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The use of anabolic-androgenic steroids (AAS) is problematic for youth because of negative effects such as reduced fertility, increased aggression and exposure to toxic chemicals. An effective programme for addressing this problem is Adolescents Training and Learning to Avoid Steroids (ATLAS). This secondary analysis expands prior research by identifying prominent mechanisms of change and highlighting key longitudinal processes that contributed to the success of ATLAS. The current sample consists of high-school football players (N = 1.068; M_age = 15.25) who began ATLAS in grades nine through eleven and participated in booster sessions for two years post-baseline. Knowledge of AAS effects, belief in media ads, reasons not to use AAS, perceived severity of and susceptibility to AAS effects and ability to resist drug offers were critical mediators of the relations between ATLAS and outcomes. Modern applications of the ATLAS programme are also discussed.

Prior mediation analyses

Drawing on the HBM, SLT and TPB, MacKinnon et al. (2001) evaluated 12 mediators targeted by ATLAS on intent to use AAS (see Table 1) and found significant effects for: knowledge of the effects of AAS, ability to turn down offers of drugs, perceived severity of AAS use, perceived susceptibility to the effects of AAS, reasons for using AAS and reasons for not using AAS. MacKinnon and colleagues also examined, whether these variables mediated the healthier alternatives to AAS use presented in the programme: nutrition behaviours and strength training self-efficacy. The significant mediators of the programmes effect on nutrition behaviours were: team as an information source, peers as an information source and belief in media advertisements. The significant mediators of strength training self-efficacy included knowledge of AAS effects, perceived coach tolerance of AAS use, team as an information source, peers as an information source, ability to turn down drug offers, perceived severity of AAS use, perceived susceptibility to AAS effects and reasons for using AAS.

Purpose of the present study

Though isolated effects of the ATLAS programme mediators have been examined (MacKinnon et al., 2001), longitudinal relations among these mediating variables have not been explored. Investigating linkages among these variables would assist in revealing specific paths that yield the most notable influences on intent to use AAS. For example, although ATLAS was based on the HBM, SLT and the TPB, these theories may not have contributed equally to key ATLAS processes. Establishing the longitudinal structure of programme variables may help those who intervene with youth at risk for AAS use by clearly identifying the major mechanisms of intervention (i.e. those mediators that change over several time points and help drive the programmes effects) and understanding how programme effects are likely to play out over the long term (which will assist in planning specific intervention targets and goals for participants). Strategies based on this information could be used to strengthen long-term prevention effects, reduce the likelihood of relapse for former AAS users who enter the programme, and help interventionists make the most of limited financial and other resources in their local schools.

In order to construct a longitudinal mediation model of ATLAS and evaluate, whether the HBM, SLT and the TPB contributed equally to its key processes, the mediators and outcomes in Table 1 must first be ordered according to the HBM, SLT and TPB as illustrated in Figure 1. According to the HBM, knowledge about AAS, specifically pros and cons of use, is the most direct effect of the ATLAS programme. AAS knowledge affects perceived ability to resist using AAS, how common AAS use is perceived to be (norms), how severe the consequences of AAS use are likely to be, and how susceptible someone may be to those consequences. In addition, AAS knowledge informs the performance of nutrition behaviours and awareness of strength training self-efficacy (i.e. in comparing various strategies to improve physique and athletic ability). The ability to resist AAS also impacts perceived severity and susceptibility because if someone is prone to substance use, they are likely not as concerned about the severity of the consequences or susceptibility to those consequences. Norms, skills and knowledge related to nutrition and strength training, and perceived severity and susceptibility also impact the ability to resist AAS in the future. Additionally, perceived severity and susceptibility affect knowledge about AAS pros and cons (i.e. if certain consequences are perceived to be more severe or more likely, participants would be more likely to report them as notable pros and cons of AAS use). Perceived severity and susceptibility, the ability to resist AAS, and pros/cons of using AAS affect AAS intent, strength training self-efficacy and nutrition behaviours.

