Virtue or Pretense? Looking behind Self-Declared
Innocence in Doping
Andrea Petro ´czi1,2*, Eugene V. Aidman1,3, Iltaf Hussain4, Nawed Deshmukh4, Tama ´s Nepusz1, Martina
Uvacsek5, Miklo ´s To ´th5, James Barker4, Declan P. Naughton1
1School of Life Sciences, Kingston University London, Kingston upon Thames, United Kingdom, 2Department of Psychology, University of Sheffield, Sheffield, United
Kingdom, 3Land Operations Division, Defence Science and Technology Organisation, Edinburgh, Australia, 4School of Pharmacy and Chemistry, Kingston University
London, Kingston upon Thames, United Kingdom, 5Faculty of Physical Education and Sport Sciences, Semmelweis University, Budapest, Hungary
Background: Social science studies of doping practices in sport rely predominantly on self-reports. Studies of psychoactive
drug use indicate that self-reporting is characterised by under-reporting. Likewise doping practice is likely to be equally
under-reported, if not more so. This calls for more sophisticated methods for such reporting and for independent, objective
validation of its results. The aims of this study were: i) to contrast self-reported doping use with objective results from
chemical hair analysis and ii) to investigate the influence of the discrepancy on doping attitudes, social projection,
descriptive norms and perceived pressure to use doping.
Methodology/Principal Findings: A doping attitudes questionnaire was developed and combined with a response latency-
based implicit association test and hair sample analysis for key doping substances in 14 athletes selected from a larger
sample (N=82) to form contrast comparison groups. Results indicate that patterns of group differences in social projection,
explicit attitude about and perceived pressure to use doping, vary depending on whether the user and non-user groups are
defined by self-report or objectively verified through hair analysis. Thus, self-confessed users scored higher on social
projection, explicit attitude to doping and perceived pressure. However, when a doping substance was detected in the hair
of an athlete who denied doping use, their self-report evidenced extreme social desirability (negative attitude, low
projection and low perceived pressure) and contrasted sharply with a more positive estimate of their implicit doping
Conclusions/Significance: Hair analysis for performance enhancing substances has shown considerable potential in
validating athletes’ doping attitude estimations and admissions of use. Results not only confirm the need for improved self-
report methodology for future research in socially-sensitive domains but also indicate where the improvements are likely to
come from: as chemical validation remains expensive, a more realistic promise for large scale studies and online data
collection efforts is held by measures of implicit social cognition.
Citation: Petro ´czi A, Aidman EV, Hussain I, Deshmukh N, Nepusz T, et al. (2010) Virtue or Pretense? Looking behind Self-Declared Innocence in Doping. PLoS
ONE 5(5): e10457. doi:10.1371/journal.pone.0010457
Editor: Conrad P. Earnest, Pennington Biomedical Research Center, United States of America
Received November 20, 2009; Accepted April 5, 2010; Published May 5, 2010
Copyright: ? 2010 Petro ´czi et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: World Anti-Doping Agency Social Science Research Grant 2007/Petro ´czi entitled ‘Measurement Tool for estimating the prevalence of doping:
development and validation of a self-report measure of performance enhancing drug use’ (http://www.wada-ama.org/en/Education-Awareness/Social-Science/).
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: A.Petroczi@kingston.ac.uk
The widespread use of performance enhancing drugs , along
with advances in performance enhancements coupled with the
increasing costs of continuous development of the testing methods
 have led anti-doping strategies to turn to identifying predictors
and/or barriers of doping behaviour, over and above sanctioning.
The recent debate around the practicalities and moral justification
of in- and out of competition testing [1,3] has reinforced the need
for preventive measures. Social science doping research has a long
standing tradition in investigating social cognition (attitudes,
norms, beliefs) and personality traits in a quest to find a set of
characters that clearly distinguishes athletes who engage in doping
practices and those who do not [4–10]. Based on these differences,
past research has strived to establish behavioural models [11–16]
with the ultimate aim of being able to predict doping use and to
inform anti-doping programmes for potential intervention points
and strategies. To date, only a few of these models have been
empirically tested [13,15], and they are exclusively based on self-
declaration of behavioural intention or behaviour; and explicit
assessment of attitudes, beliefs, norms and motivation.
Previously, researchers assumed that social cognitive determi-
nants of behaviour are accessible and explicitly endorsed by
individuals, hence relied exclusively on individual’s self-reports
when investigating thoughts and feelings that underlie human
behaviour. However, over the past two decades, convincing
evidence has led to suggestions that the human mind operates in
dual, conscientious and unconscientious, mode [17–19], therefore
key components of the cognitive processes influencing behaviour
are partially hidden from people’s awareness or under limited
PLoS ONE | www.plosone.org1 May 2010 | Volume 5 | Issue 5 | e10457
ability to control. Owing to this phenomenon, it has been
acknowledged that self-report measures are restricted in capturing
the complexity of the cognitive processes that underlie social
actions, thus social psychologists have turned to incorporating
implicit assessment of the relevant cognitions. This approach has
particularly intrigued researchers in socially sensitive domains
where it is fair to assume that socially desirable responding is likely
to confound explicit assessments .
Individual differences in implicit cognition exert a profound
influence on social behaviour, including attitudes, stereotypes and
self-concept. Their assessment poses one of the most intriguing
challenges in psychological measurement. In addition to projective
testing and similar interpretive methods traditionally employed to
assess ‘the unspoken’, recent developments in cognitive method-
ology offer a host of new methods ranging from priming  and
implicit association  through semi-projective techniques 
to performance based methods such as video-game embedded
assessment protocols [24,25].
