Skip to main content
Download PDF

Androgenic-anabolic steroids use among bodybuilders in western Iran: application of ridge logistic regression model

BMC Sports Science, Medicine and Rehabilitation volume 15, Article number: 7 (2023) Cite this article

Abstract

Introduction

Nowadays, the use of androgenic-anabolic steroids (AAS) by competitive and non-competitive bodybuilders and its side effects have become a major public health problem. Many studies have focused on determining the role and severity of various factors in AAS use, but the existence of collinearity between the factors leads to the non-significance of important factors. The study aimed to determine factors affecting the androgenic-anabolic steroids use in Iranian bodybuilders.

Method

This descriptive-analytical study was performed on 280 male bodybuilders (142 non-competitive and 138 competitive bodybuilders) in Hamadan, west of Iran, in 2016. The participations were recruited a multistage sampling method. A self-administrated questionnaire was used, which included parts such as intrapersonal, interpersonal, and behavioral factors affecting on AAS use. To solve the consequences of collinearity was used ridge logistic regression model (RLRM) in R.3.5.1 software.

Results

The mean age of bodybuilders was 25.21 years (SD = 6.31). The prevalence rate of AAS use among non-competitive and competitive bodybuilders was 27.5% and 34.1%, respectively. Factors such as age, time of starting bodybuilding, attitude, physical self-concept, behavioral intention, coach and friend use AAS, alcohol consumption, and supplement use were associated with AAS use among non-competitive and competitive bodybuilders.

Conclusion

The results of the study indicated that a combination of intrapersonal, interpersonal and behavioral factors was effective on the androgenic-anabolic steroids use among Iranian bodybuilders. Adequate education about the side effects of AAS and improvement of individual skills seem to be helpful in reducing AAS use.

Peer Review reports

Introduction

The widespread use of muscular men in media has challenged the body image of men. Developments in bodybuilding, promotion in bodybuilding contests, and financial rewards in these contests can be very attractive for young men. Such issues have caused high interest in men toward this area, whether for fitness and gaining muscle or preparing for bodybuilding competitions. Thus, some competitive or non-competitive bodybuilders use androgenic-anabolic steroids (AAS) to speed up the muscle-building process [1,2,3,4].

Androgenic-anabolic steroids are classified under different trade names which are synthetic derivatives of the male hormone testosterone. AAS Abuse has become an important public health concern worldwide due to its serious and dangerous side effects. The evidence indicates that infertility, hair loss, pimples, acne, thickening of the voice, heart disease, cancer, and testicular atrophy are reported as the side effects of AAS abuse [5,6,7,8]. The AAS abuse has also led to psychological problems like depression, aggression, anxiety, and sleep disorders [7, 9,10,11]. The results of a meta-analysis (2014) reported that the worldwide prevalence rates of AAS use by athletes and recreational athletes were estimated at 13.4% and 18.4%, respectively [12]. The prevalence rate of AAS use by Iranian bodybuilders has been estimated 32.9%. [13].

The role of intrapersonal, interpersonal, and behavioral factors affecting the androgenic-anabolic steroids use has been documented among bodybuilders [14, 15]. In previous studies, a point that has not been paid attention to is that modeling these factors may have inherent collinearity. Collinearity is defined as the relationship between predictor variables that it has some serious effects such as inflation in the variance of estimators and the non-significance of important variables. On the other hand, the contribution of each predictor variable is not separable due to collinearity [16].

Due to the non-significance of the predictor variables arising from collinearity, some researchers fit a regression model for each predictor variable. Although this seems to eliminate the collinearity, it leads to model underestimation [17]. Because of the inherent collinearity between intrapersonal, interpersonal, and behavioral factors, it is essential to control the consequences of collinearity. In this study, factors affecting the use of anabolic steroids among bodybuilders in Hamadan were determined with an appropriate model.

Methods

Study setting, population, and sampling method

This descriptive-analytical study was performed on 280 male bodybuilders in Hamadan, west of Iran, in 2016. The participations were recruited a multistage sampling method. For this, each geographical region of Hamadan city was considered as a region (three regions). Then, 12 gyms were randomly chosen from each region and from each gym, 25 bodybuilders were selected by simple sampling method. In totally, 300 bodybuilders who 280 bodybuilders completed a questionnaire (Response rate = 93.3%). Inclusion criteria were considered ages > 15 years, attending gyms at least twice a week, and no history of physical or mental disorders. Before filling out the questionnaire, an informed consent form was obtained from bodybuilders. The questionnaires with incomplete information were excluded. In this study, a competitive and non-competitive bodybuilder were defined as a person who participates in bodybuilding competitions and a person who does not participate in any bodybuilding competition, respectively. The study was approved by the Ethics committee of Hamadan University of Medical Sciences, Iran (No. IR.UMSHA.REC.1395.356).

