Psychology
2014. Vol.5, No.1, 53-61
Published Online January 2014 in SciRes (http://www.scirp .org/journal/psych) http://dx.doi.org/10.4236/psych.2014.51010
53
An Exploratory Analysis of the Correlates of Risk-Taking
Propensity in Canadian Military Personnel*
Jennifer E. C. Le e1, Ann-Renée Blais2
1Department of National Defence, Ottawa, Canada
2Defence Research and Development Canada, Toronto , Canada
Received October 23rd, 2013; revised November 28th, 2013; ac cepted February 14th, 2014
Copyright © 2014 Her Majesty the Queen in Right of Canada, as represented by the Minister of National De-
fence. This is an open access article distributed under the Creative Commons Attribution-Non Commercial-No
Derivatives 4.0 International License, which permits use, distribution, and reproduction in any medium for
non-commercial purposes, provided the original work is unchanged and properly cited.
There has been growing interest in the impacts of combat exposure on behaviora l health outcomes such as
alc ohol us e, risk y driving a nd smok ing in r esearc h on milit ar y personnel in rec ent year s. One ps ycholo gi-
cal factor that may explain such outcomes i s an indi viduals’ risk-tak ing propensit y. The present study thus
examined the relationships of risk-taking propensity with demographic variables, deployment history, as
well a s a number of heal th a nd ris k beha viors. Data coll ected as part of a compr ehens ive hea lth s urvey i n
the Canadian Armed Forces (CAF) in 2008 and 2009 were analyzed. Participants included a sample of
2157 R egular Force members, stratified to reflect the Regular Force in ter ms of rank , sex, a nd deployment
history. Using subscales of the Domain-Specific Risk Taking Scale (DOSPERT), participants’ levels of
risk-taking pr opens i ty in t he hea lt h and saf et y and in t he r ecr eat iona l do mains wer e as sess ed. Res ul ts c on-
sistently pointed to the higher levels of risk-taking propensity among younger respondents and men.
While non-commissioned members (NCMs) reported higher levels of health and safety risk-taking pro-
pensity than officers, officers reported higher levels of recreational risk-taking propensity than NCMs.
Variation in health and safety, but not recreational risk-taking propensity was found by deployment his-
tory. H ealth and safety risk-taki ng propensi ty was assoc iated with a number of hea lth-compromis ing be-
haviors (e.g., p oor eating hab its, inconsi stent helmet us e, smoking, p roblem drink ing), whil e recreational
risk-taking propensity was associated with a number of health-enhancing behaviors (e.g., good eating
habits, physical activity, never smoking). Results thus point to noteworthy variations in the correlates of
risk-taking propensity by risk domain among milita ry personnel.
Key words: Risk-Taking Propensity; Ris k Behavior; Lifestyle; Deployment; Health
Introduction
Whether in combat or training, risk is a fundamental part of
military service (Killgore, Cotting, Thomas, Cox, McGurk, Vo
et al., 2008). Hence, it may come as no surprise that a propen-
sity to take risks has, in some instances, been regarded as a
desirable attribute for military personnel (Momen, Taylor, Pie-
trobon, Gandhi, Markham, Padilla et al., 2010). However, this
very propensity may also lead to a greater engagement in un-
safe behavior (Killgore, Vo, Castro, & Hoge, 2006) and, possi-
bly, increased risk of injury and harm (RTI International, 2006).
Recentl y, it has b een suggested that th e exper iences of military
personnel, particularly during combat, might influence their
risk-taking behaviors once they return from deployment. This
has been an area of significant interest to various military or-
ganizations (North Atlantic Treaty Organisation [NATO] Re-
search and Technology Organisation [RTO] Task Group 164,
2012), in light of the growing evidence of increased risk beha-
vior (e.g., substance use or risky driving) and rates of injury
post-deployment (e.g., Bray, Pemberton, Lane, Hourani, Mat-
tiko, & Babeu, 2010; Hooper, Debakey, Bellis, Kang, Cowan,
Lincoln et al., 2006; Jacobson, Ryan, Hooper, Smith, Amoroso,
Boyko et al., 2008; Kelley, Killgore, Athy & Dretsh, 2010;
Killgore et al., 2008; Thomsen, Stander, McWhorter, Raben-
horst & Milner, 2011; Zamorski & Kelley, 2012). In one analy-
sis, however, Thomsen et al. (2011) observed that the effect of
deployment on increased risk behavior was only significant
among individuals with a history of engaging in risk behavior.
Such findings raise the question of whether individuals with a
predisposition towards risk behavior are particularly vulnerable
to the effects of depl oyment.
Risk-Taking Propensity and Military Deployment
The idea that individuals inherently differ in their tendencies
to engage in risk behavior is supported by both theory and em-
pirical findings on risk-taking. I ndeed , res earch h as p oin ted to a
high degree of inter-correlation among different types of risk
behavior (Donovon & Jessor, 1985; Jessor, Donovon, & Costa,
1991), suggesting that these may share common psychosocial
determinants (e.g., perceived environment or personality). Re-
flecting one’s natural inclination towards taking risks, risk-
*The present paper, accepted on February 14th
, 2014, is the final corrected
version.

J. E. C. LEE, A.-R. BLAIS
54
taking propensity could play a role in this regard.