Using SLT, the most salient direct effect of ATLAS is increased awareness of and attention to social sources of information about AAS (the media, football teammates, knowledge and experience gained from being part of the football team). These information sources affect perceived tolerance of AAS use by the coach, as well as participants’ knowledge of AAS, the pros and cons of AAS use, and AAS use norms (particularly among other members of the football team). Perceived coach tolerance affects AAS norms because if the coach is perceived to be less tolerant of AAS, the players are less likely to use AAS (and vice versa). Knowledge about AAS affects perceived severity and susceptibility and the pros and cons of AAS use. The pros and cons of AAS use also affect perceived severity of and perceived susceptibility to those effects. Norms affect the perceived ability to resist AAS because it may seem more difficult to resist AAS offers if more people within someone’s immediate social circle are also using AAS. Norms, perceived severity and susceptibility, pros and cons of AAS use, and the ability to resist AAS affect AAS intent, strength training self-efficacy and nutrition behaviours.

Based on the TPB, the most salient direct effect of ATLAS would be information about social acceptance of AAS use based on the media, norms, peers, and the football coach. These beliefs and knowledge affect pros and cons of using AAS (because this is the information most likely to be transmitted to potential users), perception of the team and peers as sources of information about AAS (i.e. whether they provide accurate information), perceived peer tolerance of AAS and norms. Pros and cons of AAS use affect perceived severity of and susceptibility to these consequences. Perception of the team and peers as information sources and perceived peer tolerance affect norms, particularly in the participant’s immediate social circle. Norms affect the ability to resist AAS offers. Severity and susceptibility, norms, and the ability to resist AAS affect intent to use AAS, nutrition behaviours and strength training self-efficacy.

Next, the individual HBM, SLT and TPB models of ATLAS in Figure 1 were combined into a comprehensive longitudinal mediation model. This model not only integrates HBM, SLT and TPB but also includes the timing of the variable measurements, which were selected for analysis based on the hypothesized order of processes in these three theories. The process of refining and modifying this model to improve overall fit is described in the Results section.

Design of original ATLAS study

Football players on varsity teams from 34 high schools in the Pacific north-west were recruited for the IRB-approved ATLAS programme. Schools were matched on socio-economic status and win/loss records, then randomized to condition. Three schools dropped out prior to the intervention, resulting in 15 schools in ATLAS and 16 in the control condition. All participating schools received $3000 worth of weight room equipment (Goldberg, Elliot, Clarke, MacKinnon, Zoref, et al., 1996). The experimental schools received 14 sessions scheduled over seven weeks. Seven sessions were devoted to strength training and conducted in the weight room, while the others were delivered in a classroom setting (MacKinnon et al., 2001). Control schools were given a pamphlet about AAS use (Goldberg, Elliot, Clarke, MacKinnon, Moe, et al., 1996).

Data were collected using a self-report questionnaire and anthropomorphic measures (e.g. body fat percentage; MacKinnon et al., 2003). Students in both conditions were measured prior to the start of the football season and immediately after the season. Seniors were measured again in the spring prior to graduation. Non-seniors who remained on the team the following year were measured again before and after the season, during which they received a booster session. Juniors from the first year were measured again in the spring prior to graduating. This progressed for four years. Students who joined the team each year became part of a new cohort (e.g. students who joined the team during the second year of data collection joined the second cohort). Parental consent was obtained prior to enrollment of students in the study.

Data for present study

Only students in Cohort 1 (N = 1.506) were used for the current study, which represents a secondary analysis of the original ATLAS data-set. Seniors from Cohort 1 were removed (N = 438) as they did not have data for the second year, resulting in a final sample size of 1068. Students in the final sample were measured prior to the football season to establish a baseline (zero months), after the season ended (three months), immediately prior to the following season (12 months) during which they received a booster intervention, immediately after the second season (15 months), and a final time at either 21 months for students graduating at the end of the second year or 24 months for non-graduating students (time points combined; 24 months). The final sample (M_age = 15.25; SD = .887) was mostly white (77.8%) and had mothers (67.7%) and fathers (72.7%) with at least a high-school diploma.