Recently, the utility of implicit measures of social cognition have
been investigated in relation to doping. A recent study 
showed that the adapted Implicit Association Test (IAT) has the
capacity to uncover automatic evaluative bias toward doping
among self-confessed users and was able to predict behaviour in
hypothetical situations above and beyond the explicit measures.
Although the authors concluded that the doping IAT could further
benefit from a refined stimuli set and improved protocol, the
results indicated that implicit assessment of doping attitude has the
ability to make a key contribution to the understanding of
cognitive processes behind doping behaviour. A study using an
emotional Stroop task with doping words suggested that allocation
of attentional resources presents among young adolescents, but the
source of this attentional bias has remained speculative .
Young people might be tuned for doping related stimuli because of
external exposure (media, anti-doping education), and not
necessarily internal motivation.
Assessment of doping attitude-behaviour links
The majority of the quantitative research into doping behaviour
has been based on self-reports, where athletes are not only asked to
report on their own attitudes, perceived injunctive and/or
descriptive norms but also asked to confess their compromising
behaviour (i.e. taking prohibited substances). Self-reports among
athletes in Olympic sports have yielded prevalence data ranging
between 1 and 30%, which itself is higher than the yearly rate
(,2.%) of adverse analytical findings in the World Anti-Doping
Agency accredited laboratories . This 2% constitutes a yearly
average of some 3,500 positive tests.
Alternative approaches to self-report methods
Despite the widespread use, self-report techniques come with
considerable limitations. With regard to self-reported behaviour, it
must be assumed that individuals are willing to disclose this, often
discriminating, information to the researcher. When self-reports
are used to assess social cognitive processes, it is further assumed
that people have introspective access to the construct in question
(e.g. attitude) and have no intention of distorting their responses.
Violations of either of these two assumptions negate the validity of
self-report assessment and conclusions derived solely from self-
Doping is a decidedly ostracised behaviour. Admitting use or
even expressing supportive opinions against the general view is
likely to prompt many athletes to conceal their true behaviour and
thoughts about doping if they could be discriminating for the
person or the group he/she represents. Recently, researchers have
recognised this problem and made attempts to use indirect
methods to obtain information on doping behaviour. One notable
example being the use of the Random Response Technique (RRT)
where estimation of doping prevalence is made on aggregated
levels [29,30]. Another line of research has made attempts to
estimate the likelihood of self-involvement in doping utilising the
False Consensus Effect (FCE) which has been evidenced in various
socially sensitive situations [31,32]. Despite the advances these
latter approaches have brought to doping behaviour research,
results still carry the inevitable caveat of being based on self-
declarations. Independent validation or calibration  of these
results remains an issue.
Objective verification of self-reported drug use
Previously reported validity studies of self-reported drug
behaviour used chemical analysis for the presence of mainly social
drugs in urine, saliva or hair [34–39]. Beyond the expected
discrepancies, it was also demonstrated that inconsistencies in self-
reported drug use by adolescents are not random but are
associated with socioeconomic parameters, personality character-
istics and/or underlying social cognitive determinants . For
example, reporting and under-reporting of drug use was
discordant and driven by social desirability concerns .
Discordance between self-reports and objective validation also
occurred in the unexpected direction with a considerable
proportion (34%) of self-report data unconfirmed by urinalysis
. This may be explained by the difference between the time
and/or duration of use, drug half-life and the detection window of
the chosen chemical validation. To our knowledge, no research
has been published that focuses on verifying self-reported
performance enhancing drug use with chemical analysis of hair
samples which covers prolonged periods.
In spite of the limited validity of self-reports in socially sensitive
behaviour being well documented, how this discrepancy affects the
conclusions drawn on the differences in social cognitive measures
between those involved vs. those who are abstinent remains
unknown. Whereas social psychology research routinely considers
the effect of social desirability on explicitly assessed data, we are
unaware of studies that investigated differences in related social
cognition under different scenarios where user vs. non-user groups
were established based on self-report admissions, chemical findings
or validated self-reports, and used both explicit and implicit
assessments. Therefore, the aims of this study were: i) to contrast
self-reported doping use with objective results from chemical hair
analysis and ii) to investigate the influence of the discrepancy on
doping attitudes, social projection, descriptive norms and per-
ceived pressure to use doping.
Previous research using a larger sample pool, from which the
current study sample was selected, investigated the FCE regarding
doping and social drug use and provided compelling evidence of
the differences in projected use of doping among peers and
attitude between those athletes who confessed to having personal
experience with doping and those who claimed no use . The
differences were in the expected direction with self-confessed
doping users giving higher prevalence estimates, showing a more
lenient explicit attitude toward performance enhancements than
their no-user counterparts. In this study, we expanded the
investigation by using hair analysis to verify self-reported doping
use or abstinence, and added implicit assessments in a selected
group of athletes.
We hypothesised that:
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H1: Accurately reported doping use was expected to be
associated with more positive explicit doping attitudes
and higher estimates of projected use by others; while
denied use would lead to lower explicit attitudes but
realistic or elevated estimates of doping use in others; and
accurately reported abstinence (‘clean athletes’) would be
associated with relatively low scores on both measures.
H2: Doping use was expected to result in greater correlation
between explicit and implicit doping attitudes, whereas
larger discrepancy between explicit (cognitively con-
trolled) and implicit (‘unconscious’) measures was expect-
ed in those who do not use doping.
