Vol.1, No.3, 143-153 (2011)
doi:10.4236/ojpm.2011.13019
C opyright © 2011 SciRes. Openly accessible at http://www.scirp.org/journal/OJPM/
Open Journal of Preventive Medicine
Perception and prevalence of behavioral risk factors:
the lifestyle risk scale (LRS)*
Beatrix Algurén1,2#, Rolf Weitkunat3,4
1School of Health Sciences, Jönköping University, Jönköping, Sweden;
2Institute for Health and Rehabilitation Sciences (IHRS), Ludwig-Maximilians-University, Munich, Germany;
#Corresponding Author:
3Institute for Medical Informatics, Biometry and Epidemiology, University of Munich, Munich, Germany (during the study period);
4R&D Fellow, Philip Morris Products SA, Neuchâtel, Switzerland (current affiliation).
Received 29 August 2011; revised 23 September 2011; accepted 21 October 2011.
ABSTRACT
Objective: To de v e lop a life style risk scale (L RS )
of health-related behaviors based on risk as-
sessments of study participants. Method: By
means of pairwise comparisons of assessed
risks associated with tobacco, alcohol, obesity,
fast-food, physical inactivity, and lack of sleep,
each at four lev els, 24 behav iors were ranked on
a unidimensional risk scale. Results: Overall,
use of tobacco was assigned the highest risk
score (3.7), consumption of fast-food and lack
of sleep the lowest (1.7, 1.6). Minor risk factors
(lack of sleep and fast-food) were, at their
highest levels, assigned similar risk values as
major risk factors (tobacco, alcohol, obesity) at
their lowest levels. Lifestyles of female partici-
pants were less hazardous than those of male
participants, as measured with the LRS. In con-
trast, perception of behavioral health risks was
more precise in men. Conclusions: The LRS
provides a practical quantification to identify
and compare groups with different risk behavior
patterns as well as clusters of risky health be-
haviors in and across populations. It can also
support the communication of behavi oral health
risks.
Keywords: Health Behavior; Lifestyle Score; Risk
Communication; Risk Perception
1. INTRODUCTION
It is generally accepted that in Western populations
chronic diseases are largely due to unhealthy lifestyles
[1,2]. In addition, health-related behaviors affect a va-
riety of acute illness conditions [3]. The need for moni-
toring and promoting healthy lifestyles arises as being
probably the major public health challenge to decrease
the burden of non-communicable diseases [2-4]. One of
the problems related to lifestyle-prevention is the causal
and structural complexity of lifestyles. A more general
problem in health-education is that epidemiologic risk
measures are not easily understood by most addressees
[5,6]. To address unhealthy lifestyles effectively, it ap-
pears that four aspects need to be considered: 1) an
association of the target risk behaviors with negative
health outcomes must have been verified, 2) risk groups
and clusters of risk factors have to have been identified,
3) successful procedures must be developed to address
the target groups effectively, and 4) effectiveness of
interventions must be rigorously evaluated. Step one has
been achieved generally for several risk factors [7-16].
To succeed with step two, several scores have been
developed [17-20], the chronic disease risk index (CDRI)
[17] and the Lifestyle Index (LI) [18] are two examples.
Diet, physical activity, smoking, and alcohol consump-
tion are considered in both instruments. The CDRI addi-
tionally includes the body mass index (BMI). In these
studies, behavioral risk factors were assigned scores
relative to their epidemiological risks. The CDRI scoring
system helped to identify different populations at dif-
ferent levels of risk in a multiethnic cohort [17]. In a
cross national comparison between China and the United
States similarities of lifestyle patterns but also different
unhealthy behaviors were identified with the Lifestyle
Index [18]. Both indices seem to solve the problem of
identifying and comparing high risk groups. On the other
hand, neither directly allows for comparing risks across
different behaviors and behavior patterns. Consequently,
neither supports evaluating interventions with multidi-
mensional behavioral effects, since so far it is not possi-
ble to assess changes in health risks associated with
*The opinions and conclusions of the researchers are their own and do
not necessarily reflect Philip Morris International, Inc.’s position.

B. Algurén et al. / Open Journal of Preventive Medicine 1 (2011) 143-153
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144
complex changes in health risk behavior patterns. It
would therefore be desirable to have a unidimensional risk
scale available for combinations of health-related beha-
viors, which could also support communicating lifestyle
risks in an easily understandable way [6].
