Figure 1.
Flow chart of analysis data.
Spent coffee grounds, the residue from coffee brewing, are still underutilized even though they contain several useful organic compounds including proteins. To valorize the spent coffee grounds, the spent coffee ground protein was investigated using ultrasonic-assisted extraction as a pretreatment to conventional extraction. The pretreatments involved different ultrasound amplitudes (40%, 60% and 80%) and extraction times (10, 20 and 30 min) and their effects on the physicochemical and functional properties including antioxidant activity of protein extract. It was found that the protein content extracted was increased approximately 2 times, compared to the initial spent coffee grounds. Furthermore, the ultrasonic-assisted extraction affected the physicochemical properties, functional properties and antioxidant activity of the protein extract. The 80% amplitude for 10 min extraction time improved the foaming capacity, foaming stability, emulsifying activity index and the emulsifying stability index of protein extract. The pretreatment at 20 min extraction time provided the highest antioxidant activity (933.92-976.03 mM Trolox eq/g protein extract) and the highest total phenolic content (267.66-304.81 mg GAE/g protein extract). Nonetheless, protein extract using ultrasonic-assisted extraction resulted in higher total phenolic content and antioxidant activity without changes in the protein structure as confirmed by changes in FT-IR spectra and SDS-PAGE profiles. Thus, the spent coffee ground protein can be an interesting and alternative plant protein with functional properties for food application. Moreover, this work showed the feasibility to reduce waste and the food waste valorization.
Citation: Namfon Samsalee, Rungsinee Sothornvit. Physicochemical, functional properties and antioxidant activity of protein extract from spent coffee grounds using ultrasonic-assisted extraction[J]. AIMS Agriculture and Food, 2021, 6(3): 864-878. doi: 10.3934/agrfood.2021052
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Spent coffee grounds, the residue from coffee brewing, are still underutilized even though they contain several useful organic compounds including proteins. To valorize the spent coffee grounds, the spent coffee ground protein was investigated using ultrasonic-assisted extraction as a pretreatment to conventional extraction. The pretreatments involved different ultrasound amplitudes (40%, 60% and 80%) and extraction times (10, 20 and 30 min) and their effects on the physicochemical and functional properties including antioxidant activity of protein extract. It was found that the protein content extracted was increased approximately 2 times, compared to the initial spent coffee grounds. Furthermore, the ultrasonic-assisted extraction affected the physicochemical properties, functional properties and antioxidant activity of the protein extract. The 80% amplitude for 10 min extraction time improved the foaming capacity, foaming stability, emulsifying activity index and the emulsifying stability index of protein extract. The pretreatment at 20 min extraction time provided the highest antioxidant activity (933.92-976.03 mM Trolox eq/g protein extract) and the highest total phenolic content (267.66-304.81 mg GAE/g protein extract). Nonetheless, protein extract using ultrasonic-assisted extraction resulted in higher total phenolic content and antioxidant activity without changes in the protein structure as confirmed by changes in FT-IR spectra and SDS-PAGE profiles. Thus, the spent coffee ground protein can be an interesting and alternative plant protein with functional properties for food application. Moreover, this work showed the feasibility to reduce waste and the food waste valorization.
Maintaining and improving labor productivity has become essential in a society with a very low birth rate and an aging population in Japan [1]. Therefore, Japanese companies are required to improve their employees' work performance. The Ministry of Health, Labor, and Welfare (MHLW) promotes corporate productivity through health promotion, such as improving employee lifestyle habits [2], and requires employers to provide “specific health checkups and specific health guidance”, which involves health checkups focused on preventing metabolic syndrome and improving lifestyle habits. The specific health checkups include a biological examination and a questionnaire to measure the lifestyle habits of employees; those who are at high risk for lifestyle-related diseases are provided with active support to improve their lifestyle habits [3]. Moreover, the Ministry of Economy, Trade, and Industry (METI) promotes “health and productivity management (H&PM)”, with many companies aiming to improve productivity through employee health promotion [4].
