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Performance Enhancement & Health xxx (2015) xxx–xxx

Contents lists available at ScienceDirect

Performance Enhancement & Health

journa l homepage: www.elsevier.com/locate/peh

Perceptions of assisted cognitive and sport performance enhancement

among university students in England

a,∗ a a,1 a,1

Elisabeth Julie Vargo , Ricky A. James , Kofi Agyeman , Thomas MacPhee ,

a,1 a,1 a,b

Ross McIntyre , Flaminia Ronca , Andrea Petróczi

a

Kingston University, Faculty of Science, Engineering and Computing, United Kingdom

b

University of Sheffield, Department of Psychology, United Kingdom

a

r t i c l e i n f o a b s t r a c t

Article history: There has been an ongoing research effort to understand the morality of athletes using prescription and

Received 18 January 2014

illicit drugs to enhance sporting performance. By comparison, perceptions around the ethics of university

Received in revised form 13 February 2015

students using prescription drugs to enhance academic performance (known as cognitive enhancement

Accepted 15 February 2015

or neuroenhancement) are less well understood. This study compared how university students responded

Available online xxx

to the ethical considerations of using performance enhancing substances across sporting and academic

contexts. A total of 98 participants from universities in the United Kingdom completed a Brief Implicit

Keywords:

Association Test, a brief version of the Performance Enhancement Attitude Scale, an explicit cognitive

Doping

Neuroenhancement enhancer attitude assessment and reported their views on four scenarios regarding sports doping and

the use of cognitive enhancers by university students. The implicit association did not show a significant

Moral attitudes

Students polarisation of students’ moral attitudes. Explicit measures showed a stronger disagreement towards

Zero sum game doping behaviours. Those professionally involved in sport found chemical enhancement more accept-

able than other respondents, suggesting an instrumental viewpoint and a transfer of social knowledge

from one domain of drug use to the other. Participants perceived the use of enhancers in sport and edu-

cation as “cheating” when it affected others, but believed cognitive enhancement could be necessary

due to competitiveness of the job market. Results suggest that chemical enhancement was considered

acceptable by some student groups. The proportion of the sample knowing someone who used cognitive

enhancers (13%) or someone who doped (19%) suggests that substance based performance enhancement

may be normalising and increasing in popularity.

© 2015 Elsevier Ltd. All rights reserved.

1. Introduction the use of prescription drugs to improve cognitive capacity) in the

absence of any medical need is only the most recent addition to

There is a growing debate around the widespread use of drugs the already extensive array of drugs that enhance human perfor-

to enhance physical performance and cognitive capacity, including mance or experience (Franke et al., 2013; Møldrup, Traulsen, &

the use of prescription drugs beyond therapeutic use (Møldrup & Almarsdóttir, 2003; Savulescu, ter Meulen, & Kahane, 2011). Even

Rie Hansen, 2006; Petersen, Nørgaard, & Traulsen, 2014; Smith & though such “academic doping” is by no means new, the side effects

Farah, 2011). The pressure arising from the real or perceived need arising from the unsupervised use of powerful new amphetamines,

for performance excellence can lead to using artificial enhance- narcoleptics and analeptics present a significant threat to individual

ment (McVeigh, Evans-Brown, & Bellis, 2012). Emerging evidence and public health. Given the prominent role of ethicality in models

suggests that using “neuroenhancement” (a term utilised to define of athlete doping and anti-doping interventions (Miah, 2006), ethi-

cality may prove a viable basis for interventions designed to control

the health threat posed by misuse or abuse of substances to improve

academic performance (Cakic, 2009; Outram & Racine, 2011). The

∗

Corresponding author at: Kingston University, Faculty of Science, Engineer-

current study therefore explores how university students construct

ing and Computing, Penrhyn Road, Kingston upon Thames, Surrey KT1 2EE, United

the ethicality of using prescription drugs to enhance academic per-

Kingdom. Tel.: +44 (0)20 8417 9000.

formance in relation to the ethicality of using substances to enhance

E-mail address: [email protected] (E.J. Vargo).

1

These authors contributed equally. performance in sport.

http://dx.doi.org/10.1016/j.peh.2015.02.001

2211-2669/© 2015 Elsevier Ltd. All rights reserved.

Please cite this article in press as: Vargo, E. J., et al. Perceptions of assisted cognitive and sport performance enhancement among

university students in England. Performance Enhancement & Health (2015), http://dx.doi.org/10.1016/j.peh.2015.02.001

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2 E.J. Vargo et al. / Performance Enhancement & Health xxx (2015) xxx–xxx

1.1. Cognitive enhancement in academia performance enhancing drugs is strictly regulated by the World

Anti-Doping Agency (WADA) which defines doping as contrary to

With the rise in university enrolments and increasing univer- fair play and to the virtues of sport. Despite the intensified effort to

sity tuition fees (Hübner, 2012), students in many post-industrial curb doping use in sport, a concerning level has been documented

societies endure more pressure to perform well, aiming for high in and outside WADA auspices (e.g., Dimeo & Taylor, 2013; Pitsch &

marks in response to the increased competitiveness of the gradu- Emrich, 2012). Similar to neuroenhancement, doping in sport raises

ate job market. These shifts in the academic environment have led issues regarding functionality for performance enhancement and

to a reported rise according to frequent media reports, of “smart ethicality in competitive contexts.

drug” use among students who wish to optimise academic per-

formance (Forlini & Racine, 2009; Partridge, Bell, Lucke, Yeates, & 1.2. Performance enhancers in sport

Hall, 2011). The evidence suggests that students use these sub-

stances to increase studying periods and levels of concentration, General attitudes towards doping have been extensively

and decrease anxiety (DeSantis, Webb, & Noar, 2008; Judson & researched (Sjöqvist, Garle, & Rane, 2008; Stamm, Lamprecht,

Langdon, 2009; Rabiner et al., 2009). It has also been evidenced that Kamber, Marti, & Mahler, 2008; Yager & O’Dea, 2014). The most

students attempt to self-medicate the lack of sleep through these commonly identified motives for taking performance enhanc-

substances (Wolff & Brand, 2013). Neuroenhancement appears to ing substances are mainly related to external pressures (Bilard,

be correlated with faculty of study, attitude and the use of other Ninot, & Hauw, 2011; Curry & Wagman, 1999; Pappa & Kennedy,

substances (Mazanov, Dunn, Connor, & Fielding, 2013). 2013; Singhammer, 2013) and a desire to win (Baron, Martin, &

