Predictors of Inactivity in the Working-Age Population

Abstract

Burden of diseases attributable to low physical activity is increasing worldwide mainly among working age populations. The aim of the study was to evaluate the association between selected (including demographic and socioeconomic) factors and leisure-time physical activity.
The study was performed in the randomly selected group of 450 men and 502 women in the working age. Logistic regression models were applied to assess factors related to physical activity limitations. Physical activity was determined by the physical activity questionnaire.
Over 55% of the study participants were inactive, 34.1% were insufficiently active, and only 10.6% of the subjects achieved the level of physical activity recommended by experts in health promotion. Significant differences in physical activity behaviors across age, education, income levels, and marital status were found in the study participants. Unhealthy weight and smoking habit also formed certain barriers to exercise in both men and women.
Low number of physically active working-age citizens is a challenge for public health, and it confirms the need for promoting active lifestyles. Effective strategies to encourage leisure-time physical need to be targeted at specific age and socioeconomic groups.

Full text

IJOMEH 2007;20(2) 175
International Journal of Occupational Medicine and Environmental Health 2007;20(2):175 – 182
DOI 10.2478/v10001-007-0019-z
PREDICTORS OF INACTIVITY
IN THE WORKING-AGE POPULATION
DOROTA KALETA
1
and ANNA JEGIER
2
1
Department of Preventive Medicine
Chair of Socialized and Preventive Medicine
2
Department of Sports Medicine
Chair of Socialized and Preventive Medicine
Medical University of Łódź
Łódź, Poland
Abstract
Objectives: Burden of diseases attributable to low physical activity is increasing worldwide mainly among working
age populations. The aim of the study was to evaluate the association between selected (including demographic and
socioeconomic) factors and leisure-time physical activity. Materials and Methods: The study was performed in the randomly
selected group of 450 men and 502 women in the working age. Logistic regression models were applied to assess factors
related to physical activity limitations. Physical activity was determined by the physical activity questionnaire. Results:
Over 55% of the study participants were inactive, 34.1% were insuffi ciently active, and only 10.6% of the subjects achieved
the level of physical activity recommended by experts in health promotion. Signifi cant differences in physical activity
behaviors across age, education, income levels, and marital status were found in the study participants. Unhealthy weight
and smoking habit also formed certain barriers to exercise in both men and women. Conclusions: Low number of physically
active working-age citizens is a challenge for public health, and it confi rms the need for promoting active lifestyles. Effective
strategies to encourage leisure-time physical need to be targeted at specifi c age and socioeconomic groups.
Key words:
Leisure-time physical activity, Socioeconomic factors, Demographic factors, Adults
Received: January 24, 2006. Accepted: April 23, 2007.
Address reprint requests to D. Kaleta, PhD, Department of Preventive Medicine, Medical University of Łódź, Żeligowskiego 7/9, 90-752 Łódź, Poland
(e-mail:dkaleta@op.pl).
INTRODUCTION
Rapid changes in lifestyles, including physical activity and
diets, associated with the progress of industrialization, ur-
banization, and economic development, have accelerated
over the past decade, making a signifi cant impact on the
health status of populations, particularly in the develop-
ing countries and countries in transition. While standards
of living have improved and access to new medical tech-
nologies has increased, signifi cant negative consequences
in terms of decreased physical activities, inappropriate
dietary patterns, and a corresponding increase in chronic
diseases, especially among poor people have also emerged
[1,2]. Because of these changes, chronic diseases, includ-
ing cardiovascular diseases (CVD), obesity, diabetes mel-
litus, and some types of cancer are becoming increasingly
signifi cant causes of disability and premature death, plac-
ing additional burdens on already overburdened national
health budgets.
Indirect costs of diseases attributable to low physical activ-
ity are also very high among people in the working-age and
are associated with work days lost, physician visits, disabil-
ity pensions, and lowered work ability [3,4]. Physical activ-
ity is one of the most important modifi able determinant of
chronic diseases. There is convincing evidence that regu-
lar physical activity is protective against unhealthy weight
gain whereas sedentary lifestyles, particularly sedentary
occupations and inactive recreation (e.g., watching tele-
vision), promote this ailment. For health promotion and
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ORIGINAL PAPERS D. KALETA AND A. JEGIER
IJOMEH 2007;20(2)
176
chronic disease prevention practicing an endurance activ-
ity at moderate or higher level of intensity for one hour or
more per day on most days of the week is recommended.