Measurement

A self-administrated questionnaire was used. Detailed information is provided in the Additional file 1.

Statistical methods and software

In this study the collinearity between predictor variables was examined by extracting the condition number. Because the condition number is based on the eigenvalues of the correlation matrix, it gives an accurate view of the collinearity status between the predictor variables. This index is the square root of the maximum eigenvalue divided by the minimum eigenvalue of the correlation matrix. This value was 198, which indicates severe collinearity [18]. In this survey, the response variable was AAS use (yes/no). The ridge logistic regression model (RLRM) was used to solve the consequences of collinearity, such as inflation, in the variance of estimators and non-significance of important variables [19]. Indeed, the RLRM is an extension of the ordinary logistic model. In RLRM, the variance of the estimator remarkably decreases by adding a small bias. Here, all the analyses were conducted with R.3.5.1 software at the significance level of 0.05.

Results

Totally, 142 non-competitive bodybuilders and 138 competitive bodybuilders (113 people at the regional level and 25 people at the national level) participated in this study. Characteristics of participated bodybuilders is reported in Table 1. The mean age of bodybuilders was 25.21 years (SD = 6.31).

Table 1 Characteristics of participated bodybuilders (n = 280)
Full size table

The mean (SD) ages of non-competitive and competitive bodybuilders were 24.88(6.49) and 25.54 (6.21), respectively. The prevalence rates of AAS use in non-competitive and competitive bodybuilders were 27.5% and 34.1%, respectively. Also, the relation between AAS use and bodybuilders' status (competitive and non-competitive) were tested with chi-square (p = 0.232).

Table 2 shows the results of affecting factors on AAS use for non-competitive bodybuilders. These results showed that people who are older are less likely to use AAS (OR = 0.881 [0.856–0.908]; p < 0.001). Also, subjects with more gym history are less likely to use AAS (OR = 0.902 [0.899–0.906]; p < 0.001). BMI and physical self-concept are other factors that have a negative relationship with AAS use. Bodybuilders with more BMI is less likely to use AAS (OR = 0.937 [0.896–0.980]; p = 0.005). This result is also true for physical self-concept (OR = 0.804 [0.776–0.835]; p < 0.001). On the other hand, the odds of AAS use increase by 16% in people who have a more positive attitude (OR = 1.164 [1.117–1.213]; p < 0.001).

Table 2 Affecting factors on AAS use for non-competitive bodybuilders
Full size table

Bodybuilders with higher behavioral intentions have 19% higher odds of taking AAS (OR = 1.190 [1.127–1.257]; p < 0.001). When the coach is an AAS user, the odds of taking AAS for bodybuilders are 2 times (OR = 2.012 [1.561–2.671]; p < 0.001). In addition, for bodybuilders whose friends take AAS, the odds of taking AAS increase by 2.3 times (OR = 2.332 [1.815–2.998]; p < 0.001). Consumption of alcohol and supplements by bodybuilders increases the chance of steroid use by 82% and 70% (OR = 1.827 [1.323–2.525]; p < 0.001, OR = 1.702 [1.312–2.209]; p < 0.001). Moreover, bodybuilders who smoke are 2.1 times more likely to use steroids (OR = 2.120 [1.407–3.192]; p < 0.001).

Table 3 provides the results affecting factors on AAS use for competitive bodybuilders People who are older or have longer time to start bodybuilding is higher are less likely to use AAS (OR = 0.927 [0.641–1.344]; p < 0.001, OR = 0.913 [0.847–1.416]; p < 0.001). Also, bodybuilders who have higher Physical self-concept have fewer odds to use AAS (OR = 0.798 [0.151–4.225]; p < 0.001). For people whose scores of attitude, behavioral intention, and desire are higher, the odds of using AAS increases by 10%, 24%, and 13%, respectively (OR = 1.108 [0.270–4.550]; p < 0.001, OR = 1.245 [0.216–7.180]; p < 0.001, OR = 1.132 [0.582–2.200]; p < 0.001).

