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. 2021 Mar 23;13(1):30.
doi: 10.1186/s13102-021-00257-5.

Random forest model to identify factors associated with anabolic-androgenic steroid use

Affiliations

Random forest model to identify factors associated with anabolic-androgenic steroid use

Zohreh Manoochehri et al. BMC Sports Sci Med Rehabil. .

Abstract

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.

Keywords: Androgenic-anabolic steroids; Prototype willingness model; Random forest.

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Conflict of interest statement

The authors report no conflict of interest in the undertaking of this research.

Figures

Fig. 1
Fig. 1
The prototype willingness model. Adapted from Gibbons et al [15].
Fig. 2
Fig. 2
The variables importance based on random forest for prediction of behavioral Intention of AAS use
Fig. 3
Fig. 3
The variables importance based on random forest for prediction of behavioral willingness of AAS use
Fig. 4
Fig. 4
The variables importance based on all factors by random forest for prediction of AAS use
Fig. 5
Fig. 5
Relationship between behavioral willingness and subjective norms (left), Relationship between behavioral intention and behavioral willingness (Right) For subjects with (continuous line) and without currently AAS use (dashed line)

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