Risk-taking propensity may represent or result from a com-
bination of personality traits that predispose individuals to en-
gage in risk behaviors. To be sure, research has shown that
individuals differ in their generalized attitudes towards risk, or
risk attitudes, on a continuum from risk aversion to risk seeking,
and that these may subsequently influence the process of risky
decision-making (Blais & Weber, 2009). In addition to risk
attitudes, a wide range of personality factors have been thought
to increase one’s propensity to engage in risk behavior. Exam-
ples may include perceived invincibility (i.e., one’s perception
of being immune to the negative consequences associated with
a given risky behavior), sensation seeking (i.e., the degree to
which one enjoys and seeks out thrilling or exciting experiences)
or impulsivity (i.e., the tendency to act in haste, with little
thought) (Cherpitel, 1993; 1999; Kelley et al., 2010; Killgo r e et
al., 2008) .
Along with risk behaviors, such as alcohol use, drug use and
smoking, there is evid ence that risk-takin g prop ensity increases
among military personnel after deployment (Kelley, Athy, Cho,
Erickson, King, & Cruz, 2012). Kelley et al. (2012), for exam-
ple, found that perceived invincibility as well as risk-related
self-confidence and risk/thrill seeking evaluations significantly
increased in US sold iers from pre-deployment to post-deploy-
ment. Over the same period, both frequency of alcohol use and
risky dri ving p racti ce s (i. e., f ailu re t o wear a mot orc ycle h el met)
increased. While acknowledging that risk-taking propensity
may serve as a det erminant of being deployed in the first place
(Bell, Amoroso, Wegman, & Senier, 2001), some authors have
posi ted that in creases in ri sk-taking propensity post-deployment
reflect u nderl ying changes i n healt h and well-being (Killgore et
al., 2008; NATO RTO Task Group 164, 2012). Killgore et al.
(2008) argued that physical trauma or prolonged exposure to
emotional stressors during deployment may have impacted
regions of the brain, resulting in altered decision-making under
risk. Results of one study demonstrated that US soldiers who
screened positive for post-traumatic stress disorder (PTSD)
with or without mild traumatic brain injury (mTBI) after re-
turning from a deployment to Iraq, reported greater risk/thrill
seeking than those who screened negative. These soldiers also
demonstrated a more pronounced increase in risk-related self-
confid ence evaluations (e.g., greater assuredness and preferen ce
for danger) from pre-deployment to post-deplo yment relative to
soldiers who screened negative (Kelley et al., 2012). In their
review, Zamorski and Kelley (2012) suggested that personality
characteristics, such as having a high tolerance for risk, a ten-
dency to seek sensational or novel experiences and being im-
pulsive, might explain increases in risky driving behaviors
among military personnel post-deployment. Along this line,
risk-taking propensity could serve as a factor explaining the
impact of deployment on risk behaviors.
Aside from having been found to increase after deployment
(e.g., Killgore et al., 2008; Kelley et al., 20 12), risk-taking p ro-
pensity has been found to be significantly associated with risk
behavior in some studies of US militar y person n el. Specifically,
Killgore and his colleagues found that various measures of
risk-taking propensity, such as the Evaluation of R is ks (EVAR)
scale and the Invincibility Beliefs Index, were associated with
greater engagement in behaviors such as consuming alcohol,
binge drinking, getting angry or yelling at others, getting into
fights, an d th reaten ing oth ers (Killgor e, Cast ro , & Hoge, 20 10 a;
Killgore, Kelley, & Balkin, 2010b).
Domain-Specific ity of Risk-Ta king P r opensity
Among the various measures that have been used as indices
of risk-taking propensity in military personnel research (e.g.,
Evaluati on of Risks scal e, Brief Sensati on Seekin g scale, In vin-
cibility Beliefs Index; Kelley et al., 2010; Killgore et al., 2008),
none were designed to account for possible differences in risk-
taking propensity across domains. Yet, individuals’ risky choi-
ces (and hence their associated risk attitudes) have been found
to vary across different domains and situations (MacCrimmon
& Wehrung, 1986, 1990; Schoemaker, 1990). Domains in
which individuals have typically displayed different degrees of
risk-taking include gambling, financial investing, business de-
cisions, and personal decisions (MacCrimmon & Wehrung,
1986, 1990). Personal decisions can be further broken down
into sub-categories, which differ in their associated concerns
and go als (Weber, Ames, & Blais, 2005; Web er & Lindeman n,
2007), such as ethical (e.g., plagiarizing a term paper), health/
safety (e.g., unprotected sex), and social (e.g., confronting a
coworker) decisions.
Inspired by the domain-specificity of risk attitudes, Weber,
Blais, and Betz (2002) developed the Do main-Specific Risk-
Taking (DOSPERT) Scale—a 40-item self-report instrument
that evalu ates risk attitudes (as well as percei ved-risk attitudes,
i.e., the tradeoff between perceived risks and benefits) in six
domains (i.e., ethical, gambling, health, investing, recreational,
and social). Researchers have used the 2002 DOSPERT in a
wide range of settings, populations, and cultures. For example,
Harrison, Young, Butow, Salkeld, and Solomon (2005), in their
review of a large number of instruments assessing risk propen-
sity in healthcare decisions, alluded to the 2002 DOSPERT as
one of three instruments that are “relevant to a clinical envi-
ronment as they directly measure risk propensity across a num-
ber of everyday situations, including the propensity to take
health-related risks” (p. 1394). Supporting the validity of the
2002 DOSPERT scores, Hanoch, Johnson, and Wilke (2006)
showed that individuals who engaged in risky recreational ac-
tivities (i.e., bungee jumpers, sky divers, hang gliders, and scu-
ba di vers) h ad the hi ghest scor es on the r ecreati on al risk- taking
propensity subscale. As well, individuals who engaged in
health seeking behaviors (i.e., gym members) and health risk
behavio rs ( i.e. , smokers) e ach h ad t he lo west and highes t sco res,
respectively, on the health and safety risk-taking propensity
subscale.