Neuroenhancing drugs act on a variety of neurotransmitter Magd, 2007; Lucidi et al., 2008). Taken together, available research

systems and appear to be able to enhance cognition, mood and suggests that doping is used as a way to cope with training and com-

pro-social behaviour (De Jongh, Bolt, Schermer, & Olivier, 2008). petition demands, as well as recover from injury quickly and more

Nonetheless, their efficacy in enhancing overall memory and intel- efficiently. In this sense, doping in the athletic domain could be seen

lectual performance is yet to be established, and side effects can as a means to cope with environmental demands, presenting sim-

be detrimental to the individual’s health and psychological well- ilarities to students’ motivations related to using neuroenhancers.

being (Farah, Smith, Ilieva, & Hamilton, 2014). Misuse or abuse of Athletes often acquire performance enhancing substances via the

prescription medication can be very dangerous and is an ongoing black market, thus the health risks of their conduct can be even

challenge for public health. Inappropriate use of these compounds more critical and unpredictable (Paoli & Donati, 2014).

can impair cognitive function and cause substance dependency; the Prevalence rates emerging across samples and methods (e.g.,

side effects of long-term use are not yet fully understood (Sahakian James, Nepusz, Naughton, & Petroczi, 2013; Mottram, 2005; Pitsch

& Morein-Zamir, 2011). & Emrich, 2012) indicate higher rates of doping than official

Stimulants like Ritalin, a drug normally prescribed to attention records of adverse analytical findings suggest (approximately 2%;

deficit hyperactive disorder patients, are estimated to be used by WADA, 2013). Furthermore, the Athlete Biological Passport (ABP)

5–35% of the student population in the United States (DeSantis has shown an estimated average of 14–19% blood dopers among

et al., 2008; Wilens et al., 2008). While a low prevalence of use track and field athletes (Sottas et al., 2011), suggesting a consider-

(1.3%) was observed among German students (Franke et al., 2011), able discrepancy between doping prevalence rates based on direct

the same study reported 80% of participants stating that they evidence and the ABP. However, prevalence rates can only be inter-

would consider using these stimulants. Swiss university students preted in the contexts in which the information is obtained. Often,

had experience with neuroenhancement but only 4.1% reported the target populations vary in sporting levels and investigations

methylphenidate (Ritalin) use, finding that a significant propor- lack a uniformly accepted definition of what constitutes doping

tion of students felt neuroenhancement was acceptable as long as (Lentillon-Kaestner & Ohl, 2011).

it served performance related (as opposed to “recreational”) goals Many athletes do not consider taking performance enhancers

(Maier, Liechti, Herzig, & Schaub, 2013). Neuroenhancer use has as deceitful and believe these are necessary to compete, regardless

also been observed among Australian university students (Mazanov of health consequences (Curry & Wagman, 1999; Kayser & Broers,

et al., 2013; Partridge, Lucke, & Hall, 2012), although these students 2013; Morente-Sanchez & Zabala, 2013). Research on doping has

were concerned about possible side effects and the drugs’ effective- often focused on attitudes of elite athletes and suggests that moti-

ness in improving grades. In a study surveying UK students (Singh, vations tied to initiating or maintaining doping use are extremely

Bard, & Jackson, 2014), less than 10% reported lifetime prevalence, diverse (Bloodworth & McNamee, 2010; Kirby, Moran, & Guerin,

but one third expressed an interest in experimenting with neu- 2011; Lentillon-Kaestner & Carstairs, 2010; Overbye, Knudsen, &

roenhancement. One university student newspaper has reported Pfister, 2013). Regarding stimulants in particular, it is suggested

that 20.5% of a convenient sample of local students has used cog- that athletes consider them as “performance enablers”, as they are

nitive performance enhancing drugs, and 54% indicated that they required to maintain homeostasis during prolonged and intense

would use stronger substances than coffee or energy drinks if such training (Bilard et al., 2011). Athletes believe hard work alone

substances were available to them (Ibrahim, 2012). To date, no epi- is insufficient when competing against someone who is doping

demiological study has comprehensively examined and compared (Maycock & Howat, 2005). Considering the social and economic

prevalence rates. impact of the sports enterprise, doping and anti-doping attempts

Despite students’ willingness to try these stimulants (Forlini & are to be considered a public issue (Kayser, Mauron, & Miah,

Racine, 2009) is associated with the belief that they are not dan- 2007). Athletes are often confronted with a competitive environ-

gerous (De Santis et al., 2009), side and long term effects are still ment which enables the functionality of doping, thus interventions

of concern (Forlini & Racine, 2012). Due to the strong contrast in based on morality and ethicality do not appear to successfully

responses (Sattler, Forlini, Racine, & Sauer, 2013) and the high vari- contain the phenomenon (Kayser & Broers, 2013; Petróczi, 2013a,

ability in prevalence rates, more research is needed to understand 2013b).

these differences. Although the non-prescribed use of neuroen-

hancers is – in most cases – illegal and students are obtaining 1.3. Comparing cognitive enhancers to performance enhancers

these drugs from the black market (Greely et al., 2008), no regu-

lations exist regarding their use in academia (Coenen, Schuiff, & Neuroenhancement is a relatively new phenomenon, and peo-

Smits, 2014). Conversely, in the sporting environment the use of ple may not have a ready-formed social representation unless they

Please cite this article in press as: Vargo, E. J., et al. Perceptions of assisted cognitive and sport performance enhancement among

university students in England. Performance Enhancement & Health (2015), http://dx.doi.org/10.1016/j.peh.2015.02.001

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have personal experiences or have been in close contact with these 2. Aims

drugs via friends and family. Research into cognitive enhancing

drugs typically anchors the investigation to an established line of The aim of the present study was to explore university stu-

research, comparing it directly to illicit drugs (Svetlov, Kobeissy, & dents’ implicit and explicit moral mental representations relative

Gold, 2007) or doping (Bell, Partridge, Lucke, & Hall, 2013; Dodge, to neuroenhancement and doping in sports. Students’ attitudes

Williams, Marzell, & Turrisi, 2012). These comparisons inevitably were explored in a multidimensional manner. The scarcity of stud-

provide contextual valence, as exemplified by the use of terminol- ies which explore attitudes towards neuroenhancement, as well

ogy like “brain doping” (Dietz, Striegel, et al., 2013), “dope” (Maier as the public concern expressed on regulating their use, make this

et al., 2013), “smart drugs” or “viagra of the brain” (Lucke, Bell, exploration vital and necessary.