In H&PM, employers are recommended to evaluate employees' work performance as presenteeism using a worldwide scale such as the World Health Organization Health and Work Performance Questionnaire (WHO-HPQ). While absenteeism is defined as an employee being absent from work due to health concerns, presenteeism is defined as “a decline in work performance due to the health problems of workers who are present at work” [5]. Since presenteeism accounts for approximately 64% of the total loss among health-related costs (absenteeism, presenteeism, and medical and pharmaceutical costs) [6], improving presenteeism is vital for companies. As part of H&PM, companies have implemented various initiatives focused on improving employees' lifestyle habits, such as diet (e.g., creating and supporting an environment in which employees can eat according to their dietary needs, providing applications, and other support to improve dietary habits) and exercise (e.g., encouraging exercise and providing tools to promote exercise, organizing sports events, and providing sports equipment outside the workplace) [7].
To implement effective measures to improve employee presenteeism, it is necessary to identify the key lifestyle habits that affect presenteeism; however, it remains unelucidated. Several cross-sectional studies found that smoking [8],[9], heavy alcohol consumption [8], insufficient fruit and vegetable intake [9], lack of exercise [10]–[12], and poor sleep [13],[14] were associated with presenteeism. Furthermore, our sex-based, cross-sectional study found that among 11 lifestyle habits (e.g., smoking, exercise, diet, drinking, and sleep), insufficient sleep, lack of regular exercise, and eating late evening meals were related to presenteeism in both sexes. In addition, slow walking speed, smoking, and skipping breakfast in men, while fast eating speed was associated with presenteeism in women were related to presenteeism [15].
However, there have been few longitudinal studies on the relationship between the changes in lifestyle habits and presenteeism change, and their findings are inconsistent. A study reported that among four lifestyle habits (smoking, heavy drinking, poor diet, and physical inactivity), only improvement in physical inactivity was associated with reduced presenteeism in United States corporate employees [16]. Another reported that among changes in Breslow's seven health habits (sleep, breakfast, snacking, physical exercise, alcohol drinking, smoking, and body mass index), only worsened sleep was associated with increased presenteeism in Japanese corporate employees [17]. Furthermore, there are sex differences in presenteeism [18], lifestyle habits [19],[20], and their relationship [15], suggesting that there may also be sex differences in the relationship between lifestyle habit change and presenteeism change.
Therefore, it is necessary to identify which of various lifestyle habits changes are associated with presenteeism changes in each sex This study aimed to examine the relationship between either the worsening or improvement of 11 comprehensive lifestyle habits, namely current smoking, regular exercise, physical activity, walking speed, eating late evening meals, eating snacks after dinner, skipping breakfast, eating speed, frequency of drinking, alcohol consumption per day, and sleeping obtained from specific health checkups and change of presenteeism by sex in Japanese employees. This will allow us to precisely identify interventions and target lifestyle habits that effectively maintain or improve work performance in each sex, based on real-world data from company employees. Considering previous findings [15]–[20], we hypothesized that (1) worsening of sleeping habits is associated with increased presenteeism, (2) improvement of exercise habits is associated with reduced presenteeism, and (3) there are sex differences in the relationship between lifestyle habit changes and presenteeism changes.
A longitudinal study was conducted using anonymous data from specific health checkups, self-administered questionnaires on presenteeism, and health insurance claims of a Japanese company employees in 2015 and 2016. This data was provided under the non-disclosure agreement by the company and health insurance association of a private sector, and was approved for use in academic studies.
As shown in Figure 1, of 13,331 individuals aged under 69 years who belonged to the company and had data from specific health checkup and self-administered questionnaire on presenteeism in both 2015 and 2016, 9366 were included in the analysis, excluding 3965 with incomplete data on measurements described hereinafter. For the sensitivity analysis, 13,331 individuals were included by using multiple imputations to handle missing data.
This study was approved by the Research Ethics Committee of the Institute of Health and Sport Sciences of the University of Tsukuba, Japan (approval number: Tai 29–132).
Attributes including sex, age, job position (i.e., non-manager or manager), and department (i.e., sales, customer service, and administration) were obtained.