Study Obj e ctives
In light of recent work pointing to the domain-specificity of
risk-taking propensity, the aim of the present study was to ex-
plore the correlates of risk-taking propensity in different do-
mains among military personnel. For this purpose, analyses
were carried out on data collected as part of a comprehensive
health su rvey in th e Can adian Armed Fo rces (C AF). In addi tion
to assessing a wide range of health and lifestyle factors, this
survey assessed health and safety, as well as recreational do-
mains of risk-taking propensity using elements of the DOS-
PERT. Hence, it was determined whether risk-taking propensity
in both domains varies as a function of various demographic
variables and deployment history. As well, the relationships of
both domains of risk-taking propensity with engagement in risk
behaviors (e.g., substance use, smoking) and health behaviors
(e.g., eating habits, physical activity, safety practices) were

J. E. C. LEE, A.-R. BLAIS
55
examined. Based on previous research, it was expected that
risk-taking propensity would be greater among CAF personnel
who were recently deployed. It was also expected that higher
risk-taking propensity would be associated with greater en-
gagement in risk behavior and, conversely, lesser engagement
in health behavior.
Method
Participants
Participants were respondents of the 2008/9 Health and Life-
style Information Survey (HLIS). This paper and pencil survey
was mailed between November 2008 and November 2009 to a
sample of 4744 CAF Regular Force members, which was stra-
tified by rank, sex and deployment history to reflect the overall
CAF Regular Force population. Among the 4744 CAF mem-
bers who were mailed a survey, 2315 provided a response for a
gross response rate of 49%. An inverse proportional weight was
applied to account for the stratified complex sampling design
based on sex, rank and deployment history, after adjusting for
non-response. Because some respondents did not provide
enough information to be assigned a population weight, the
final sa mple includ ed 2157 member s of the CAF Regular Force.
With population weights applied, participants were primarily
male (87%), under the age of 40 years (28% was 18 to 29 years,
28% was 30 to 29 years) and of lower ranks (51% was Pri-
vate/Ordinary Seaman to Master Corporal/Master Seaman).
Also, most of them had been deployed in the past two years
(76%). More detailed information about the survey procedure is
provided elsewhere (see Whitehead & Hawes, 2010). The sur-
vey was approved by an independent human research ethics
review board.
Measures
Consisting of multiple sections (e.g., overall health status,
mental and social wellness, and occupational health and safety
issues, among others), the 2008/9 HLIS was designed to pro-
vide a comprehensive assessment of health and its various de-
terminants in the CAF. Details regarding the items or measures
used to assess variables of relevance to the present study are
provided below.
Risk-taking propensity. Risk-taking propensity was as-
sessed using two 6-item subscales of the DOSPERT—recrea-
tional risk-taking propensity and health and safety risk-taking
propensity. Items in these subscales represent various types of
risky activities. Risky recreational activities include:
• Going camping in the wilderness
• Going down a ski run that is beyond your ability
• Going white water rafting at high water in the spring
• Takin g a s kydiving clas s
• Bungee jumping off a tall bridge
• Piloting a small plane
Risky health and safety activities include:
• Drinking heavily at a social function
• Engaging in unprotected sex
• Driving a car withou t wearin g a seat belt
• Riding a motorcycle without wearing a helmet
• Sunbathing without sunscreen
• Walking home alone at night in an unsafe area of town
Using a 7-point rating scale (1 = extremely unlikely, 2 =
moderately unlikely, 3 = somewhat unlikely, 4 = not sure 5 =
somewhat likely, 6 = moderately likely, 7 = extremely likely),
respondents indicated the likelihood with which they would
engage in each activity if they had an opportunity to do so. The
subscales demonstrated adequate reliability (i.e., Cronbach’s
alphas of .68 for the recreational and .80 for the health and
safety risk-taking propensi ty subscales, respectively).
Demographic characteristics. Demographic variables that
were considered included age group (18 - 29 years, 30 - 39
years, 40 - 49 years, 50 - 64 years), educat ion ( some/completed
secondary, completed college/some university, completed uni-
versity), element (air, sea, land), first official language (English,
French), rank (non-commissioned member [NCM] or officer)
and sex.
Deployment history. A variable was created to identify the
number of times each participant was deployed in the past two
years, based on responses to two items. Specifically, categori-
zation was derived from responses to: 1) “When were you last
deployed overseas?” and 2) “How many times have you been
deployed overseas in the past 2 years?” Participants who ans-
wered “I’ve never been deployed” or “More than 2 years ago”
to the former question, were categorized as having been dep-
loyed overseas 0 times in the past t wo years, whil e participants
who indicated that they were deployed “In the last 12 months”
or “Between 12 and 24 months ago” were assigned the values
they provided to the latter question. While this variable did not
take into consideration the duration or nature of the deployment,
previous analyses revealed that 76% of the reported overseas
deployments were in Afghanistan and 18% were in the Middle
East (Whi tehead & Hawes, 2010).
Healt h a nd ri sk beha viors . A broad array of health and risk
behaviors was assessed in the 2008/9 HLIS, ranging from eat-
ing habits to the use of energy supplements. In the interest of
parsimony, only a subset of behaviors was considered in the
presen t study. As a starti ng point, variables were selected on the
basis of their face validity as indicators of risk behavior. How-
ever, it was decided to also investigate the relationships of
risk-taking propensity with health behaviors, since a greater
propensity for risk-taking could also result in a decreased en-
gagement in health behaviors. Therefore, some health behaviors
were selected. In addition to face validity, the reliability of the
measures (as d etermined through past research ) was consid ered
in the selection of variables. Broadly speaking, health and risk
behaviors pertained to diet, physical activity, safety practices,
and substance use.
