１

傷病休業と関連する因子の検討―ある製造企業における後ろ向きコホート研究研究者（代表者）
宋裕姫

帝京大学大学院公衆衛生学研究科

共同研究者
矢野

栄二

帝京大学大学院公衆衛生学研究科−1−

平成29年度研究助成報告書.indd 1

2018/08/28 8:32

平成29年度研究助成報告書.indd 2

2018/08/28 8:32

傷病休業と関連する因子の検討―ある製造企業における後ろ向きコホート研究宋裕姫 矢野栄二

帝京大学大学院公衆衛生学研究科【目的】傷病休業の削減は産業保健活動の重要な課題のひとつであり、その原因を特定できれば、一次予防が可能となる。企業には、労働安全衛生法に基づく健康診断、職種などの人
事情報、傷病休業、健康保険組合には、特定健康診査のデータがあるものの、これまでこれらを統合した上で、傷病休業と関連する要因を検討した報告はない。今回某製造企業において、傷病休業と関連する要因を特定するために、企業、健保にあるデータを用いた後ろ向き
コホート研究を行った。
【方法】某製造企業正規社員 24945 名を対象に、2015 年 4 月 1 日から 2016 年 3 月 31 日の期間の 7 日以上の傷病休業と関連する要因について後ろ向きコホート研究を行った。傷病休業と関連する要因としては、2015 年 4 月 1 日時点の年齢、性別、事業場（本社、研究所、工場、その他）、役割等級（管理職/工場監督者/工場一般職/非工場リーダー/非工場一般職）、2014 年 4 月 1 日から 2015 年 3 月 31 日の期間に実施された健康診断（BMI、収縮期・拡張期血圧、LDL コレステロール、空腹時血糖値）、と高齢者医療確保法に基づく特定
健康診査の質問項目とした。統計学的解析は、全年齢層、40 歳以上それぞれにおいて、Stepwise 法を用いて変数を選択した上で、多重ロジスティック回帰分析を用いて、傷病休
業と上記因子との関連を 95％信頼区間（CI）でオッズ比（OR）として計算した。
【結果】3.2％（801 人）が傷病休業を取得した。全年齢層では、工場、非工場いずれにおいても役割等級が低いほど、拡張期血圧が高いほど、高血圧治療、糖尿病治療、貧血既往、摂食速度が遅いこと、睡眠障害、飲酒頻度が少ないこと、喫煙していることは傷病休業と有意に関連していた （Hosmer-Lemeshow 検定、p = 0.58）。40 歳以上の被験者では、工場、非工場いずれにおいても役割等級が低いほど、収縮期血圧が高いほど、LDL コレステロールが 109mg / dl 未満であること、糖尿病治療、歩行速度が遅いこと、飲酒頻度が少ないこと、
喫煙していることが、傷病休業と有意に関連していた（Hosmer-Lemeshow 検定、p = 0.44）。
【考察】低い役割等級やいくつかの健康や生活習慣に関する因子が 7 日以上の傷病休業と関連性がある可能性がある。以上より、健康診断事後措置において、適切な就業区分判定、保健指導、衛生委員会への報告などによって傷病休業を予防することができる可能性がある。今後は、労働安全衛生法のストレスチェック制度のデータ等も追加することによって、傷病休業と関連する要因がさらに特定される可能性がある。
(1,107 文字)

1−3−

平成29年度研究助成報告書.indd 3

2018/08/28 8:32

The role of occupational class, health, and lifestyle-related factors in sickleave: a retrospective cohort study in a Japanese manufacturing company

Authors
You Hwi Song and Eiji Yano
Authors affiliations
Teikyo University Graduate School of Public HealthCorrespondence to You Hwi Song
2-11-1 Kaga, Itabashi-ku, Tokyo, Japan
TEL +81-3-3964-1211 (Ext: 46210)
FAX +81-3-3964-1058
Email; sph-ysong@med.teikyo-u.ac.jp
Type of contribution
Field Study
Running title
Sick leave and health-related factors in a Japanese company
Conflict of interest (COI)
The authors declare that are are no conflicts of interest.

2−4−

平成29年度研究助成報告書.indd 4

2018/08/28 8:32

Sources of Funding
This study was funded by the Occupational Health Promotion Foundation.
Number of words
Abstract: 245
Text: 3,342
Number of tables and figures
Tables: 4 (including 1 appendix)
Figures: 03−5−

平成29年度研究助成報告書.indd 5

2018/08/28 8:32

Abstract:
Objectives:
The objective of this study was to comprehensively investigate the healthrelated factors associated with sick leave (SL) in a Japanese manufacturing
company through a pilot study.
Methods:
The subjects of the study were 24,945 permanent employees in a retrospective(2014–2016) cohort study in a Japanese manufacturing company. SL as an
outcome variable was defined as medically certified SL with a duration ofseven or more days undertaken between April 1, 2015, and March 31, 2016.
The explanatory variables at baseline were demographic characteristics,
occupational class, health indicators, and life-related factors. The association
between SL and the above factors was calculated as an odds ratio (ORs) with
95% confidence intervals (CIs) using multiple logistic regression analysis
after selecting variables using the Stepwise method.
Results:
In total, 3.2% (801 individuals) were classified as taking SL. For all subject
ages of a lower occupational class, higher diastolic blood pressure,4−6−

平成29年度研究助成報告書.indd 6

2018/08/28 8:32

hypertension and medication-controlled diabetes, a medical history of anemia,
slower eating speed, sleep disturbances, lower frequency of alcohol intake,and current smoking were significantly associated with SL (Hosmer–
Lemeshow test, p = 0.58). For subjects aged 40 or over, in addition to the above
factors, a higher systolic blood pressure, low-density lipoprotein cholesterol(<109 mg/dl), and slow walking speed were also significantly associated withSL (Hosmer–Lemeshow test, p = 0.44).
Conclusions:
A lower occupational class and several health indicators and lifestyle
behaviors were associated with SL. Hence, primary lifestyle interventions
have noticeable contributions to preventing SL.