Measures

Table 1 contains basic descriptions of all variables used in the current study; information about the overall questionnaire has been provided elsewhere (Goldberg, Elliot, Clarke, MacKinnon, Moe, et al., 1996; Goldberg, Elliot, Clarke, MacKinnon, Zoref, et al., 1996). Multi-item construct scores were obtained using the mean of the item scores with the following exceptions: knowledge of AAS effects, which was scored on a seven-point Likert scale and then recoded into agree/disagree scores for each item and summed, and reasons to use and not to use AAS, which were based on the sum of reasons endorsed. Descriptive statistics and reliabilities for mediators and outcome variables are displayed in Table 2; standardized alphas were used because constructs varied in their number of items and scales (Falk & Savalei, 2011).

Analyses

Models were estimated using Mplus (Version 6.12, Muthén & Muthén, 2011). Missing data were handled using full information maximum likelihood (Enders, 2010). Fit criteria included chi-square goodness of fit (χ²), Bayesian Information Criterion (BIC), Comparative Fit Index (CFI; Hu & Bentler, 1999) and root-mean-square error of approximation (RMSEA). Mediation effects were estimated using MODEL INDIRECT and 95% confidence intervals (CIs) were created using the bias-corrected bootstrap (MacKinnon, 2008) with 1000 resamples. The CLUSTER command in Mplus was initially used to adjust standard errors for nesting of students within schools, but the bootstrapping function cannot be combined with this command. An examination of results with and without the CLUSTER command yielded minimal differences, so these results do not include the CLUSTER command so that bootstrap CIs may be presented.

Overall model fit

Initially, a complex path model based on the theorized relations from the HBM, SLT, and TPB (as displayed in Figure 1) was created. The model fit was somewhat poor, with some disagreements between indices (χ² [302] = 947.844, p < .001; BIC = 85760.031; RMSEA = 0.045; CFI = 0.868). This model was simplified in an attempt to improve fit by removing variables at certain time points that did not contribute substantially to the model (e.g. those that did not relate significantly to at least one outcome variable) and reducing the number of non-significant paths. In total, six variables at two different time points (although they were all represented at other time points in the model) and 38 paths were removed from the model.

The overall fit of the revised model was adequate (χ² [186] = 515.384, p < .001; BIC = 67340.790; RMSEA = 0.041; CFI = 0.904). Modification indices were evaluated to further improve fit. In order to not over fit the model to this sample, only modifications that could be supported by the HBM, SLT- and/or TPB were considered; the theoretical basis for each of the new paths is described in the discussion. Four new paths were added in the final model; no other changes were made between the initial and final model. These added paths are shown in Figure 2, along with a simplified view of only the significant indirect effects from the revised model. Final fit was good (χ² [182] = 450.688, p < .001; BIC = 67303.988; RMSEA = 0.037; CFI = 0.922) and significantly better than the unmodified model (χ²_D [4] = 64.696, p < .001).

Simplified final longitudinal model of the ATLAS programmes effects, with path coefficients and standard errors.

Notes: Variable names correspond to abbreviations in Table 1. Variable numbers correspond to time point (i.e. 0, 3, 12, 15 or 24 months) as shown in Table 2. Also, though not displayed here, all variables occurring at the same time were correlated. Variables measured at 3 months or later were regressed onto prior time points (except baseline; model fit was compared with and without baseline included and no noteworthy differences were observed between the two versions, so baseline measures were omitted) as shown, where applicable. Only the significant indirect effects from the final mediation model are shown to maximize readability of this figure. Dash–dotted lines represent paths added based on modification indices.

According to the final model (see Figure 2), the ATLAS programme increased knowledge and reasons not to use AAS, while reducing belief in media ads at 3 months. These changes predicted increases in perceived severity and susceptibility, as well as an improved ability to resist at 12 months. Additionally, knowledge was related to an increase in strength training self-efficacy at 24 months. Ability to resist and perceived severity and susceptibility at 12 months predicted increases in ability to resist, reasons not to use and perceived severity at 15 months, which in turn were related to a reduction in intent and an increase in self-efficacy at 24 months. Notably, this figure does not represent a modified version of the revised model; rather, for ease of viewing, this figure depicts only the significant indirect effects from the revised model, along with the modification indices that were added to this model to improve fit, as described above.

