A comparative study of health-promoting lifestyles in agricultural and non-agricultural workers in Japan
Environ Health Prev Med
A comparative study of health-promoting lifestyles in agricultural and non-agricultural workers in Japan
Shi-chen Zhang 0 1 2
Chang-nian Wei 0 1 2
Kumiko Fukumoto 0 1 2
Koichi Harada 0 1 2
Kimiyo Ueda 0 1 2
Keiko Minamoto 0 1 2
Atsushi Ueda 0 1 2
0 K. Harada K. Ueda Graduate School of Medical Sciences, Kumamoto University , Kumamoto , Japan
1 K. Fukumoto Kyushu University of Nursing and Social Welfare , Kumamoto , Japan
2 S. Zhang (&) C. Wei K. Minamoto A. Ueda Department of Preventive and Environmental Medicine, Graduate School of Medical Sciences, Kumamoto University , 1-1-1 Honjo, Kumamoto, Kumamoto 860-8556 , Japan
Objectives To clarify the difference in health-promoting lifestyles between agricultural and non-agricultural workers in Japan, a cross-sectional study was conducted on 627 residents living in a town with a mixed rural-urban population. Methods The subjects were divided into 8 groups by job (agricultural and non-agricultural), age (young and old), and gender (male and female). To evaluate the subjects' lifestyles, the Health Promoting Lifestyle Profile II (HPLP-II) was applied. The Bartlett test and the Kendall rank test were performed for statistical analysis. Results There was no significant difference in the overall score of the HPLP-II between the two job groups. However, for the HPLP-II subscales, a significantly higher score for ''spiritual growth'' and a significantly lower score for ''physical activity'' were seen in the agricultural group than in the non-agricultural group. In general, the old and female groups showed higher scores than the corresponding groups, regardless of job type. Conclusions It was determined that the major countermeasures to maintain a healthy lifestyle in agricultural
Lifestyle; Health promotion; Quality of life; Health Promoting Lifestyle Profile-II; Agriculture
workers should be associated with how to introduce daily
activities that maintain and enhance ‘‘spiritual growth’’ and
improve ‘‘physical activity’’.
The Scientific Committee on Rural Health in the
International Commission on Occupational Health stated in the
action plan for 2006–2009 that agriculture was still one of
the most dangerous and unsafe of all human activities [
In Japan, among industrial employees who received a legal
periodical medical examination, a higher rate of abnormal
findings was seen in those working in the primary
industrial sector [
]. Yamane reported that a relative
disadvantage in the overall supply of primary health care and a
severe erosion of hospital-based and emergency medical
services were urgent problems in rural communities in the
first decade of the twenty-first century [
]. Ueda reported
that, in addition to the issues of health and safety, the
agricultural sector has been facing severe socioeconomic
problems, such as rapid reductions in the size of the
workforce and the output of agricultural products, an
increasing proportion of women and elderly workers, and
changes in agricultural management [
]. These situations
may counteract attempts to maintain a healthy lifestyle in
agricultural workers, resulting in a fall in their quality of
As described in the Declaration of Health Promotion
proposed by the WHO [
], the significant association of
lifestyle with QOL is widely understood. In the Healthy
Japan 21 proposed by the Japanese Ministry of Health,
Labor and Welfare [
], in which the concept and
techniques of health promotion were to be introduced, the
importance of improving lifestyle and maintaining a
healthy lifestyle was emphasized.
To measure and evaluate lifestyle or daily activity, the
Health-Promoting Lifestyle Profile (HPLP) is now widely
]. The HPLP-II is an upgraded version of the HPLP,
which has been used extensively in health promotion
] and has been reported to have sufficient
validity and reliability by various studies that explored the
determinants and actual state of a health-promoting
]. In Japan, the Japanese version of the HPLP-II
was developed by Wei et al. , and has been used by
many researchers [
Several studies have found certain sociodemographic
variables related to a health-promoting lifestyle. In terms of
age, some studies reported that older age groups tended to
have healthier lifestyles than younger age groups [
]. In terms of gender, many studies suggested that the
frequency and intensity of performance of healthy
behaviors were better in females than in males [
have also been some studies on the importance of job type,
which showed some differences in health behaviors among
people with different occupations for individuals with
various ethnic origins [
As mentioned above, there are various negative factors
in the daily life of agricultural workers that greatly affect
their lifestyles. The situation indicates that it is necessary
and important to clarify these precisely and to improve the
lifestyles of agricultural workers. However, to date, there
have been few studies on the lifestyles of agricultural
workers undertaken via a comparison with those of workers
in other industrial sectors.
