Doping can be defined as the occurrence of one or more anti-doping code violations and is usually observed by the presence of a prohibited substance, its metabolites or markers in a biological specimen from an athlete (Sajber et al., 2013). Doping usage in sport is known to be related to negative health-related consequences and even death (Honour, 2016; Mazanov et al., 2012). Additionally, doping corrupts the main essence of sport and fair play and is therefore considered non-ethical behavior (Ljungqvist et al., 2008). As a result, the global fight against doping is highly prioritized in all organized sport societies (Ljungqvist, 2014).
The World Anti-Doping Agency (WADA), a global governing body for anti-doping in sports, has put special efforts into the development and application of differentially targeted approaches in the fight against doping. Generally, two approaches can be recognized in an anti-doping campaign. The first approach includes the development of reliable and applicable measurement tools and protocols that allow the precise identification and consequent penalization of athletes who have resorted to doping (Kiss et al., 2013; Malm et al., 2016). The second approach in global anti-doping efforts is more "preventive" in its nature and includes the identification of the cultural and sport-specific factors that influence doping behavior in each sport society (Fuljan Mandic et al., 2013; Morente-Sanchez et al., 2013; Rodek et al., 2013). The idea is to identify certain precipitating factors of doping behavior in sports and to evaluate the nature of their influence (i.e., risk or protective effects on doping behavior) (Kisaalita and Robinson, 2014).
Several factors have been studied to identify possible associations with doping in sports, including sport-specific factors, socio-demographic variables, socio-cognitive factors (i.e., variables identified throughout the self-determination theory), and motivational variables (Barkoukis et al., 2013; Chan et al., 2015; Kondric et al., 2011; Matosic et al., 2016; Zenic et al., 2010). However, the studies conducted to date suggest that the factors associated with doping behavior (either actual or potential behavior) in one group of athletes (sport, gender, socio-cultural environment) could be differentially associated with doping behavior in other sport-specific groups (Rodek et al., 2013).
The specific influence of precipitating factors on doping behavior in different sports and societies is even more aggravated by the fact that the prevalence of doping is very different in relation to sport, gender, age and athlete (Lentillon-Kaestner and Ohl, 2011). Moreover, the doping intentions of athletes are influenced by distal influences (e.g., self-determination, sportpersonship orientations, and achievement goals), and proximal influences (e.g., situational temptation and perceived behavioral control, descriptive and subjective norms, and attitudes) (Barkoukis et al., 2013; Ntoumanis et al., 2017). Therefore, specific analyses of different sports and socio-cultural environments are needed.
It is widely accepted that characteristic relationships that exist between coaches and athletes should be observed as important determinant of athletes' attitudes toward doping, and the importance of coaches as potential agents in the prevention of doping amongst athletes has been repeatedly emphasized (Backhouse and McKenna, 2012; Barkoukis et al., 2013; Lonsdale et al., 2017). In studies where type of coaching (i.e. style of coaching) was observed as a determinant of athletes' doping-behavior, the authors used the self-determination theory to evidence coaches' personal behavior, and observed the influence of the contextual climate that the coach creates on athletes' motivation and pro-and antisocial behavior, including doping attitudes (Chen et al., 2017; Hodge and Lonsdale, 2011). Indeed, the coaching style (i.e., coaching behavior: autonomy supportive vs. controlling) is an important determinant that can modulate athletes' attitudes (Chen et al., 2017; Hodge et al., 2013). However, behavioral characteristics are partially inherited but are mostly shaped throughout maturation-experience interactions, and each individual (i.e., coach) develops distinct behavioral characteristics as a result of the specific maturational-experimental interactions that influence him/her (Lerner, 2013). Therefore, the possible influence of coaching behavior on athletes' doping tendencies can be used to identify at-risk athletes.
