine supplementation among gym goers in Bragança. A QR-code survey collected responses from 158 participants, including 65 non-consumers and 95 consumers. Five machine-learning algorithms (Logistic Regression, Gradient Boosting, AdaBoost, and XGBoost) were evaluated with K-fold cross-validation. Women more often reported insufficient information about creatine effects and side effects (22.2%) than men (8.47%). AdaBoost achieved the best overall performance (86%), highlighting associations with smoking habits, grams consumed per day, and lack of side-effect information.","MACHINE LEARNING CLASSIFICATION OF CONSUMPTION HABITS OF CREATINE  \nSUPPLEMENTS IN GYM GOERS  \nPatrícia C. Magalhães 1 , Samuel Encarnação2,3,4,5 , André C. Schneider2 , Pedro Forte2,4,5 , José E. Teixeira2,5,6,7,8 , António Miguel Monteiro2,5 , Tiago M. Barbosa2,5 , Ana M. Pereira5  \nABSTRACT  \nThe aim is to identify usage patterns and the main factors that influence creatine supplementation, providing a basis for future  \neducational interventions and  \nrecommendations 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 nonconsumers 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.  \nKey words: Creatine supplementation. Gyms. Characteristics. Adults.  \n1-Instituto Politécnico de Bragança, 5300-253 Bragança, Portugal.  \n2 - Department of Sports Sciences, Instituto Politécnico de Bragança, 5300-253 Bragança, Portugal.  \n3 - Department of Physical Education, Sport and Human Movement, Universidad Autónomade Madrid (UAM), Ciudad Universitaria de Cantoblanco, 28049 Madrid, Spain.  \nRESUMO  \nClassificação por machine learning dos hábitos de consumo de suplementos de creatina em frequentadores de ginásios  \nO objetivo é identificar padrões de uso e os principais fatores que influenciam asuplementação de creatina, fornecendo uma base para futuras intervenções educacionais erecomendações para um uso seguro e eficaz. O estudo foi aplicado a frequentadores de academia em Bragança, onde um código QR para uma pesquisa foi disponibilizado. 158 pessoas participaram, 65 não consumidores desuplementação de creatina (37,34% homens; 22,78% mulheres) e 95 consumidores (15,19% homens; 24,68% mulheres) . Cinco algoritmos de aprendizado de máquina foram implementados para classificar o consumo decreatina em frequentadores de academia: Regressão Logística, Classificador de Impulso Gradiente, Classificador de Impulso Ada, Classificador Xgboost. A validação cruzada de K-folds foi implementada para validar odesempenho de aprendizado de máquina. Houve uma proporção maior de mulheres que se consideravam insuficientemente informadas sobre os efeitos/efeitos colaterais da creatina (22,2%) em comparação com homens (8,47%), p=0,03 . O classificador AdaBoost expôs omelhor desempenho geral (86%) na classificação do uso excessivo de creatina em frequentadores de academia com base em seus hábitos de fumo (r = 0,33), gramas decreatina usadas por dia (r = 0,50) e falta deinformação sobre os efeitos colaterais da ingestão de creatina (r = -0,33) . O método Kfolds validou os resultados com um desempenho muito bom (86%) . Em conclusão, os cinco métodos de aprendizado de máquinaempregados caracterizaram bem o uso excessivo de creatina em frequentadores de academia com base em hábitos de fumo, gramas de creatina por dia e falta deinformação sobre os efeitos colaterais da ingestão de creatina.  \nPa","cbCaitTuNkH5Hzz2","https://ap.wps.com/l/cbCaitTuNkH5Hzz2","pdf",474071,1,13,"English","en",105,"# Abstract\n# Introduction\n## Creatine background and metabolism\n## Dietary sources and storage in the body\n## Role in high-intensity exercise\n## Storage levels and biochemical limits","[{\"question\":\"What is the main objective of the study on creatine supplementation?\",\"answer\":\"To identify usage patterns and the primary factors that influence creatine supplementation, supporting future educational interventions and safe, effective recommendations.\"},{\"question\":\"How was the data collected and how many participants took part?\",\"answer\":\"A QR code survey was released to gym goers in Bragança. In total, 158 participants responded, including 65 non-consumers and 95 consumers.\"},{\"question\":\"Which machine learning model performed best and what factors did it emphasize?\",\"answer\":\"AdaBoost provided the best overall performance (86%) for classifying overuse, with related signals from smoking habits, daily grams consumed, and insufficient information about side effects.\"}]","Machine Learning Classification of Consumption Habits of Creatine Supplements in Gym Goers - Report | PDF",1785736333,33,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-classification-of-consumption-habits-of-creatine-supplements-in-gym-goers-report","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-classification-of-consumption-habits-of-creatine-supplements-in-gym-goers-report/121579/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main objective of the study on creatine supplementation?","Question",{"text":75,"@type":76},"To identify usage patterns and the primary factors that influence creatine supplementation, supporting future educational interventions and safe, effective recommendations.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How was the data collected and how many participants took part?",{"text":80,"@type":76},"A QR code survey was released to gym goers in Bragança. In total, 158 participants responded, including 65 non-consumers and 95 consumers.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning model performed best and what factors did it emphasize?",{"text":84,"@type":76},"AdaBoost provided the best overall performance (86%) for classifying overuse, with related signals from smoking habits, daily grams consumed, and insufficient information about side effects.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]