Partridge, & Hall, 2011). Yet they are nonetheless relevant, as they A brief Implicit Association Test (b-IAT) (Sriram & Greenwald,

provide insight on how representations from one drug domain may 2009) served for the exploration of implicit moral attitudes. This

be transferred to others, both in scientific terms as by general social instrument measures participants’ automatic responses to focal

knowledge (Petróczi, Mazanov, & Naughton, 2011). These ready- categories, providing insight of their associative preference (Sriram

made social representations can then directly influence drug use & Greenwald, 2009). In previous research, IATs have contributed

trajectories as well as the interventions and approaches aimed at to understanding athletes’ decision-making examining the role of

reducing and controlling them. attitudes in doping (Brand, Melzer, & Hagemann, 2011; Petróczi,

In an attempt to investigate attitudes towards neuroenhance- Aidman, & Nepusz, 2008).

ment through a comparison with doping in sport, Dodge et al. The empirical hypotheses for the present study were as follows:

(2012) explored how individuals judge others who use enhancers in

H1. There will be a stronger acceptance of neuroenhancent in

either the athletic or the academic sector by applying the zero-sum

comparison to doping on all attitude measures utilised (IAT, explicit

and non-zero sum paradigm (Goodman, 2010). Goodman (2010)

attitude scales and scenario evaluations).

defines zero sum tasks as either/or situations where there is a win-

ner and one or many losers. Non-zero sum tasks on the other hand H2. Young students with athletic experience will show more pos-

describe situations where success is independent from others’ per- itive attitudes towards performance enhancing in sports than their

formance. Through this differentiation Dodge et al. (2012) explain non-athletic counterparts.

why their sample judged steroid use as more immoral: academic

situations are primarily considered non-zero sum (e.g., an exam- 3. Method

ination), while sporting competitions are seen as zero sum (e.g.,

a tournament). However, the wording of the scenarios differed 3.1. Sample characteristics

importantly, as the term “effort” was mentioned only in the cog-

nitive enhancer scenario, possibly leading participants to assume 3.1.1. Demographics

that the use of anabolic steroids required less or no effort (Dodge Ninety-eight students (mean age = 24 ± 5.98 years, 60.2% male)

et al., 2012). were recruited from universities in England. In the sample, 12.2%

Although the goal to pursue can influence views of performance had no sporting experience, 31.6% practiced sports recreationally

enhancing, Dodge et al.’s study (2012) still leaves issues unresolved. and 27.6% belonged to a local or university sports club, whereas

It is still possible to utilise neuroenhancement and doping for 17.3% practiced sport at a semi-professional level, and 11.2%

both zero-sum and non-zero tasks. Moral standards co-exist with practiced sports at a professional level. The distinctions in sport

other decision-making processes which may rationalise assisted involvement were based upon the level of financial support avail-

performance enhancement as a functional adaptation (Petróczi, able, according to the information provided by the participants

2013a). Implicit attitudes can also be seen as key variables in during the compilation of the questionnaire. Professional athletes

predicting intentions, as they play an important role in deter- receive a salary for doing sport whereas those classed as semi-

mining subjective mental representations of these practices (De professionals only have their expenses covered or receive a nominal

Houwer, 2006). Indirect reaction-time based attitude tests, such as amount for playing or participating in competitions.

the implicit association test, have been used to explore attitudes

towards doping (Brand, Heck, & Ziegler, 2014; Petróczi, 2013b) 3.1.2. Prevalence and visibility

but no studies have yet used these instruments to explore atti- Participants were asked to provide information relative to cog-

tudes relative to neuroenhancement. Previous research indicates nitive enhancer and steroid use of their close friends. The study

that the general population takes a predominantly negative view of sample was asked to differentiate between close friends who they

doping (Backhouse & McKenna, 2011; Solberg, Hanstad, & Thøring, believed were using these substances and reporting the number of

2010) while those from sporting contexts show a tendency to those they knew for certain were using them. Regarding steroid

view performance enhancers as necessary (Mazanov, Hemphill, use, 24.5% (n = 4 females and n = 20 males) participants estimated

Connor, Quirk, & Backhouse, 2014; Pappa & Kennedy, 2013). Atti- that at least one of their close friends was using this performance

tudes towards cognitive enhancers remain controversial and still enhancer, while 19.4% declared that their friends’ steroid use was

poorly researched (Bostrom & Sandberg, 2009; Savulescu et al., known to them (n = 1 female and n = 18 males).

2011). When enquiring about cognitive enhancer use, 17.3% (n = 2

Neuroenhancement in academia and doping in sports may females and n = 15 males) participants estimated that at least one

draw back on similar psychological representations based on the of their friends was utilising these substances. The number of par-

need to excel (Farah et al., 2014; Wolff & Brand, 2013). The drug ticipants who declared to be sure of this conduct among their

instrumentalization theory (Müller & Schumann, 2011) explores friends was 13.3% (n = 2 females and n = 11 males). Furthermore,

non-addictive drug use through individuals’ expectations, where 6.1% participants (n = 2 females and n = 4 males) of the study sample

drug use represents a functional adaptation to modern environ- indicated that their close friends were using both of these sub-

ments based on previous learning mechanisms. In a similar manner, stances.

neuroenhancement may represent a functional means to achieve The proportion of athletes knowing at least one steroid user

a highly valued end, or an instrument utilised as a coping strategy increased with the level of sport involvement. Estimations of

(Hildt, Lieb, & Franke, 2014; Ilieva & Farah, 2013; Wolff & Brand, neuroenhancement were fairly similar across sporting levels. The

2013). overall estimated level of neuroenhancement arising from the

Please cite this article in press as: Vargo, E. J., et al. Perceptions of assisted cognitive and sport performance enhancement among

university students in England. Performance Enhancement & Health (2015), http://dx.doi.org/10.1016/j.peh.2015.02.001

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Table 1

chosen as target words were synthetic compounds (see Table 1).

Brief IAT category labels and stimuli.

The attribute categories were “Ethical” and “Unethical” with uneth-

Targets Attributes ical being the non-focal category in analysis (Sriram & Greenwald,

2009). Using a validated algorithm, the result is presented as a D-

Cognitive enhancer Physical enhancer Ethical Unethical (non-focal)

score, which is the difference between mean latencies of two b-IAT

Adderall Deca-durabolin Fair Dishonest

blocks (“Ethical” with either “performance enhancers” or “cognitive

Ritalin Nandrolone Correct Unjust

enhancers”) divided by the inclusive standard deviation of these

Dexedrine Winstrol Moral Immoral

Modafinil Anabol Truthful Cheating two latencies (Greenwald, Nosek, & Banaji, 2003). A participant’s

preference to doping as being more ethical was indicated by a posi-

tive D-score, while preference to cognitive enhancers was indicated

sample was commensurate to the informally reported 20.5% by

by a negative score.