Results and Discussion
Verifying self-reported doping behaviour
Hair samples from the participants in our previous study 
were tested for performance enhancing and social drugs. Of the 82
athletes, 12 (14.6%) reported having personal experience with
prohibited performance enhancing substances, one with thera-
peutic use exemption. Twelve hair samples were positive for
anabolic steroids and/or erythropoietin (EPO), of which 10
(12.2%) were confirmed with no overlap between confessed
lifetime experience and current use. None of the positives reported
medical use of anabolic steroids or EPO. The pattern was very
similar for social drugs with 15% overlap between self-reported use
(27, 32.5%) and current use (12, 14.6%). Three of the confirmed
doping positives also tested positive for social drugs.
The observed discrepancies between self-reports and objectively
verified social drug taking behaviour is in line with previous
research and although not surprising, they highlight the fact that a
significant proportion of respondents simply choose to deny their
real current or recent behaviour, even under circumstances when
the verification is known to the participants. This phenomenon
that has already cast doubt over drug use survey research expands
to, or even magnifies the unreliability of doping use epidemiology
surveys. The evaluation of anti-doping interventions is seriously
hindered by the absence of reliable information on athletes’ true
behaviour; opens the field to wild guesses and speculations, often
about other athletes, sports and nations. Devising more reliable
ways to gauge this crucial information is an important issue but
beyond the foci of this research and shall be addressed by future
research. The present investigation aims to interpolate the
tendency of giving misleading information about the behaviour
to selected self-reported social cognitive processes.
Hair sample results were combined with self-reported doping use to
inform the selection of 14 athletes to populate the groups in Table 1.
Among the athletes selected for this study, 4 athletes admitted having
used performance enhancing substances (PEDs) with no (or undetect-
able) current use. Of the remaining 10 athletes claiming that they have
never used such substance 6 hair samples were positive for steroids,
for nandrolone. Of these 6 athletes, 2 tested positive for a selection of
social drugs despite that they both denied such drug use.
Based on self-report and hair analysis results for doping
substances, athletes were categorised into disjoint groups of: i)
clean athletes (matching negative self-report and hair screening), ii)
denier (negative self-report coupled with positive hair samples), iii)
open users (matching positive self-report and hair) and iv)
unverified/non-current user (admitted use with currently negative
hair sample). Although hair samples were also tested for
recreational drugs (5 out of the 10 positive samples for doping
were also positive for recreational drugs), parallel psychological
testing was only performed in relation to doping, hence the
confessed use of recreational drugs and/or positive hair samples
for such substances will not be addressed in this report. In our
previous study we have shown that whilst self-reported use of
recreational drugs and doping substances was not independent,
related social cognition were domain specific . That is, self-
admitted doping users gave significantly higher estimates of doping
prevalence among athletes but not social drug prevalence, and vice
versa. Similarly, differences in doping attitude scores were related
to doping use but independent of social drug use. However, two
athletes in the current sample denied any type of drug use whilst
their hair samples contained evidence of both PEDs and social
drugs. As this category of athletes demonstrated repeated denial on
a single survey, they were treated as a separate group in this study.
Attitudes, perceived pressure and social projection by
Assuming that direct experience increases attitude salience and
the level of attitude - behaviour consistency , athletes’ explicit
and implicit attitudes and social projections were contrasted in the
four user groups. The relationship between explicit and implicit
doping attitude was investigated separately in each group (with
repeat deniers excluded from the analysis owing to the insufficient
variation in the sample) but included in Table 1 and Figure 1.
Prior to in-depth analysis, it is important to note the distinction
between the two types of information. In the questionnaire phase,
participants were asked if they have ever used performance
enhancing substances or social drugs. Hence an affirmative to this
question does not necessary mean current or recent use. Hair
analysis covered approximately the last 3 months (minus the last 2
weeks when the hair is still in the body); therefore results reflect
relatively recent use. It should be noted that the hair analysis at
Table 1. Mean tests results (6SD) for self-report measures and implicit association effects (implicit doping attitude) by user
Explicit doping attitude
(raw scale score)
attitude (IAT effect, ms)
Perceived pressure to
dope (raw scale score)
(raw scale score)
2255.986153.46 2.5065.00 32.50626.30
‘Denier’28.5064.93 27.486132.410.00 9.50612.50
Self-reported non-user group: 29.9065.64
294 986185.18 1.0063.1620.80622.12
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this stage was limited to the list of most often used performance
enhancing and social drugs. Contradicting answers can be derived
from two legitimate sources: i) respondent answered truthfully
about having an experience but the last occasion when drugs were
used happened before the 6-month maximum detection window;
or ii) the drugs used were not among those tested for. Theoretically
there is also a possibility that a respondent did not answer
truthfully but there is very little reason to admit a socially
unacceptable behaviour when it in fact did not happen. On the
contrary, a ‘no’ answer on the questionnaire coinciding with
positive analytical results in the matching hair sample can be seen
as a denial on the self-report because the denied ‘ever use’ is
contradicted by the very presence of a drug or drugs in the hair.
In self-reports, ‘deniers’ and ‘repeat
deniers’ are classified as ‘non-users’ with explicit scores and
measures below those who admit to doping and close to those who
are truly clean. This phenomenon holds clearly for two of the three
explicit measures, doping attitude and perceived pressures.
Interestingly, social projections were given the lowest percentage
by those who denied doping use where hair results indicated
otherwise and reached the highest estimation by those who admit
using PEDs. Users denying their actions claimed that they feel no
pressure at all to use PEDs, followed by the clean athletes (with a
low 2.5%) and self-admitted users scoring the highest with 37.5%.