According to the European Guidelines on CVD Preven-
tion in Clinical Practice endurance training with moder-
ate intensity (60–75% of max heart rate) undertaken at
least 4–5 times a week for 30–45 min is required. Energy
expenditure on exertion should exceed 1000 kcal/week
[5]. A higher volume or intensity of activity would confer
a larger protective effect.
Regardless of the established health benefi ts resulting
from regular leisure-time physical activity, currently a ma-
jority of the world population, including Poles, do not take
up physical exercises at a suffi cient level, or any training at
all [6]. The currently available scientifi c evidence indicates
numerous factors that can negatively infl uence the level
of leisure-time physical activity [7,8]. Such factors include
a complex combination of interacting socioeconomic, cul-
tural, and other environmental parameters. Recognizing
the factors affecting participation in physical activity is
a fi rst step that may
help to develop
effective preventive
programs.
The aim of the study was to evaluate association between
selected (including socioeconomic or demographic) fac-
tors and leisure-time physical activity among men and
women in the working age.
MATERIALS AND METHODS
The study was performed in the population of adults ran-
domly selected by the Local Data Bank in Łódź, which
rendered the data available with the proportional draw
scheme. As an operator the personal identifi cation num-
ber (in Polish PESEL) was used. Of the directly drawn
2000 persons, 954 answered all the questions included in
the questionnaire. Physical activity was determined by the
physical activity questionnaire based on the Country Wide
Noncomunicable Disease Intervention Program, World
Health Organization (CINDI, WHO) health monitoring
questionnaires [9]. The questionnaire sought information
concerning leisure-time physical activity (LTPA). In the
assessment of LTPA, people were asked whether they reg-
ularly practiced any physical exercises (walking, jogging,
cycling, swimming, gymnastics) for at least 30 min. More-
over, those who did were asked to recall the frequency
of such activities. Satisfactory level of LTPA (compliance
with physical activity guidelines) was defi ned as practicing
exercises on most days of the week, insuffi cient LTPA was
defi ned as being active less than twice a week. Individuals
who had declared lack of physical activity, practicing any
physical exercises were classifi ed as inactive. Three groups
of television watching were also defi ned on the basis of
the distribution of the time spent watching television in
our sample: less than 1 h/day, 1 to 3 h/day, and ≥ 3 h/day.
While interviewing the subjects, the data on education, in-
come, marital status and smoking were also collected.
Statistical analysis
For the statistical analysis of the measurable character-
istics, their range (minimum–maximum), mean values
(arithmetic mean and median), and also standard devia-
tion were calculated. To compare the frequency of the
given categories of quantitative characteristics in the
analyzed groups the Chi
2
test or the Chi
2
test with Yates’
correction were implemented. The distribution of measur-
able characteristics was analyzed using the Shapiro-Wilk
test. A level of signifi cance was established at p = 0.05 for
the values included in the critical region of a given distri-
bution. The Chi
2
test was used to determine differences in
activity by selected characteristics, including age, educa-
tion, employment status, level of monthly income, marital
status, body mass index (BMI), smoking, and television
watching. In addition, to identify risk factors that can con-
tribute to lack of leisure-time physical activity, the logistic
regression analysis was performed. At the fi rst stage, crude
coeffi cients — odds ratios (OR) of the impact of singular
variables on the risk of lack of recreational physical activ-
ity in men and women were calculated. Subsequently, the
multifactorial analysis, considering simultaneous effect
of all variables on the risk of lack of leisure-time physical
activity in the study subjects, was employed. All p values
were two-sided and p < 0.05 was set as statistically sig-
nifi cant. The statistical analysis was performed with the
STATGRAPHICS plus 5.1 program.