Ibrahim (2012) in a comparable setting.

3.3.2. Explicit measures

3.2. Procedure

3.3.2.1. Attitudes towards neuroenhancement and doping. Explicit

attitudes towards doping and neuroenhancement were measured

The study was conducted between February and July 2013 in

via 16 questions: eight questions were selected from the PEAS

South East England and was approved by the Kingston Univer-

(Petróczi & Aidman, 2009) while the remaining eight were related

sity Faculty of Science, Engineering and Computing Research Ethics

to neuroenhancement in education. Three statements of the Cog-

Committee. Participants were students recruited within public

nitive Enhancer Attitude Assessment (CEAA) did not provide

spaces of university campuses and asked if they were willing to

satisfactory validity after factor analysis and were excluded from

participate in a study regarding doping and cognitive enhancers.

further data analyses.

Volunteers were then asked to take three sets of assessments on a

The full version of the PEAS (Petróczi & Aidman, 2009) is a val-

portable computer: (a) b-IAT (Sriram & Greenwald, 2009) which is

idated attitude scale which has been used to assess views towards

based on the association of cognitive enhancers or performance

sports doping use in various populations (e.g., professional athletes;

enhancers with ethical or unethical words, (b) a 16-item ques-

university students). Items 2, 5, 6 and 7 of the short version of the

tionnaire assessing explicit attitudes towards doping in sports and

PEAS overlapped with an alternative 6-item short version used by

neuroenhancement in education, and (c) four vignettes, two of

Elbe and Brand (2014). The CEAA items were created by adapting

which described doping in sports (Appendix A Scenarios 1 and

PEAS statements, in particular by replacing doping related termi-

2) and the other two neuroenhancement in educational contexts

nology with neuroenhancement related terms. This adaptation is

(Appendix A Scenarios 3 and 4). Socio-demographic data were

similar to Wolff and Brand’s (2013) previous method of assessing

also collected. The researchers assisted and provided guidance to

attitudes relative to neuroenhancement. Participants marked their

participants performing the different tasks; testing resulted in a

level of agreement to each question on a 6-point Likert-type scale

procedure lasting approximately 20 min. In order to avoid con-

(from Strongly Disagree to Strongly Agree). Psychometric properties

textual influences (Payne & Gawronski, 2010), participants were

for the short version of PEAS (PEAS-S) and CEAA are reported in

asked to complete the implicit task first, followed by the explicit

measures. Section 4. The items used are provided in Table 2. Response scores

to the PEAS-S and the CEAA were added for each scale separately.

Personal information was not disclosed. Recruited individuals

were fully informed of the study’s objectives prior to partaking in

the test and participation was voluntary. All participants provided

3.3.2.2. Scenarios. The study used four vignettes (Appendix A)

implied consent via the completion of the test and survey.

which were systematically constructed to represent a 2 × 2 factorial

design contrasting neuroenhancement to doping in the presence

3.3. Measures

or absence of a zero-sum game. The scenarios were differentiated

by the effect that the main character’s drug using conduct had

3.3.1. Implicit measure

on others, on the basis of Dodge’s zero sum and non-zero sum

The integration of indirect implicit measures to obtain infor-

task differentiation (Dodge et al., 2012). In other words, one set of

mation relative to subconscious attitudes through an indirect

scenarios depicted the use of these substances in a competitive con-

association task was desirable for several reasons. Participants

text, while the second set of scenarios regarded solely the effect that

could respond to explicit measures with socially desirable answers

the enhancer use had on the person taking them (non-competitive

(Nosek, 2007). Moreover, this allows a representation of partici-

scenarios). Besides requiring participants to give their opinion on

pants’ mental associations outside conscious awareness, enabling

the intensity of this effect, and their level of agreement to the char-

a different measure of attitude that parallels explicit measures (De

acter’s decision, an open question was also provided for qualitative

Houwer, 2006). A b-IAT (Sriram & Greenwald, 2009) was used to

data collection. Participants chose from a 5-point (No Affect to Major

assess implicit moral attitudes. The b-IAT has two blocks of trials

Affect) and a 6-point Likert-type scale (Strongly Disagree to Strongly

with the same mappings as the standard IAT but with one third the

Agree) for the first two questions. All four scenarios depicted male

number of trials. Validation studies demonstrate this approach has

protagonists. This would guarantee that gender affiliations do not

satisfactory validity and can be used to explore implicit attitudes

influence participants’ preferences, but inevitably depicts doping

while dramatically reducing testing times (Sriram & Greenwald,

and neuroenhancement as a male only practice and detracts from

2009). The b-IAT measures the amount of time that participants

face validity. Implications of this methodological approach are elab-

utilise to sort words into categories. If there is a perceived conflict

orated in Section 5.

between a word and a category it will take slightly longer to sort,

thus the fastest category would be the preferred association.

Doping and neuroenhancement “target words” are shown in 3.3.2.3. Software. The b-IAT was set-up using the desktop appli-

®

Table 1. Compounds used as target words were chosen to avoid cation of the Inquisit software. The online software program

overlapping of stimulants from one domain of artificial enhance- (www.surveymonkey.com) utilised for test administration pre-

ment to the other, as some substances may be used for both sented the attitude scales and scenarios in a randomised manner.

cognitive and physical performance enhancement. All substances Statistical analyses were performed using IBM SPSS v19 and AMOS

Please cite this article in press as: Vargo, E. J., et al. Perceptions of assisted cognitive and sport performance enhancement among

university students in England. Performance Enhancement & Health (2015), http://dx.doi.org/10.1016/j.peh.2015.02.001

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Table 2

a

Factor loadings of the PEAS-S and CEAA.