The lack of such an instrument was the motivation to
develop a unidimensional risk scale for ranking beha-
vioral risks according to their health impact. In the ab-
sence of comprehensive epidemiological data on the
health impacts of multiple behavioral risk factors, that
would allow a direct analysis of risks, the underlying
quantification can only be based on subjective knowl-
edge represented in a sample of individuals. In the pre-
sent study, based on pair-wise comparisons, six behave-
ioral risks were ranked on a lifestyle risk scale, reflecting
subjective risk perceptions using a multidimensional
scaling method [21]. The development of the Lifestyle
Risk Scale (LRS) is descrybed, as well as the risks of the
study population, based on applying the scale to the re-
ported actual behaviors.
2. METHODS
2.1. Design
This cross-sectional survey sampled primarily health
experts from public health schools in Germany and staff
from the Munich university hospital. No random sample
could be drawn since a population-representative list of
potential participants with information on professions
was not available. Initially, 713 questionnaires were e-
mailed with the request to reply anonymously and to
possibly forward the questionnaire on to others. Recruit-
ment of the targeted convenience sample was ongoing
from May to August 2004. The sample size was deter-
mined by the available number of addresses of health ex-
perts.
2.2. Measurements
The self-administered questionnaire covered three ar-
eas: sociodemographic and anthropometric variables
(sex, age, weight, height, citizenship, professional train-
ing, current occupation, partnership, children), individ-
ual health related behaviors (smoking, alcohol consump-
tion, physical activity, fast food consumption, duration
of sleep) using four ordinal response categories, and the
appraisal of 24 pairs of lifestyle risk factors. The latter
were defined considering the guidelines for reducing
chronic diseases [2], and study participants were asked
about their risk assessment regarding tobacco and alco-
hol consumption, obesity, daily physical activity, weekly
fast food consumption and sleeping hours, each dimen-
sion divided into four increasing degrees of risk. The 24
intensities (or levels of manifestation) of the six con-
sidered behavior related risk dimensions are shown in
Table 3 Each of the 24 manifestations was to be com-
pared with each other by participants indicating in each
pairwise comparison which specific health-related be-
havior they considered more dangerous. The instruction
was to imagine an 18-year-old man, sticking to either
behavior to be assessed for the rest of his life. Due to the
large number of 240 not permuted pairs of risk behavior
manifestation comparisons (276 possible minus six triv-
ial intra-dimension comparisons for each of the six be-
havior-related risk dimensions), ten different question-
naires were used, each containing a randomly selected
fixed set of 24 comparisons. Prior to the main study the
questionnaires were answered by 39 public health stu-
dents and no difficulties were reported during this pre-
test.
2.3. Statistical Analyses
All measured and derived sociodemographic, anth-
ropometric, and health behavior variables were des-
cribed according to their measurement scale by absolute
and relative frequencies or by mean ±standard deviation
as well as median and 25th and 75th percentiles. The
BMI (weight (kg)/height (m2)) was derived and BMI
under 18.5 indicated underweight, BMI between 18.5
and 25 normal weight, BMI between 25 and 30
overweight, and BMI above 30 obesity (WHO 1998).
LRS item scores were estimated using the Bradley-Terry-
Luce (BTL) model for paired comparisons of ranked
stimuli [22]. The model is based on the assumption that
the probability of choosing an alternative is proportional
to the “utility” of this alternative in terms of its health
risk impact [23]. It is assumed that the utility of a
specific item is linked to the response probability by a
logistic function. The regression parameters were derived
from fitting logistic models without offset to the data
obtained in the study. By subtracting the smallest re-
gression parameter from each of the 24 regression
parameters [21], a rational scale with a minimum value
of zero was obtained. The lifestyle risk scale was derived
using the SAS program provided in [24]. In addition to
analyzing the total sample, separate models were fitted
to the data obtained from health professionals and laymen.
For every participant, an individual risk score cor-
responding to his or her actual lifestyle was calculated
by using the parameter estimates derived with the BTL
scoring of the total sample data. In addition, individual
risk perception scores based on individual appraisals of
life-styles were determined for each participant. This
was done accounting for the fact that the 240 com-
parisons were distributed to ten versions of the question-
naire. In the first step, each risk specification as assessed
by each participant was quantified using the final LRS

B. Algurén et al. / Open Journal of Preventive Medicine 1 (2011) 143-153
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145
parameters. Then the sum of the 24 values of each par-
ticipant was subtracted from the maximal value which
was possible with the specific version of the question-
naire. Finally, to warrant comparability across the ten
versions of the questionnaire, the score was divided by
the range of risk appraisals possible with the specific
questionnaire version. For convenience, the score was
finally multiplied by 100. Higher values represent larger
deviations of the group estimates, i.e. poorer risk
perception in terms of underestimation of risk.
