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Random forest model to identify factors associated with anabolic-androgenic steroid use
BACKGROUND: One of the types of doping that is commonly used by bodybuilders, is androgenic-anabolic steroids (AAS). The use of AAS besides violating sporting ethics would have serious consequences on physical and mental health statuses. This study aimed to determine the most important factors of us...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7988984/ https://www.ncbi.nlm.nih.gov/pubmed/33757585 http://dx.doi.org/10.1186/s13102-021-00257-5 |
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author | Manoochehri, Zohreh Barati, Majid Faradmal, Javad Manoochehri, Sara |
author_facet | Manoochehri, Zohreh Barati, Majid Faradmal, Javad Manoochehri, Sara |
author_sort | Manoochehri, Zohreh |
collection | PubMed |
description | BACKGROUND: One of the types of doping that is commonly used by bodybuilders, is androgenic-anabolic steroids (AAS). The use of AAS besides violating sporting ethics would have serious consequences on physical and mental health statuses. This study aimed to determine the most important factors of using AAS among bodybuilders by prototype willingness model (PWM). METHODS: In this analytical cross-sectional study, 280 male bodybuilders were selected from the bodybuilding clubs in Hamadan city using multistage sampling in 2016. A self-administered questionnaire consisting of demographic information and constructs of the PWM was then used to collect data and random forest model was also applied to analyze the collected data. RESULTS: Behavioral willingness, attitude, and previous AAS use were found as the most important factors in determining the behavioral intention. Moreover, subjective norms, attitude, BMI, and prototypes were the factors with the greatest effect on predicting behavioral willingness of AAS use. As well, behavioral intention was observed to be more important than behavioral willingness for predicting of AAS use. DISCUSSION: The obtained results show that the reasoned action path has a greater impact to predict AAS use among bodybuilders compared to social reaction path. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13102-021-00257-5. |
format | Online Article Text |
id | pubmed-7988984 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-79889842021-03-25 Random forest model to identify factors associated with anabolic-androgenic steroid use Manoochehri, Zohreh Barati, Majid Faradmal, Javad Manoochehri, Sara BMC Sports Sci Med Rehabil Research Article BACKGROUND: One of the types of doping that is commonly used by bodybuilders, is androgenic-anabolic steroids (AAS). The use of AAS besides violating sporting ethics would have serious consequences on physical and mental health statuses. This study aimed to determine the most important factors of using AAS among bodybuilders by prototype willingness model (PWM). METHODS: In this analytical cross-sectional study, 280 male bodybuilders were selected from the bodybuilding clubs in Hamadan city using multistage sampling in 2016. A self-administered questionnaire consisting of demographic information and constructs of the PWM was then used to collect data and random forest model was also applied to analyze the collected data. RESULTS: Behavioral willingness, attitude, and previous AAS use were found as the most important factors in determining the behavioral intention. Moreover, subjective norms, attitude, BMI, and prototypes were the factors with the greatest effect on predicting behavioral willingness of AAS use. As well, behavioral intention was observed to be more important than behavioral willingness for predicting of AAS use. DISCUSSION: The obtained results show that the reasoned action path has a greater impact to predict AAS use among bodybuilders compared to social reaction path. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13102-021-00257-5. BioMed Central 2021-03-23 /pmc/articles/PMC7988984/ /pubmed/33757585 http://dx.doi.org/10.1186/s13102-021-00257-5 Text en © The Author(s) 2021 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Article Manoochehri, Zohreh Barati, Majid Faradmal, Javad Manoochehri, Sara Random forest model to identify factors associated with anabolic-androgenic steroid use |
title | Random forest model to identify factors associated with anabolic-androgenic steroid use |
title_full | Random forest model to identify factors associated with anabolic-androgenic steroid use |
title_fullStr | Random forest model to identify factors associated with anabolic-androgenic steroid use |
title_full_unstemmed | Random forest model to identify factors associated with anabolic-androgenic steroid use |
title_short | Random forest model to identify factors associated with anabolic-androgenic steroid use |
title_sort | random forest model to identify factors associated with anabolic-androgenic steroid use |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7988984/ https://www.ncbi.nlm.nih.gov/pubmed/33757585 http://dx.doi.org/10.1186/s13102-021-00257-5 |
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