As such, we conducted the present research to clarify the
actual state of health-promoting lifestyles in agricultural
workers by comparison with those of non-agricultural
workers living in the same district. This research may
contribute to the understanding of occupational and
regional health personnel, especially those in mixed rural–
urban societies, to clarify the actual state of daily activities
of agricultural and non-agricultural workers and to develop
programs to improve them.
Town A conducted an action plan for creating a healthy
town along with the Healthy Japan 21 [
] in 2005 and we
participated in that project. As the first part of the project,
the present questionnaire survey was carried out to clarify
the actual state of subjective health, lifestyle, health
behavior, and lifestyle-related factors among the residents
of Town A.
Structure of the questionnaire
The Japanese version of the [
] was used to evaluate the
actual state of each resident’s lifestyle. This version, which
is the same as the original English-language HPLP-II [
contains six subscales (a total of 52 items); namely, health
responsibility (HR), spiritual growth (SG), physical activity
(PA), interpersonal relations (IR), nutrition (N), and stress
management (SM). The overall score of the HPLP-II as
a comprehensive evaluation of a person’s lifestyle was
calculated from the mean score of the 52 items. Each
respondent was asked to rate each item on a Likert
response scale as follows: 1 never, 2 sometimes, 3 often, 4
routinely. The scoring of each item was calculated by the
procedure proposed in the original article [
], with a higher
score indicating a better health-promoting lifestyle. In the
preceding study, the values of Cronbach’s a in the overall
HPLP-II and six subscales had been reported to be 0.91 and
0.70–0.87, respectively [
The information on health and lifestyle-related factors
was obtained, on the basis of the framework of the
PRECEDE-PROCEED model [
], by group work organized
with residents of Town A and our colleagues, who
participated in the project. The final questionnaire was composed
of a fact sheet, the HPLP-II, and additional factors related
to health and daily behavior. In the present study, only the
data for HPLP-II, some demographic data (job, age, and
gender) and data on subjective health were utilized. The
results of more detailed analysis using all of the data in the
questionnaire will be described in another article.
Study population and data collection
Town A was a mixed rural–urban society with 19.0% of the
population working in agriculture (the national average
was 4.4%) and the main agricultural activity was
greenhouse cultivation of vegetables [
]. A total of 3141
residents (18–64 years) were selected randomly from the list
of registered residents in Town A, to whom the
questionnaire was posted with a form concerning informed consent;
1270 (40.4%) questionnaires were returned for this study,
and after uncompleted questionnaires were discarded 1176
remained. In the present study, the sample respondents
consisted of the following occupational groups: (1) primary
industry workers, almost all of whom were agricultural
workers in Town A; (2) secondary industry workers,
generally engaging in manufacturing, a fairly small population
in Town A; (3) tertiary industry workers, including office
workers, public employees, and health service workers,
who had similar employment characteristics, such as a
regular salary, a limited working life (retire at around 60),
and regular work hours. So we considered these people as
one group (as regular employees); (4) informal sectors,
such as irregular employees, the self-employed, and the
unemployed; (5) students; and (6) housewives (see Table
1). Among these groups we chose groups (1) and (3) for the
present research samples, because groups (1) and (3) had a
homogeneous working mode, as mentioned above,
differing from the other groups (2), (4), (5), and (6). Of course,
groups (2), (4), (5), and (6) are important working
populations in a community but we could not gather enough
samples with clear characteristics for analysis for the
present comparative study in Town A. And as for the
housewife group (6), the term ‘‘housewife’’ is not defined
clearly in Japan and housewives are not considered to have
a homogeneous lifestyle; also there are rare cases of
housewives participating in agricultural households. From
this viewpoint, we did not consider this group for our
research sample, and our subjects were groups (1) and (3).