The assumption of homogeneity of the assessments of
the study participants regarding their comparisons of
pairs of risk behaviors was analyzed by an adaptation of
Cochran’s Q-test [23], based on calculating the ratio of
inter-item and inter-individual variability. To assess the
split-half reliability of the scale, Cronbach’s alpha (coef-
ficient of reliability) was calculated [25], considering a
value of 0.9 or above as being indicative of high reliabil-
ity [26]. U-tests were performed to investigate differ-
ences in sex and partnership status as well as differences
between subgroups of age, professional training, and
current occupation with respect to risk scores and risk
perception scores. Spearman rank correlation coeffi-
cients (rsp) were used to correlate risk scores with age as
well as with risk perception scores. All tests were per-
formed two-sided at local alpha levels of 5 percent
without adjustment for multiplicity. All statistical analy-
ses were carried out with SAS (Statistical Analysis Sys-
tem, Version 8.2).
3. RESULTS
3.1. Demographic and Lifestyle
Characteristics
As expected, the number of returned questionnaires
was different for each of the ten versions. To obtain an
equal distribution, 32 returned questionnaires of each of
the ten versions were randomly retained. Therefore, only
320 of the 434 returned questionnaires (64 question-
naires were undeliverable because of wrong emailad-
dresses) were used for further analyses, 43 percent of
which were from health professionals.
The analyses included 202 women with an average
age of 33.3 ± 8.2 years and an average BMI of 21.6 ±
2.2 kg/m2. The 116 men were 33.2 ± 9.0 years old and
had an average BMI of 24.2 ± 2.8 kg/m2. Two partici-
pants did not report their sex. Most participants lived in
Germany (77%) and did not lead risky lifestyles: 85
percent were non-smokers, 83 percent had normal
weight, 71 percent drank less than one drink per day and
69 percent ate fast food less than once a week. Sample
characteristics and lifestyle variables are described in
Table 1 and Table 2, respectively.
3.2. Assessment of Risk Behaviors
The assessments of risk behaviors were homogeneous
in the sample (p < 0.001). Table 3 contains the LRS
scores based on the pairwise assessments of the 24 risk
behavior manifestations. The reported results refer to
models based on the total sample data set, as well as on
data obtained from laymen and from health profes-
sionals.
The values range from 0 (‘sleep less than six hours
once a week’) to 4.8 (‘four drinks per day’). On average,
smoking was perceived as the most risky behavior (3.7),
followed by alcohol consumption (3.1) and obesity (3.0).
Lack of sleep and fast-food consumption were assessed
as being less risky (1.7 and 1.6). The assessed health risk
of physical inactivity was 57 percent of that of smoking
(2.1 vs. 3.7). Less major risks such as lack of sleep and
fast-food consumption were at higher levels of mani-
festation assigned similar risk scores as the major risks
of smoking, alcohol and obesity at lower levels. For
instance, “fast-food four times per week” scored 2.8, a
similar value to that obtained for “two drinks per day”
(2.7) or “five cigarettes per day” (2.6). “Daily sleep
under six hours four times per week” scored as even
riskier (3.2). Figure 1 contains the unidimensional LRS
scores based on the total sample analysis.
As can be seen, in order to apply the scale, only
information related to the considered health-behaviors is
required at sufficient granularity, whereas there is no
need for using a particular questionnaire. This has the
advantage that data collected with different instruments
can be analyzed using the LRS scoring, given the
required information is available. An individual lifestyle
risk index can be determined by adding up the values
corresponding to an individual’s health-behaviors and
levels of manifestation.
To evaluate the reliability of the scores, the two scales
were compared which were derived separately for the
assessments made by health professionals (participants
with professional medical or public health background,
N = 136) and laymen (participants with neither profes-
sional medical, public health, sociological/pedagogical,
psychological, biological, nor pharmacological back-
ground, N = 129). To warrant some homogeneity of the
two assessment populations, participants which did not
fall in one of these two subgroups were not considered in
the reliability analysis. The two scales were found to
correlate strongly (Cronbach’s Alpha = 0.98, p < 0.0001).
For almost all specific risk behaviors, scores were higher
in the assessments of health professionals than in those
of laymen. Overall, the highest scores were obtained for
obacco, the lowest for fast food consumption. t

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146
Table 1. Characteristics of the sample.