Correspondingly, 3 out of 4 of the self-admitted users believed that
most high-performing athletes used performance enhancing
substances in training and competition with the 4thathlete
believing that doping is used by most athletes in training but not
in competition. Of those who denied doping use but their hair
samples indicated otherwise, half (3/6) agreed that performance
enhancing substances are used in both training and competition by
most high performing athletes, followed by 2/6 stating that most
athletes do not use doping (1 in each ‘denier’ group) with 1 athlete
believing that doping is used by most high performing athletes but
used only in competition. This view was generally shared by the
clean athletes, where 2 of the 4 thought that doping is used in both
training and competition with the remaining 2 votes being split
between training only and competition only.
Therefore, relying solely on self-report data, the observed
differences in deliberate judgment were in the direction expected
from known groups, with differences in three of the four measures
reaching statistical significance. These are, in diminishing order of
significance: explicit attitude (|t|=4.901), pressure to use PEDs
(|t|=3.217) and social projection (|t|=2.343; all ts,CV=1.782
directed, at df=12, a=0.05). In reality, however, the membership
of the self-reported non-user group was seriously confounded by a
number of distorted answers about athletes’ doping use. When
these denials were corrected by hair analysis verification, a
considerably different pattern of group differences emerged.
The highest estimation of doping prevalence given by self-
confessed users is consistent with previous results [31,32]. The
elevated estimation may be explained by the desire to find comfort
in big numbers (also called False Consensus Effect) by which
people who are involved in a socially disputable act tend to
overestimate the number of others doing the same . The
opposite trend has also had some support from literature ,
when socially endorsed behaviour may correlate with slight
underestimation of the proportion of other well-behaved individ-
uals to reinforce one’s uniqueness. Our results are consistent with
this observation: our self-reported non-users, overall, gave a
considerably lower estimation (21% vs. 52%) of doping prevalence
in others. However, past research has mainly based these
interpretations on self-reported behaviour. With the added insight
from the hair analysis, the description of this phenomenon can
further be refined. Contrary to the expectation, those athletes who
denied PED use did not give realistic or elevated estimates of
doping use in others. In fact, their projection was the lowest among
all groups. Those who were determined to create a good
impression to hide their real behaviour gave a very low estimate
of doping prevalence, scored the lowest on the explicit doping
attitude scale (indicating strong disapproval) and claimed that they
felt no pressure at all to use PEDs. By contrast, they performed the
implicit doping association task with ease when doping words were
combined with good words; the task that was more difficult to non
users, for whom doping had little or no salience. As would be
expected, self-admitted users’ performance on the same task fell
somewhere in between. All together, the discrepancy of the
different inferences that could be drawn under the two scenarios
(self-report vs. validated behavioural data) highlights not only the
unreliability of self-reports in social sensitive domains but also their
effects on related constructs of social cognition.
Theoretically there are two fundamental and mutually exclusive
assumptions underpinning the observed low scores on explicit social
cognition measures among verified doping users. On the one hand, it
may be reduced introspective accessibility of the constructs in
question: having no insight into their feelings and biases the
respondents produced low scores are no reflection of their actual
doping-related cognition, but instead represent an extraneous
influence, such as generic social desirability. On the other hand,
answers on the explicit tests are consciously and deliberately distorted
Figure 1. Scatterplot between explicit doping attitude scores (‘explicit’) as measured by PEAS and implicit doping associations
(‘Implicit’) as measured by the Brief IAT-D by doping user groups.
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in order to create a favourable (but false) impression. Our results,
however,suggestthat objectivelyverified dopingusershad,infactnot
only introspective access to the construct (doping attitude) but also
had positive feelings toward it. Investigating accessibility effects on
varied implicit social cognition, Gawronski and Bodenhausen
demonstrated that performance on a latency-based response
compatibility task (such as the IAT) is affected by the practiced ease
of and subjective feelings about the retrieval of relevant information
(i.e. valence attached to doping) from memory .
By contrast, the implicit association test
was more revealing. Responding to pairings of positive-connotation
words (the ‘good’ category) with doping substance words was fastest
among those who currently use doping but denied it, followed by
those who are currently using doping and admit it. Not surprisingly,
responding to the same word pairings was slowest for those who
claimed to have no experience with doping, followed by those who
reported having used PEDs. Interestingly, athletes with doping
experience performed the task quite well, indicating a closer
association of doping with positive connotations than observed in
thosewho have notused doping.Currentusers,asindicatedbytheir
hair analysis results, performed the good+doping pair the fastest with
the results being close to the good+nutritional supplement pairing.
These differences, however, are very small with large variance,
based on small groups, and hence should be treated as preliminary
observations, rather than definite conclusions.
Relationship between explicit and implicit measures:
indicators for method development
The triangulation of self-reported explicit measures and
objective verification of behaviour data using hair analysis with
an implicit measure provided some preliminary evidence that the
reason behind underreporting explicit cognitions is not a genuine
effect but more likely a strategic response. In order to take a step
forward to identifying deniers without the advantage of hair
sample analysis, we examined the correlation between explicitly
and implicitly assessed doping attitudes separately for the four
groups. In the literature, the correlation between explicit and
implicit measures of the same construct tends to be small .
This is especially true when social desirability is thought to
confound explicit responses. In several studies, implicit measures
had incremental predictive power in criterion validity over and
above self-reports in socially sensitive domains . Scatterplots
by user groups depicted in Figure 1 and corresponding
correlation coefficients in Table 2 suggest that the relationship
between the parallel explicit and implicit measures is indicative of
deliberate distortion. We assume that the implicit association is
close to the true reflection of people’s feelings toward the attitude
object. For example, those who endorse doping would be able to
perform the lexical sorting task of doping words when they share
the same key with positive-connotation words faster compared to
those who associate doping with negative connotations.
Athletes who honestly admitted PED use performed congru-
ently on the explicit and implicit measures. The more they
endorsed doping in self-report and deliberate judgement, the faster
they performed the good+doping pair test. Note that this is a trend
between the two measures. In terms of sign of their attitudes, even
these athletes were negative towards doping, albeit not as negative
as their non-user counterparts.