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IJOMEH 2007;20(2)
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RESULTS
Based on the information collected during interviews, the
subjects were characterized by means of basic anthropo-
metric indices and selected socio-economic variables (Ta-
bles 1 and 2). In Tables 1 and 2, the subjects are categorized
as inactive, insuffi ciently active and achieving satisfactory
level of physical activity. Over 55% of study participants
(including 52.9% of men and 57.6% of women) were inac-
tive, 34.1% (including 34.6% of men and 33.7% of women)
were insuffi ciently active and only 10.6% (including 12.6%
of men and 8.8% of women) of the subjects achieved the
Table 1. Characteristics of the study population — men
Characteristics
Men
(n = 454)
Leisure-time physical activity status
Inactive
(n = 240)
Insuffi cient
(n = 157)
Meets
recommendations
(n = 57)
n%n%n%n%
Age (years)*
25–34 100 22.0 40 16.7 49 31.2 11 19.3
35–44 95 20.9 50 20.8 34 21.7 11 19.3
45–54 125 27.5 70 29.2 32 20.4 23 40.4
55–64 134 29.5 80 33.3 42 26.7 12 21.0
Education*
Primary/Secondary 184 40.5 120 50.0 51 32.5 13 22.8
High school 178 39.2 100 41.7 61 38.08 17 29.8
University 92 20.3 20 8.3 45 26.7 27 47.4
Employment status*
Employed 311 68.5 144 60.0 122 77.7 45 78.9
Not employed 143 31.5 96 40.0 35 22.3 12 21.2
The level of monthly income in EUR*
< 124 148 32.6 90 37.5 48 30.6 10 17.5
125–249 212 46.7 117 48.8 74 47.1 21 36.8
250–374 54 11.9 21 8.7 19 12.1 14 24.6
> 374 40 8.8 12 5.0 16 10.2 12 21.1
Marital status
Single 70 15.4 29 19.1 30 19.1 11 19.3
Married 340 74.9 184 76.7 117 74.5 39 68.4
Divorced/Widowed 44 9.7 27 11.2 10 6.4 7 12.3
Body mass index (BMI) (kg/m
2
)*
Below 24.9 165 36.3 88 36.7 56 35.7 21 36.8
25.0–29.9 219 48.2 113 47.1 82 52.2 24 42.1
30 and more 70 15.4 39 16.2 19 12.1 12 21.1
Smoking status *
Never smoker 111 24.5 43 17.9 47 29.9 21 36.8
Former smoker 135 29.7 71 29.6 46 29.3 18 31.6
Current smoker 208 45.8 126 52.5 64 40.8 18 31.6
Television watching (h/day) *
< 1 60 13.2 26 10.8 24 15.3 10 17.5
1–3 214 47.1 95 39.6 87 55.4 32 56.1
> 3 180 39.7 119 49.6 46 29.3 15 26.4
*Chi
2
test p < 0.05 for differences in activity by selected characteristics.
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ORIGINAL PAPERS D. KALETA AND A. JEGIER
IJOMEH 2007;20(2)
178
level of physical activity recommended by experts in health
promotion. These tables also show the results of the Chi
2
test analysis and distribution in leisure-time physical activ-
ity levels by selected groups of variables. It should be em-
phasized that among men and women, the highest numbers
of inactive subjects were in the oldest age group (33.3%
and 30.8%, respectively), less educated (50% and 43.6%,
respectively), current smokers (49.6% and 40.5%, re-
spectively) and among people watching television 3 h/day
or longer (49.6% and 47.8%, respectively). Moreover, lo-
gistic regression analysis was used to identify factors that
can contribute to the lack of recreational physical activity in
Table 2. Characteristics of the study population — women
Characteristics
Women
(n = 502)
Leisure-time physical activity status
Inactive
(n = 289)
Insuffi cient
(n = 169)
Meets
recommendations
(n = 44)
n%n%n%n%
Age (years) *
25–34 112 22.3 48 16.6 50 29.6 14 31.8
35–44 116 23.1 64 22.2 42 24.8 10 22.7
45–54 141 28.1 88 30.4 48 28.4 5 11.4
55–64 133 26.5 89 30.8 29 17.2 15 34.1
Education *
Primary/Secondary 172 34.3 126 43.6 41 24.3 5 11.4
High school 220 43.8 122 42.2 78 46.2 20 45.4
University 110 21.9 41 14.2 50 29.6 19 43.2
Employment status *
Employed 304 60.6 148 51.2 123 72.8 33 75.0
Not employed 198 39.4 141 48.8 46 27.2 11 25.0
The level of monthly income in EUR *
< 124 173 34.5 127 43.9 40 23.7 6 13.6
125–249 251 50.0 138 47.8 91 53.8 22 50.0
250–374 55 10.9 18 6.2 25 14.8 12 27.3
> 374 23 4.6 6 2.1 13 7.7 4 9.1
Marital status *
Single 78 15.5 33 11.4 36 21.3 9 20.4
Married 330 65.7 192 66.4 112 66.3 26 59.1
Divorced/Widowed 94 18.7 64 22.2 21 12.4 9 20.5
Body mass index (BMI) (kg/m
2
) *
Below 24.9 281 56.0 148 51.2 100 59.2 33 75.0
25.0–29.9 149 29.7 90 31.1 52 30.8 7 15.9
30 and more 72 14.3 51 17.7 17 10.1 4 9.1
Smoking status *
Never smoker 209 41.6 111 38.4 78 46.1 20 40.5
Former smoker 116 23.1 61 21.1 40 23.7 15 34.1
Current smoker 177 35.3 117 40.5 51 30.2 9 20.5
Television watching (h/day) *
≤ 1 77 15.3 35 12.1 31 18.3 11 25.0
1–3 240 47.8 116 40.1 94 55.6 30 68.2
> 3 185 36.9 138 47.8 44 26.1 3 6.8
*Chi
2
test p < 0.05 for differences in activity by selected characteristics.