Women (N = 202) Men (N = 116) Total (N = 320)
Va riab l e No. % No. % No. %
Age [years]
20 - 29
30 - 39
40 - 49
50 - 59
60 +
no answer
59
67
22
7
1
46
29.2
33.2
10.9
3.5
0.5
22.8
45
29
14
3
2
23
38.8
25.0
12.1
2.6
1.7
19.8
104
96
36
10
3
71
32.5
30.0
11.3
3.1
0.9
22.2
Weight
underweight
normal weight
overweight
adiposity
no answer
9
175
17
0
1
4.5
86.6
8.4
0
0.5
1
76
33
6
0
0.9
65.5
28.5
5.2
0
10
253
50
6
1
3.1
79.1
15.6
1.9
0.3
Partnership
alone
with partner
67
135
33.2
66.8
38
78
32.8
67.3
107
213
33.4
66.6
Children
yes
no
no answer
49
153
0
24.3
75.7
0
30
85
1
25.9
73.3
0.9
79
240
1
24.7
75.0
0.3
Citizenship
Germany
Switzerland
Austria
other
157
29
5
11
77.7
14.4
2.5
5.5
89
22
2
3
76.7
19.0
1.7
2.6
247
52
7
14
77.2
16.3
2.2
4.4
Professional training
Medicine
Public health
Sociology/Pedagogy
Psychology
Biology
Pharmacy
other
no answer
23
76
13
14
8
3
63
2
11.4
37.6
6.4
6.9
4.0
1.5
31.2
1.0
15
20
6
4
1
2
66
2
13.0
17.2
5.2
3.5
0.9
1.7
56.9
1.7
38
98
19
18
9
5
129
4
11.9
30.6
5.9
5.6
2.8
1.6
40.3
1.3
Current occupation
self-employed
company-employed
civil servant
unemployed
student
other
no answer
16
123
5
5
46
6
1
7.9
60.9
2.5
2.5
22.8
3.0
0.5
18
67
4
3
19
5
0
15.5
57.8
3.5
2.6
16.4
4.3
0
34
191
9
9
65
11
1
10.6
59.7
2.8
2.8
20.3
3.4
0.3
3.3. Lifestyle Risk Scores and Risk
Perception Scores in the Study
Population
The risk scores according to actual health-related be-
haviors of the study participants are shown in Table 4,
stratified by sex, age, professional training, occupation
and partnership.
The observed risk scores in the sample ranged from 0
to 12.5, the median being 2.2. Men scored higher (2.9)
than women (1.5, p < 0.001), singles higher (2.6) than
non-singles (1.5, p < 0.013), self-employed participants
higher (2.5) than students (1.5, p < 0.083), and parti-
cipants with a medical background scored higher (2.7)
than participants without a health-professional back-
ground (2.0, p < 0.078).
Table 5 contains the risk perception scores, stratified
by sex, age, professional training, occupation, partner-
ship, and by their risk behaviors. Higher values denote
more pronounced underestimation of the health risks
relative to the assessment of the total sample. The scores
ranged from 0 to 29.4, the median was 4.0. The risk
perception score correlated slightly negatively with age
(rsp = –0.15, p < 0.05). Risk perception in men (3.7) was
more accurate than in women (4.1, p < 0.91), more
accurate in participants with medical background (3.2)
than in those with no health professional background
(4.2, p < 0.071), and more accurate in students (4.4) than

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147
Figure 1. The lifestyle risk scale (LRS) derived from the health risk assessments of the study partici-
pants. Measures of tobacco, alcohol, physical inactivity: daily; measures of fast food, sleep deficit:
weekly.
Table 2. Actual lifestyles of the sample.
Women (N = 202)Men (N = 116)Total (N = 320)
No. % No. % No. %
Obesity
no 185 91.6 77 66.4 264 82.5
10% 15 7.4 27 23.3 42 13.1
20% 2 1.0 6 5.2 8 2.5
30% 0 0 5 4.3 5 1.6
40% 0 0 1 0.9 1 0.3
Fast Food per week
< 1 148 73.3 72 62.1 221 69.1
up to once 41 20.3 29 25.0 70 21.9
up to twice 9 4.5 5 4.3 14 4.4
up to three times 4 2.0 4 3.5 8 2.5
> three times 0 0 6 5.2 7 2.2
Cigarettes per day
non-smoker 179 88.6 91 78.5 271 84.7
up to 5 10 5.0 13 11.2 23 7.2
up to 10 2 1.0 4 3.5 6 1.9
up to 15 4 2.0 7 6.0 11 3.4
>15 7 3.5 1 0.9 9 2.8
Alcohol per day
<1 drink 159 78.7 65 56.0 226 70.6
up to 1 drink 38 18.8 29 25.0 67 20.9
up to 2 drinks 5 2.5 19 16.4 24 7.5
up to 3 drinks 0 0 3 2.6 3 0.9
> 3 drinks 0 0 0 0 0 0
Physical activity per day
> 30 minutes 99 49.0 51 44.0 151 47.2
up to 30 minutes 38 18.8 27 23.3 65 20.3
up to 20 minutes 44 21.8 26 22.4 71 22.2
up to 10 minutes 18 8.9 12 10.0 30 9.4
no physical activity 3 1.5 0 0 3 0.9
Days with sleep under 6h per week
<1 92 45.5 52 44.8 145 45.3
up to 1 54 26.7 32 27.6 87 27.2
up to 2 31 15.4 20 17.3 51 15.9
up to 3 6 3.0 9 7.8 15 4.7
>3 19 9.4 3 2.6 22 6.9

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Table 3. LRS scores derived by the BTL-model in the total sample, in health experts, and in laymen (separate models).