Interestingly, trends expected and observed in research using
self-reports change dramatically when the behavioural categories
are based on objective measures (chemical analysis) and not on
self-reports. Whilst patterns of explicitly assessed social cognition
and tend to be consistent with self-reported behaviour, data from
hair analysis revealed that distorted responses tend to bias these
results. It can be argued that cultural context (i.e. doping use is
unaccepted, un-sportsmanlike behaviour) influenced the athletes’
automatic associations when performing the IAT task, as
evidenced by the general trend of doing better on the good+
nutritional supplements pair compared to the task when the good+doping
shared the same response key . If that is the case, athletes who
denied PEDs use appeared to be less affected by this, showing little
differences in response latencies between these two tasks.
The fact that self-reports on behaviour are very consistently
associated with explicit social-cognitive outcomes is indeed
informative. It is also consistent with mainstream social cognition
literature [44–46] linking self-report to consciously controlled,
deliberate outcomes – as distinct from more automatic and less
controlled outcomes linked to implicit attitudes and dispositions. In
our context this could indicate how athletes want to be seen to the
outside word. In future studies, the strength and effect of this desire
should be taken into account in explicitly measured doping-related
constructs. Self-reports reflect what respondents want to reveal
about themselves in that particular context, which has a non-trivial
relationship to their actual feelings, thoughts or behaviour.
Following the recommendation , we use the terms explicit
and implicit with reference to measurement, not the construct (e.g.
attitude). Based on the implicit doping attitude data at hand, no
assertions can be made about the level of awareness among the
selected athletes, especially in the denier group, of their own
attitude demonstrated in the IAT. Rather, the considerable
discrepancy between explicitly and implicitly measured attitudes
in the denier group only differ qualitatively in their doping
behaviour and their willingness to disclose this information suggest
that athletes, indeed, were aware of their attitudes but owing to the
sensitive nature of the issue, they made a deliberate effort to
conceal their feelings about doping when it was under their
cognitive control (i.e. explicitly measured) and deliberated. By
contrast, automatic activation of these attitudes during the implicit
association test was something that is very difficult to manipulate
at will. The fact that the task was presented as a timed exercise to
respondents who were competitive athletes may have further
enhanced the validity of the test. That is, athletes were likely to be
focused on performing fast and accurately on the task, instead of
pondering about what the test might be measuring.
Limitations of this study arise from the sample size. Whilst the
number of hair samples screened and positives samples confirmed
are considerably higher than what is typically used in publications
focusing on the chemical analyses for steroids [47,48], it is
somewhat below the typical sample size in similar experimental
psychology studies . Results from this study were presented as
evidence for the need for chemical validation of self-reports and
mixed methodology, rather than drawing firm inferences regard-
ing user vs. non user groups. In order to do this, similar
Table 2. Correlation between explicit and implicit doping
attitudes by user groups.
PEAS * Brief IAT-D
(average time diff)
PEAS * Brief
‘Confirmed clean’ (n=4).281 .270
‘Self-reported user’ (n=4).991 .942
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investigations need to be conducted on sufficiently large samples to
establish representative groups and improve confidence with
which practically meaningful differences/relationships can be
observed. All together, the discrepancy of the different inferences
that could be drawn under the two scenarios (self-report vs.
validated behavioural data) highlights not only the questionable
validity of self-reports in social sensitive domain but also their
profound effects on related social cognitive outcomes. Sample
descriptions (e.g. means and standard deviations) in this study are
only indicative and presented here to assist in estimating the
required sample sizes for future studies.
Incorporating developments in hair sample analysis for the
detection of performance enhancing substances, this initial study
examines the prospects of objective validation of athletes’ doping
attitude estimations and admissions of use. Overall the results
indicate that patterns of group differences in deliberately expressed
attitudinal outcomes, such as social projection, explicit attitude to
doping and perceived pressure to use, vary depending on whether
the user and non-user groups are defined by self-report or by
objective verification such as hair sample analysis. When user and
non-user groups were defined by self-report, the differences
between them on several attitudinal outcomes were observed in
the expected direction (i.e. self confessed user groups scored higher
on social projection, explicit attitude to doping and perceived
pressure to use). However, data from hair analysis revealed that
deliberate response distortion may have biased these results.
Subjects, whose hair sample returned positive for doping but who
denied doping use in self-reports, were observed to manipulate
their questionnaire responses to a greater degree than all other
groups. Implicit doping attitude and its correlation to the explicit
attitude towards doping are indicative of this distorted responding.
Therefore, the observed discrepancy between self-report and
objectively (e.g. chemically) validated behavioural data needs to be
considered when drawing conclusions from self-report findings.
Our results pose a challenging question about the veracity of
studies where doping-related behaviours and attitudinal outcomes
are examined through group or individual differences that are
themselves based on self-report. Our findings not only confirm the
need for improved self-report methodology for future research in
socially-sensitive domains but also indicate where the improve-
ments are likely to come from: as chemical validation remains
expensive, a more realistic promise for large scale studies and
online data collection efforts is held by measures of implicit social
Owing to the time and resource-intensive nature of chemical
validation (including equipment, personnel and know-how), large
scale adoption of such validation for self-reported behaviour data
across doping research does not seem feasible. However,
improving self-report methodology remains imperative. One
possible avenue is incorporating implicit assessments to gain
Figure 2. Sample characteristics and group means for 2 explicit and 1 implicit assessments.