To examine diet, questions assessed the number of times that
respondents had skipped breakfast, skipped lunch, and felt too
rushed to eat regular meals in the past week. Daily fruit and
vegetable consumption was also assessed using a measure
adapted from one used in the Canadian Community Health
Survey (CCHS; Statistics Canada, 2001), which has been found
to significantly correlate with the Healthy Eating Index (Garri-
guet, 2009). This measure requires respondents to report the
frequency (daily or weekly) with which they consume six dif-
ferent types of fruits or vegetables (e.g. , frui t jui ces, green salad ,
carrots). An index of daily frequency of fruit and vegetable
consumption is then derived based on responses.
Physical activity was measured using another measure
adapted from the CCHS (Statistics Canada, 2001). Total daily
energy expenditure (EE) was estimated based on the frequency
(number of times) and average duration (1 - 15 minutes, 16 - 30
minutes, 31 - 60 minutes or more than one hour) of respondents’
participation in 18 activities. Respondents were categorized as

J. E. C. LEE, A.-R. BLAIS
56
inactive, moderately active or active according to pre-specified
cutoff values (Statistics Canada, 2005; total EE of less than 1.5
for inactive, total EE of 1.5 to 2.9 for moderately active and
total EE of 3 or more for active).
Bicycle helmet use was examined as an indicator of safety
practices . Respon dents were aske d to repo rt the frequ ency with
which they wear a helmet when riding a bicycle (always, most
of the time, rarely, never, don’t ride a bicycle). Respondents
who indicated that they did not ride a bicycle were excluded
from an y analysis involving this question.
Regardin g substance use, respondents were asked t o indicate
whether or not they had used energy drinks (such as Red Bull,
Full Throttle, Monster, AMP, Jolt or Wired) and performance
enhancers (such as synephrine, glutamine, Co-enzyme Q10,
amino acids, creatine, pro-hormones, hydroxymethyl butyrate/
HMB) in the past year.
Smoking status was assessed based on two questions:
whether respondents have smoked at least 100 cigarettes (4 to 5
packs) in their lifetime, and whether they currently smoke cig-
arettes every day, occasionally or not at all. Respondents who
indicated that they had not smoked 100 cigarettes in their life-
time were considered never smokers. Among respondents who
had smoked more than 100 cigarettes, those who indicated that
they currently smoke every day or occasionally were consi-
dered smokers, while those who indicated that they currently do
not smoke at all were con s idered ex-smokers.
Two indicators of alcohol use were examined: whether res-
pondents had engaged in binge drinking (six or more drinks on
one occasion) in the past year (less than monthly versus on a
monthly basis or more), and their scores on the Alcohol Use
Disorders Identification Test (AUDIT). Scores on the AUDIT
range from 0 to 40 and reflect one’s frequency of alcohol use,
engagement in hazardous drinking and symptoms of possible
alcohol dependence. Scores of 8 or more are recommended
indicators of hazardous and harmful alcohol use (Babor, Hig-
gins -Biddle, Saunders, & Montneiro, 2001).
Analyses
All analyses were carried out using the SPSS 17.0 Complex
Samples module, which allowed the adjustment for effects due
to the stratified sampling design. A series of analyses of va-
riance (ANOVAs) were conducted to examine variation in
risk-taking propensity scores by age, education, element, lan-
guage, rank, sex, and deployment history. Linear regression
analyses were conducted to examine the relationship between
risk-taking propensity and health or risk behaviors measured on
a continuous scale, while logistic regression analyses (multi-
nomial or binary logistic regression) were conducted to ex-
amine the relationship between risk-taking propensity and
health or risk behaviors measured on a categorical or nominal
scale. In these analyses, recreational risk-taking propensity and
health and safety risk-taking propensity were simultaneously
entered as independen t variables in ord er to reduce family-wise
error and account for intercorrelations among the two. These
analyses, however, were not adjusted for demographic cova-
riates, given the exploratory nature of the work.
Results
Demographic Characteristics
Mean scores obtained by participants on the DOSPERT re-
creation al risk-taking propensity and health and safety risk-tak -
ing propensity subscales are presented in Table 1 by demo-
graphic groupings.
Recreational risk-taking propensity significantly differed ac-
cording to age group (F[3, 2084] = 38.72 , p < .001), element
(F[2, 2080] = 5.91 , p < .01 ), language (F[1, 2076] = 13.16 , p
< .001), rank (F[1, 208 6] = 6.9 5 , p < .01), and sex (F[1, 2086]
= 44.24 , p < .001 ). Specificall y, there was a tend ency for recr-
eational risk-taking propensity to be greater among younger
respondents, members of the Air Force, those with English as a
first official languag e, officers and men.
Health and safety risk-taking propensity was found to differ
according to age group (F[3, 2 065] = 38. 27 , p < .001), educa-
tion (F[2, 2040] = 4.99 , p < .01), rank (F[1, 2067] = 17.18, p
< .001) and sex (F[1, 2067] = 141.46, p < .001). In line with
results regarding recr eational risk-taking propensity, health and
safety risk-taking propensity was greater among younger res-
pondents and men. However, it was greater among NCMs (ra-
ther than officers) and among those without post-secondary
educat ion.
Deployment History
Mean scores by deployment history (i.e., 0, 1, 2, or 3 or more
deployments in the past two years) are presented in Table 2.