Table 3 Affecting factors on AAS use for competitive bodybuilders
Full size table

The odds of using AAS is 70% higher in bodybuilders whose coach also uses AAS (OR = 1.749 [1.334–2.292]; p < 0.001). The odds of taking AAS is 2.8 times higher for those whose friend uses AAS (OR = 2.820 [0.983–8.129]; p < 0.001). Subjects who use alcohol or supplements have 2, and 2.2 times more likely AAS use (OR = 2.045 [1.789–2.336]; p < 0.001, OR = 2.256 [1.376–3.695]; p < 0.001, respectively).

Tables 4 and 5 show the result of RMLM for competitive bodybuilders at the national and regional levels. According to Table 4, national-level bodybuilders who are older are less likely to use AAS. Also, at this level of bodybuilders, people who smoke or take supplements are less likely to use AAS (OR = 0.307 [0.148–0.634]; p = 0.001, OR = 0.562 [0.328–0.964]; p = 0.036, respectively). By increasing BMI, the odds of steroid use increase by 14% (OR = 1.148 [1.019–1.293]; p = 0.023).

Table 4 Affecting factors on AAS use for national level competitive bodybuilders
Full size table
Table 5 Affecting factors on AAS use for regional level competitive bodybuilders
Full size table

Based on Table 5, in competitive bodybuilders at the regional level, older subjects have less likely to use AAS (OR = 0.952 [0.918–0.988]; p = 0.009). Also, people who have been doing bodybuilding for a longer period of time are less likely to use AAS (OR = 0.932 [0.928–0.935]; p < 0.001). At this level of bodybuilders, when physical self-concept or behavioral willingness is higher, the odds of steroid use are lower (OR = 0.809 [0.771–0.848]; p < 0.001, OR = 0.872 [0.805–0.945]; p = 0.001, respectively). People who have a higher score in attitude and behavioral intention increase the odds of using AAS by 11% and 25% (OR = 1.113 [1.062–1.165]; p < 0.001, OR = 1.250 [1.159–1.349]; p < 0.001, respectively).

The coach's use of steroids and alcohol consumption for these bodybuilders increase the odds of using AAS by 39% and 86% (OR = 1.394 [1.059–1.835]; p = 0.018, OR = 1.862 [1.162–2.985]; p = 0.010, respectively). When a bodybuilder’s friend uses AAS or the bodybuilder takes supplements, the odds of using AAS increases by 2.8, and 2.6 times (OR = 2.842 [2.147–3.762]; p < 0.001, OR = 2.636 [1.919–3.621]; p < 0.001, respectively).

Discussion

This study investigated the factors affecting AAS use in non-competitive and competitive bodybuilders. The prevalence rate of AAS use among non-competitive and competitive bodybuilders was 27.5% and 34.1%, respectively,

although the relationship between AAS use and the bodybuilders' status (competitive and non-competitive) was not significant. In the available evidence, more AAS use is seen in both competitive bodybuilders [20] and non-competitive bodybuilders [21]. In our study, about one-third of competitive and non-competitive bodybuilders used AAS. It seems that only competitive goals can't justify the use of AAS. Adolescents' and young adults' attention to body shape and body image through increasing muscle mass has been documented, so that some of these people believe that boys/men with bigger muscles are more attractive [22]. The evidence indicates that the reasons for the use of AAS could be the pressure of the press to compete and win, positive attitude towards doping to achieve success, people's unrealistic expectations from competitions, the pressure of coaches, the lack of monitoring of consumption, the competitive personality of the bodybuilder, attracting the attention of the spectators of sports competitions, the heavy schedule of a bodybuilder's competitions, increasing strength, muscle mass, and endurance pointed out [23,24,25].

The ridge logistic regression was applied with respect to severe collinearity between predictors. This study showed that younger bodybuilders and those with less experience in the gym were more likely to use steroids in both groups of bodybuilders, which was consistent with Wichstrom [26]. The possible reason for this result could be that with increasing age and more experience using the gym, the intensity of emotions decreases and the person understands the facts more than in the past.

An inverse relationship was found between BMI and AAS use in non-competitive bodybuilders, which confirms the results of the study by Mattila et al. [27]. It is possible that non-competitive bodybuilders attend the gym to build muscles and may use AAS to speed up the process.