Indirect effects

Though all paths in Figure 2 are significant, MacKinnon (2008) recommends testing for the significance of indirect effects using a confidence interval around each estimate. The confidence intervals used here were created using the bias-corrected bootstrap procedure because it does not assume a normal distribution of effects, typically has higher power than the percentile bootstrap, and has a low risk of elevated Type I error rate in large sample sizes (i.e. > 500, Fritz, Taylor, & MacKinnon, 2012). The seven significant indirect effects are shown in Table 3. Note that no significant indirect effects were found between the ATLAS programme (at zero months) and nutrition behaviours (at 24 months); this finding is revisited in the discussion.

Five significant indirect effects were found between the ATLAS programme (at zero months) and intent to use AAS (at 24 months). ATLAS group status increased the number of reasons not to use AAS at 3 months, which increased ability to resist drug offers at 12 months, which reduced intent to use AAS (β = −0.005; SE = 0.003; 95% CI = [−0.015, −0.001]). ATLAS increased reasons not to use AAS at three months, which increased reasons not to use AAS at 15 months, which reduced intent (β = −0.011; SE = 0.006; 95% CI = [−0.029, −0.002]). ATLAS also increased knowledge of AAS effects at three months, which increased perceived severity of AAS effects at 12 months, which increased perceived severity at 15 months, which reduced intent (β = −0.005; SE = 0.003; 95% CI = [−0.013, −0.001]). ATLAS increased reasons not to use AAS at three months, which increased perceived severity at 12 months, which increased perceived severity at 15 months, which reduced intent (β = −0.004; SE = 0.003; 95% CI = [−0.014, −0.001]). Finally, ATLAS reduced belief in media ads at three months, which reduced perceived susceptibility at 12 months, which increased ability to resist at 15 months, which reduced intent (β = −0.002; SE = 0.001; 95% CI = [−0.007, −0.001]).

Two significant indirect effects were found between ATLAS and strength training self-efficacy (at 24 months). ATLAS increased knowledge at three months, which increased self-efficacy (β = 0.027; SE = 0.013; 95% CI = [0.008, 0.064]). Also, ATLAS increased knowledge at three months, which increased perceived severity at 12 months, which increased perceived severity at 15 months, which increased self-efficacy (β = 0.002; SE = 0.001; 95% CI = [0.001, 0.007]).

Strengths and limitations

The use of a longitudinal design and inclusion of a strong theoretical framework are the major strengths of this study, although not all variables were available at all time points given the structure of the data. The major weakness of the study is that we utilized secondary data analysis, which necessitates a data-driven model. In addition, intent to use AAS was used in place of actual AAS use as a major outcome variable because actual AAS use was reported by few participants and in small quantities, and intent to use AAS demonstrated a less restricted range of responses. Given the young age of the ATLAS participants, however, intent to use AAS may be a more relevant variable than actual AAS use, particularly from a primary prevention standpoint. Additionally, a potential weakness of the original programme design is that schools that received the intervention received more hands-on training than control schools, which may potentially have positively influenced the success of the programme for those schools that received it.

Implications for prevention

AAS use differs in several ways from the use of other substances and more research and specified preventative efforts for AAS should be generated. However, aside from ATLAS, few programs for AAS have been developed and peer-reviewed (Kuehn, 2009). The present study suggests that AAS prevention may be implemented using a multi-faceted approach: providing accurate information about AAS, addressing popular misconceptions about AAS, teaching skills to help participants resist offers of AAS, allowing participants to evaluate their own perceptions about AAS use, and discussing healthier alternatives to AAS use for improving body image and performance. Findings from the original ATLAS papers and the salient components identified herein may be useful for creating and structuring other prevention programs for youth AAS use.