Items Component

1 2 3

PEAS-S1 There is no difference between drugs, fibreglass poles, and speedy swimsuits that are all used to enhance performance. .844 .213 .085

PEAS-S2 The risks related to doping are exaggerated. .808 .205 .127

PEAS-S3 Legalising performance enhancements would be beneficial for sports. .801 .237 .082

PEAS-S4 Doping is not cheating since everyone does it. .798 .330 .246

PEAS-S5 Athletes should not feel guilty about breaking the rules and taking performance-enhancing drugs. .791 .314 .103

PEAS-S6 Only the quality of performance should matter, not the way athletes achieve it. .782 .259 .123

PEAS-S7 Doping is necessary to be competitive. .734 .264 .287

PEAS-S8 Doping is unavoidable part of the competitive sport. .644 .193 .113

CEAA1 Cognitive enhancers are needed to attain a high mark in exams. .271 .814 .038

CEAA2 In order to get consistently good grades cognitive enhancers are necessary. .347 .753 .185

CEAA3 Taking cognitive enhancers is not cheating as students in exams are not directly competing against others. .288 .740 .009

CEAA4 Cognitive enhancers can take the pressure off of exams. .431 .643 −.056

CEAA5 By taking cognitive enhancers less time can be spent revising. .386 .588 −.027

COMP1 Performance enhancers have a more negative effect than cognitive enhancers. .021 .754 .036

COMP2 Taking cognitive enhancers are on par with doping in sport. .137 .030 .889

COMP3 Athletes taking steroids to enhance performance is the same as students taking Ritalin to get better grades. .260 .014 .848

Values in bold illustrate the factor structure of the final attitude measure, showing that items 1 to 8 (PEAS-S1–PEAS-S8) load on component 1 and items 9 to 13 (CEAA1–CEAA5)

load on component 2.

a

Extraction method: principal component analysis. Rotation method: Varimax with Kaiser normalisation. Kaiser–Meyer–Olkin measure of sampling adequacy = 0.872;

2

Bartlett’s test of sphericity approx. (120) = 930.201, p < 0.001.

v19. Raw data from the open questions were inserted and codified Group comparisons were performed using student t-test or one

using Atlas.ti 7 software. way/factorial Analysis of Variance (ANOVA) followed by Tukey’s

Honestly Significant Difference (HSD) post hoc test, with level of

significance set at 0.05 for all analyses. Examination of interac-

3.4. Data analysis tion effects using factorial ANOVA between gender and sporting

level found no interactions. Scenario ratings were compared

3.4.1. Quantitative analyses using Kruskal–Wallis (H) and Mann–Whitney (U) tests. When the

Exploratory factor analysis for the PEAS-S and CEEA used Mann–Whitney U was used for post hoc testing, the significance

ˇ

principal component analysis with Varimax rotation for clear sep- level was adjusted using Sidàk’s (1967) correction to control for

aration. The structural integrity of the PEAS-S was confirmed Type I error.

using confirmatory factor analysis with chi-square statistics for

overall fit and selected fit indices. The incremental fit indices

were selected based on the comprehensive guide by Kenny 3.4.2. Qualitative analysis

(http://davidakenny.net/cm/fit.htm). For assessing incremental fit, Data deriving from open questions were analysed by a single

both Comparative Fit Index (CFI) and Tucker Lewis Index (TLI) val- researcher through Grounded Theory (GT) methodology (Strauss &

ues were reported. This promotes parsimonious models through Corbin, 1990). GT uses inductive-deductive interpretation for the

imposing a penalty for each added parameter. CFI is more com- analysis of qualitative raw data. Theory was discovered through

monly used in literature and is more lenient in terms of penalty than the data and the researcher’s categorisation of key points, creat-

TLI. Root Mean Square Error of Approximation (RMSEA), which is ing a circular process. The researcher organised concepts emerging

routinely included for confirmatory factor analysis as an indicator from the participants’ responses into codes. These present a level

of absolute measure of fit, was also reported with a note that this of groundedness (prevalence) which provides information relative

measure has a tendency for being positively biased in cases of small to how common a social representation is in the study sample’s

df and N (Kenny, Kaniskan, & McCoach, 2014). narrations. Qualitative data analysis software (Atlas.ti) aided this

Following Tabachnick and Fidell (2000), parametric assump- process through the count of frequencies and the identification of

tions were tested using Shapiro–Wilk’s statistics for normality co-occurrences of codes. Concepts and their categories were also

and Levene’s test for homogeneity of variances. Normality Q–Q organised in conceptual networks which contributed to the inter-

plots, along with skewness >|2| and kurtosis values >|2|, were pretation of the responses.

also examined (standard errors (SE) reported in square brac-

kets). Of the three key outcome variables, the b-IAT D-score

clearly met all parametric assumptions (Shapiro–Wilk’s = 0.978, 4. Results

p > 0.125; Levene statistics = 1.424, p = 0.232; skewness = −0.047

[SE = 0.254]; kurtosis = 1.045 [SE = 0.503]). The explicit measures 4.1. Psychometric properties of the brief PEAS and CEAA

diverged but violations of the parametric assumptions were

not serious (attitude towards doping: Shapiro–Wilk’s = 0.878, Confirmatory factor analysis provided reassurance that the

p < 0.001; Levene statistics = 0.422, p = 0.792; skewness = 0.353 shortened version of the PEAS maintained a unidimensional struc-

[SE = 0.254]; kurtosis = −0.064 [SE = 0.503]; attitude towards cog- ture with strong standardised regression weights (>0.64), and good

nitive enhancement: Shapiro–Wilk’s = 0.966, p < 0.018; Levene model fit after allowing correlation between the error terms of

2

statistics = 2.639, p = 0.039; skewness = 1.297 [SE = 0.254]; kurto- items 2 and 8. Overall, the CFA results showed good fit ( = 35.416,

2

sis = 1.541 [SE = 0.503]). Acknowledging that psychological vari- df = 19, p < 0.012; = 1.864, RMSEA = 0.094 (null model = 0.381), p

ables are seldom distributed normally in the population (Micceri, of close fit < 0.071, CFI = 0.968, TLI = 0.939). The reliability coefficient

1989), we concluded that parametric methods were more robust of the PEAS-S was excellent (Cronbach alpha = 0.926). After exclud-

and offered better statistical power over their non-parametric vari- ing three items from analysis, the initial psychometric properties of

ants. the CEAA provided reassurance for construct validity and reliability

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Fig. 1. Box-plot of the participants’ D scores obtained on the brief IAT (A). Histogram of the D scores (B) where positive scores indicate preference for performance enhancers,

while negative scores indicate preference for cognitive enhancers.

Table 3

4.3.1. Doping attitude

Mean attitude scores (±SD) by gender and sporting levels; normalised for the scale.