While no differences were observed in recreational risk-taking
propensity across deployment history groups, significant dif-
ferences wer e observed in h ealth and safety risk-taking propen-
sity (F[3, 2054] = 3.11, p < .05). An examination of simple
effects revealed that health and safety risk-taking propensity
was greater among those who were deployed once (F[1, 2056]
= 4.36, p < .05) or twice (F[1, 2056] = 5.82, p < .05) rel ative to
those who were not deployed in the past two years.
Health and Risk Behaviors
Diet. Table 3 provides a summary of regression coefficient
estimates (B) and corresponding standard errors (SE B) of linear
regression analyses predicting eating habits. Results revealed
that risk-taking propensity significantly predicted the number of
days participants felt too rushed to eat regularly (R2 = .02, p
< .05), skipped breakfast (R2 = .06, p < .001) and skipped lunch
(R2 = .01, p < .05) in the past week. While greater health and
safety risk-taking propensity was associated with engaging in
each of these unfavorable eating behaviors more frequently,
greater recreational risk-taking propensity was associated with
skipping breakfast less frequently.
Risk-taking propensity was also found to be significantly as-
sociated with daily fruit and vegetable servings in a multinomi-
al logistic regression analysis (Nagelkerke R2 = .06, p < .001).
While health and safety risk-taking propensity was negatively
associated with daily fruit and vegetable servings, recreational
risk-taking p ropensi ty was po sitively associated with daily fruit
and vegetab le servings. Ta ble 4 provides a summary of regres-
sion coefficient estimates (B), odds ratios (OR) and corres-
ponding 95% confidence intervals (CI) resulting from the anal-
ysis. Sp ecifically, for every one po int increase in score on recr-
eational risk-taking propensity, the odds of consuming 5 to 9
servings increased by 5% and the odds of consuming 10 or
more increased by 7%. For every one point increase in score on
health and s afety risk-taking propensity, the odds of consuming
5 to 9 servings decreased by 4% and the odds of consuming 10

J. E. C. LEE, A.-R. BLAIS
57
Tabl e 1.
Mean sc ores and standa rd errors on measu res of recrea tion al and health
and safety risk-taking propensity by demographic group.
Demographic Variab le
Recreational
Risk-Taking
Propensity
Health and Safety
Risk-Taking
Propensity
M SE M SE
All Participants 21.0 0.3 17.4 0 .3
Age
18 - 29 years 25.6a 0.7 21.2a 0.6
30 - 39 years 21.2b 0.5 17.6b 0.5
40 - 49 years 18.3c 0.5 15.3c 0.4
50 - 64 years 15.8d 0.7 13.1d 0.6
Education
Some/Completed Secondary 20.6a 0.6 17.7a 0.5
College/Some Uni versity 21.2a 0.6 1 7.8a 0.4
University Completed 21.7a 0.4 16.2b 0.4
Element
Air 22.1a 0.5 16.8a 0.4
Sea 18.9b 0.7 17.7a 0.7
Land 20.9c 0.5 17.9 a 0.4
Language
English 21.8a 0.4 17.7a 0.3
French 19.2b 0.6 16.7a 0.5
Rank
NCM 20.7a 0.4 17.8a 0.3
Officer 22.1b 0.4 16.0 b 0.3
Sex
Men 21.4a 0.4 18.0a 0.3
Women 18.4b 0.3 13.8 b 0.2
Note. M e a ns with s ubscripts that differ are s igni ficantly different at p < .05.
Table 2.
Mean sc ores and standa rd errors on measu res of recrea tion al and health
and safety risk-taking propensity by deployment history.
Number of Deployments
in Past Two Years
Recreational
Risk-Taking
Propensity
Health and Safety
Risk-Taking
Propensity
M SE M SE
0 21.0a 0.4 17.1a 0.3
1 21.3a 0.5 18.2b 0.4
2 21.8a 1.2 19.8b 1.0
3 or more 18.6b 0.9 17.1a 1.1
Note. M e a ns with s ubscripts that differ are s igni ficantly different at p < .05.
or more decreased by 8%.
Physical activity. Risk-taking propensity was significantly
associated with physical activity (Nagelkerke R2 = .04, p
< .001). A summary of results is provided in Table 5. Specifi-
cally, recreational risk-taking propensity was positively associ
ated with physical activity such that a one point increase in
score on its corresponding measure was associated with a 5%
Table 3.
Results of multiple regression analyses predicting problem eating ha-
bits.
Risk Domain B SE B
Too Rushed Recreational <0.01 0.01
Health and Safety 0.03* 0.01
Risk Domain B SE B
Skipped
Breakfast
Recreational −0.03** 0.01
Health and Safety 0.09*** 0.0 2
Risk Domain B SE B
Skipped Lunch Recreational −0.01 0.01
Health and Safety 0.02* 0.01
Note. *p < .05, **p < .01, ***p < .001.
Table 4.
Results of multinomial logistic regression analysis differentiating Re-
gula r Fo rc e mem b ers consuming less than five daily fruit and vegetable
servings f rom those c onsuming fi ve to nine and ten or m ore.
Risk Domain 5 to 9 Servings 10 + Servings
B OR (95% CI) B OR (95% CI)
Recreational 0.05 1.05
(1.03 - 1.07) 0.07 1.07 (1.03 - 1.12)
Health and Safety −0.05 0.96
(0.93 - 0.98) −0.08 0.92 (0.88 - 0.97)
Note. C I = confidence interva l; OR = odds ratio.
Tabl e 5.
Results of multinomial logistic regression analysis differentiating phy-
sic ally i nact iv e Regula r Force m embe rs f rom th ose who a re mod erat ely
active and ac tiv e .