The present study revealed a positive association between AAS use with intention and attitude toward AAS use in both groups. Also, AAS use was associated with a lower physical self-concept in both groups. This finding supports in the other studies [28,29,30,31]. Positive intention and attitude towards an issue usually lead to doing that behavior, and a negative physical self-concept may cause a person haste in muscle building. For competitive bodybuilders, there was a relationship between behavioral willingness and AAS use. It may be because using steroids is not banned for bodybuilders in competitions hence, they easily show a desire to use.

The use of AAS by coaches and friends has a positive effect on bodybuilders' AAS use. This association was observed in both competitive and non-competitive bodybuilders. These results support previous research findings [32, 33] and can be attributed to the fact that the coach usually gives the bodybuilding exercise program and consultation, and may also sell these steroids. On the other hand, compliments are common in Iranian culture and a bodybuilder may be tempted to receive it by a close friend. Additionally, a friend has the role of a training opponent who is to be beaten by the bodybuilder.

Among behavioral factors, alcohol and supplement use were positively associated with AAS use. This relationship was seen in both groups of bodybuilders. In accordance with the present results, previous studies have demonstrated a positive relationship between alcohol and supplement consumption and AAS use [34,35,36,37]. Smoking had a positive effect on steroid use among bodybuilders which the previous study has reported such results [38].

Limitations

The results of the current study should be interpreted in light of some limitations. First limitation was related to cross-sectional design and observed associations did not show the causality. Another limitation of our study was that data was obtained using self-reported data that may be associated with social desirability bias and due to the risky nature of the behavior of using androgenic-anabolic steroids, some bodybuilders may refuse to tell the truth. Finally, the study was conducted only among male bodybuilders. So, the generalizability of the results is limited because our sample consisted of men.

Conclusions

The results of the study indicated that a combination of intrapersonal, interpersonal and behavioral factors was effective on the androgenic-anabolic steroids use among Iranian bodybuilders. Adequate education about the side effects of AAS and improvement of individual skills seem to be helpful in reducing AAS use.

Availability of data and materials

The analyzed dataset in this study is available from the corresponding author on reasonable request.

References

  1. Bateman O, Žeželj D. Steroid solidarity: the culture of juicing at the Mr. Olympia competition. Va Q Rev. 2017;93(3):60–72.

    Google Scholar 

  2. Pereira E, Moyses SJ, IgnÁCio SA, Mendes DK, Silva DSDA, Carneiro E, et al. Prevalence and profile of users and non-users of anabolic steroids among resistance training practitioners. BMC Public Health. 2019;19(1):1650.

    Article  CAS  Google Scholar 

  3. Perry PJ, Lund BC, Deninger MJ, Kutscher EC, Schneider J. Anabolic steroid use in weightlifters and bodybuilders: an internet survey of drug utilization. Clin J Sport Med. 2005;15(5):326–30.

    Article  Google Scholar 

  4. Zahnow R, McVeigh J, Bates G, Hope V, Kean J, Campbell J, et al. Identifying a typology of men who use anabolic androgenic steroids (AAS). Int J Drug Policy. 2018;55:105–12.

    Article  Google Scholar 

  5. Sekuła MJ, Świerczyńska B, Smoluchowski K, Undziakiewicz A, Piecewicz-Szczęsna H. Hepatotoxicity of anabolic androgenic steroids in sport. J Educ Health Sport. 2020;10(9):349–56.

    Article  Google Scholar 

  6. Torrisi M, Pennisi G, Russo I, Amico F, Esposito M, Liberto A, et al. Sudden cardiac death in anabolic-androgenic steroid users: a literature review. Medicina. 2020;56(11):587.

    Article  Google Scholar 

  7. van Amsterdam J, Opperhuizen A, Hartgens F. Adverse health effects of anabolic–androgenic steroids. Regul Toxicol Pharmacol. 2010;57(1):117–23.

    Article  Google Scholar 

  8. Vorona E, Nieschlag E. Adverse effects of doping with anabolic androgenic steroids in competitive athletics, recreational sports and bodybuilding. Minerva Endocrinol. 2018;43(4):476–88.

    Article  Google Scholar 

  9. Angoorani H, Halabchi F. The misuse of anabolic-androgenic steroids among Iranian recreational male body-builders and their related psycho-socio-demographic factors. Iran J Public Health. 2015;44(12):1662.