Participants could achieve a minimum score of 8 and a maxi-

PEAS-S CEAA mum of 48 on the PEAS-S (scoring: from 8 to 14 strongly disagree,

Gender from 15 to 21 disagree, from 22 to 28 slightly disagree, from 29 to

Male 2.636 ± 1.234 3.014 ± 1.064 35 slightly agree, from 36 to 42 agree, and from 43 to 48 strongly

±

±

Female 1.780 0.413 2.316 0.959 agree). The study population’s average score fell within the “Dis-

Sporting levels

agree” range; the average score was 18.473 ± 8.612 (2.309 ± 1.076

Professional 3.264 ± 1.491 3.325 ± 1.063

standardised for the 6-point scale). Gender differences were sta-

Local/university 2.417 ± 0.990 3.000 ± 1.038

tistically significant (t(88) = −3.853, p < 0.001, d = −0.842). Females’

Amateur 1.842 ± 0.844 2.557 ± 1.090

±

±

±

Recreational 2.329 1.121 2.740 1.022 average score was 14.242 3.307 (Strongly Disagree) while males’

None 1.885 ± 0.515 1.945 ± 0.854

average score was 21.088 ± 9.869 (Disagree). There was a notable

difference in response distributions between males and females,

with scores for females tightly clustered around strong disagree-

with good internal consistency (Cronbach alpha = 0.851) and clear

ment and a wide dispersion in agreement among males.

separation from the PEAS-S (Table 2).

When observing attitudes in relation to sporting experience,

strong differences were evident as well (F(4,88) = 3.315, p < 0.014,

4.2. Brief IAT 2

= 0.131). Post hoc comparisons using the Tukey HSD test indi-

cated that the mean score of professional athletes was significantly

Results from the b-IAT presented a distribution tending to neu-

different to that obtained from the subgroup with no sporting

tral (Fig. 1A). Similar error rates and normality in the distribution

experience (p < 0.025) and for the semi-professional subgroup

of D-scores give confidence that familiarity to category items did

(p < 0.012). The most tolerant subsample was the professional

not interfere with the associative task. Fig. 1B shows that 59%

sport players with a mean score of 26.111 ± 11.932 (Slightly Dis-

of the study sample preferred associating cognitive enhancers

agree). Remaining average scores were: local or university sport

with ethical words (negative score), while 41% showed a prefer-

players 19.333 ± 7.923 (Disagree), semi-professional sport players

ence for associating physical performance enhancers with ethical

14.733 ± 6.756 (Disagree), recreational 18.633 ± 8.969 (Disagree)

words (positive score). The overall average of the b-IAT scores was

and no sporting experience 15.083 ± 4.122 (Disagree).

−

0.069 ± 0.282. The majority of the study sample’s D-scores fell

very close to neutrality in preference.

4.3.2. Neuroenhancement attitude

Comparing D-scores of those involved in sport to those with

Participants could achieve a minimum score of 5 and a maxi-

no sporting experience did not reach statistical significance

mum of 30 on the CEAA (scoring: from 5 to 9 strongly disagree,

2

(F(4,93) = 0.504; p = 0.733; = 0.021), although it is likely to be the

from 10 to 13 disagree, from 14 to 17 slightly disagree, from 18 to

function of the small sample size, not the complete lack of differ-

21 slightly agree, from 22 to 25 agree, and from 26 to 30 strongly

ence. The effect size (Cohen’s d = 0.33 after considering the unequal

agree). The average was equivalent to disagreement but somewhat

sample sizes) suggests the presence of a small to medium effect.

less strongly compared to the doping assessment (13.808 ± 5.333;

2.762 ± 1.066 standardised for the 6-point scale). Results were

4.3. Explicit attitudes

not significantly different between males and females: scores

were equivalent to “Disagree” and “Slightly Disagree”, respectively

Explicit attitude scales presented a scenario where sport-

(females 12.059 ± 5.093; males 15.017 ± 5.290).

ing experience determined more tolerant views towards both

In addition to what was initially hypothesised, sporting experi-

neuroenhancement and doping. To facilitate direct comparison,

ence also led to more tolerant views towards neuroenhancement

attitudes scores normalised for the respective scales are provided 2

(F(4,29) = 3.326, p = 0.014, = 0.130). Post hoc comparisons using

in Table 3.

the Tukey HSD test indicated that the mean scores of professional

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and local/University athletes were significantly different to those

obtained from the subgroup with no sporting experience (p < 0.011 (.002) (.085) (.091) (.484)

(<.001) (<.001) (<.001) (<.001) (.940)

(<.001)

and p < 0.037, respectively). Participants with no sporting experi-

.176 .320 .173 .072

± .390 11 .008 .456 − − − .667 − –

ence showed the strongest disagreement (9.727 4.268; Strongly 476 .442

Disagree). Remaining averages were: recreational 13.581 ± 5.071

(Disagree), semi-professional 13.062 ± 5.247 (Disagree), local or

university sport activity 15.000 ± 5.189 (Slightly Disagree), and (.204) (.004) (.054) (.232) (.873) (.534) (.289) (.612) (.951)

professional 17.333 ± 5.408 (Slightly Disagree).

.197 .123 .006 .017 .291 .053 .134 .064 .109 10 – − − − − −

4.3.3. Scenarios

As can be observed in Fig. 2A and B, the highest percent-

age in Scenario 1 (depicting doping in a competitive context) (<.001) (<.001) (.031) (<.001) (<.001) (<.001) (.016) (.578)

corresponded to major effect on others (77.6%), with the major- .058 .222 .367 .247 .565 .476 .564 .495

ity of participants strongly disagreeing to James’ decision (54%). – 9

− − − −

For Scenario 2 (describing doping in a non-competitive context),

35.7% of the participants believed that Steve’s decision had no

effect on others, although the majority disagreed with his deci- (.138) (<.001) (.255) (.658) (.308) (.500) (.987)

sion (Fig. 2B). For Scenario 3 (neuroenhancement for competitive

.070 .155 .046 .399 .105 .120 .002 reasons), 58.2% of the participants believed that Dave’s decision 8 –

− − −

had a major effect on others, and the majority also disagreed with

him. The overall majority of participants for Scenario 4 (cognitive

enhancement in a non-competitive context) believed there to be no (<.001) (.005) (<.001) (<.001) (156) (.718)

affect (40.8% no affect, 22.4% minor affect) and participants’ agree-

ment/disagreement to Martin’s decision was evenly distributed. .286 .037 .337 .146 .557 .605

– 7

− −

Fig. 2C and D describes trends from Scenarios 3 and 4.