Health professionals
(N = 136)
Laymen
(N = 129)
Total
(N = 320)
Risk factor

Score

Score

Score
Obesity Mean 4.2 Mean 3.6 Mean 3.0
10% above normal weight
20% above normal weight
30% above normal weight
40% above normal weight
–2.9257
–0.2255
0.9285
1.7184
1.4
4.1
5.3
6.1
–2.5611
–1.1138
0.7983
1.1689
1.5
3.0
4.9
5.2
–2.1593
–0.5908
0.71
1.2238
1.0
2.6
3.9
4.4
Fast Food (e.g., hamburger, hot dogs,
pizzas, french fries, currywurst) Mean 2.3 Mean 2.1 Mean 1.6
Once a week
Twice a week
Three times a week
Four times a week
–4.3664
–2.3563
–1.1345
–0.2205
0
2.0
3.2
4.1
–3.6121
–2.3964
–1.3054
–0.6798
0.5
1.7
2.8
3.4
–3.1223
–1.9093
–0.9146
–0.4349
0.1
1.3
2.3
2.8
Tobacco Mean 5.3 Mean 4.5 Mean 3.7
Up to 5 cigarettes a day
Up to 10 cigarettes a day
Up to 15 cigarettes a day
Up to 20 cigarettes a day
–0.6156
0.6778
1.571
2.0311
3.8
5.0
5.9
6.4
–0.8145
–0.0106
1.3056
1.0855
3.3
4.1
5.4
5.2
–0.6357
0.308
1.2042
1.3025
2.6
3.5
4.4
4.5
Alcohol (Definition “drink”: 0.25 l
wine or 0.5 l beer or one little glass of
liquor)
Mean 4.4 Mean 3.6 Mean 3.1
1 drink a day
2 drinks a day
3 drinks a day
4 drinks a day
–3.2276
–0.594
1.3141
2.4534
1.1
3.8
5.7
6.8
–2.8617
–0.8962
0.4244
1.4899
1.2
3.2
4.5
5.6
–2.3394
–0.4649
0.7275
1.6203
0.9
2.7
3.9
4.8
Physical inactivity Mean 3.2 Mean 2.4 Mean 2.1
Maximal 30 min a day
Maximal 20 min a day
Maximal 10 min a day
None a day
–2.8638
–1.6235
–0.8944
0.7417
1.5
2.7
3.5
5.1
–3.0063
–2.4243
–1.1975
–0.0837
1.1
1.7
2.9
4.0
–2.1958
–1.748
–0.8167
0.3006
1.0
1.5
2.4
3.5
Lack of sleep (“Sleeping less than six
hours a day”) Mean 2.4 Mean 2.2 Mean 1.7
Once a week
Twice a week
Three times a week
Four times a week
–4.0309
–2.8267
–0.944
0
0.3
1.5
3.4
4.4
–4.0741
–2.4233
–1.1113
0
0
1.7
3.0
4. 1
–3.2046
–1.9859
–0.8788
0
0
1.2
2.3
3.2
in self-employed participants (5.2, p < 0.078). When the
distribution of the risk perception score was examined
across body weight strata as well as across tobacco and
alcohol consumption patterns, no substantial associations
were apparent. There was virtually no correlation be-
tween actual behavior-based scores on the one hand and
risk perception scores on the other (rsp = 0.02, p < 0.72).
4. DISCUSSION
A lifestyle risk scale (LRS) was developed, based on
pairwise comparisons of health-risks of specific health-
related behaviors, using the BTL model. Lifestyles were
treated as objects with specific manifestations of risk
behaviors as their attributes. For a realistic evaluation of
the attributes, the measures had to be easy to com-
prehend and overloading study participants with too
many pairwise comparisons [22] had to be avoided.
Consequently, the 240 pairwise comparisons were ran-
domly distributed to ten questionnaires, each comprising
24 pairwise comparisons. Six health-related behavior in-
dicators, each of which had four ordinal levels of mani-
festation, were to be assessed. In addition to LRS scores,
risk scores corresponding to actual individual behaviors
as well as to individual LRS risk perception scores were
determined for each participant.