Consequently, a total of 627 questionnaires were
selected and the respondents were divided into two
occupational groups: 130 agricultural workers (20.7% of the
present sample), who engaged in agriculture for 150 days
in a year; and 497 non-agricultural workers (79.3%),
including office workers, public employees, and health
service workers. There were 65 males (50% of the
agricultural group) and 65 females (50.0%) in the agricultural
group; in contrast, there were 292 males (58.7%) and 205
females (41.3%) in the non-agricultural group, showing a
significant difference in sex ratios between the two job
groups (p = 0.045). In terms of age, the average for the
males in the agricultural group was 50.0 ± 11.4 years, and
that of females was 49.4 ± 10.7 years. The average age of
males in the non-agricultural group was 45.0 ± 11.3 years
and that of females was 40.6 ± 11.3 years, showing a
significant difference in mean age by sex in both of the job
groups (p \ 0.0001).
As mentioned above, the proportions of individuals with
regard to age and gender were different in the two job
groups, reflecting the present demographic situation of
Town A and also that in Japan; it would be difficult to
compare these two job groups by a
simple-distributiondependent statistical method.
In the present analysis, the age distribution of the
present samples was shown to be 47–48 years in the 50th
percentile, and the overall HPLP-II score showed a
significant difference when compared between the age groups
of 18–49 and 50–64 years (p \ 0.0001). In the report of
Breslow et al. [
] the mean health status score of the
people in the community in their follow-up samples was
indicated to be in those aged 50–55 years. Accordingly, we
divided our samples into two age groups with the cutoff
at 50 years.
Finally, the subjects were divided into 8 groups according
to their job (agricultural and non-agricultural), age (young
18–49 years and old 50–64 years), and gender (male and
female) (Table 2).
Firstly, the Bartlett test [
] of one-way analysis of
variance was used to clarify the differences in overall
scores and the scores for the subscales of the HPLP-II
between the 8 groups. In addition, according to each
overall score and the scores for the 6 subscales of the
HPLP-II, each group was ranked and assigned a ranking
position from first to eighth.
Next, in order to clarify the difference between the
agricultural and non-agricultural groups, as well as the
differences between age (young and old) and gender (male
and female) groups, in terms of overall scores and scores for
subscales of the HPLP-II, we applied the Kendall
rankorder correlation coefficient (Kendall’s s) [
] as a
nonparametric method. Briefly, the scores of the eight groups,
shown in Fig. 1, were ranked from high to low; namely,
from that ranked first to that ranked eighth. In terms of the
job group, each of the 4 agricultural groups (young/old and
male/female) was given the same rank of first (each
assigned the number of 2.5 as the average rank value of each
group) and each of the 4 non-agricultural groups was given
the rank of fifth (the number being 6.5). The same
procedures were performed for gender groups [males given the
rank of first (the number being 2.5) versus females given the
rank of fifth (6.5)] and age [the young given the rank of first
(2.5) versus the old given the rank of fifth (6.5)]. In this
analysis, a positive value of Kendall’s s indicated that the
agricultural, male, and young groups were assumed to have
a better HPLP-II score than the opposite groups, whereas a
negative value indicated the converse. In both analyses (i.e.,
the Bartlett test and Kendall’s s), the difference was
considered to be statistically significant when p \ 0.05.
NAP version 4.0 (NAP, Mac (c) 1994*S.Aoki, Japan)
and SPSS package version 11 (SPSS, Chicago, IL, USA)
were used for statistical analyses.
In the present study, implemented as a part of community
health services, all selected subjects were informed of the
purpose of the study and were assured of confidentiality
upon receiving the questionnaire. Consent to participate in
the study was confirmed for those who completed and
returned the questionnaire. Study data were processed in a
restricted place, using a personal unidentifiable code for
.460 .480 .40 .50 .50 .60 .20 .40 .40 .40 .40 .40 .05 .03 .04 .04 .04
8 1 7 3 2 4 7 3 7 5 0 2 1 7 9
± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
IR .582 .592 .972 .803 .872 .083 .782 .223 .093 .243 .053 .802 .063 .852 .123 .812 .073 S,no
.047 .055 .057 .053 .063 .071 .042 .050 .062 .048 .065 .067 .052 .075 .052 .060 .059 tirnu
± ± ± ± ± ± ± ± ± ± ± ± ± ± ± N
PA .612 .619 .717 .172 .201 .192 .200 .181 .215 .176 .192 .184 .175 .200 .150 .182 .181 ,ison
.005 .074 .053 .056 .061 .065 .048 .045 .050 .047 .051 .054 .056 .061 .056 .054 .053 llrea
± ± o
± ± ± ± ± ± ± ± ± ± ± ± ± ± ± n
SG .472 .962 .542 .642 .562 .802 .582 .662 .003 .912 .692 .542 .632 .512 .526 .529 .628 rrsep
.047 .054 .049 .050 .051 .062 .049 .046 .043 .841 .053 .057 .055 .063 .061 .051 .053 in
Reliability of the questionnaire
Cronbach’s a coefficient for the overall score and subscales
of the HPLP-II was shown to have a high value, the same
as that in previous research by Wei et al. [
], with an a
coefficient of 0.92 overall and 0.68–0.83 for the subscales.