Since ATLAS was first implemented, the characteristics of AAS use have changed in several ways. Discussion of performance-enhancing substances, including AAS, has become more commonplace with emerging news stories of state-sponsored doping programmes at the 2016 Summer Olympics, along with recent testimonies of AAS use by athletes in the Major Baseball League (MLB) and Tour de France, among other sports leagues and organizations (Momaya, Fawal, & Estes, 2015). The use of these substances has expanded beyond professional athletes, however, and spread more widely to young athletes (like those who participated in the present study), gym members concerned with their physical image, and prisoners (Sjöqvist, Garle, & Rane, 2008). AAS are now widely available over the Internet and can be easily and discreetly obtained, often without much questioning as to whether a physician has authorized the use of AAS; this avenue of purchase may be particularly preferred by youth who are concerned about the social stigma related to buying AAS (Clement, Marlowe, Patapis, Festinger, & Forman, 2012). While, Internet purchases of AAS are convenient, misinformation about the benefits and dangers of AAS use may also be easily disseminated online (Clement et al., 2012). The rise of social media has exacerbated both of these problems by connecting potential AAS users with sellers around the world and allowing for the mixing of facts with dangerous myths. Exposure to sports-, fitness- and body image-related media, which is facilitated by the user-friendly, wide-spread connectivity of social media, has been found to predict AAS use in European youth (Frison, Vandenbosch, & Eggermont, 2013) and more research on the influence of modern technology on adolescent AAS use needs to be conducted with participants from the US. Additionally, as the theories that first inspired the development of the ATLAS programme have evolved, the programme itself would also benefit from evolving further to match them more closely.

Despite the passage of time, however, the ATLAS programme remains very relevant for youth growing up in the current climate of AAS use. One of the programmes key strengths is its focus on both positive and negative aspects of AAS use, and this dual focus allows facilitators to effectively address misinformation about AAS taken from the Internet or social media. As part of the ATLAS curriculum, participants discuss media myths and claims about AAS effects and refer to advertisements containing professional athletes and celebrities as examples. Performance-enhancing substance use in prominent sports leagues and organizations are good examples to use in these discussions because they are recent and likely to generate comments. Based on the importance of HBM in the results of the present study, these types of modules serve an important role in reducing intent to use AAS and promoting healthy alternative behaviours. Additionally, ATLAS and similar programmes can help call adolescents’ attention to real-life models of sports performance, particularly if teammates and coaches can speak personally to their negative experiences with or exposure to AAS use and its dangerous consequences.

These findings, which need to be replicated, suggest that an increase in knowledge of AAS, reduced belief in advertisements, and an increase in reasons not to use AAS are the most direct effects of ATLAS. The programme dispels potential myths about advertisements related to AAS and also provides accurate information about the benefits and consequences of AAS use. Skills training for resisting AAS and discussions about perceived severity and susceptibility to negative consequences of AAS use are also important. AAS use reduction is a good target for further research, and the ATLAS programme provides a good template for the development of new AAS use prevention programmes, particularly if the components highlighted in this study are included. In keeping with the HBM, future research may wish to augment findings from ATLAS by exploring the extent to which youth perceive benefits of and barriers to engaging in alternatives to AAS use, in order to bolster preventative effects of programmes for reducing intent to use AAS. Additionally, as mentioned above, more information about the connection between modern technology (particularly the Internet and social media) and youth AAS use would be beneficial for shaping future applications of ATLAS and similar interventions.

The authors wish to thank Diane Elliot, Linn Goldberg, David MacKinnon, and Esther Moe for providing access to the ATLAS database and feedback on the current project. The authors also thank Lee Cooper for providing comments on the current project.

Funding

This work was supported in part by the National Institute on Drug Abuse (NIDA) [Grant DA 009758-14]. Additionally, funding for open access publication was provided by the Virginia Tech Open Access Subvention Fund.

Amanda Halliburton is a doctoral candidate in the Clinical Science area of the Department of Psychology at Virginia Tech, USA. Her research interests focus on adapting empirically-supported, cognitive-behavioral psychotherapy interventions to better meet the needs of youth, as well as identifying key mechanisms in existing interventions, including prevention programmes.

Matthew Fritz is an assistant professor in the Quantitative, Qualitative and Psychometric Methods Program in the Department of Educational Psychology at the University of Nebraska, Lincoln, USA. His research interests include mediation, longitudinal data and the design and evaluation of prevention interventions.

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