We used data obtained from self-administered questionnaires established in the standard Specific Health Check-up and Guidance program (2013 Edition) [3], which included the following 11 items: current smoking (“Do you smoke cigarettes regularly?”), regular exercise (“Are you in a habit of doing exercise to sweat lightly for over 30 minutes a time, 2 times weekly, for over a year?”), physical activity (“In your daily life do you walk or do any equivalent amount of physical activity for more than one hour a day?”), walking speed (“Is your walking speed faster than the speed of those of your age and sex?”), eating late evening meals (“Do you eat supper two hours before bedtime more than 3 times a week?”), eating snacks after dinner (“Do you eat snacks after dinner three days or more a week?”), skipping breakfast (“Do you skip breakfast more than 3 times a week?”), and sleeping (“Do you sleep well and enough?”). Each question was asked using a two-point rating scale representing either “yes” or “no.” Eating speed (“Is your eating speed quicker than others?”) was rated on a three-point scale as “quicker,” “normal,” or “slower”. Frequency of drinking (“How often do you drink?”) was also rated on a three-point scale as “everyday”, “sometimes”, or “rarely drink (cannot drink)”. Alcohol consumption per day (“How much do you drink per day?”) was rated on a four-point scale as “<180 mL/day”, “180–360 mL/day”, “360–540 mL/day”, or “≥540 mL/day”, where a glass (180 mL) of refined saké (rice wine) was equivalent to a medium bottle (500 mL) of beer, 110 mL of shochu (alcohol content, 25%), a glass (double, 60 mL) of whiskey, and two glasses (240 mL) of wine.
Of these, we dichotomized the items rated on either a three- or four-point scale as follows: eating speed as “quicker”, or “normal or slower”, frequency of drinking as “everyday” or “sometimes or rarely drink (cannot drink)”, and alcohol consumption per day as “light” (<360 mL/day for men; <180 mL/day for women) or “heavy” (≥360 mL/day for men; ≥180 mL/day for women) based on the criteria on the Second Phase of Health Japan 21 [21].
We evaluated eight diseases reported to be associated with presenteeism in prior studies (hypertension [22], dyslipidemia [22], diabetes [23], cancer [24], mental illness [25], infectious diseases [26], musculoskeletal diseases [27],[28], and oral diseases [29]). The assessed disease information, including related treatments, hospital procedures, drugs, and duration of primary disease, were obtained from health insurance claims data. We determined the presence of disease based on whether the individual received any treatments, procedures, or drugs for the primary disease, regardless of the duration.
The eight disease items corresponded to the ICD-10 (International Statistical Classification of Diseases and Related Health Problems 10th Revision) as follows: 1, hypertension (I10–I15); 2, dyslipidemia (E78); 3, diabetes (E10–E14); 4, cancer (stomach cancer [C16], colon cancer [C18–C20], lung/bronchial cancer [C33,C34], biliary/liver/pancreatic cancer [C22–C25], esophageal cancer [C15], oral/pharyngeal/laryngeal cancer [C00–C14 and C32], breast cancer [C50], and cervical cancer [C53]); 5, mental illness (mood (affective) disorders [F30–F39], neurotic, stress-related, and somatoform disorders [F40–F48], other mental and behavioral disorders (all of F00–F99 including nonorganic sleep disorders [F51], with excepting the above and dementias [F01,F03], mental and behavioral disorders due to psychoactive substance use [F10–F19], schizophrenia [F20–F29], and mental retardation [F70–F79]), and disorders of autonomic nervous system [G90]); 6, infectious diseases (intestinal infectious diseases [A00–A09], sequelae of infectious and parasitic diseases [B90–B94], acute upper respiratory infections [J00–J06], pneumonia [J12–J18], acute bronchitis [J20,J21]); 7, musculoskeletal diseases (M00–M99); and 8, oral diseases (diseases of the oral cavity, salivary glands, and jaw [K00–K14]).
We used the validated short-form Japanese version of the World Health Organization Health and Work Performance Questionnaire (WHO-HPQ) [30],[31]. The respondents were asked to answer the question “On a scale from 0 to 10, where 0 is the worst job performance anyone could have at your job and 10 is the performance of a top worker, how would you rate your overall job performance on the days you worked in the past 4 weeks (28 days)?” The final score was obtained by multiplying the respondents' answers by 10 (range: 0–100). A higher WHO-HPQ score indicated lower presenteeism, and thus, higher work performance.