Key words
Sick leave, occupation, lifestyle, risk factors, alcohol, tobacco

5−7−

平成29年度研究助成報告書.indd 7

2018/08/28 8:32

Introduction
Sick leave (SL) is considered a global measure of health status and a marker
of social, psychological, and physical functioning in working populations. SL
also has a profound impact on individuals’ wellbeing and is an economic
burden for a society. SL has further been shown to predict future adverse
health outcomes, mortality, and an increased future risk of requiring adisability pension for aging populations. The aging of the working population
is a crucial issue in developed societies. Because Japanese society has anaging population and a decreasing population of working age, it is also
important to decrease the loss caused by SL to improve employee productivity.
In aging populations, there is a need to keep the workforce healthy andproductive. If the determinants of SL are identified, primary interventions toaddress these factors may have a noticeable contribution to the prevention of
SL1.
The causes of SL are multifactorial. Several studies have investigated
the association of SL with occupational class. However, findings on whether
a lower occupational class was related to a higher risk of SL have been
inconsistent23456. One study did not find any association3, whereas others
6−8−

平成29年度研究助成報告書.indd 8

2018/08/28 8:32

confirmed an association between lower occupational class and a higher
likelihood of taking SL245. Another study showed that routine non-manual
workers took more SL than manual workers. This was more prevalent among
younger employees6. A further previous study found that lower occupationalclass was related with a higher risk of SL because of greater exposure toadverse working conditions27. In terms of health- and lifestyle-related factors,
smoking891011 and obesity1012131415 were associated with SL. By contrast,
evidence of a relationship between alcohol consumption and SL has been
inconsistent161718. Findings on the effects of worksite physical activity
interventions on SL had inconsistent associations with SL19202122, althoughsome studies showed a significant association between physical activity andSL232410. A study using the multidimensional multimorbidity score showedthat the greater the number of health conditions, the higher the risk of SL,
even after adjusting for prior SL and other covariates25. An association
between sleep disturbances and an increased likelihood of taking SL has been
repeatedly observed26-33.
In Japan, a cross-sectional study found an association between
occupational class and SL5. Another prospective cohort study found that7−9−平成29年度研究助成報告書.indd 9

2018/08/28 8:32

employees with functional impairment at work were more likely to take SL34.
In a cross-sectional study, white-collar workers with poor sleep quality (asmeasured by the Pittsburgh Sleep Quality Index) showed higher risks of SL35.
However, in Japan, there are few studies that have investigated the healthand lifestyle determinants of SL, and most of those used non-law regulated
data which are not affordable in Japanese occupational practice. Moreover,those studies have concentrated on one or a few working conditions, and few
studies have included a wide range of health-related factors, including
occupational class, chronic health conditions, and lifestyle.
All workers at worksites with 50 or more employees in Japan have anannual health checkup because the Industry Safety and Health Act requiresthat employers provide a regularly employed worker with a medical
examination by a physician once every a year36. Medical insurers must holdannual health checkups, including a lifestyle questionnaire, for those who are
insured and aged 40 and over, according to the Act on Assurance of Medical
Care for Elderly People37 (APPENDIX).
It could be hypothesized that lower occupational class, chronic healthconditions, and an unhealthy lifestyle are associated with SL. To investigate
8− 10 −

平成29年度研究助成報告書.indd 10

2018/08/28 8:32

whether occupational class, chronic health conditions, and lifestyle factors areassociated with SL, a large retrospective cohort study in Japan was conductedusing an integrated approach, which included all types of exposure such as
occupational class, health status, and lifestyle, where the variables were both
law-regulated and affordable in Japanese occupational practice.

Subjects and Methods
Study settingThis study was a retrospective cohort study using data from April 1, 2014, to
March 31, 2016.

Study population

The subjects of this study were 24,945 permanent employees at a
manufacturing company as of April 1, 2014. The manufacturing company wasa global enterprise headquartered in Japan. The subjects of this study were
employees working at headquarters, in research and development (R&D), fiveplants, and other small worksites in Japan. Worksites were dispersed
between cities and rural areas and from north to south.

9− 11 −

平成29年度研究助成報告書.indd 11

2018/08/28 8:32

Outcome variable

Sick leave as an outcome variable was defined as medically certified sick leave
with a duration of seven days or more undertaken between April 1, 2015, andMarch 31, 2016. A diagnosis (International Statistical Classification of
Disease and Related Health Problems, 10th revision-ICD-10) of every SL wasgathered from the company’s occupational health management registers.

Explanatory variables at baseline

The continuous variables used as explanatory variables in this study wereage, body mass index (BMI), systolic blood pressure (SBP), diastolic bloodpressure (DBP), low-density lipoprotein cholesterol (LDL), and fasting blood
glucose level (FBS). These variables were converted to categorical variables
using tertiles. The categorical variables were gender, worksite (headquarters,
R&D, plant, and others), occupation (manager, high-skilled white-collar, lowskilled white-collar, high-skilled blue-collar, and low-skilled white-collar), andchange of occupation, including job promotion, job demotion, and change ofcourse between white-collar and blue-collar. Variables related to life history,
medical history, and lifestyle were also used (see the Appendix). These were
the following:10− 12 −

平成29年度研究助成報告書.indd 12

2018/08/28 8:32

1. Medical history: hypertension, diabetes and hyperlipidemia while using
medication, stroke, heart attack, chronic renal failure, and anemia
2. Life history: body weight gain and change, walking speed, and sleep
3. Lifestyle: exercise, eating speed, eating habits, alcohol intake, andsmoking.
Questions on the above issues were formulated as requiring Yes or No
responses, and alcohol intake was categorized into “every day,” “sometimes,”and “rarely (cannot drink).” Smoking was classified into “current,” “exsmoker,” and “never.”In Japan, the Labor Safety and Health Act requires employers to
conduct an annual health checkup on employees, which includes checking the
BMI, SBP, and DBP for all ages and the LDL and FBS for those aged 40 andabove. The Act on Assurance of Medical Care for Elderly People requires
health insurance associations to carry out an annual health checkup for
insured persons aged 40 and over. Consequently, health insurance
associations have data on life history, medical history, and lifestyle.
Information on age, gender, worksite, occupation, and change ofoccupation were collected from the human resource department. BMI, SBP,11

− 13 −

平成29年度研究助成報告書.indd 13

2018/08/28 8:32

DBP, LDL, and FBS were collected from the company’s health management
department. Life history, medical history, and lifestyle data were collectedfrom the medical insurers.

Statistics

The associations between SL and the above factors were calculated as an odds
ratio (ORs) with 95% confidence intervals (CIs). In Model 1, we calculated
ORs and CIs using simple logistic regression analyses. In Model 2,
adjustments for age and gender were made using logistic regression analyses.
In Model 3, we conducted multiple adjustments using the multiple logistic
regression mode. We used the Stepwise method for variable selection, withage and gender forced on, and other variables were introduced and removed
as P = 0.2. Variable selection was done in terms of factors. If any of the factor
categories met the criteria, that item was selected and added to the multiple
adjustment model (Model 3). Finally, the Hosmer–Lemeshow goodness-fit
tests were used to validate the models.

Ethics statement

The study protocol was approved by the Teikyo University Review Board (1712− 14 −

平成29年度研究助成報告書.indd 14

2018/08/28 8:32

074). The data obtained anonymized participant data after the study’s
approval and the signing by all stakeholders of an agreement assuring
participant confidentiality. Data were treated confidentially in accordancewith current Japanese legislation on data protection. All data were deidentified before being delivered to the research team. All participants were
informed that their data were to be included in the study.