Risk Domain Moderate ly Active Active
B OR (95% CI) B OR (95% CI)
Recreational 0.03 1.02
(1.00 - 1.05) 0.05 1.0 5 (1.03 - 1.07)
Health and Safety <−0.01 0.99
(0.96 - 1.03) 0.01 1.01 (0.99 - 1.04)
Note. CI = confidence interval; OR = odds ratio.
increase in odds of being active relative to inactive. However,
health and safety risk-taking propensity was not significantly
associated with physical activity.
Safety. Among Regular Force members who ride a bicycle,
it was found that the use of a bicycle helmet was significantly
associated with risk-taking propensity (Nagelkerke R2 = .14,
p< .001). In particular, the odds of consistently (i.e., always)
using a bicycle helmet while riding a bicycle significantly de-
creased as a fun ctio n of heal th and safet y risk-taking propensity
but were not related to recreational risk-taking propensity. For
every one point increase in score on health and safety risk-
taking propensity, the odds of consistently using a bicycle hel-
met decreased by 10% (Table 6).
Substance use. Risk-taking propensity was significantly as-
sociated with the use of both energy drinks (Nagelkerke R2
= .10, p < .001) and performance enhancers (Nagelkerke R2
= .04, p < .001). In both cas es, the relationship was only signif-
icant for health and safety risk-taking propensity, with a one
point increase in score associated with an 8% increase in the
odds of using energy drinks and 5% increase in the odds of
usin g performance enhancers (Table 7).

J. E. C. LEE, A.-R. BLAIS
58
Tabl e 6.
Results of binary logistic regression analysis predicting consistent bicy-
cle h el met use.
Risk Domain B OR (95% CI)
Recreational 0.01 1.01 (0.98 - 1.03)
Health and Safety −0.11 0.90 (0.88 - 0.93)
Note. CI = confidence interval; OR = odds ratio.
Tabl e 7.
Results of binary logistic regression analyses predicting energy sup-
plement use in the past yea r .
Risk Domain B OR (95% CI)
Energy Drinks Recreational 0.01 1.01 (0.99 - 1.04)
Health and Safety 0.07 1.08 (1.05 - 1.11)
Risk Domain B OR (95% CI)
Performance
Enhancers
Recreational 0.01 1.01 (0.98 - 1.04)
Health and Safety 0.05 1.05 (1.02 - 1.08)
Note. C I = confidence interva l; OR = odds ratio.
The relationship of risk-taking propensity with smoking sta-
tus was also found to differ across risk domains. While it was
significantly associated with smoking status overall (Nagel-
kerke R2 = .09, p < .001), the odds of being a current or
ex-smoker decreased as recreational risk-taking propensity
increased (by 3% and 8% per one point increase, respectively)
and increased as health and safety risk-taking propensity in-
creased (by 8% and 6%, respecti vel y), as shown in Table 8 .
Risk-taking propensity was significantly associated with
having engaged in binge drinking (i.e., consuming six or more
drinks on a single occasion) on a monthly basis or more over
the past year (Nagelkerke R2 = .19, p < .001). However, this
relationship was primarily driven by health and safety risk-
taking propensity, with the odds of binge drinking increasing
by 14% for every one point increase in score on the subscale
(Table 9).
In line with results pertaining to binge drinking, risk-taking
propensity was also significantly associated with scores on the
AUDIT (R2 = .14, p < .001). Again, the association was only
statistically significant for health and safety risk-taking pro-
pensity, with greater scores predicting higher AUDIT scores
(Table 10).
Discussion
The aim o f the present study was to explore the various cor-
relates of risk-taking propensity in different domains among
military personnel. While some of the findings converge with
results of previous studies (Kelley et al., 2012; Killgore, 2010a,
2010b), others highlight the value of considering the domain-
specificity of risk-taking propensity for providing a more
nuanced perspective of its correlates, particularly those related
to health and risk behaviors.
Summary o f Findings
Both recreational and health and safety risk-taking propensity
were found to differ according to key demographic factors,
including age, rank and sex. Younger respondents and men
invariably demonstrated greater risk-taking propensity, and
these results are consistent with past research. Women appear
Tabl e 8.
Results of multinomial logistic regression analysis differentiating cur-
rent smokers and ex -smokers from never smokers.
Risk Domain Current Smoker Ex-Smoker
B OR (95% CI) B OR (95% CI)
Recreational −0.03 0.97 (0.94 - 0.99) −0.08 0.92 (0.90 - 0.95)
Health and Safety 0.08 1.08 (1.05 - 1.11) 0.06 1.06 (1.02 - 1.09)
Note. C I = confidence interva l; OR = odds ratio.
Tabl e 9.
Results of binary logistic regression analysis predicting binge drinking
beha vior (on a monthly basis or more) in the past year.
Risk Domain B OR (95% CI)
Recreational <−0.01 1.00 (0.98 - 1.02)
Health and Safety 0.13 1.14 (1.11 - 1.18)
Note. C I = confidence interva l; OR = odds ratio.
Tabl e 1 0 .
Results of linear regression analysis predicting AUDIT scores.
Risk Domai n B SE B
Recreational 0.02 0.02
Health and Safety 0.18*** 0.03
Note. *p < .05, **p < .01, ***p < .001.
to be more risk averse in many situations and contexts, a find-
ing that can at least partly be explained by the fact that they
perceive greater risks in most domains (all but the social do-
main; Weber & Johnson, 2009). Older adults have been found
to be more risk averse than younger adults in some studies, yet
the evidence for this effect remains mixed (Weber & Johnson,
2009).