Characteristics of the subjects
Some characteristics of the total number of subjects in the
present study, such as age, the percentage of those having
good subjective health, and the scores for the domains of
the HPLP-II stratified by sex and occupational status are
summarized in Table 1.
In the present study, we focused on agricultural and
nonagricultural workers by dividing the subjects into 8 groups,
as mentioned above. The mean age of each group is shown
in Table 2, showing that there was no statistically
significant difference in the mean age between the 4 young
groups, or between the 4 old groups.
Fig. 1 Mean overall Health Promoting Lifestyle Profile (HPLP)-II c
scores (a) and mean scores for the HPLP-II subscales (b-g), as well as
rank orders (a-g) for agricultural (n = 130) and non-agricultural
groups (n = 497). Data are means ± SD. Statistical analysis was
performed by the Bartlett test of one-way analysis of variance.
Significant differences between corresponding groups are indicated as
follows: *p \ 0.05, **p \ 0.01, ***p \ 0.001. Numbers in circles
-¼ indicate the rank order of each score in each group. Symbols for
the agricultural group are as follows: open squares young-male, open
triangles old-male, open circles young-female, open diamonds
oldfemale. Symbols for the non-agricultural group are as follows: filled
squares young-male, filled triangles old-male, filled circles
youngfemale, filled diamonds old-female
A healthy subject was defined as one who answered
‘‘yes’’ to the question concerning subjective health of ‘‘I
think I am healthy’’.
In the present subjects a significant correlation was seen
in the score for subjective health (0–2) to overall HPLP-II
(r = 0.146 p \ 0.0001), and also a significant correlation
was seen to each subscale of the HPLP-II (r = 0.23,
p \ 0.001 for SG, r = 0.08, p \ 0.05 for PA, r = 0.12,
p \ 0.01 for IR, r = 0.07, p \ 0.05 for N, r = 0.18, p \
0.001 for SM) except for HR (r = -0.04, not significant).
5. Interpersonal relations
Mean 2.47 2.63 2.83 2.95
The positive rates for good subjective health of the
subjects in each group are summarized in Table 3. A
significant difference was observed between the 8 groups
(p \ 0.0001 by the v2test) and the
young-female-agricultural group showed the lowest value and the
youngfemale-non-agricultural group showed the highest value. In
the female groups, a lower rate was generally seen in the
agricultural group than in the non-agricultural group;
however, no significant difference was found for
agricultural versus non-agricultural in the young-female group
(p = 0.068), or in the entire female group (p = 0.052).
Differences in overall scores and scores for the six
subscales of the HPLP-II between the 8 groups
by the Bartlett test
The overall scores and scores for the six subscales of the
HPLP-II in the agricultural and non-agricultural groups are
summarized in Fig. 1a–g. The rank of the score of each group
is indicated below the mean value of each group in the figure.
For the overall HPLP-II score, as shown in Fig. 1a, the
female group and the old group showed, in general, higher
scores than the male and the young group in both
occupational groups; however, a significantly higher score was
seen only in the old male non-agricultural group compared
with the young male non-agricultural group (p \ 0.001).
For the subscale HR (Fig. 1b), the female
non-agricultural group showed a significantly higher score than the
male non-agricultural group [both in the young (p \ 0.01)
and old groups (p \ 0.01)]. In addition, old male workers
showed significantly higher scores than young males in the
non-agricultural group (p \ 0.001). The old female
agricultural group showed significantly higher scores than the
old male agricultural group (p \ 0.01) and the young
female agricultural group (p \ 0.001).
For the subscale SG (Fig. 1c), the agricultural group
showed, in general, a higher score than the non-agricultural
group; however, no significant difference was found.