Results
Table 1 shows the distribution of explanatory variables at baseline. In total,
3.2% (801 individuals) were classified with taking SL.
The results of Model 1, Model 2, and stepwise variable selection are
shown in Table 2. Model 1 found that the risks were significantly higher inthe following cases: plants rather than worksites (OR = 1.62, 95%CI, 1.23–
2.13) and occupation (high-skilled white-collar: OR = 2.86, 95%CI, 1.89–4.33;
low-skilled white-collar: OR = 4.75, 95%CI, 3.07–7.35; high-skilled bluecollar: OR = 3.21, 95%CI, 2.08–4.96; low-skilled blue-collar: OR = 4.50, 95%CI,
2.98–6.78; manager: reference).
Under medical history, the risks were significantly higher in the13− 15 −

平成29年度研究助成報告書.indd 15

2018/08/28 8:32

following cases: higher DBP (70–79 mmHg: OR = 1.27, 95%CI, 1.06–1.52; 80
and more mmHg: OR = 1.36, 95%CI, 1.14–1.63; less than 70 mmHg:
reference) and lower LDL in those aged 40 and over (109–133 mg/dl: OR =0.60, 95%CI, 0.45–0.75; 134 and more mmHg: OR = 0.73, 95%CI, 0.60–0.90;
less than 109 mmHg: reference), hypertension (OR = 1.62, 95%CI, 1.31–2.00),
diabetes (OR = 2.25, 95%CI, 1.66–3.06), hyperlipidemia (OR = 1.40, 95%CI,
1.08–1.81) controlled by medication, heart attack (OR = 1.73, 95%CI, 1.09–
2.72), and anemia (OR = 1.43, 95%CI, 1.08–1.90).
Under life history, the risks were significantly higher in the following
cases: subjects not exercising 2 days per week (OR = 1.31, 95%CI, 1.08–1.58),
smoker (OR = 1.34, 95%CI, 1.14–1.56) of lifestyle, slow walking speed (OR =1.34, 95%CI, 1.15–1.55), body weight change over last year (OR = 1.16, 95%CI,
1.00–1.35), and sleep disturbance (OR = 1.26, 95%CI, 1.09–1.47).
By contrast, faster eating speed (OR = 0.85, 95%CI, 0.73–0.98) andalcohol intake (every day: OR = 0.76, 95%CI, 0.64–0.91; sometimes: OR = 0.62,
95%CI 0.53–0.73; rarely or cannot drink: reference) showed a significantly
lower risk. All of those significant associations remained even after
adjustment by age and gender in Model 2.
14− 16 −

平成29年度研究助成報告書.indd 16

2018/08/28 8:32

The results of Model 3’s multiple logistic regression analysis, after
selecting variables using the Stepwise method, are shown in Table 3. Multiple
logistic regression analyses were conducted by separating subjects into twogroups (all ages and those aged 40 and over) because available variables foranalysis depended on subjects’ ages. For the all ages group, the following weresignificantly associated with SL (Hosmer–Lemeshow test, p = 0.58):
occupation (high-skilled white-collar: OR = 3.18, 95%CI, 2.02–5.02; lowskilled white-collar: OR = 6.38, 95%CI, 3.88–10.48; high-skilled blue-collar:
OR = 3.22, 95%CI, 1.92–5.41; low-skilled blue-collar: OR = 4.75, 95%CI, 2.86–
7.88; manager: reference), DBP (70–79 mmHg: OR = 1.50, 95%CI, 1.20–1.87;
80 and more mmHg: OR = 1.68, 95%CI, 1.28–2.19; less than 70 mmHg:
reference), hypertension (OR = 1.34, 95%CI, 1.05–1.72), diabetes mellitus (OR= 1.74, 95%CI, 1.25–2.44), anemia (OR = 1.41, 95%CI, 1.05–1.89), fastereating speed (OR = 0.85, 95%CI, 0.72–0.99), sleep disturbance (OR = 1.20,
95%CI, 1.03–1.40), alcohol intake (every day: OR = 0.75, 95%CI 0.62–0.92;
sometimes: OR = 0.69, 95%CI, 0.58–0.83; rarely or cannot drink: reference),and smoker (OR = 1.37, 95%CI, 1.16–1.63).
For the group aged 40 and above, the following were significantly
15− 17 −

平成29年度研究助成報告書.indd 17

2018/08/28 8:32

associated with SL (Hosmer–Lemeshow test, p = 0.44): occupation (highskilled white-collar: OR = 3.31, 95%CI, 2.03–5.38; low-skilled white-collar: OR= 5.88, 95%CI, 3.31–10.46; high-skilled blue-collar: OR = 2.62, 95%CI, 1.49–
4.60; low-skilled blue-collar: OR = 3.82, 95%CI, 2.19–6.64; manager:
reference), SBP (116–125 mmHg: OR = 1.38, 95%CI, 1.08–1.76; 126 and more
mmHg: OR = 1.49, 95%CI, 1.15–1.91; less than 116 mmHg: reference), LDL(109–133 mg/dl: OR = 0.65, 95%CI, 0.52–0.82; 134 and more mmHg: OR =0.81, 95%CI, 0.65–1.01; less than 109 mmHg: reference), diabetes mellitus(OR = 1.70, 95%CI, 1.20–2.41), slow walking speed (OR = 1.26, 95%CI, 1.04–
1.53), alcohol intake (every day: OR = 0.76, 95%CI 0.61–0.95; sometimes: OR= 0.73, 95%CI, 0.58–0.92; rarely or cannot drink: reference), and smoker (OR= 1.29, 95%CI, 1.04–1.60).

Discussion
This study investigated the association between SL and health-related factors
in the general working population of a manufacturing company. SL wassignificantly associated with lower occupational class, higher DBP,
medication-controlled diabetes mellitus, a medical history of anemia, slower16− 18 −

平成29年度研究助成報告書.indd 18

2018/08/28 8:32

eating speed, sleep disturbances, alcohol intake habits (rarely or cannot
drink), and current smoking. When age was limited to subjects aged 40 andabove, lower occupation class, higher SBP, lower LDL, medication-controlled
diabetes mellitus, slower walking speed, alcohol intake habits (rarely orcannot drink), and current smoking were significantly associated with SL. In
both the all age groups and the aged 40 and above group, there was a higher
risk of SL in low-skilled white-collar workers than in low-skilled blue-collar
workers.
We found that those of a lower occupational class had a high risk ofsick leave, which was consistent with several previous studies showing an
association between lower occupational class and higher risk of SL 245. Our
results also showed that lower-skilled white-collar workers took more SL thanlower-skilled blue-collar workers, which was the same outcome as a previous
report6. This outcome indicated that we might be able to prevent SL bymodifying the job design and managing the work environment to decrease job
stress and work-life conflicts and to motivate lower occupational classworkers.
Ubalde-Lopez et al. measured multimorbidity in a working population17− 19 −