Doping Self-Report Distortion
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incremental predictive validity over and above explicit self-report
measures. This approach has also been advocated by Greenwald
et al.  upon meta-analysis of 122 empirical studies using
explicit and implicit measures to predict behavioural, judgemental
and physiological outcomes.
The chemical validation of self-reported information on doping
and drug taking behaviour was part of a multi-centre study
investigating social projection in doping and social drug use
[31,32]. This part of the study aimed to detect the presence of
selected drugs and metabolites in hair in order to investigate the
validity of self-reports and the effect on any expected discrepancy
between self-declaration and objective behavioral data on doping
related social cognition.
This study was based on mixed methods using a questionnaire,
computerised psychological test and hair analysis for selected
performance enhancing drugs. Self-report questionnaire results on
doping behaviour were compared to the data gleaned from hair
sample analyses for 14 selected athletes (4 per group plus 2) based
on their self-reported behavior and hair sample results from the
ELISA screening. In groups with more than 4 athletes (e.g. ‘clean’,
‘denier’ and ‘self-reported’), 4 athletes were randomly selected for
confirmation and further testing. The sample pools were as
follows: 61/115 clean athletes, 11/115 self-reported users (only 1
was confirmed), and 12/115 deniers (2 erythropoietin and 9
steroids users, one was not confirmed). The representativeness of
this random selection is shown in Figure 2. Participants with
unconfirmed positive ELISA results were eliminated from the
sample pool. Participation was anonymous, voluntary and based
on fully informed written consent. Participants were told that the
hair samples will be analysed for various chemicals. All athletes
were aware of the hair sampling procedure before completed the
questionnaire and performed the computerised assessment. As the
completion of the testing protocol required at least one hour,
participants were compensated for their time with a small payment
(value of less than 10 Euros).
The study was approved by the Faculty Research Ethics
Committee in Kingston University.
Athletes were asked to complete a web browser based test
consisting of the explicit and implicit attitude measures, comple-
mented with a paper-and-pencil questionnaire. A brief self-esteem
IAT with ‘good’, ‘bad’, ‘self’ or ‘others’ stimuli set separated the
two doping measures and served as method practice. Results for
the implicit self-esteem test are not reported in this study. Implicit
assessments preceded the explicit questionnaire measures (includ-
ing questions about PED use), separated by other, non-doping
related computerised tests, hence explicit did not influence the
implicit assessment . Although respondents were presented
with an Information Sheet detailing the hair sampling procedure
when seeking consent, the emphasis of the research was not on
doping but investigating resource depletion in executive function-
ing, where doping appeared to be one avenue of evoking self-
control and was mixed with other tasks (e.g. Donders’ task
switching and Stroop response inhibition).
Testing took place in a well-lit, quiet room containing two
desktop computers. One or two athletes were present and
completed the task at a time under supervision. The data
collection was conducted between 8 am and 6 pm during
Validation of self-reports
Validation of self-report was conducted using hair samples. The
key advantage of using hair, as opposed to blood, urine or saliva, is
its wide detection window, coupled with being non-invasive, easily
stored and free of biohazards. The selection of drugs for screening
was based on frequency of detection in WADA reports over the past
five years . Thus, along with testosterone, stanozolol, nandro-
lone and boldenone are frequently used anabolic steroids which
differ in their licensing status . In addition, tests were conducted
for Naltrexone and most commonly used recreational drugs (for the
full list, see Table 3). This research has adhered to the WADA
CODE for laboratories . Proper chain of custody was followed
for hair samples collection, storage and disposal. Any unusual
conditions like colour, pH and specific gravity were recorded.
The hair sample consisted of a lock of untreated hair with a
diameter of 3 to 4 mms (approximately 50 hairs), minimum 3 cm
in length (equal 100 mg in weight), cut directly at the skin surface
at the vertex posterior whenever possible. The sample was stored
individually in labelled, sealable paper envelopes, according to the
protocols established and approved by the Kingston University
Faculty Research Ethics Committee.
Chemicals and reagents.
stanozolol, amphetamine, methamphetamine, cocaine, delta-9-
tetrahydrocannabinol (THC), ketamine, erythropoietin (EPO),
and their metabolites, were obtained from Neogen Corporation
(Lexington KY 40511 USA), with enzyme immunoassay (EIA)
ELISA kits for nandrolone,
Table 3. Limits of detection (LOD) and WADA general
Minimum Required Performance Limit (MRPL) values.
Drugs Category ELISA LODMRPL value
Nandrolone Anabolic steroid0.07 ng/ml2 ng/ml
Testosterone Anabolic steroid0.5 ng/ml2 ng/ml
Naltrexone Anabolic steroid1.3 ng/ml2 ng/ml
Boldenone Anabolic steroid6 ng/ml2 ng/ml
StanozololAnabolic steroid 1 ng/ml2 ng/ml
3-HydroxyStanozololAnabolic steroid12 ng/ml 2 ng/ml
AmphetamineStimulant 11.5 ng/ml 500 ng/ml
N-DesmethylselegilineStimulant 1.27 ng/ml 500 ng/ml
EphedrineStimulant 23.4 ng/ml500 ng/ml
Methamphetamine Stimulant9.5 ng/ml500 ng/ml
D8THCStimulant 0.6 ng/ml500 ng/ml
D9THCStimulant0.5 ng/ml 500 ng/ml
CocaineStimulant 5.1 ng/ml500 ng/ml
Cocaethylene Stimulant 5.5 ng/ml500 ng/ml
Benzoylecgonine Stimulant6.8 ng/ml500 ng/ml
m-Hydroxycocaine Stimulant7.1 ng/ml 500 ng/ml
Ketamine Stimulant7 ng/ml 500 ng/ml
NorketamineStimulant 137 ng/ml500 ng/ml
1.2 mU/ml5 mU/ml or
Doping Self-Report Distortion
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being part of the ELISA kits. Drugs, their metabolites and internal
standard (stanozolol D3) were obtained from LGC standards
(Teddington, UK). All chemicals and silanized amber glassware
were from Sigma Aldrich (UK). Blank hair was obtained from
healthy non-athlete volunteers.