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IJOMEH 2007;20(2)
179
the subjects. The risk of lack of leisure-time physical activ-
ity in men and women was signifi cantly associated with age
(Table 3). Among men aged 55–64 years, the risk for inac-
tivity was about 2-fold higher than in men aged 25–34 years
(adjusted OR = 1.95; 95% CI: 1.14–3.32). Among women
the highest risk of inactivity was in those aged 45–54 years
(adjusted OR = 2.89; 95% CI: 1.52–3.51). In the women
aged between 55–64 years, the risk of lack of activity was
over 2-fold higher than in those aged under 34 years (ad-
justed OR = 2.51; 95% CI: 2.43–4.35). In both genders,
Table 3. Odds ratios (OR) and 95% confi dence intervals (CI) for inactivity during leisure-time to selected characteristics
in men and women
Variables
Men Women
Crude OR Adjusted OR ** Crude OR Adjusted OR **
OR 95% CI OR 95% CI OR 95% CI OR 95% CI
Age (years)
25–34 1.00 Referent 1.00 Referent 1.00 Referent 1.00 Referent
35–44 1.20 0.59–2.44 1.35 0.78–2.34 1.7 0.79–3.64 1.64 0.97–2.78
45–54 1.93 1.16–3.21* 1.55 1.21–2.94 3.86 2.39–4.96* 2.89 1.52–3.51*
55–64 1.98 1.03–3.79* 1.95 1.14–3.32* 2.78 1.70–4.89* 2.51 2.43–4.35*
Educational
Primary 4.37 1.21–6.66* 3.27 1.16–5.47* 4.51 4.22–1.18 4.23 4.14–0.39*
High school 3.26 1.14–5.23* 2.25 1.15–3.48* 1.43 0.27–1.69 1.18 0.58–2.36
University 1.00 Referent 1.00 Referent 1.00 Referent 1.00 Referent
Employment status
Employed 1.00 Referent 1.00 Referent 1.00 Referent 1.00 Referent
Not employed 1.98 1.31–3.01* 1.18 0.71–1.94 1.63 1.11–2.38* 0.95 0.53–1.69
The level of income in EUR
< 124 4.70 2.17–10.17* 1.63 0.65–7.54 3.94 1.83–9.73* 1.65 1.57–9.40*
125–249 2.43 0.14–5.20 2.57 0.34–4.91 3.30 0.75–62.0 1.25 0.73–4.11
250–374 1.28 0.72–2.30 1.23 0.79–1.91 1.94 0.76–2.97 1.35 0.45–4.03
> 374 1.00 Referent 1.00 Referent 1.00 Referent 1.00 Referent
Marital status
Single 1.00 Referent 1.00 Referent 1.00 Referent 1.00 Referent
Married 2.64 0.97–7.22 1.89 0.87–4.12 2.50 1.40–3.05* 2.16 1.10–3.83*
Divorced/Widowed 1.45 0.58–3.58 1.17 0.61–2.56 1.06 0.65–1.73 0.98 0.41–2.34
Body mass index (BMI) (kg/m
2
)
Below 24.9 1.00 Referent 1.00 Referent 1.00 Referent 1.00 Referent
25.0–29.9 1.15 1.04–3.65* 1.18 1.05–3.36* 2.03 1.69–5.98* 1.34 1.71–2.54*
> 30 2.87 1.44–4.74* 1.91 1.21–3.61* 2.64 1.23–8.04* 2.27 1.26–1.07*
Smoking status
Never smoked 1.00 Referent 1.00 Referent 1.00 Referent 1.00 Referent
Former smoker 1.34 0.77–2.36 1.28 0.82–2.01 1.50 0.98–2.30 1.05 0.52–2.09
Current smoker 2.39 1.49–3.84* 1.79 1.02–3.14* 1.81 1.28–3.71* 1.66 1.12–2.71*
Television watching (h/day)
< 1 1.00 Referent 1.00 Referent 1.00 Referent 1.00 Referent
1–3 1.27 0.39–2.94 1.25 0.76–2.74 1.89 0.79–3.64 1.53 0.94–1.78
> 3 1.89 1.06–3.21* 1.63 0.51–2.94 3.86 2.39–4.96 2.89 1.52–3.51*
* Statistically signifi cant p < 0.01.