平成29年度研究助成報告書.indd 19

2018/08/28 8:32

and found that the greater the number of health conditions, the higher therisk of SL, even after adjusting for prior SL and other covariates25. Our study
also found that some chronic health conditions (hypertension, diabetes
mellitus, and anemia) and unhealthy lifestyles (current smoking) showed ahigher risk of SL.
We also found that sleep disturbances increased the risk of SL, whichis consistent with previous studies2627282930313233. This finding indicated thatsleep hygiene education, screenings for sleep disorders such as obstructive
sleep apnea, and optimization of working hours and scheduling might prevent
SL.
However, our findings on obesity were inconsistent with previous
findings showing that obesity was associated with SL1012131415. There areseveral reasons for this inconsistency. First, most previous studies wereconducted in developed Western countries where obesity is a very importantsocial and health issue, whereas in Japan, obesity has been a less serious
issue, and consequently, there has been little evidence of an association
between obesity and SL in Japan. Second, BMI levels were different between
our study and those previous studies. BMI as an explanatory continuous
18− 20 −

平成29年度研究助成報告書.indd 20

2018/08/28 8:32

variable in our study was converted to categorical variables using tertile. As
a result, the highest BMI category was ≧24.1 kg/m2. By contrast, previous
studies used 25.0–29.9 and ≧30 kg/m2 BMI as indicators of overweight andobesity, respectively. Therefore, even the highest BMI category in our study
was much lower than both the overweight and obesity levels in previous
studies.
In terms of physical activity, our findings were consistent with theprospective study by Proper et al.24 and inconsistent with Eriksen et al.'s23
prospective study. Proper et al. found that there was no relation between
moderate physical activity and SL, whereas vigorous physical activity was
associated with significantly less SL. Eriksen et al. used a questionnaire
which classified brisk walks, aerobics, gymnastics, and physical leisureactivities for 20 min or more at least once a week as physical activity. Our
questionnaire included the following questions: “Have you been exercising atleast 2 days per week, at least 30 minutes per session, at an intensity thatcauses a slight sweat, for at least 1 year?” to measure regular exercise and“Have you walked for at least 1 hour every day or carry out equivalent
physical activities in your daily life?” to measure daily physical activity. These19− 21 −

平成29年度研究助成報告書.indd 21

2018/08/28 8:32

questions might of lower sensitivity than questions used in previous studies.
We found that drinking alcohol sometimes and everyday showed lower
risks of SL than drinking rarely and not being able to drink, which isconsistent with a previous study showing the U-shaped relation between
alcohol intake and SL1638. Moderate alcohol consumption is sometimes
presented as possibly health-promoting39404142. These assumptions are basedon a curve-linear relationship between alcohol intake and several healthissues, where low and high intakes appear to have an increased risk comparedto moderate intake. Some believe that the outcome among low-level drinkers
is due to the health-promoting effect of alcohol. However, others argue thatthese findings are more likely to be caused by unmeasured confounding
factors such as genetic and environmental factors influencing both alcohol
consumption and the outcomes. Østby has suggested, in a discordant twin
design, that the increased level of SL observed with low-level alcohol
consumption seemed to be mainly explained by confounding genetic factors43.
We found an association between low levels of LDL and high risk of SL.
A meta-analysis demonstrated significantly lower serum LDL in cases ofdepression44. This finding possibly indicates that depression and SL might20− 22 −

平成29年度研究助成報告書.indd 22

2018/08/28 8:32

have similar biological or social mechanisms, although evidence of therelationship remains controversial and the mechanisms of this association
have not yet been explored.
This study has several strengths. It was a large study which used aprospective design, with variables that were affordable in occupational healthpractice. SL was an important outcome as it is a health event both employers
and employees care about. We should try to understand, predict, interpret,
and change these outcomes when caring for workers. Moreover, all data werecollected from registration systems, which might lead to less information bias.
This study also had several limitations. First, the study population
includes extremely few female workers, as this company has far fewer female
employees (8.77%). That might be because historically there have beenoriginally fewer female students in the fields of science and engineering inJapan compared to foreign countries and women might be more likely to avoid
manufacturing companies because of handling of heavy materials and shift
work. Moreover, it is more difficult for female workers to continue theircareers because of Japan’s work–life conflict compared to Organization for
Economic Co-operation and Development (OECD) countries. Therefore, this21

− 23 −

平成29年度研究助成報告書.indd 23

2018/08/28 8:32

study might miss some important problems in terms of women’s health in theworking age population. Second, although the study was a retrospective
cohort study, the possibility of reverse causation might remain because wehave no information about the history of SL at baseline. For example, thismight be the reason why there was a less risk of SL in subjects who consumed
alcohol sometimes and every day compared with subjects who rarely or cannot
drink. Moreover, eating speed and walking speed might be presented as aperceived health status, which means that there might be a poor healthcondition at baseline which presents through slow eating speed and slow
walking speed. Third, there might be additional potential confounding factors
than the explanatory variables used in the study. However, all considerable
affordable health-related factors in Japanese occupational health practicewere used in the study. Fourth, it is necessary to consider multiple
comparisons because many variables were used in the study. However,affordable data in occupational practice regulated by law should be utilized
to understand, predict, interpret, and prevent this important health outcome.
These understandings have been limited using those variables in Japan. Fifth,
the study subjects were solely workers at one manufacturing company in22− 24 −

平成29年度研究助成報告書.indd 24

2018/08/28 8:32

Japan. These results might be difficult to generalize to the general worker
population. However, the company surveyed was huge, and it has a variety ofworkers in terms of age, gender, and occupation.

Conclusions
Regardless of these limitations, some health-related conditions associatedwith future SL in the general working population might be identified throughour study. A possible exposure-response relationship was observed for
occupation, blood pressure, diabetes mellitus, insomnia, exercise, walking
speed, sleep, and smoking. We might be able to prevent future SL by focusing
on these determinants and modifying high-risk employees’ lifestyles as afollow-up to legally mandated annual health checkups. Interventions aimed
at reducing SL in the general working population may benefit from a
prevention strategy that focused on both individual behavioral changes andthe improvement of work environments. Such interventions may have thelargest impact.
However, this study’s subjects were exclusively derived from one
manufacturing company in Japan, which might lead to difficulty generalizing23− 25 −

平成29年度研究助成報告書.indd 25

2018/08/28 8:32

these results to the worker population. Moreover, psychosocial factors areimportant in predicting future SL, especially because of mental or
musculoskeletal disorders. Therefore, further studies are needed togeneralize this study’s results to all Japanese workers.

Acknowledgments
The present study was a part of thesis work in DrPH degree and funded by
the Occupational Health Promotion Foundation, Japan. The authors would
like to thank Enago (www.enago.jp) for the English language review.

Conflict of interest
The authors declare that there are no conflicts of interest.

24− 26 −

平成29年度研究助成報告書.indd 26

2018/08/28 8:32

References1.