Screening by ELISA
The hair sample was rinsed twice with 5 ml dichloromethane
for 2 minutes. After complete drying, hair was finely cut into circa
1 mm segments. Hair segments (ca 50 mg) were weighed in a glass
tube. Calibrants and controls for each kit were prepared by spiking
blank hair with the required amount of drug. Hair samples,
calibrants and controls were then incubated in 1 mL of 1 M
NaOH at 95uC for 15 minutes. After cooling, the homogenate was
neutralized (pH 7) with required amount of 1 M HCl (approx
1 mL) and then diluted with equal amount of enzyme immuno-
assay (EIA) buffer (1:1 v/v). Screening methods were fully
validated in accordance with the WADA Code of validation for
urine and plasma which was extended to hair samples. Neogen
Corp. (USA) forensic ELISA kits were used on a Biotek-ELx808
(USA) and Varian Cary 50 MPR Microplate Reader (UK). The
full range of drugs and their metabolites are given in Table 4. In
addition to the steroid results, the application of the Neogen
ELISA methods have been extended from biofluids to hair
samples for the detection of EPO and the most frequently used
drugs of abuse that are on the WADA 2009 List of Prohibited
Substances . These include amphetamine, methamphetamine,
cocaine, marijuana and ketamine (currently not prohibited) and
their selected metabolites. This process involved developing
extraction methods along with devising a protocol for analysis.
Methods for extraction of the drugs from hair were developed and
subsequent ELISA analyses were validated in-house.
For all non-threshold and threshold substances appropriate
controls near the appropriate threshold levels were included in the
initial screening, although uncertainties of measurements were not
taken into account. Table 4 shows the detection limit of ELISA
kits supplied by Neogen Corporation (USA) and general MRPL
levels set by WADA.
The ELISA results were confirmed by liquid chromatography-
mass spectrometic (LC-MS/MS) methods using a ThermoScien-
tific LC-MS/MS Accela UPLC coupled with Triple Quadrupole
TSQTMQuantum Access system. These confirmatory quantitative
methods are more sensitive than the initial screening procedures
with the LOD’s of the three key substances in hair are shown in
Table 4. There are no therapeutic use exemptions (TUE) for the
prohibited substances detected.
Analyses by LC-MS/MS.
After decontamination, hair was
finely cut into 1 mm segments. Following a previously established
method , hair segments (ca 20 mg) were weighed in a glass
tube and incubated in 1 ml of 1 M NaOH at 95uC for 15 minutes
in the presence of stanozolol D3 as an internal standard (I.S). After
cooling, the homogenate was neutralized with approximately
1 mL of 1 M HCl followed by addition of 0.2 M phosphate buffer
(pH 7.0). Liquid – Liquid extraction was employed for all three
steroids analyzed. Pentane (3.5 ml) was added to the homogenate.
After agitation and centrifugation (4 minutes at 1257 g) the
organic layer was separated and evaporated to dryness under a
stream of nitrogen gas at 60uC. The dried residue was
reconstituted with 100 mL acetonitrile. An aliquot (4 mL) of
reconstituted extract was injected into the ThermoScientific LC-
An Agilent ZORBAX column (SB-
C18, 2.1650 mm, 1.8 mm) was used. Formic acid (0.1%) and
acetonitrile were used as mobile phase. The LC mobile phase
gradient flow used was: A: acetonitrile (%), B: 0.1% formic acid;
start: 50% A, after 10 min: 80% A–20%B, after 11 min: 100%A,
after 12 min: 50%A. Total flow rate through the column was set at
100 ml/min using gradient flow. Column temperature was set at
60uC. The mass spectrometer was operated in the positive
electrospray ionisation mode. SRM (single reaction monitoring)
was used to confirm each analyte as shown in Table 5. A standard
calibration curve and quality controls were prepared by spiking
negative control of hair (blank hair) with the required amount of
drug and internal standard.
Psychological assessment consisted of computerized word
sorting task (used to assess implicit associations) and a paper-
and-pencil questionnaire seeking information on explicit doping
attitude, and basic demographic information (gender, age,
ethnicity, sport, level of competition, nationality). In order to
protect athletes’ anonymity, only mean age and gender distribu-
tion is reported.
Implicit doping attitude (the brief version ).
test block, respondents were presented by words falling into four
categories (good, bad, nutritional supplements or doping). The
stimuli used in each category are shown in Table 6. Two of those
four category names were shown on the left hand side of the screen
during the test. Respondents were asked to press ‘E’ if the stimulus
word matches either of the categories or to press ‘I’ if it does not
match them. Words were presented in 24pt Arial font. Each
Table 5. Main qualifier ions of analytes used for steroid
AnalyteParent mass Product mass
Stanozolol 329.2 81.2
Testosterone 289.2109.2, 97.2
Stanozolol D3 (I.S) 332.281.2
Table 4. Limits of detection using LC-MS/MS.