First, the following were classified as poor lifestyle habits: “yes” for current smoking, “no” for regular exercise, “no” for physical activity, “no” for walking speed, “yes” for eating late evening meals, “yes” for eating snacks after dinner, “yes” for skipping breakfast, “quicker” for eating speed, “everyday” for frequency of drinking, “heavy (men: ≥360 mL/day, women: ≥180 mL/day)” for alcohol consumption per day, and “no” for sleeping.
Changes in lifestyle habits (“worsening” and “improvement”) were classified into four patterns by combining lifestyle habits (good/poor) in 2015 and 2016. First, of those habits that were “good” in 2015, those that were “good” in 2016 were defined as (a) “no worsening”; whereas those that were “poor” in 2016 were defined as (b) “worsening”. Second, of those habits that were “poor” in 2015, those that were “poor” in 2016 were defined as (c) “no improvement”; whereas those that were “good” in 2016 were defined as (d) “improvement”. The presenteeism change was defined as changes in the WHO-HPQ score by subtracting the WHO-HPQ score in 2015 from that in 2016.
After observing the basic statistics of all variables at baseline, the relationship between the worsening or improvement of lifestyle habits and changes in the WHO-HPQ score was analyzed using a t-test. Subsequently, multiple regression analyses were performed by sex, with changes in the WHO-HPQ score as the objective variable; the pattern of each lifestyle habit change as the explanatory variable; and age, job position, department, diseases (hypertension, dyslipidemia, diabetes, cancer, mental illness, infectious diseases, musculoskeletal diseases, and oral diseases), lifestyle habits, and WHO-HPQ score at baseline as the adjusted variables. During the analysis, the reference categories were “no worsening” and “no improvement”. Furthermore, in order to verify the robustness of the results, a sensitivity analysis was conducted using a data set with multiple imputation by chained equations to handle the missing values. An analysis of the imputed datasets reduces the potential bias introduced by missing data. This method assumes that data are missing at random, whereby any systematic differences between the missing and observed values could be explained by differences in the observed data [32]. The missing values were imputed according to a model that consisted of other all variables, and we used multiple imputations to create and analyze 20 multiply imputed datasets.
In all analyses, a p-value correction using the Benjamini–Hochberg method was conducted to consider type I errors. We ensured that there was no multicollinearity among variables. All data analyses were performed using the IBM SPSS Statistics for Windows (version 26.0; IBM Corp., Armonk, NY). The statistical significance was set at p < 0.05.
The subject characteristics at baseline are presented in Table 1. The subjects of this study were 4899 men (52.3%) and 4467 women (47.7%), with a mean age of 43.1 ± 11.9 years (21–69 years). The mean WHO-HPQ scores of the subjects in 2015 and 2016 were 60.5 ± 17.1 and 61.1 ± 16.8, respectively.
Tables 2 and 3 show the changes in lifestyle habits and of the WHO-HPQ scores. The change in the WHO-HPQ score differed by worsening of sleeping in both men and women (Table 2) and improvement of sleeping only in women (Table 3).
The results of the multiple regression analysis adjusted for age, job position, department, diseases (hypertension, dyslipidemia, diabetes, cancer, mental illness, infectious diseases, musculoskeletal diseases, and oral diseases), lifestyle habits, and WHO-HPQ score at baseline are shown in Tables 4 and 5. Additionally, the results of the sensitivity analysis using a data set with the multiple imputation by chained equations to handle missing values are shown in Tables 4 and 5.
Regarding worsening of lifestyle habits (Table 4), for men, worsening of regular exercise (β = −0.058, p = 0.024), frequency of drinking (β = −0.044, p = 0.009), and sleeping (β = −0.063, p < 0.001) were associated with negative changes in the WHO-HPQ score. For women, skipping breakfast (β = −0.034, p = 0.018) and sleeping (β = −0.048, p = 0.003) were associated with negative changes in the WHO-HPQ score. Moreover, the sensitivity analysis showed similar results, except that for women, worsening of skipping breakfast was not associated with change in the WHO-HPQ score.