Kuoppala, J., Lamminpää, A. & Husman, P. Work health promotion,
job well-being, and sickness absences -- a systematic review and metaanalysis. Journal of Occupational & Environmental Medicine 50,
1216–1227 12p (2008).

2.

Piha, K., Laaksonen, M., Martikainen, P., Rahkonen, O. & Lahelma,
E. Interrelationships between education, occupational class, income
and sickness absence. European Journal of Public Health 20, 276–280(2010).

3.

Sumanen, H., Pietiläinen, O., Lahti, J., Lahelma, E. & Rahkonen, O.
Interrelationships between education, occupational class and income
as determinants of sickness absence among young employees in 2002–
2007 and 2008–2013. BMC Public Health 15, 332 (2015).

4.

Laaksonen, M., Piha, K., Rahkonen, O., Martikainen, P. & Lahelma,
E. Explaining occupational class differences in sickness absence:
results from middle-aged municipal employees. Journal of

Epidemiology & Community Health 64, 802–807 (2010).
5.

Ishizaki, M. et al. Psychosocial work characteristics and sickness
25− 27 −

平成29年度研究助成報告書.indd 27

2018/08/28 8:32

absence in Japanese employees. International Archives ofOccupational and Environmental Health 79, 640–646 (2006).
6.

Sumanen, H., Lahelma, E., Pietiläinen, O. & Rahkonen, O. The
magnitude of occupational class differences in sickness absence: 15year trends among young and middle-aged municipal employees.

International Journal of Environmental Research and Public Health14, 19–23 (2017).
7.

Niedhammer, I., Chastang, J. F., David, S. & Kelleher, C. The
contribution of occupational factors to social inequalities in health:Findings from the national French SUMER survey. Social Science andMedicine 67, 1870–1881 (2008).
8.

Lundborg, P. Does smoking increase sick leave? Evidence using
register data on Swedish workers. Tobacco control 16, 114–8 (2007).

9.

Seyed, M., Berg, V. Den, Ij, T., Duivenbooden, V. & Leo, A. M. Impact
of work-related factors, lifestyle, and work ability on sickness.pdf.
(2009).

10.

Robroek, S. J. W., van den Berg, T. I. J., Plat, J. F. & Burdorf, A. The
role of obesity and lifestyle behaviours in a productive workforce.
26− 28 −

平成29年度研究助成報告書.indd 28

2018/08/28 8:32

Occupational and Environmental Medicine 68, 134–139 (2011).
11.

Rabacow, F. M. et al. The influence of lifestyle and gender on sickness
absence in Brazilian workers. BMC Public Health 14, 317 (2014).

12.

Van Duijvenbode, D. C., Hoozemans, M. J. M., Van Poppel, M. N. M. &Proper, K. I. The relationship between overweight and obesity, andsick leave: A systematic review. International Journal of Obesity 33,
807–816 (2009).

13.

Lehnert, T., Stuhldreher, N., Streltchenia, P., Riedel-Heller, S. G. &König, H.-H. Sick Leave Days and Costs Associated With Overweightand Obesity in Germany. Journal of Occupational and Environmental

Medicine 56, 20–27 (2014).
14.

Neovius, K., Neovius, M., Kark, M. & Rasmussen, F. Association
between obesity status and sick-leave in Swedish men: Nationwide
cohort study. European Journal of Public Health 22, 112–116 (2012).

15.

Proper, K. I., Koppes, L. L. J., Meijer, S. & Bemelmans, W. J. E. The
Association Between Body Mass Index Status and Sick Leave and theRole of Emotional Exhaustion—A Mediation Analysis Among a
Representative Sample of Dutch Employees. Journal of Occupational27− 29 −

平成29年度研究助成報告書.indd 29

2018/08/28 8:32

and Environmental Medicine 55, 1213–1218 (2013).
16.

Vasse, R. M., Nijhuis, F. J. & Kok, G. Associations between workstress, alcohol consumption and sickness absence. Addiction(Abingdon, England) 93, 231–241 (1998).
17.

Upmark, M., Moller, J. & Romelsjo, a. Longitudinal, population-based
study of self reported alcohol habits, high levels of sickness absence,and disability pensions. Journal of epidemiology and community

health 53, 223–229 (1999).
18.

Johansson, E., Böckerman, P. & Uutela, A. Alcohol consumption andsickness absence: Evidence from microdata. European Journal of

Public Health 19, 19–22 (2009).
19.

von Thiele Schwarz, U. & Hasson, H. Effects of Worksite HealthInterventions Involving Reduced Work Hours and Physical Exercise
on Sickness Absence Costs. Journal of Occupational andEnvironmental Medicine 54, 538–544 (2012).
20.

Odeen, M. et al. Systematic review of active workplace interventions
to reduce sickness absence. Occupational Medicine 63, 7–16 (2013).

21.

Jorien, E., Karin, I., Mechelen, V., Beek, V. Der & Allard, J.
28− 30 −

平成29年度研究助成報告書.indd 30

2018/08/28 8:32

Effectiveness of a worksite lifestyle intervention on vitality , work ...
(2013).
22.

Pereira, M. J., Coombes, B. K., Comans, T. A. & Johnston, V. The
impact of onsite workplace health-enhancing physical activity
interventions on worker productivity: a systematic review.

Occupational and Environmental Medicine 72, 401–412 (2015).
23.

Eriksen, W. Physical leisure-time activities and long-term sick leave:
A 15-Month Prospective Study of Nurses’ Aides. JOEM 44, 530–538(2002).

24.

Proper, K. I. Dose-response relation between physical activity and sickleave. British Journal of Sports Medicine 40, 173–178 (2006).

25.

Ubalde-Lopez, M., Delclos, G. L., Benavides, F. G., Calvo-Bonacho, E.
& Gimeno, D. Measuring multimorbidity in a working population: theeffect on incident sickness absence. International Archives ofOccupational and Environmental Health 89, 667–678 (2016).
26.

Sivertsen, B. et al. The long-term effect of insomnia on work disability:
The HUNT-2 historical cohort study. American Journal of

Epidemiology 163, 1018–1024 (2006).
29− 31 −

平成29年度研究助成報告書.indd 31

2018/08/28 8:32

27.

Akerstedt, T. & Kecklund, G. Predicting long-term sickness absencefrom sleep and fatigue. Journal of Sleep Research 16, 341–345 (2007).

28.

Westerlund, H. et al. Work-related sleep disturbances and sickness
absence in the Swedish working population, 1993-1999. Sleep 31,
1169–1177 (2008).

29.

Sivertsen, Bø. et al. Insomnia and long sleep duration are risk factors
for later work disability. the Hordaland Health Study. Journal of

Sleep Research 18, 122–128 (2009).
30.

Kleinman, N. L., Brook, R. A., Doan, J. F., Melkonian, A. K. & Baran,
R. W. Health benefit costs and absenteeism due to insomnia from theemployer’s perspective: A retrospective, case-control, database study.