Drugs CategoryMRPL value LC-MS LODCalibration curve in hair pg/mg
Nandrolone Anabolic steroid 2 ng/ml1 ng/ml2.5 pg/mg3 to 400
Testosterone Anabolic steroid 2 ng/ml0.1 ng/ml0.25 pg/mg 1 to 400
Stanozolol Anabolic steroid 2 ng/ml 0.2 ng/ml0.5 pg/mg 1 to 400
Doping Self-Report Distortion
PLoS ONE | www.plosone.org8 May 2010 | Volume 5 | Issue 5 | e10457
stimulus was preceded by a fixation cross which stayed on-screen
for 400 ms. Stimuli stayed on-screen until the respondent pressed
either ‘E’ or ‘I’. A large red X was shown on the bottom of the
screen for 400 ms when the answer was wrong; respondents had to
press the correct button to proceed.
The Brief Doping IAT test consisted of two blocks. In the first
block, categories ‘good’ and ‘nutritional supplement’ were assigned
to the ‘E’ key; the second block used categories ‘good’ and
‘doping’. Each block consisted of 32 stimuli and each word was
presented twice. Brief instructions were presented before each
block; the instructions specified the words of the categories that
were selected as target categories (i.e. good and nutritional
supplement in the first block; good and others in the second
block) but not the other two. The ‘good’ combinations (good +
nutritional supplement and good + doping) were fixed as focal
categories. Respondents were instructed to proceed as fast as they
could. The order of the two blocks was counterbalanced.
The Doping IAT effect was calculated as the difference time
difference between the two focal test blocks as shown in Figure 3.
The difference was also divided by the variance to derive the D-
scores . Because the difference was calculated as: [Good +
Nutritional Supplement] – [Good + Doping], difference time.0 means
that completion of the good + nutritional supplement combination
task took longer, whereas difference time,0 suggests that the
[Good + Doping] completion took longer.
The computerised test application also included an explicit
measure of doping attitude using the Performance Enhancement
Attitude Scale (PEAS). The PEAS consists of 17 statements related to
performance-enhancing drugs. Respondents were asked whether
they agree with the statements. Answers were recorded using a 6-
point Likert-type scale (1=strongly disagree, 6=strongly agree). The
PEAS has shown good evidence for scale reliability and validity .
The anonymous questionnaire included key questions on drug
and doping taking behaviour: Have you ever used a social drug? (Yes/
No); Have you ever used a banned substance? (Yes/No) and Do you use
nutritional supplements? (Yes/No). The question regarding nutritional
supplement use (beyond and above the normal diet and taken in a
concentrated form) was included as a control (not reported). At the
beginning of the questionnaire, athletes were presented with clear
definitions: ‘doping’ or ‘banned substances’ were those substances
that are prohibited by the World Anti-Doping Agency or other
governing body in training and/or competition (e.g. steroids,
EPO). ‘Social’ or ‘recreational’ drugs were defined as psychoactive
drugs (e.g. stimulants, opiates, cannabis, cocaine, etc.) used for
recreational purposes rather than for work, medical or spiritual
reasons with caffeine, alcohol and tobacco excluded. Nutritional
supplements were vitamins, minerals, and non-vitamin non-
mineral substances including herbals and botanicals. Exemplars
were given for all three groups. In addition to these key questions,
athletes were also asked about the perceived pressure to use doping
(0–100%), estimated prevalence of doping among fellow athletes
(0–100%) and their general belief about the doping use pattern
(descriptive norm). For the exact wording and answer options of
these questions, see File S1.
Figure 3. Illustration of the Implicit Association Test (IAT) effect.
Table 6. Stimuli of the Brief Implicit Doping Attitude test.
Good peace, joy, love, smile
Badsick, hell, poison, fail
Doping nandrolone, stanozolol, testosterone, amphetamine
Supplements vitamins, ginseng, garlic, calcium
Doping Self-Report Distortion
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The hair samples of the selected 14 athletes were analysed for
PEDs. Athletes competed in track & field (5), triathlon (4),
volleyball (2), orienteering (1), basketball (1) and karate (1). The
mean age was 20.4363.18 years, 10 females and 4 males in the
sample. In this small sample, age and gender appear to be
unrelated to doping use.
Table 7 summarised the self-report and hair analysis results for
the selected 14 athletes. Note that positive hair samples for social
drugs were not confirmed beyond the ELISA screening at this
stage. The focus of the paper was performance enhancing drugs
and social cognition relating doping, hence the test did not contain
explicit or implicit measures of social cognition about social drugs.
Figure 2 shows the selected athletes’ position in relation to the
group mean for the full sample (N=482).
Group differences in and relationship between explicit doping
attitude and implicit doping associations and social projection
were compared for groups based on self-reports and hair analyses.
Group means are reported with standard deviation. Independent
samples t-tests were used to compare scores achieved on social
cognitive measures, where user vs. non-user groups were formed
by the self-reported PEDs taking. Graphs and statistical analysis
were conducted by SPSS 17.0 and Excel 2007.
regarding athletes’ drug and doping behaviour, doping attitude,
descriptive norm, social projection and perceived pressure.
Found at: doi:10.1371/journal.pone.0010457.s001 (0.06 MB
This file contains the questionnaire used to collect data
The authors thank the athletes who participated in this study.
Conceived and designed the experiments: AP DPN. Performed the
experiments: MU MT. Analyzed the data: AP EVA TN. Contributed
reagents/materials/analysis tools: AP EVA IH ND TN JB DPN. Wrote the
paper: AP EVA IH ND JB DPN. Developed the implicit doping attitude
test: AP EVA. Contributed to the design of the study and behavioural data
analysis: EVA. Developed the methods for chemical analysis and
performed the chemical analyses: IH ND. Developed the computerised
test: TN. Contributed to the data acquisition and statistical analyses: TN.
Contributed to drafting the manuscript: MU MT JB. Contributed to the
chemical analysis: JB. Contributed to the method development for
chemical analysis and interpretation of the results: DPN.
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