Meanwhile, coaching strategy and training methodology (CS&TM) have not been studied as factors potentially related to doping behavior in athletes. In short, CS&TM can be defined as a set of methods that coaches use throughout the sport training process to improve the athlete's physiological capacities and sport-specific skills. The CS&TM includes (but is not limited to) the application of different types of training, training regimes and methodological/didactical approaches in sport training. Of particular importance is the fact that the CS&TM is modifiable and adaptable (Bompa and Haff, 2009). Therefore, if some aspects of the CS&TM are identified as being related to athletes' doping susceptibility, it would implicate its applicability in anti-doping efforts, either by detecting those athletes who are at certain risk for doping behavior, or throughout instructing the coaches and informing them that certain type of CS&TM is recognized as a risk factor for PDB in athletes.
Despite the fact that the variables of CS&TM could influence doping susceptibility in different sports, this problem is particularly important in individual, mono-structural, cyclic sports, which are performed in systematically controlled environment, such as swimming. Namely, to improve the competitive achievement (sport-result) in swimming, the variables which are controlled throughout the training process are training-volume, training-intensity, and mastering of the specific swimming-skills (i.e. swimming-technique) (Colwin, 2014). While doping in sport is mostly used in order to enhance athletes' physiological capacities (i.e. to overcome physiological stress induced by training volume and intensity, and to boost the mechanism of supercompensation) (Colwin, 2014; Rodek et al., 2013), it is reasonable to expect that the influence of CS&TM on doping susceptibility in swimmers is higher than influence of CS&TM on doping susceptibility in athletes involved in "multifaceted" sports (i.e. sport games, team sports).
The aims of this study were to identify the prevalence of potential doping behavior (PDB) in high-level swimmers and to identify the factors associated with their PDB. We were mainly focused on variables of CS&TM but also studied those factors that were previously reported as being potentially important determinants of PDB in sports The increased knowledge on a problem will allow the development of a meaningful and accurate anti-doping strategy in swimming, Initially, we hypothesized certain associations between CS&TM variables and PDB in swimmers. As a methodological remark, it must be emphasized that this study included practically the whole population of high-level competitive swimmers older than 18 years in a country (see Methods for more details) and therefore allows a substantial generalization of the findings.
The original sample in this study comprised 97 swimmers from Slovenia (35 females; 19.7 [+ or -] 2.3 years of age; 11.3 [+ or -] 3.1 years of experience in swimming sport). All participants were older than 18 years and were tested during the 2017 National Championship. An invitation to participate in the study was sent by the national swimming federation, and none of the athletes refused to participate; therefore, all swimmers who participated in the championship were included. The study was originally initiated and approved by the national swimming federation, complied with all ethical guidelines and received approval from the Institutional Ethics Review Board at the corresponding author's institution (EBO 10/09/2014-1).
Variables and measurement
The previously validated questionnaire on substance use (QSU) was used to test the athletes (Zenic et al., 2010). Additionally, participants were questioned about CS&TM they were exposed to.
The QSU included questions on socio-demographics (age [in years], and gender), sports factors, doping factors and questions on CS&TM. Sport factors were assessed by questions on the (i) athlete's experience in swimming (in years) and (ii) competitive results achieved in (iia) non-Olympic events (25-m pool) and (iia) Olympic events (50-m pool) ("Regional-level medalist", "National championship--finals", "National championship--medal", "European and World Championship--finals", "European and World Championship--medalist", "Olympics"), (iii) preferred style of swimming (i.e., front crawl, butterfly, breaststroke, backstroke, medley), and (iv) competitive discipline in which they mostly compete (i.e., short distance, middle distance, long distance).
Doping-related factors were assessed by asking participants their opinions about (i) the occurrence of doping in swimming ("I don't think doping is used in swimming", "Not sure about it", "Occurs, but rarely", "Doping is often"), (ii) the number of doping tests ("Never tested on doping", "Once or twice", "Three times or more"), and (iii) PDB. The PDB was tested on scale which included four possible answers ("I would engage in doping if it would help me", "I would engage in doping if it would help me with no negative health consequences", "Not sure" and "I do not intend to engage in doping in the future"), but for the purpose of logistic regression analysis the responses on PDB were specifically clustered (see later text on Statistical analyses). This scale was found to be valid in evaluation of PDB in different sports, including tennis, synchronized swimming...