Journal of Clinical Psychiatry 70, 1098–1104 (2009).
31.

Salo, P. et al. Using repeated measures of sleep disturbances to predict
future diagnosis-specific work disability: a cohort study. Sleep 35,
559–69 (2012).

32.

Jansson, C., Alexanderson, K., Kecklund, G. & Åkerstedt, T. Clinicallydiagnosed insomnia and risk of all-cause and diagnosis-specificsickness absence: a nationwide Swedish prospective cohort study.
30− 32 −

平成29年度研究助成報告書.indd 32

2018/08/28 8:32

Scandinavian journal of public health 41, 712–21 (2013).
33.

Lallukka, T. et al. Sleep and sickness absence: a nationally
representative register-based follow-up study. Sleep 37, 1413–25(2014).

34.

Fujino, Y. Prospective cohort study of work functioning impairment.

Journal of Occupational and Environmental Medicine 58, 264–7(2016).
35.

Doi, Y., Minowa, M. & Tango, T. Impact and Correlates of Poor Sleep
Quality in Japanese White-Collar Employees.

36.

Justice, M. of. Industrial Safety and Health Act. at<http://www.japaneselawtranslation.go.jp/?re=01>

37.

Justice, M. of. Act on Assurance of Medical Care for Elderly People. at<http://www.japaneselawtranslation.go.jp/law/detail/?id=1107&vm=&r
e=>

38.

Alcohol intake and sickness absence: a curvilinear relation. American

Journal of Epidemiology 156, 969–76 (2002).
39.

Mukamal, K. J., Chen, C. M. & Rao, S. R. Alcohol consumption andcardiovascular mortality among U.S adults, 1987-2002. J Am Coll31

− 33 −

平成29年度研究助成報告書.indd 33

2018/08/28 8:32

Cardiol 55, 1–15 (2013).
40.

Bobak, M. et al. Alcohol, drinking pattern and all-cause,
cardiovascular and alcohol-related mortality in Eastern Europe.

European Journal of Epidemiology 31, 21–30 (2016).
41.

O’Doherty, M. G. et al. Effect of major lifestyle risk factors,
independent and jointly, on life expectancy with and without
cardiovascular disease: results from the Consortium on Health andAgeing Network of Cohorts in Europe and the United States(CHANCES). European Journal of Epidemiology 31, 455–468 (2016).

42.

Relationship of Alcohol Consumption to All-Cause, Cardiovascular,
and Cancer-Related Mortality in U.S. Adults. Journal of the American

College of Cardiology 70, 913–922 (2017).
43.

Østby, K. A. et al. Does low alcohol use increase the risk of sickness
absence? A discordant twin study. BMC Public Health 16, 825 (2016).

44.

Depression and serum low-density lipoprotein: A systematic review
and meta-analysis. Journal of Affective Disorders 206, 55–67 (2016).

32− 34 −

平成29年度研究助成報告書.indd 34

2018/08/28 8:32

Table 1. Distribution of explanatory variables at baseline
N

(%)

Age

less than 39
39-48
49 and more

8424
8857
8663

32.47
34.14
33.39

Gendermale
femaleMissing

23646
2274
24

91.14
8.77
0.09

Worksite

Headquarter
R&D
PlantsOthers

2600
8620
11726
2998

Occupation

ManagerWhite-collar

sick leave
8168
8601
8374

%
3.04
2.89
3.34

722
79

22924
2195

3.05
3.47

10.02
33.23
45.20
11.56

60
246
432
63

2540
8374
11294
2935

2.31
2.85
3.68
2.10

2667
9401
2650
3899
7327

10.28
36.24
10.21
15.03
28.24

25
248
114
115
299

2642
9153
2536
3784
7028

0.94
2.64
4.30
2.95
4.08

Change of occupation
no changechange

1795
24149

6.92
93.08

49
752

1746
23397

2.73
3.11

BMI

less than 21.5
21.5-24.0
24.1 and more
Missing

8173
8565
8613
593

31.50
33.01
33.20
2.29

241
226
301

7932
8339
8312

2.95
2.64
3.49

SBP

less than 116
116-125
126 and more
Missing

7799
8513
9037
595

30.06
32.81
34.83
2.29

211
264
293

7588
8249
8744

2.71
3.10
3.24

DBP

less than 70
70-79
80 and more
Missing

7470
9179
8700
595

28.79
35.38
33.53
2.29

173
295
300

7295
8884
8400

2.32
3.21
3.45

LDL

less than 109
109-133
134 and more
Missing

6687
6619
6810
5828

25.77
25.51
26.25
22.46

238
172
197

6449
6447
6613

3.56
2.60
2.89

FBS

less than 91
91-98
99 and more
Missing

6453
6847
6673
5971

24.87
26.39
25.72
23.01

183
206
213

6270
6641
6460

2.84
3.01
3.19

+
Missing

22513
2281
1150

86.78
8.79
4.43

646
104

21867
2177

2.87
4.56

Diabetes Mellitus+
Missing

24070
724
1150

92.78
2.79
4.43

704
46

23366
678

3.01
6.78

Hyperlipidemia+
Missing

23198
1592
1154

89.42
6.14
4.45

685
65

22513
1527

2.95
4.08

Stroke+Missing

24637
154
1153

94.96
0.59
4.44

745
5

23892
149

3.02
3.25

Attack+Missing

24398
396
1150

94.04
1.53
4.43

730
20

23668
20

2.99
5.05

Chronic renal failure+
Missing

24724
66
1154

95.30
0.25
4.45

746
4

23978
62

3.02
6.06

Anemia+Missing

23431
1291
1222

90.31
4.98
4.71

693
54

22738
1237

2.96
4.18

Body weight gain >=10Kg since 20 years oldNo
YesMissing

14395
7917
3632

55.48
30.52
14.00

403
271

13992
7646

2.80
3.42

Exerciseat least 2 days per week, at least 30
minutes each, for at least 1 year

Yes
No
Missing

5149
19594
1201

19.85
75.52
4.63

126
621

5023
18973

2.45
3.17

walk one hour or more every day

YesNo
Missing

7941
16798
1205

30.61
64.75
4.64

217
531

7724
16267

2.73
3.16

Yes
No
Missing

10963
13795
1186

42.26
53.17
4.57

281
468

10682
13327

2.56
3.39

Body weight gain or>=3Kg
loss over last year

No
Yes
Missing

16023
8663
1258

61.76
33.39
4.85

459
287

15564
8376

2.86
3.31

Eating speed

faster than others

No
Yes
Missing

14343
10427
1174

55.28
40.19
4.53

462
286

13881
10141

3.22
2.74

Evening meal

within 2 hours before bed time 3 days No
Yesor more per week
Missingsnack after evening meal 3 days orNoYesmore per week
Missing