For the subscale PA (Fig. 1d), the agricultural groups
showed, in general, lower scores than the corresponding
non-agricultural groups; however no significant difference
For the subscale IR (Fig. 1e), significant differences
were seen for the female non-agricultural group compared
with the male non-agricultural group [both in the young
(p \ 0.001) and the old (p \ 0.001)].
For the subscale N (Fig. 1f), a significantly higher score
was seen in the female agricultural group than in the male
agricultural group [both in the young (p \ 0.01) and the
old (p \ 0.01)]. Furthermore, in the non-agricultural group,
a significantly higher score was seen in the old female
group than in the young female group (p \ 0.001).
For the subscale SM (Fig. 1g), no significant difference
was found between the 8 groups.
Comparisons of the HPLP-II overall scores and scores
for the HPLP-II subscales between agricultural
and non-agricultural groups, young and old groups,
and male and female groups by Kendall rank-order
The results of the Kendall rank-order test for job
(agricultural and non-agricultural), age (young and old), and
gender (male and female) are summarized in Table 4. A
significantly higher rank value was found for SG in the
agricultural group than in the non-agricultural group
(s = 0.714, p = 0.037). PA showed a significantly lower
rank value in the agricultural group than in the
non-agricultural group (s = -0.756, p = 0.021). However, no
significant difference in rank value between the two job
groups was found for the overall score or for the scores for
the other subscales.
In terms of age, the young group showed significantly
lower values than the old group for the overall score
(s = -0.722, p = 0.029). There was no significant
difference in rank value for the other subscales.
In terms of gender, the male group showed significantly
lower values than the female group for the overall score
(s = -0.722, p = 0.029), and for HR (s = -0.661, p =
0.043), IR (s = -0.756, p = 0.021), and N (s = -0.756,
p = 0.021).
The present study was conducted to clarify the actual state
of the lifestyle in agricultural workers, for whom some
critical factors, including not only physiological and
mental workload but also various kinds of socioeconomic
and demographic situations, have generally been
recognized as affecting their lifestyle. We selected subjects and
controls by random sampling from a governmental list of
registered residents in Town A.
Town A is a typical Japanese rural–urban mixed society,
in which fulltime farmers constitute 19.0% of the
population, which provides an appropriate model and sample for
comparing the actual lifestyle situation between
agricultural and non-agricultural workers.
As summarized in Table 2, the age and gender
proportions were different between the two job groups in our
study (agricultural and non-agricultural workers), reflecting
the present demographic situation in Town A. Similar
phenomena are found throughout Japan. It was difficult to
compare these two job groups by a
simple-distributiondependent statistical method. We devised a specific
statistical procedure as follows.
As a first step, after dividing the subjects into 8 groups
of two opposite groups of job, age, and sex, we performed
the Bartlett test [
] of one-way analysis of variance to
clarify the differences in the HPLP-II overall scores and the
scores for the six subscales of the HPLP-II between the 8
groups. As a second step, after ranking the 8 groups from
first to eighth according to each mean score, we applied the
Kendall rank-order correlation coefficient (Kendall’s s)
] as a nonparametric method to clarify the difference in
the HPLP-II scores between the agricultural and
nonagricultural groups, and also between the different age
(young and old) and gender (male and female) groups.
The Kendall s rank correlation coefficient, developed by
Kendall in 1938, is commonly used to measure the degree
of correspondence between two rankings and to assess the
significance of this correspondence. Thus we applied this
method to investigate how to accumulate the rank of the
scores in the specific group by giving the rank of first to the
4 agricultural groups and fifth to the 4 non-agricultural
groups. Although the application of the Kendall rank test to
the present analysis may not always fit directly with the
statistical concept of the Kendall rank test, we think that
the present statistical procedure may have been the proper
choice as a new statistical method for this study.
As a whole, this study is the first comparative study to
have evaluated the actual lifestyle condition in agricultural
workers living in a district with a rural–urban mixed
society. The results may contribute to helping regional
public healthcare professionals to develop health service
plans, by considering the individual and occupational
differences of residents.