Among the six risk categories to be assessed, para-
mount importance was assigned to smoking as well as to
obesity. This is in agreement with epidemiological find-
ings [2,27-30]. The LRS scores thus appear to allow for
a fine-grained unidimensional ranking of perceived as
well as of actual risk, not only of dimensions of risk be-
haviors, but also of specific intensity levels at which these
behaviors are performed. The finding, for example, that
‘one drink per day’ was perceived as less risky than ‘phy-
sical activity not longer than 30 minutes a day’ seems to
reflect the available evidence on health benefits of mode-
rate red wine consumption [4,31-33] and also the recom-
mendation of daily moderate physical activity with a
minimum of 30 minutes [2]. The findings of the study
further indicate that with regar to subjective risk percep- d

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149
Table 4. Lifestyle risk scores of the sample based on applying the LRS on actual behaviors.
Women (N=202) Men (N=116) Total (N=320)
Va riables 25th
percentile Median 75th percentile Mean 25th percentile Median 75th percentile Mean 25th percentile Median 75th percentile Mean
total 1.0 1.5 3.3 2.21.1 2.9 5.1 3.71.0 2.2 4.0 2.7
Age years
20 - 29
30 - 39
40 - 49
50 - 59
60 +
no answer
0.9
0.1
1.2
1.1
2.6
1.0
1.5
1.2
1.4
3.7
2.6
1.7
3.2
3.2
2.7
4.8
2.6
3.3
2.1
2.2
2.1
3.7
2.6
2.1
1.2
0.9
1.0
0
0
2.1
2.9
2.4
2.2
3.4
1.4
4.3
5.0
4.7
5.5
6.8
2.8
7.3
3.4
3.3
3.3
3.4
1.4
5.0
0.9
0.9
1.5
1.5
0
1.0
2.1
1.5
1.7
3.5
2.6
2.4
4.2
3.5
3.6
4.8
2.8
4.1
2.7
2.5
2.6
3.6
1.8
3.1
Professional training
Medicine
Psychology
Sociology/Pedagogy
Public health
others
0.1
1.0
1.0
0.9
1.0
2.4
1.7
1.3
1.5
1.5
4.3
3.5
1.5
3.3
2.9
2.9
2.3
1.6
2.2
2.1
2.5
0.6
1.0
1.5
1.0
3.4
2.4
2.8
3.9
2.3
5.1
5.0
3.7
6.2
5.0
4.0
2.8
3.8
4.1
3.4
1.5
1.0
1.0
1.0
1.0
2.7
1.5
1.5
2.3
2.0
4.7
3.7
2.5
4.2
3.5
3.3
2.3
2.3
2.6
2.7
Current occupation
self-employed
company-employed
student
0.9
1.0
0.9
1.2
1.9
1.5
2.5
3.5
2.7
1.8
2.4
2.0
2.4
1.1
0
4.6
2.5
2.9
8.3
5.1
5.0
5.4
3.4
3.2
1.1
1.0
0.9
2.5
2.3
1.5
5.0
4.1
3.6
3.7
2.8
2.4
Partnership
single
with partner
1.0
0.9
2.4
1.5
3.5
2.9
2.6
2.0
2.0
1.0
3.3
2.6
6.7
4.7
4.5
3.2
1.1
1.0
2.6
1.5
5.0
3.6
3.3
2.5
tion, minor risk behaviors such as lack of sleep and
fast-food consumption might, if performed excessively,
reach the same levels as smoking or drinking. It should
be noted that, in addition to being in line with epide-
miologic risk assessment, individual risk perception
scores possibly allow for gauging deviations from group
level perception scores and may thereby have diagnostic
potential in terms of indicating individual-level miscon-
ceptions or information gaps regarding certain beha-
vioral risks.
The LRS appears to have a very high split-half re-
liability and was not found to depend substantially on
professional background, i.e., on whether the scoring
was based on assessments of health-experts or on assess-
ments of laymen. While the group-specific pairwise asses-
sments did not change the ordinal sequence of specific
risk behaviors, the scores were somewhat different:
Tobacco use, obesity, alcohol consumption, and physical
inactivity were perceived to be about 15 percent more
risky when assessed by health experts as compared to
laymen.
Obviously, the risk values cannot (and are not in-
tended to) substitute for clinical and epidemiological risk
quantifications. However, as the ranking of behavioral
risk factors is consistent with epidemiologic risk as-
sessment, the scale appears to be useful to identify high-
risk groups and clusters of risk behaviors. As the LRS
estimates individual lifestyle risks without requiring
clinical measurements or detailed interviews, it might be
useful in epidemiologic as well as in prevention studies,
where fine-grained individual-level assessments of beha-
vioral risk factors might be impractical.