12467
12310
1167
21616
3158
1170

48.05
47.45
4.50
83.32
12.17
4.51

376
372

12091
11938

3.02
3.02

654
94

20962
3064

3.03
2.98

Blue-collar

Under medicaion Hypertension

Diagnosis

Walking speed

faster than people of your age and sex

High-skilled
Low-skilled
High-skilled
Low-skilled

+

256
256
289

-

Breakfastskipping 3 days or more per weekNo
Yes
Missing

20799
3953
1192

80.17
15.24
4.59

621
127

20178
3826

2.99
3.21

Sleep

feeling refreshed after a night's sleep

Yes
No
Missing

15951
8479
1514

61.48
32.68
5.84

441
294

15510
8185

2.76
3.47

Alcohol

Rarely (can't drink)
Sometimes
Everyday
Missing

7532
9802
7443
1167

29.03
39.56
30.04
4.71

284
240
223
1167

7248
9562
7220

3.77
2.45
3.00

Smoking

Non-smoker
Ex-smoker
SmokerMissing

11865
4791
8248
1040

45.73
18.47
31.79
4.01

312
133
306
1040

11553
4658
7942

2.63
2.78
3.71

LDL

less than 109
109-133
134 and more
Missing

5199
5438
5899
415

30.67
32.08
34.80
2.45

198
141
171

5001
5297
5728

3.81
2.59
2.90

FBS

less than 91
91-98
99 and more
Missing

4838
5549
6058
506

28.54
32.74
35.74
2.99

147
166
194

4691
5383
5864

3.04
2.99
3.20

9458
6841
652

55.80
40.36
3.85

274
227

9184
6614

2.90
3.32

40 age and more

Body weight gain >=10Kg since 20 years oldNo
YesMissing33− 35 −

平成29年度研究助成報告書.indd 35

2018/08/28 8:32

Table 2. Crude and adjusted by age and gender OR and 95%CI, results ofvariable selection: SL associations with socio-demographic and healthvariables34− 36 −

平成29年度研究助成報告書.indd 36

2018/08/28 8:32

Crude model
95% CIOR

Adjusted by age and gender
P value

95% CIOR

step-wise selection
P value

all age

40 age or more

*
*

*
*

Age

less than 39
39-48
49 and more

1.00
0.95
1.10

0.80
0.93

1.13
1.31

0.56
0.27

Gendermale
female

1.00
1.15

0.91

1.46

0.23

Headquarter
R&D
PlantsOthers

1.00
1.24
1.62
0.91

0.94
1.23
0.64

1.66
2.13
1.30

0.13
0.00
0.60

1.00
1.32
1.72
0.95

0.98
1.29
0.66

1.76
2.28
1.37

0.06
0.00
0.80

*

*

1.00
2.86
4.75
3.21
4.50

1.89
3.07
2.08
2.98

4.33
7.35
4.96
6.78

<.0001<.0001<.0001<.0001

1.00
3.04
5.40
3.28
4.87

2.01
3.43
2.12
3.21

4.61
8.50
5.08
7.39

<.0001<.0001<.0001<.0001

*
*
*
*

*
*
*
*

Change of occupation
no changechange

1.00
0.87

0.65

1.17

0.36

1.00
0.89

0.66

1.19

0.42

BMI

less than 21.5
21.5-24.0
24.1 and more

1.00
0.84
1.12

0.70
0.95

1.00
1.33

0.06
0.17

1.00
0.85
1.14

0.71
0.96

1.02
1.36

0.08
0.13

*

less than 116
116-125
126 and more

1.00
1.07
1.12

0.90
0.94

1.28
1.33

0.46
0.20

1.00
1.09
1.13

0.91
0.94

1.31
1.36

0.35
0.18

*

less than 70
70-79
80 and more

1.00
1.27
1.36

1.06
1.14

1.52
1.63

0.01
0.00

1.00
1.32
1.43

1.09
1.17

1.59
1.74

0.00
0.00

*
*

+

1.00
1.62

1.31

2.00

<.0001

1.00
1.63

1.30

2.04

<.0001**Diabetes Mellitus

+

1.00
2.25

<.0001

1.00
2.24

1.63

3.06

<.0001**Hyperlipidemia

+

1.00
1.40

0.01

1.00
1.38

1.06

1.80

0.02

Stroke

+

1.00
1.08

0.87

1.00
1.04

0.43

2.56

0.92

Heart attack

+

1.00
1.73

1.09

2.72

0.02

1.00
1.69

1.07

2.68

0.02

Chronic renal failure

+

1.00
2.08

0.75

5.72

0.16

1.00
2.06

0.75

5.68

0.16

Anemia

+

1.00
1.43

1.08

1.90

0.01

1.00
1.39

1.04

1.87

0.03

*

*

at least 2 days per week, at least 30
minutes each, for at least 1 year

Yes
No

1.00
1.31

0.01

1.00
1.30

1.07

1.58

0.01

walk one hour or more every day

Yes
No

1.00
1.16

0.07

1.00
1.16

0.99

1.37

0.07

*

Yes
faster than people of your age and sex
No

1.00
1.34

0.00

1.00
1.33

1.15

1.55

0.00

*

0.05

1.00
1.18

1.02

1.38

0.03

0.74

1.00

0.04

*
*

WorksiteOccupation

ManagerWhite-collar
Blue-collar

SBP

DBP

Under medicaion Hypertension

Diagnosis

ExerciseWalking speed

High-skilled
Low-skilled
High-skilled
Low-skilled

1.66
1.08
0.44

1.08
0.99
1.15

3.06
1.81
2.64

1.58
1.36
1.55

*

*
*

*

*

Body weight gain or>=3Kg
loss over last year

No
Yes

1.00
1.16

Eating speed

No
Yes

1.00
0.85

0.73

0.98

0.03

1.00
0.86

within 2 hours before bed time 3 days No
Yesor more per week

1.00
1.00

0.87

1.16

0.98

1.00
1.02

0.88

1.18

0.78

snack after evening meal 3 days ormore per week

No
Yes

1.00
0.98

0.88

1.00
0.99

0.79

1.23

0.90

skipping 3 days or more per week

No
Yes

1.00
1.08

0.44

1.00
1.10

0.90

1.34

0.35

Yes
No

1.00
1.26

0.00

1.00
1.26

1.09

1.47

0.00

*

*

0.53
0.63

0.74
0.90

<.0001
0.00

*
*

*
*

*
*

*

Evening meal

Breakfast
Sleep disturbancefaster than others

feeling refreshed after a night's sleep

1.00

0.79
0.89
1.09

1.35

1.23
1.31
1.47

Alcohol

Rarely (can't drink)
Sometimes
Everyday

1.00
0.62
0.76

0.53
0.64

0.73
0.91

<.0001
0.00

1.00
0.63
0.76

Smoking

Non-smoker
Ex-smoker
Smoker

1.00
0.99
1.34

0.81
1.14

1.21
1.56

0.92
0.00

1.00
1.01
1.38

0.82
1.17

1.25
1.62

0.92
<.0001LDL

less than 109
109-133
134 and more

0.60
0.73

0.49
0.60

0.75
0.90

<.0001
0.00

0.60
0.73

0.49
0.60

0.75
0.90

<.0001
0.00

FBS

less than 91
91-98
99 and more

0.90
0.99

0.73
0.80

1.11
1.22

0.31
0.91

0.90
0.96

0.73
0.78

1.11
1.20

0.31
0.74

1.15

0.96

1.38

0.12

1.15

0.96

1.38

0.13

*
*

Aged 40 and more

Body weight gain >=10Kg since 20 years oldNoYes− 37 −

平成29年度研究助成報告書.indd 37

2018/08/28 8:32

Table 3. Multiple logistics regression analysis: multiple adjusted OR and
95%CI: SL associations with socio-demographic and health variables