Firstly, by the calculation of a single correlation
coefficient, we confirmed that the overall HPLP-II score and the
scores for the 6 HPLP-II subscales were significantly
related to the study subjects’ subjective health
Secondly we clarified that, especially from the results of
the Kendall rank test, gender and age appeared to greatly
influence the lifestyles of both agricultural and
non-agricultural workers, a finding which is consistent with
previous reports on various working populations [
11, 16, 18–21,
]. For example, in terms of the association of
age with lifestyle, it was reported that the nutritional
condition was significantly better for old workers than young
workers. In addition, in blue-collar workers, the old age
group performed less exercise but showed more health
responsibility and practiced healthier nutrition. In terms of
gender, it was reported that females showed more
healthprotective behaviors. Bagwell and Bush [
] noted that
females scored significantly higher than males in health
responsibility and interpersonal relations. Furthermore,
many studies have suggested that the frequency and
intensity of the performance of good health behavior varied
between males and females. In the present study, it was a
noteworthy result that a tendency was shown toward
higher overall HPLP-II scores and higher scores for the six
HPLP-II subscales only in the female-old-agricultural
group compared with the female-young-agricultural group;
nevertheless, higher ranks for the scores were seen in the
female-young-agricultural group than in the two male
groups. This tendency might reflect a critical situation at
present in the female-young-agricultural workers and
indicates that further investigation should be conducted to
clarify a critical situation, from social and work
physiological aspects, in e young female agricultural workers.
Finally we clarified that there was no significant
difference in the overall HPLP-II score between the
agricultural group and the non-agricultural group in the present
study. However, as to the HPLP-II subscales , a
significantly higher SG score and a significantly lower PA score
were seen in the agricultural group compared with the
As Wei et al. reported [
], the SG subscale consists of 9
items representing happy and positive feelings and strong
intentions for the future and the PA subscale consists of 8
items that reflect a persevering attitude and the practice of
exercise. The items in both of these subscales are
recognized as advantageous factors for achieving a high QOL,
by reducing stress and by enhancing physical activities,
] reported that SG was a natural attribute of
agricultural workers because agricultural work and life are
based on a creative element that is latent in primary
industry. From this viewpoint, it is of great importance for
public health professionals to educate agricultural workers
that they possess an advantage with regard to elements of
lifestyle through their agricultural work and life; such
education could thus increase the interest of community
residents in agriculture, by promoting an understanding of
the importance and benefits of agricultural work.
PA is considered to be one of the most important and
common activities for improving residents’ lifestyle.
However, it should be noted that because the items
reflecting exercise in the questionnaires used in the present
study were associated with jogging, swimming, or other
formal sports (which are the common methods of
exercising among non-agricultural workers) whereas for
agricultural workers, exercise usually includes walking, doing
housework, and carrying out job-related activities, their
scores may have been underestimated. Further
investigation is necessary to clarify this point.
We note that agricultural workers showed and
maintained higher SG scores , a finding which is considered to
be probably due to their living environment. So, it is more
applicable and effective to improve the lifestyle of
agricultural workers by further improving SG, which is
relatively easy to achieve, rather than by attempting to improve
the items with lower scores, such as PA, which is relatively
difficult to improve at present.
We concluded that introducing activities for maintaining
and enhancing the condition of SG, and improving the
condition of PA would be effective countermeasures for
maintaining a desirable lifestyle in agricultural workers in a
rural–urban mixed society.
It is also necessary and important for public health
professionals to introduce knowledge and techniques of
exercise to agricultural workers and to make arrangements
for places for training and equipment for the purpose of
exercise establishment and the development of better PA.
Besides factors such as income, education, and marital
status, job, age, and gender may also be very important
factors related to health-promoting behaviors [
16, 34, 37
Further investigations regarding these factors for regulating
individual health-promoting behaviors are warranted.
We conducted a cross-sectional survey to clarify the actual
condition of a health-promoting lifestyle in agricultural
workers in comparison with that of non-agricultural workers
living in the same town. The findings were as follows:
There was no significant difference in the overall
HPLP-II score between the agricultural group and the
For the HPLP-II subscales , a significantly higher SG
score and a significantly lower PA score were seen in
the agricultural group compared with the
The female group tended to show a significantly
higher overall HPLP-II score than the male group.
The old group had higher overall scores and higher
scores for HR, IR, and N than the young group.
Accordingly, the results suggested that introducing
daily activities to maintain and enhance SG and
improve PA would be effective measures to maintain
a desirable lifestyle for agricultural workers.
Acknowledgments The authors thank the staff of the Health and
Welfare section of Town A for their cooperation on-site during the
process of data acquisition. The present study was undertaken as part of
the national model project, Health-Up Model Project, of the National
Health Insurance, the Ministry of Health, Welfare and Labor.
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