An essential benefit of the LRS is its ability to rank
health risks of different behavioral domains on a com-
mon scale. Because obesity is a result of lifestyle and not
a risk behavior itself, and since the association between
lack of sleep and diseases is inconsistent [34-37], the
scale was, in an additional exploratory analysis, adjusted
by excluding these dimensions. The scale for the remain-
ing behavioral risk factors did not differ from the
original scale using all available assessments. From this
it might be concluded that other behavioral risk factors
could be added to the scale in the future.
In addition to the aspect of making highly different
behavioral risks comparable, the LRS allows for com-
paring different populations as well as different sub-
groups. This opens a wide field of possible applications,
for example targeting groups for the specification or
evaluation of behavioral intervention programs. Although
information on behavioral risks can be and has often
been given in an objective way by providing epide-
miologic findings, the LRS does so at a different level: It

B. Algurén et al. / Open Journal of Preventive Medicine 1 (2011) 143-153
Copyright © 2011 SciRes. Openly accessible at http://www.scirp.org/journal/OJPM/
150
Table 5. Risk perception scores of the sample.
Women (N=202) Men (N=116) Total (N=320)
Va riables 25th
percentileMedian 75th percentile Mean 25th percentile Median75th percentileMean 25th percentile Median 75th percentile Mean
total 2.1 4.1 6.6 4.81.8 3.7 7.7 5.22.0 4.0 7.1 4.9
Age years
20 - 29 2.8 4.7 7.1 5.62.8 4.6 8.0 6.42.8 4.6 7.7 6.0
30 - 39 2.0 3.4 6.0 4.20.6 4.2 9.5 5.71.8 3.5 6.7 4.6
40 - 49 0.9 3.2 8.0 4.61.4 3.2 4.3 3.41.1 3.2 6.3 4.1
50 - 59 2.1 3.2 9.4 6.21.7 2.2 2.4 2.12.1 3.8 7.9 5.0
60+ 7.6 5.7 7.6 6.20 1.4 2.7 1.40 2.7 7.6 3.4
no answer 1.9 7.6 5.8 4.30.9 2.4 7.6 4.21.0 3.9 6.9 4.2
Professional training
Medicine 1.8 3.3 6.2 4.01.0 3.1 6.9 3.71.7 3.2 6.4 3.9
Psychology 3.6 5.1 8.3 5.92.1 5.3 9.6 5.83.6 5.2 8.3 6.3
Sociology/Pedagogy 2.1 3.5 6.1 3.84.5 7.9 9.8 9.32.8 4.5 7.1 5.5
Public health 1.2 3.9 5.4 4.50.4 2.2 7.1 3.70.9 3.9 5.8 4.4
other 2.5 4.4 7.6 5.32.3 3.6 7.7 5.72.4 4.2 7.6 5.5
Current occupation
self-employed 3.6 5.2 8.7 6.12.8 6.0 8.6 7.03.1 5.2 8.6 6.6
company-employed 1.9 3.8 6.3 4.71.7 3.6 7.4 5.11.9 3.7 6.6 4.9
student 1.9 4.6 6.9 4.62.1 3.6 7.7 4.52.1 4.4 6.9 4.6
Partnership
single 2.0 4.2 7.1 4.72.4 3.9 7.7 5.32.1 4.2 7.6 4.9
with partner 2.5 4.0 6.3 4.81.7 3.6 7.6 5.21.9 3.9 6.6 4.9
Weight
underweight 1.8 6.9 7.2 5.14.6 4.6 4.6 4.61.8 5.8 7.2 5.1
normal weight 2.5 4.1 6.3 4.72.0 3.5 7.4 4 2.1 3.9 6.5 4.8
overweight 1.9 4.2 7.6 5.41.0 4.2 7.7 5.71.6 4.2 7.7 5.6
adiposity - - - - 3.9 5.8 9.5 7.23.9 5.8 9.5 7.2
Fast Food per week
<1 2.0 4.1 6.6 4.82.2 4.1 8.3 5.82.0 4.1 7.3 5.1
up to once 2.7 3.9 6.3 4.71.4 2.8 5.8 4.72.1 3.7 6.1 4.7
up to twice 1.0 4.2 7.1 4.00.0 0.5 1.8 0.80.2 1.9 5.6 2.9
up to three times 1.2 4.6 8.3 4.72.5 5.5 8.5 5.52.5 4.6 8.5
5.1
>three times - - - - 1.6 5.1 7.9 4.82.1 7.4 7.7 5.1
Cigarettes per day
Non-smoker 2.1 4.2 6.6 4.81.6 3.2 7.4 4.61.9 3.9 6.9 4.7
Up to 5 2.1 3.9 4.6 3.63.5 5.9 8.6 7.22.5 4.6 8.0 5.6
Up to 10 0 8.0 15.9 8.03.7 7.4 8.5 6.10 7.4 9.5 6.7
Up to 15 1.4 3.1 6.3 3.93.5 9.0 11.0 9.92.8 4.0 10.1 7.7
>15 2.8 4.4 8.0 6.02.1 2.1 2.1 2.12.1 3.9 4.8 4.9
Alcohol per day
<1 drink 2.1 4.2 6.7 4.81.8 4.0 7.7 5.42.1 4.1 7.3 5.0
Up to 1 drink 1.9 3.8 5.7 4.32.1 3.2 6.0 4.61.9 3.7 6.0 4.4
Up to 2 drinks 2.8 2.8 9.4 5.61.6 3.6 8.6 6.11.9 3.5 7.3 6.0
Up to 3 drinks - - - - 0 0 7.5 2.56.0 0 6.0 2.5
Physical activity per day
>30 minutes 2.1 4.2 6.4 4.82.3 4.1 7.9 5.82.2 4.1 7.1 5.2
up to 30 minutes 2.7 4.1 6.3 4.90.9 3.2 7.4 3.81.4 3.7 6.5 4.4
up to 20 minutes 1.2 4.0 5.7 4.10.8 2.6 7.5 4.40.9 3.1 6.1 4.2