    Google Scholar 

  10. Börjesson A, Möller C, Hagelin A, Vicente V, Rane A, Lehtihet M, et al. Male anabolic androgenic steroid users with personality disorders report more aggressive feelings, suicidal thoughts, and criminality. Medicina. 2020;56(6):265.

    Article  Google Scholar 

  11. Havnes IA, Jørstad ML, Wisløff C. Anabolic-androgenic steroid users receiving health-related information; health problems, motivations to quit and treatment desires. Subst Abuse Treat Prev Policy. 2019;14(1):1–12.

    Article  Google Scholar 

  12. Sagoe D, Molde H, Andreassen CS, Torsheim T, Pallesen S. The global epidemiology of anabolic-androgenic steroid use: a meta-analysis and meta-regression analysis. Ann Epidemiol. 2014;24(5):383–98.

    Article  Google Scholar 

  13. Safari Tekyeh S, Yousefi B, Astinchap A. Anabolic steroids consumption prevalence and awareness of their side effects among male body builders in Iran: a meta-analysis. J Appl Health Stud Sport Physiol. 2018;5(2):1–12.

    Google Scholar 

  14. Saeidinejat S, Hooshmand E, Zahra H, Najar AV. Evaluating the pattern of anabolic androgenic steroid use and its relation with mental health of male members of bodybuilding Clubs of Iran, in 2015. Asian J Sports Med. 2018;9(1):e60164.

    Google Scholar 

  15. Uddin Z, Iqbal Q, Haider S, Saleem F. Usage and perceptions of anabolic-androgenic steroids among male gym attendees in Quetta city, Pakistan–a descriptive analysis. Res Pharmacy Health Sci. 2019;5(2):152–7.

    Article  Google Scholar 

  16. Dormann CF, Elith J, Bacher S, Buchmann C, Carl G, Carré G, et al. Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography. 2013;36(1):27–46.

    Article  Google Scholar 

  17. Hoerl AE, Kennard RW. Ridge regression: biased estimation for nonorthogonal problems. Technometrics. 1970;12(1):55–67.

    Article  Google Scholar 

  18. Midi H, Sarkar SK, Rana S. Collinearity diagnostics of binary logistic regression model. J Interdiscip Math. 2010;13(3):253–67.

    Article  Google Scholar 

  19. Schaefer RL. Alternative estimators in logistic regression when the data are collinear. J Stat Comput Simul. 1986;25(1–2):75–91.

    Article  Google Scholar 

  20. Steele I, Pope H, Ip EJ, Barnett MJ, Kanayama G. Is competitive body-building pathological? Survey of 984 male strength trainers. BMJ Open Sport Exerc Med. 2020;6(1):e000708.

    Article  Google Scholar 

  21. Evans NA. Gym and tonic: a profile of 100 male steroid users. Br J Sports Med. 1997;31(1):54–8.

    Article  CAS  Google Scholar 

  22. Nilsson S, Baigi A, Marklund B, Fridlund B. The prevalence of androgenic-anabolic steroids by adolescent in country of Sweden. Eur J Public Health. 2001;11:195–7.

    Article  CAS  Google Scholar 

  23. Arazi H, Salehi A. Attitude and awareness of male bodybuilders toward adverse effects of anabolic-androgenic steroids in Qazvin, Iran. Iran J Epidemiol. 2014;9(3):45–53.

    Google Scholar 

  24. Halabchi F. Doping in athletes. Hakim. 2007;10(1):1–12.

    Google Scholar 

  25. Parkinson AB, Evans NA. Anabolic androgenic steroids: a survey of 500 users. Med Sci Sports Exerc. 2006;38(4):644–51.

    Article  CAS  Google Scholar 

  26. Wichstrøm L. Predictors of future anabolic androgenic steroid use. Med Sci Sports Exerc. 2006;38(9):1578–83.

    Article  Google Scholar 

  27. Mattila V, Rimpelä A, Jormanainen V, Sahi T, Pihlajamäki H. Anabolic-androgenic steroid use among young Finnish males. Scand J Med Sci Sports. 2010;20(2):330–5.

    Article  CAS  Google Scholar 

  28. Al-Falasi O, Al-Dahmani K, Al-Eisaei K, Al-Ameri S, Al-Maskari F, Nagelkerke N, et al. Knowledge, attitude and practice of anabolic steroids use among gym users in Al-Ain district, United Arab Emirates. Open Sports Med J. 2008;2:75–81.