When analysing differences between subsamples, similarly

to the previous assessments, sporting experience was the pri- (.068) (.084) (.861) (.042)

(.965)

mary independent variable influencing participants’ views. Steve’s

decision to use steroids in a non-competitive context resulted .004 .177 .208

.018 .192 6 – −

− −

in a statistically significant difference (H = 17.971, p < 0.001). The

mean score of professional athletes (M = 3.500 slightly disagree,

SD = 1.650) was significantly different to scores obtained from stu-

(<.001) (<.001) (<.001) dents with no sporting experience (M = 1.450 strongly disagree, (.684)

SD = 0.688). When observing neuroenhancement, Dave’s decision .042 .498 .482

– 5

− −

to use Ritalin in a competitive scenario presented H = 14.117,

p < 0.007. Post hoc tests evidence that local/university athletes’

scores (M = 3.556 slightly agree, SD = 1.577) were significantly

(<.001) .590

different to those obtained from participants with no sporting (.170)

experience (M = 2.000 disagree, SD = 1.095). Martin’s decision to use .141 190 (.068) .365

4 − −

Ritalin to pass a test was evenly approved by the different subcate- −

gories of the study sample (H = 6.417, p > 0.170). Steve’s steroid use

for aesthetic reasons provoked larger agreement on the part of male pairs.

measures.

participants (U = 601.00, p < 0.001), but no other gender differences (<.001) (.808)

were observed. bivariate .025 .665

– 3

4.4. Relationships between measures other

enhancement

all

As can be observed in Table 4, there were strong correlations for (.647)

r

between explicit attitude measurements. Significant and fairly cognitive .048

strong positive correlation patterns were found between the agree- – 2

−

ment/disagreement scenario items and attitudes towards sport and

and cognitive enhancement. The consistently inverse relationship Spearman’s

between the items for effect on others and the level of agreement 1–3;

to the protagonist’s choice suggests that acceptance of neuroen-

1 2 3 4

and hancement is contextually situated. This observation is in line with (CEAA) (PEAS-S)

performance-

othersothers others others –

Sattler et al.’s (2013) investigation of contextual factors influenc- -score) 2–3 the

D on on on on (

ing cognitive enhancement, which noted a tendency on the part of attitude attitude Scenario Scenario Scenario Scenario 1–2, performance

students to rationalise neuroenhancement as a means for “catching to to to to of

effect effect effect effect

between significance. up” with naturally high performers. This notion also emerged from 1 2 3 4

attitude sport cognitive

the competitive scenarios and from the qualitative data. Athletic (1 pairs

and 2) and cognitive (3 and 4) scenarios also showed significantly for

r

Implicit Explicit Explicit Scenario Agreement Scenario Agreement Scenario Agreement Scenario Agreement enhancement

enhancement

strong correlations between each other, evidencing homogeneity statistical

coefficients

name

in the participants’ views towards these two achievement contexts.

The implicit measure was independent from all explicit 4 Pearson’s

denotes :

1 3 4 8 6 7 5 9 measures. This was predictable as the psychological construct 2 10 Variable 11 Table Note

Correlation

measured implicitly was different from the explicit measures. It Bold

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Fig. 2. Left: participants’ responses to the question ‘How much do you believe this affects others?’ in sport (A) and in academic (C) settings. Right: participants’ disagree-

ment/agreement to the character’s decision from the sport (B) and in academic (D) scenarios.

is possible though that the b-IAT is not an attitude measure but as doping is common in sports environments (“Drugs are available

perhaps a reflection of familiarity and unfamiliarity (Petty, Brinol,˜ to all, most top high athletes use it, to compete you have to, however

& DeMarree, 2007; Petty, Brinol,˜ & Johnson, 2012). It may be that it is not a magic pill”).

participants associated doping with negative moral attributes Steve’s use of steroids to improve his physical appearance on

which were available to them through social knowledge. Neuroen- the other hand, was not seen as immoral behaviour. However, the

hancement, being a relatively new phenomenon, may not yet be majority of participants disapproved this conduct, as it showed a

stored with moralistic evaluative labels. lack of confidence and superficiality on the part of the main char-

acter (“He is not really affecting anyone, although he is still silly for

4.5. Students’ perceptions of the vignettes taking it”). Disapproval was reinforced by beliefs relative to the neg-

ative effects of steroid use on an individual’s wellbeing (“It has a lot

Coding and interpretation of participants’ comments regarding of nasty effects on his health and potentially can have deadly conse-

the four hypothetical scenarios provided qualitative information quences due to its side effects”), or the concern that it may provoke in

which enriched the understanding of the quantitatively assessed loved ones. A smaller but significant amount of responses declared

attitudes. Analysis of responses was divided into two primary units that Steve’s choice regarded an individual’s personal choices and

(doping and neuroenhancement). Codification of doping scenarios values.

provided 16 different conceptual categories (a total of 199 quo- Martin’s decision to take Ritalin in order to pass an exam was

tations), with Steve’s scenario (Scenario 2) provoking the most viewed by the majority of participants as a justifiable action, being

diversity in responses. Regarding the neuroenhancement scenar- an attempt to help himself. “He has paid a lot to do his degree, so

ios, a total of 14 codes were identified (136 quotations), but this if he feels he needs to take cognitive enhancers to help concentrate

unit possessed a higher density of co-occurring concepts. and smash his exams, why not, it is logical”. A smaller but notable

James’s decision to use steroids in a competitive context number of quotations referred to this behaviour as being deceitful

provoked the most polarised views, as a consistent number of and wrong, often justifying by mentioning health consequences,

participants justified their responses describing his behaviour as or that the drug was not prescribed (“If it were necessary to take

“wrong” or “cheating”. One participant declared, “It’s not fair. By these he should have consulted his GP who would have prescribed him

cheating he is cheating himself and creating unfair advantage. It is a something appropriate for his condition”).

lazy way to win and an unsatisfying way to win!” Many participants Dave’s use of Ritalin to achieve an interview was consistently

suggested that James did not respect the rules of competition, it seen as cheating by the study sample. Nonetheless, this form of

was not “natural” or that his actions would negatively influence cheating was viewed differently from a sports competition, as this

other team mates’ confidence. An opposing point of view emerging context does not present a set of predetermined rules. One partic-

from the responses believed that this behaviour was not cheating, ipant stated, “When you look at it in this scenario, it highlights how