** Adjusted OR took account of all other variables in the model.
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ORIGINAL PAPERS D. KALETA AND A. JEGIER
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the level of education was signifi cantly associated with the
level of leisure-time physical activity (Table 3). In men with
primary education, the risk of inactivity was over 3 times
higher than in men with university education (adjusted
OR = 3.27; 95% CI: 1.16–5.47). Among women with pri-
mary education, the risk of sedentary behavior was over
4 times higher than in women with university education
(adjusted OR = 4.23; 95% CI: 4.14–0.39). No correlation
between the employment status and participation in recre-
ation activities was found. Statistically signifi cant associa-
tion between the economic status and the level of leisure-
time physical activity was found only in women (Table 3).
In the group of women with the lowest monthly income
per person in family, the risk of inactivity was higher than
in women with monthly income higher than 250 euros (ad-
justed OR = 1.65; 95% CI: 1.57–9.40). Furthermore, lei-
sure-time physical activity in women was associated with
marital status. Among married women, the risk of lack of
physical activity was over two times higher than in single
women (adjusted OR = 2.16; 95% CI: 1.10–3.83).
The lack of leisure-time physical activity in the study
subjects was also associated with body mass index. In the
group of obese men risk of inactivity was almost 2 times
higher than in men with healthy weight (adjusted OR =
=1.91; 95% CI: 1.21–3.61). In the group of obese women
the risk of sedentary behaviors during leisure was over
twice higher than in women with BMI ≤ 25 (kg/m
2
) (ad-
justed OR = 2.27; 95% CI: 1.26–1.07).
In both groups, leisure-time physical activity was found
to be associated with smoking habit (Table 3). In men
who were current smokers, the risk of inactivity was sig-
nifi cantly higher than in men who never smoked (adjusted
OR = 1.79; 95% CI: 1.02–3.14). In currently smoking
women, the risk of sedentary behavior was nearly 2 times
higher in comparison with never smoking women (adjust-
ed OR = 1.66; 95% CI: 1.12–2.71). Moreover, in women
who had declared television watching longer than 3 h/day,
the risk of not taking up leisure-time physical activity was
almost 3 times higher than in women who watched TV less
than 1 h/day (adjusted OR = 2.89; 95% CI: 1.52–3.5). In
men, no association was found between time spent
watch-
ing television and inactivity (Table 3).
DISCUSSION
In Poland,
there are very few data concerning the level of
physical
activity compared with other Western European
countries and the United States. In our study, over 55%
of the study participants were inactive and only 20.7%
of the men and 10.9%
of the women achieved recom-
mended levels of physical activity.
These rates also appear
quite different from those reported in West Europe and
the United States [10–13]. In a recent French study, for
example, recommended
levels of physical activity were
achieved by 62% of men and
52% of women, whereas
10% of men and 12% of women reported no
physical ac-
tivity at all in their leisure [14]. Similar fi gures
were also
reported in U.S. studies [8,12]. In a study carried out by
Jones et al. [11], 32% of adults
were engaged in moder-
ate LTPA at least 10 times over a 2-week period
for a to-
tal duration of 30 min or more. The level of leisure-time
physical activity in Poland is comparable with that in the
Baltic countries. The data from three national surveys of
adults conducted in Estonia, Latvia, and Lithuania showed
that one in three Estonians and one in fi ve Latvians and
Lithuanians had a low physical activity level at work [15].