For other demographic factors, there was n otab le variation in
relationships with risk-taking propensity across domains. While
officers demonstrated greater recreational risk-taking propensi-
ty, they demonstrated lower levels of health and safety risk-
taking propensity. Although the specific mechani s ms that might
explain this observation are unclear, it might account for results
of previous analyses pointing to greater participation in physi-
cal activity and more consistent use of safety equipment (e.g.,
bike helmets and seatbelts) among officers (Lee & Hachey,
2011).
Only recreational risk-taking propensity was found to differ
by element and first official language, with higher levels re-
ported by members of the Air Force and those with English as a
first official language. One factor that may account for higher
levels of risk-taking propensity among members of the Air
Force i s the fact th at one o f the scale ite ms assesse s one’s li ke-
lihood of piloting a small plane. Blais and Weber (2006) found
a similar difference between English- and French-speaking
adult civilians, with English-speaking participants showing
greater ris k-taking propensity in both the health and recreation-
al domains.
Finally, only health and safety risk-taking propensity was
found to differ by educational attainment, with the highest le-
vels reported by those with no university degree. While it
would be easy to assume that individuals with lower levels of
education might demonstrate more risk-taking propensity in
this domain due to lesser awareness of risks, the possible in-

J. E. C. LEE, A.-R. BLAIS
59
volvement of other influences should be recognized. For in-
stance, CAF members with lower levels of education may be
employed at lower ranks in occupations that require them to be
exposed to health and safety risks or to be deployed overseas.
The r elationship between deployment history and risk-taking
propensity also varied across domains in that health and safety,
but not recreational risk-taking propensity differed by deploy-
ment history. As was expected, respondents who were deployed
one to two times in the past two years demonstrated higher
levels of health and safety risk-taking propensity compared to
those who were not deployed. On the other hand, those who
were deployed three or more times demonstrated similar levels
of health and safety risk-taking propensity. Results of a recent
stud y by Kelley et al. ( 2012) revealed a medi um to large effect
of deployment on risk-taking propensity. One important differ-
ence, however, is that variations were examined by comparing
risk-taking propensity before and after deployment rather than
comparing risk-taking propensity among service members who
have and have not been deployed. Military personnel with dif-
ferent levels of deployment experience may differ on factors
other than the number of times they have been deployed, such
as their level of health. The “healthy warrior” effect, for in-
stance, refers to the tendency for military personnel who have
been deployed to demonstrate better health than their non-de-
ployed counterparts, in part due to screening and selection
processes (Haley, 1998). Similarly, the fact that those who were
deployed three or more times demonstrated similar levels of
health and safety risk-taking propensity than those who had not
been d eplo yed might have rel ated to the need to h ave extremel y
good health in order to be able to go on multiple deployments
and the fact that any propensity to take health and safety risks
would have compromised health.
Analyses of the relationships between risk-taking propensity
in both domains and various risk behaviors yield ed an inter est-
ing pattern of results. As expected, greater health and safety
risk-taking propensity was associated with a number of health-
compromising behaviors, including poor eating habits (i.e.,
skipping meals, lower fruit and vegetable consumption), lesser
use of bicycle helmets, and greater use of various substances
(i.e., energy drinks, performance enhancers, tobacco and alco-
hol). Given that some of the items used to assess health and
safety risk-taking propensity related to alcohol consumption
and motorcycle helmet use, its relationship with bicycle helmet
use and alcohol consumption may not be entirely remarkable.
Still, it is reiterated that the purpose of these items is to assess
one’s propensity to engage in these behaviors, which is distinct
from an individual’s actual engage ment in them.
Contrary to expectations, recreational risk-taking propensity
was associated with a number of health-enhancing behaviors.
Specifically, respondents who reported greater recreational
risk-taking propensity demonstrated better eating habits (i.e.,
not skipping lunch, higher fruit and vegetable consumption),
higher levels of physical activity, and lower odds of being a
current or ex-smoker. Such findings recall the distinction that
has been made between behavioral immunogens, as behaviors
that promote health and prevent disease, and behavioral patho-
gens, as behaviors that impair health and increase the risk of
disease (Matarazzo, 1984). Having focused on risk behaviors,
such as alcohol use and fighting, much of the work on risk-
taking propensity in military personnel has addressed the latter.
Yet, the current findings suggest that there may be value in
considering other types of behavioral outcomes, as these may
result from different factors and processes.
Limitations
On the whole, findings bring to light noteworthy variations
in the potential outcomes of risk-taking propensity in different
domains. However, some important limitations are noted. First,
while a causal relationship may be assumed between risk-taking
propensity and the health and risk behaviors, the direction of
these relationships may not be inferred due to the cross-sec-
tional na t ur e of the study.
A second limitation is the fact that only a small set of DOS-
PERT subscales were considered in the present study. In addi-
tion to including measures of risk-taking propensity in different
risk domains (health and safety, recreational, financial, ethical
and social domains), the original DOSPERT includes measures
to assess perceptions of risk in these domains (Blais & Weber,
2009). Having considered both perceptions and behavioral in-
tentions related to risks in all of the domains could have pro-
vided a more d etai led per spect ive of th e mechan is ms th at migh t
account for the relationship between risk-taking propensity and
health and risk behaviors.
Implications for Theory and Research
Previously, it was recognized that a certain degree of risk-
taking may be beneficial in the military context (Momen et al.,
2010). Specificall y, Mo men et al . (2 010 , p. 130) noted, “[s]ome
risk-takers are more impulsive and are more likely to expe-
rience preventable negative consequences as result of their
thrill- seeking propensity. Some risk-takers, on the other hand,
go through a process of deliberation where they contemplate
before taking risks. These individuals are more likely to expe-
rience positive consequences for their risk-taking behavior”.