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wrong and unfair it actually is, sports is one thing but for a professional were dichotomous. This conduct was either considered “immoral”

role or career it’s just wrong”. Nonetheless, many participants still and “wrong”, or essential in contemporary competition for a “level

did not consider it as completely unethical, and sometimes neces- playing field”. Steroid use in a non-competitive context was also

sary as the job market is very competitive (“I can understand how it’s poorly tolerated, as the students considered the risks and bene-

unfair to others, but I would do it, it is a competitive working market, so fits of the behaviour. Neuroenhancement in a competitive context

you need to be different”). Health consequences were considered a solicited a general disagreement, yet concerns regarding the com-

concern as well. Interestingly, both of the neuroenhancement sce- petitiveness of the job market led to an increased diversity in

narios presented very little quotations (3 in total) referring to this opinions. This theme became more evident when participants

practice as “unnatural”, contrary to the doping scenarios. judged the use of Ritalin for academic success, as a significant

amount of individuals expressed agreement to the non-competitive

use of these enhancers. These results are in line with Rudski’s

5. Discussion (2014) observation that a competitive framing influences attitudes

towards neuroenhancing drugs. In a non-competitive situation

In order to explore students’ moral attitudes and represen- “smart drugs” can have a compensatory value for students’ emo-

tations relative to neuroenhancement, three sets of assessments tional distress (Vrecko, 2013).

comparing this practice to sports doping were utilised. Both quan- Norms and stereotypes are available to this population in

titative and qualitative data were considered in analysis, enabling relation to doping, and many students embrace the internation-

a multidimensional investigation of participant attitudes. It should ally promoted “zero tolerance” attitude towards this conduct

be noted that a significant proportion of participants declared to (Vangrunderbeek & Tolleneer, 2011). This view seems to be shared

have close friends which used performance and neuroenhancing by individuals with little sporting experience and females. While

substances (19% and 13%, respectively). These contextual data open moral values can influence attitudes towards neuroenhancement,

up the possibility that chemical enhancing practices may be quite contextual demands have a greater impact on students’ views. Par-

common within the population assessed. ticipants agreed more to Scenario 4, where Martin uses cognitive

The b-IATs used in this study explored participants’ preference enhancers to gain unfair advantage. This scenario concerned a topic

in associating doping and neuroenhancement related terms to eth- that these students were preoccupied with (i.e., success in finding a

ical or unethical words. This assessment indicated a low level of job). Also, regular sports players regarded doping as tolerable, and

polarisation in attitudes towards these forms of artificial enhance- appeared to transfer this instrumental attitude from one domain

ment, although there was a slight preference towards viewing (doping) to another (neuroenhancement).

neuroenhancement as more ethical. A larger sample size may have Overall results showed more negative attitudes towards doping

seen a stronger polarisation. Although the b-IAT did not correlate in sports than towards neuroenhancement. Males may be at risk for

to the explicit scales, experts of the instrument (Nosek et al., 2007) the initiation of these conducts (Byrnes, Miller, & Schafer, 1999), as

demonstrate that correlation between implicit and explicit meas- they have more lenient attitudes. A potential confounding factor of

ures of attitudes is not always obvious. As in the case of attitudes this study could be the students’ presumed lack of knowledge and

that are infrequently considered or unimportant to the partici- experience regarding neuroenhancement. Besides indirect preva-

pant, it is expected to find weak correlations between implicit lence data, the analysis of qualitative data clearly indicates that

and explicit attitude measurements (Nosek, 2007). The mental pro- students of the study sample were well aware of this practice, and

cesses involved in these two types of measures are related, but they the more negative doping attitudes were enforced by general social

represent two distinct constructs (Nosek, 2007). beliefs. An implementation of the project could explore the influ-

The explicit attitude scales (PEAS-S and CEAA) identified impor- ence of gender bias on attitudes. The vignettes used in this study

tant differences between subgroups of the study sample. In general, utilised only male protagonists. Depicting female or gender neu-

participants disagreed with using chemical enhancement, although tral protagonists (e.g., as in Huybers & Mazanov, 2012) may have

doping was viewed slightly more negatively. Males showed higher rendered different representations relative to the ethicality of phar-

agreement to performance enhancing practices, a tendency in line macological enhancement. This potential gender effect needs to be

with literature suggesting that males are prone to taking more risks explored in future experimental studies.

and less concerned about negative health consequences, especially Although this study cannot be considered representative due

during young adulthood (Baron et al., 2007). Sporting experience to the limited sample size and the limitations relative to the par-

was the primary independent variable influencing perceptions ticipant recruitment methodology, important inferences can still

towards doping and neuroenhancement. be made. The hypothesis-generating nature of this study was sup-

The scenarios showed that students were more tolerant to neu- ported by detailed statistics regarding newly constructed scales

roenhancement. Results also demonstrated coherence with the and implicit measures. The study also highlighted the presence of

zero sum or non-zero sum tasks hypothesis (Dodge et al., 2012). a moral - functional differentiation. Following Petróczi’s (2013a,

Scenarios depicting zero sum situations were viewed more neg- 2013b) argument on conflicting normative expectations in relation

atively. However, what is observed from the non-competitive to the goal (enhanced performance) and the means (use of chemical

scenarios (Fig. 2B and D) is that steroid use (despite its lack of assistance) with regards to doping, future research avenues should

effect on others) is ill tolerated but Ritalin use for academic suc- further explore this duality. Similarly to doping in professional

cess is considered much more acceptable. Moreover, males were sports, emphasising the ethical implications of these conducts may

more tolerant towards using steroids for body enhancement. This not contribute to the containment of the phenomenon (Ragan, Bard,

demonstrates that achievement contexts are viewed as distinct, & Singh, 2013). The perception of an assumed functionality can be

and that contextual demands are at play when these individuals a better predictor of attitudes and intentions in regards to these

are formulating their opinions (Petersen et al., 2014; Wolff & Brand, forms of enhancement, rather than the individuals’ moral standards

2013). Differences found in the attitudes of professional athletes or ethical values. It is plausible that a utilitarian approach, such as

demonstrate that sports involvement elicits more instrumental the drug instrumentalization theory (Müller & Schumann, 2011)

views towards doping practices, and that athletes may have a gen- can be more useful in explaining and predicting these conducts

eral propensity to enhance (Dietz, Ulrich, et al., 2013). and their outcomes, at least in the case of young adults.

Qualitative analysis of participants’ open responses suggested To better guide effective policy (Bourgois, 2002), future research

that social representations of steroid use in competitive contexts should investigate pharmacological enhancement as a broader

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university students in England. Performance Enhancement & Health (2015), http://dx.doi.org/10.1016/j.peh.2015.02.001

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