36− 38 −

平成29年度研究助成報告書.indd 38

2018/08/28 8:32

all age
Multiple logistic regoression analysis
OR
95% CI
P value

40 age and more
Multiple logistic regoression analysis
OR
95% CI
P valueAgeless than 39
39-48
49 and more

1.00
0.90

0.82
0.73

1.23
1.11

0.98
0.33

0.99
0.93

0.78
0.73

1.26
1.17

0.93
0.51

Worksite

Headquarter
R&D
PlantsOthers

1.09
0.99
0.70

0.80
0.68
0.47

1.50
1.44
1.03

0.58
0.96
0.07

0.88
0.99
0.64

0.59
0.62
0.40

1.31
1.55
1.04

0.52
0.95
0.07

Occupation

ManagerWhite-collar

3.18
6.38
3.22
4.75

2.02
3.88
1.92
2.86

5.02
10.48
5.41
7.88

<.0001<.0001<.0001<.0001

3.31
5.88
2.62
3.82

2.03
3.31
1.49
2.19

5.38
10.46
4.60
6.64

<.0001<.00010.00<.0001less than 21.5
21.5-24.0
24.1 and more

0.95
1.12

0.78
0.91

1.15
1.36

0.60
0.28

0.86
1.02

0.68
0.80

1.10
1.30

0.23
0.89

SBP

less than 116
116-125
126 and more

1.01
0.88

0.82
0.69

1.24
1.13

0.95
0.31

1.38
1.49

1.08
1.15

1.76
1.91

0.01
0.00

DBP

less than 70
70-79

1.50
1.68

1.20
1.28

1.87
2.19

0.00
0.00

LDL

less than 109
109-133
134 and more

0.65
0.81

0.52
0.65

0.82
1.01

0.00
0.06

Blue-collar
BMI

Under medicaion Hypertension

High-skilled
Low-skilled
High-skilled
Low-skilled

+

1.34

1.05

1.72

0.02

1.25

0.97

1.62

0.09

Diabetes Mellitus

+

1.74

1.25

2.44

0.00

1.70

1.20

2.41

0.00

Diagnosis

Anemia

+

1.41

1.05

1.89

0.02

1.33

0.91

1.94

0.14

Exercisewalk one hour or more every day

Yes
No

1.12

0.95

1.33

0.18

at least 2 days per week, at least 30
minutes each, for at least 1 year

Yes
No

1.16

0.90

1.49

0.24

1.26

1.04

1.53

0.02

1.19

0.98

1.44

0.08

Walking speed

faster than people of your age and sex Yes
No

1.15

0.98

1.35

0.09

Body weight gain or>=3Kg
loss over last year

No
Yes

Eating speed

faster than others

No
Yes

0.85

0.72

0.99

0.04

Evening meal

within 2 hours before bed time 3 days No
Yesor more per week

1.12

0.96

1.30

0.16

Sleep

feeling refreshed after a night's sleep

Yes
No

1.20

1.03

1.40

0.02

1.18

0.98

1.42

0.09

Rarely or cannot drink
Sometimes
Everyday

0.69
0.75

0.58
0.62

0.83
0.92

<.0001
0.00

0.73
0.76

0.58
0.61

0.92
0.95

0.01
0.02

Non-smoker
Ex-smoker
Smoker

1.16
1.37

0.93
1.16

1.44
1.63

0.18
0.00

1.15
1.29

0.89
1.04

1.47
1.60

0.29
0.02

Alcohol

Smoking

Hosmer-Lemshow test

0.58

0.44

37

− 39 −

平成29年度研究助成報告書.indd 39

2018/08/28 8:32

Appendix
Variable
Under medication

Medical history

Life style

Life history

Question

AnswerHypertension

Are you currently taking the following medications?- a drug to lower blood pressure

yes or no

Diabetes mellitus

Are you currently taking the following medications?- insulin injections or a drug to lower blood glucose

yes or noHyperlipidemias

Are you currently taking the following medications?- a drug to lower blood glucose

yes or no

StrokeHave you ever been told by a doctor that you have stroke (e.g., cerebral hemorrhage, cerebral infarction) or have you ever received
treatment for stroke?

yes or no

Heart disease

Have you ever been told by a doctor that you have heart disease (e.g., angina, myocardial infarction) or have you ever received treatment
yes or nofor heart disease?

Chronic renal failure

Have you ever been told by a doctor that you have chronic renal failure or have you ever received treatment for chronic renal failure(dialysis)?

yes or no

AnemiaHave you ever been told by a doctor that you have anemia?

yes or no

Smoking

Are you a current regular smoker? (A "current regular smoker" is a person who has smoked a total of 100 or more cigarettes or smokedsmoker, no-smoker, ex-smoker
for 6 months or longer and has been smoking for the last 1 month.)

Regular exercise

Have you been exercising at least 2 days per week, at least 30 minutes each at an intensity that causes a slight sweat, for at least 1 year?

yes or no

Daily physical activity

Have you walk for at least 1 hour everyday or have equivalent physical activities in your daily life?

yes or no

Eating speed

Do you eat faster than others?

yes or no

Evening meal

Do you have an evening meal within 2 hours before bedtime 3days or more per week?

yes or no

Fourth meal

Do you eat after the evening meal (have a fourth meal) 3 days or more per week?

yes or no

Skipping breakfast

Do you skip breakfast 3 days or more per week?

yes or no

Alcohol intake

How often do you drink alcohol (sake, shochu [distilled spirits], beer, liquor, etc.)?

everyday, sometimes or rarely (cannot drink)

Body weight gain

Have you gained <=10Kg since you were 20 years old?

yes or no

Walking speed

Do you walk faster than people of your age and sex?

yes or no

Body weight change

Have you had a weight gain or loss of >=3 Kg over the last year?

yes or no

Sleep disturbanceDo you feel refreshed after a night's sleep?

yes or no38− 40 −

平成29年度研究助成報告書.indd 40

2018/08/28 8:32