    Article  Google Scholar 

  29. Bahrke MS, Yesalis CE, Kopstein AN, Stephens JA. Risk factors associated with anabolic-androgenic steroid use among adolescents. Sports Med. 2000;29(6):397–405.

    Article  CAS  Google Scholar 

  30. Goulet C, Valois P, Buist A, Côté M. Predictors of the use of performance-enhancing substances by young athletes. Clin J Sport Med. 2010;20(4):243–8.

    Article  Google Scholar 

  31. Shahhosseini M, Noury GR. Comparing body image, self-esteem and gender roles In the bodybuilder anabolic-androgenic steroid (AAS) users and nonusers. Sports Psychol. 2016;1(1):19–29.

    Google Scholar 

  32. Chng CL, Moore A. A study of steroid use among athletes: knowledge, attitude and use. Health Educ. 1990;21(6):12–7.

    Article  Google Scholar 

  33. Sepehri G, Fard MM, Sepehri E. Frequency of anabolic steroids abuse in bodybuilder athletes in Kerman city. Addict Health. 2009;1(1):25.

    Google Scholar 

  34. Brower KJ. Anabolic steroid abuse and dependence. Curr Psychiatry Rep. 2002;4(5):377–87.

    Article  Google Scholar 

  35. Hildebrandt T, Harty S, Langenbucher JW. Fitness supplements as a gateway substance for anabolic-androgenic steroid use. Psychol Addict Behav. 2012;26(4):955.

    Article  Google Scholar 

  36. Ip EJ, Trinh K, Tenerowicz MJ, Pal J, Lindfelt TA, Perry PJ. Characteristics and behaviors of older male anabolic steroid users. J Pharm Pract. 2015;28(5):450–6.

    Article  Google Scholar 

  37. Wright S, Grogan S, Hunter G. Motivations for anabolic steroid use among bodybuilders. J Health Psychol. 2000;5(4):566–71.

    Article  CAS  Google Scholar 

  38. Nakhaee MR, Pakravan F, Nakhaee N. Prevalence of use of anabolic steroids by bodybuilders using three methods in a city of iran. Addict Health. 2013;5(3–4):77–82.

    Google Scholar 

Download references

Acknowledgements

The authors are grateful to all of the bodybuilders who participated in this study.

Funding

This study was supported by the Vice Chancellor for Research and Technology of Hamadan University of Medical Sciences financially (No. 9804253413).

Author information

Authors and Affiliations

  1. Department of Biostatistics School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran

    Sanaz Khalili

  2. Social Determinants of Health Research Center, Hamadan University of Medical Sciences, Hamadan, Iran

    Sahar Khoshravesh

  3. Autism Spectrum Disorders Research Center, Hamadan University of Medical Sciences, Hamadan, Iran

    Majid Barati

  4. Department of Biostatistics School of Public Health, Modeling of Noncommunicable Diseases Research Center, Hamadan University of Medical Sciences, Hamadan, Iran

    Hossein Mahjoub & Javad Faradmal

Authors
  1. Sanaz Khalili
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sahar Khoshravesh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Majid Barati
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hossein Mahjoub
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Javad Faradmal
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Study design: SK, JF, HM, Data acquisition, analysis and interpretation: SK, SK, MB, JF, Writing of the first draft: SK, SK, and JF. Revising first draft for important intellectual content: SK, SK, MB, HM and JF. All the authors have read and approved the final version of the manuscript.

Corresponding author

Correspondence to Javad Faradmal.

Ethics declarations

Ethics approval and consent to participate

The study was approved by the Ethics committee of Hamadan University of Medical Sciences, Iran (No. IR.UMSHA.REC.1395.356). The informed consent form was obtained from all bodybuilders. All methods were carried out in accordance with the Declaration of Helsinki and relevant regulations.

Consent for publication

Not applicable.

Competing interests

The author declares that they have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1:

 Measurement details.

Rights and permissions

Open Access This 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.

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Khalili, S., Khoshravesh, S., Barati, M. et al. Androgenic-anabolic steroids use among bodybuilders in western Iran: application of ridge logistic regression model. BMC Sports Sci Med Rehabil 15, 7 (2023). https://doi.org/10.1186/s13102-023-00616-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s13102-023-00616-4

Keywords

  • Bodybuilder
  • Steroid
  • Collinearity
  • Ridge logistic regression model

BMC Sports Science, Medicine and Rehabilitation

ISSN: 2052-1847

Contact us