up to 10 minutes 2.6 3.8 9.3 5.82.6 4.8 10.2 7.62.8 4.0 10.2 6.5
no physical activity 2.1 7.8 8.1 6.1- - - - 2.1 7.8 8.1 6.1
Days with sleep under 6h per week
<1 2.0 4.4 6.9 5.11.7 2.8 8.6 5.51.8 4.2 7.6 5.2
up to 1 1.8 3.5 6.7 4.21.2 3.9 7.6 5.12.0 3.7 6.8 4.6
up to 2 2.1 4.1 6.2 5.02.4 3.6 7.1 4.42.2 4.1 6.4 4.7
up to 3 0.0 2.8 4.2 2.40.4 3.8 6.6 4.20.0 2.8 5.1 3.5
>3 2.4 4.6 7.3 5.13.4 12.214.8 11.02.7 4.9 9.1 5.9

B. Algurén et al. / Open Journal of Preventive Medicine 1 (2011) 143-153
Copyright © 2011 SciRes. Openly accessible at http://www.scirp.org/journal/OJMP/
151
allows people to obtain, based on fine-grained levels of
assessment, feedback on their risk perception and/or
health-related behavior as compared to others, thus pos-
sibly supporting them in adjusting their views and life-
styles.
By applying the LRS to the actual behaviors of the
study population, some interesting findings were ob-
tained, although the present sample had a rather low
average risk score and reported rather healthy lifestyles.
The findings that lifestyles were healthier in female than
in male participants, that participants living with a part-
ner led healthier lifestyles than singles, and that younger
participants engaged more often in risky lifestyles than
older ones correspond well with findings reported in the
literature [38-45]. Although men perceived risks more
accurately, their lifestyles were more risky than those of
females. Also, professionals with medical training did
not show less risky behaviors than others, even though
they perceived health risks as more serious. This cor-
responds to the well-known fact that being aware of
risks does not automatically lead to their avoidance [46].
It is clear that independent validation of the LRS
should be undertaken prior to applying it without reser-
vation in research or individual-level diagnostic or inter-
ventional settings. Out-of-sample replication studies and
possibly recalibration of the proposed scoring should be
undertaken. Other than the present sampling and measure-
ment procedures might provide important insights re-
garding the generalizability of the scale and the reported
findings. The response rate of 67 percent was reasonably
high and selection bias seems to be unlikely. While there
appears to be no prima facie reason to assume that the
sampling procedure might have biased the results, this
possibility cannot be fully excluded. Replication studies
might therefore attempt to draw population-repre-
sentative random samples of study participants. Also,
epidemiological analyses of the predictive value of the
LRS should be undertaken, either based on existing data
(where available) or on data of future case-control or
(preferably) prospective cohort studies. By addressing
different disease-specific endpoints and settings, a fine
grained purpose-specific assessment of the instrument
might eventually become available.
In summary, it was possible to quantify multidimen-
sional health-behaviors and risk perceptions concisely
with a unidimensional risk scale. The LRS, although pre-
liminary at present, can be useful in epidemiologic
research as well as in developing and evaluating inter-
ventions, and possibly as a tool for risk communication
in prevention contexts. There is ample evidence that for
most diseases, effective strategies for risk communi-
cation need to focus on health-related behaviors [2,4].
Scoring multidimensional health-related behavioral risks
on a single dimension of subjective risk appears now
feasible, but additional work is required to further va-
lidate and calibrate the instrument.
5. ACKNOWLEDGEMENTS
There was no funding for this project.
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