Recognizing the potential benefits of readiness to accept risks,
some military organizations have considered sensation seeking
as one el ement of recru iting ca mpaigns (P armak, 2011 ; Sackett
& Mavor, 2004). However, a greater propensity to take risks
may pose problems if it leads to behaviors that may compro-
mise health. While analyses were exploratory in nature, results
demonstrated a fair degree of consistency in support of the role
of recreational risk-taking propensity in health-enhancing be-
havior and the role of health and safety risk-taking propensity
in health-compromising behavior in the present study. Addi-
tional research in which the domain-specificity of risk-taking
propensity is considered could bring us closer to understanding
which specific aspects of risk-taking propensity are desirable
and which ones are not.
A common feature of items used to assess recreational
risk-taking propensity is their focus on the propensity for en-
gaging in extreme sports or activities that would require skill,
knowledge o r p rep arati on . Whi le t hese act ivit ies en tail a cer tain
degree of risk, success in these activities also requires partici-
pants to be in top shape. In a qualitative study of extreme sport
practit ioners, it was revealed t hat the physical and mental chal-
lenge posed by extreme sports was an important factor in indi-
viduals’ reasons for engaging in them (Willig, 2008). For some,
improving their skills and gaining experience with the sport
generated a sen se o f master y. In o rd er t o d evelo p th eir cap abi li-
ties and further push themselves, athletes had to be disciplined
and self-aware. Hence, the psychological processes that in-
crease individuals’ propensity for recreational risk-taking may,
in some way, overlap with those that compel them to monitor

J. E. C. LEE, A.-R. BLAIS
60
and think about their behaviors more closely. This could be
interp reted as a form of care ful delib eration and contemplation,
which so me have argued may enabl e mor e effective risk-taking
and positive outcomes (Momen et al., 2010), and could account
for wh y recreation al risk-taking propensity was associated with
health-enhancing behaviors. While further research is necessary
to fully understand the psychological processes involved in this
relationship, it could be worthwhile for military organizations
to target individuals with a specific propensity for recreational
risk-taking in military recruitment campaigns rather than a
broader sensation seeking temperament.
As a rule, items used to assess health and safety risk-taking
propensity focused on the propensity for engaging in activities
that may threaten individuals’ health or safety. The relationship
between health and safety risk-taking propensity in risk beha-
vior was therefore not surprising. Kelley et al. (2012) noted that
risk-taking propensity may not only put the health and safety of
individuals and their families in jeopardy, it may also have a
detrimental i mpact on operati onal readiness. Given the potential
effects of deployment on health and safety risk-taking propen-
sity, it could be worthwhile to assess the value of incorporating
discussions on the impacts of deployment into pre-deployment
training and education. Raising awareness about possible in-
creases in risk-taking among military personnel upon their re-
turn from deployment and the implications for health and safet y
could encourage service members and th eir families t o monitor
and regulate their behavior. Furthermore, the possible link be-
tween risk-taking propensity and overall well-being should be
addressed in these discussions to ensure that emotional needs
are not ove r l ook e d.
Next Steps
A major limitation of the present analyses is their cross-sec-
tional nature. Measures of risk-taking propensity will be admi-
nistered as part of another survey in the CAF—the Recruit
Health Questionnaire—which serves as a bas eline h ealth moni-
toring tool administered in the early stages of basic military
training (Whitehead, Lee, & McCreary, 2012). In future work,
it will thus be possible to conduct prospective analyses to ex-
amine the predictive validity of risk-taking propensity for
health and risk behaviors, as well as other outcomes, such as
injuries or work performance. Future research using a longitu-
dinal study design will provide a better platform for determin-
ing whether risk-taking propensity does change as a function of
military experiences, such as training or deployment, and
whether it plays a role in other outcomes, such as injury.
As well, additional work could address the possible role of
risk-taking propensity in the performance of military duties.
While so me aspects of risk-taking propensity may be negatively
associated with beh avioral healt h, they could still play a role in
the su ccessful perfo rmance of military du ties, p articularl y those
involving a high degree of risk. It may be useful to consider the
nature of the relationship between risk-taking propensity and
performance of military duties in addition to its relationship
with beh avioral heal th to arrive at a mor e balanced u nderstand-
ing of the implications of risk-taking propensity for military
organizations.
Conclusion
Although analyses were exploratory in nature, results provide
support for the role of recreational risk-taking propensity in
promoting health-enhancing behaviors and the role of health
and safety risk-taking propensity in promoting health-com-
promising behaviors. Ultimately, these observations bring us
closer to understanding which specific aspects of risk-taking
propensity may be desirable and which ones may be undesira-
ble. Given that a fair degree of risk-t aking may be b eneficial in
the military context (Momen et al., 2010), these findings raise
the question of whether it may be beneficial to target individu-
als demonstrating more adaptive forms of risk-taking propensi-
ty (such as recr eat io nal risk-taking propensity) rather than those
with a broader sensation seeking nature in military recruitment
campaigns. Additional longitudinal research on the relation-
ships o f risk-taking propensity in different domains with injury
or performance in military duties (in addition to health and risk
behaviors) could help shed light on this issue. Longitudinal
research in this area would also be fruitful to better understand
how risk-taking propensity may change over time, both as a
normal part of the aging process and as a function of military
experien ces , and how it may influence h ealth.
Acknowledgement s
The authors would like to thank Dr. Jeff Whitehead for
granting access to the 2008/9 Regular Force Health and Life-
style Info rmation Sur vey dataset, as well as t he var ious review-
ers for their critical feedback on earlier drafts of this manu-
script .
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