Furthermore, half of the respondents participated only in
sedentary activities during their leisure time. According to
our data, in the Baltic countries leisure-time inactivity was
inversely related to the education level in men and women
and to income in men. Our results show healthier physical
activity patterns in subjects younger than 45 years of age,
as
recently confi rmed by the Behavioral Risk Factor Surveil-
lance System data [13]. Macera et al.[12] have
also shown
that the percentage of subjects meeting the recommended
physical activity levels were higher in younger than in oth-
er age groups (18–29
years vs. 30–64 years). Other studies
confi rm as well a negative relationship between age and
recreational physical activity level [16].
Several studies have shown that LTPA performance can be
related to educational
level.
Signifi cant
associations were
documented by Jones et al. [11] in a study carried out un-
der the Behavioral Risk Factor Surveillance
System and
the National Physical Activity Surveys in Australia [8,13,
17,18]. It is suggested that higher education may increase
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IJOMEH 2007;20(2)
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awareness of the benefi ts of healthy behaviors, including
exercise, and improve individuals’ ability to follow health
education messages. In our sample, the compliance with
the peak heart rate (PHR) for physical
activity was related
to educational level and this relationship
was statistically
signifi cant after multivariate adjustment. Furthermore,
statistically signifi cant association between the economic
status and LTPA level was found in women. This relation-
ship was not statistically signifi cant among the men, but
the trend of alterations remained. Possible explanations
for socioeconomic differences in taking-up physical ac-
tivity during leisure involve differences between socio-
economic groups in health-related knowledge, behavior,
values, and attitudes as well as in economic barriers
to
exercise. Moreover, according to Parks et al. [18] lower
income residents
were more likely than others to report
poor health or fear of injury as barriers to physical activ-
ity.
Evidence
for a dose-response relation emerged among
these personal barriers
as well. For all urban residents
each additional barrier reported
resulted in an incremen-
tal decrease in the likelihood of meeting
physical activity
recommendations.
Our results showed healthier physical
activity patterns in
single than in married women. It is possible that married
women are overburdened with high level of occupational
or household-related physical activities. Adults who expe-
rience a larger number of personal barriers, such
as lack of
time, work fatigue or lack of energy are known to be less
active [7,18].
Our study showed that obese people were less likely to be
active during leisure than those with healthy weight. These
results are consistent with the majority of data yielded by
studies of the relation between LTPA and obesity. Longi-
tudinal study of Petersen et al. [19] indicates that obesity
may cause some limitations and lead to physical inactivity.
According to this fi ndings, the inverse cross-sectional re-
lation may be due to the reduction of physical activity as
a consequence of obesity, assuming that the worse discom-
fort of physical activity the greater the overweight. In our
study, we failed to determine which obesity or inactivity
is the cause and which is the effect. Our study results are
rather limited and cannot solve this problem, but the level
of leisure-time physical activity plays an important role in
energy balance.
Negative association was found
between leisure-time
physical activity and smoking in both men and women. It
is well known that health risk behaviors, such as smoking,
insuffi cient
levels of physical activity, improper diet, have
a tendency to cluster, especially among people with lower
education as also observed in the present study [18]. An-
other possibility for this fi nding is that in smokers, various
health limitations (as a consequence of smoking) to exer-
cise may occur.
We also found
that LTPA level was related to time spent
watching television in women.
This association was not
statistically signifi cant in men but the trend of alterations
remained. Several studies have revealed independent as-
sociations
between LTPA, television watching and health
outcomes, such as
obesity and other cardiovascular risk
factors [20–22]. These
fi ndings suggest that public health
policies should encourage
both an increase in physical ac-
tivity and a decrease in sedentary
occupations, especially,
time spent on TV watching [14]. Furthermore, interven-
tion studies evidence that a reduction in TV and video-
tape watching, and playing video
games can be effective
in children to encourage LTPA and limit body weight
gain
over time [23].
Low number of physically active working-age citizens is
a challenge for public health in Poland, and it confi rms
the need for promoting active lifestyles. Effective strate-
gies to encourage leisure-time physical activity need to be
targeted at specifi c age and socio-economic groups.
CONCLUSIONS
1. The results of the present study reveal that over 80% of
men and women in the working-age do not meet recom-
mendations for leisure-time physical activity.
2. Signifi cant
differences in physical activity behaviors
across age, education, income levels or marital status
were
found in the study participants.
3. Certain barriers
to exercise in men and women also in-
cluded unhealthy weight and smoking habit.
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ORIGINAL PAPERS D. KALETA AND A. JEGIER
IJOMEH 2007;20(2)
182
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