Regarding improvement of lifestyle habits (Table 5), for men, no variables were associated with changes in the WHO-HPQ score. For women, improvement of sleeping (β = 0.051, p = 0.013) was associated with positive changes in the WHO-HPQ score. However, the sensitivity analysis showed that improvement of sleeping for men and of regular exercise and sleeping for women were associated with positive changes in the WHO-HPQ score.
| All (n = 9366) | |||
| n (%) or Mean ± SD | |||
| Attributes | |||
| Sex | Men | 4899 | (52.3) |
| Women | 4467 | (47.7) | |
| Age | 43.1 | ±11.9 | |
| 21–29 y | 1736 | (18.5) | |
| 30–39 y | 1897 | (20.3) | |
| 40–49 y | 2560 | (27.3) | |
| 50–59 y | 2372 | (25.3) | |
| 60–69 y | 801 | (8.6) | |
| Department | Sales | 4831 | (51.5) |
| Customer service | 3405 | (36.4) | |
| Administration | 1130 | (12.1) | |
| Job position | Non-manager | 7700 | (82.2) |
| Manager | 1666 | (17.8) | |
| Lifestyle habits | |||
| Current smoking (Yes) | 1611 | (17.2) | |
| Regular exercise (No) | 7690 | (82.1) | |
| Physical activity (No) | 7285 | (77.8) | |
| Walking speed (No) | 5386 | (57.5) | |
| Eating late evening meals (Yes) | 3372 | (36.0) | |
| Eating snacks after dinner (Yes) | 1152 | (12.3) | |
| Skipping breakfast (Yes) | 2077 | (22.2) | |
| Eating speed (quicker) | 3476 | (37.1) | |
| Frequency of drinking (Everyday) | 2940 | (31.4) | |
| Alcohol consumption per day (Heavy) | 3826 | (40.8) | |
| Sleeping (No) | 3180 | (34.0) | |
| Diseases | |||
| Hypertension | 1120 | (12.0) | |
| Dyslipidemia | 1378 | (14.7) | |
| Diabetes | 569 | (6.1) | |
| Cancer | 241 | (2.6) | |
| Mental illness | 610 | (6.5) | |
| Infectious diseases | 5062 | (54.0) | |
| Musculoskeletal diseases | 2309 | (24.7) | |
| Oral diseases | 4735 | (50.6) | |
| WHO-HPQ score | 60.5 | ±17.1 | |
Note: SD: standard deviation; WHO-HPQ: World Health Organization Health and Work Performance Questionnaire.
DownLoad:
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| Men |
Women |
||||||||
| Worsening | n | (%) | Mean ± SD | P-value* | n | (%) | Mean ± SD | P-value* | |
| Current smoking | No | 3503 | (97.8) | 0.7 ± 15.9 | 0.091 | 4144 | (99.3) | 0.8 ± 18.2 | 1.000 |
| Yes | 80 | (2.2) | −2.4 ± 17.9 | 28 | (0.7) | −0.7 ± 14.1 | |||
| Regular exercise | No | 923 | (75.7) | 1.5 ± 15.5 | 0.132 | 311 | (68.1) | 0.0 ± 17.2 | 0.617 |
| Yes | 296 | (24.3) | −0.2 ± 15.6 | 146 | (31.9) | −1.4 ± 18.5 | |||
| Physical activity | No | 778 | (64.1) | 0.4 ± 16.1 | 1.000 | 529 | (61.0) | 0.8 ± 18.2 | 1.000 |
| Yes | 436 | (35.9) | 0.5 ± 15.9 | 338 | (39.0) | 0.9 ± 17.7 | |||
| Walking speed | No | 1977 | (81.3) | 0.7 ± 15.6 | 1.000 | 1224 | (79.0) | 0.4 ± 17.2 | 0.836 |
| Yes | 454 | (18.7) | 0.6 ± 16.7 | 325 | (21.0) | 1.1 ± 19.7 | |||
| Eating late evening meals | No | 2241 | (80.6) | 1.2 ± 15.1 | 0.289 | 2786 | (86.7) | 0.8 ± 17.6 | 1.000 |
| Yes | 539 | (19.4) | 0.1 ± 17.3 | 428 | (13.3) | 0.6 ± 19.4 | |||
| Eating snacks after dinner | No | 4216 | (94.4) | 0.7 ± 16.1 | 1.000 | 3472 | (92.7) | 0.6 ± 18.2 | 1.000 |
| Yes | 251 | (5.6) | 0.9 ± 16.1 | 275 | (7.3) | 1.0 ± 18.1 | |||
| Skipping breakfast | No | 3592 | (95.1) | 1.0 ± 15.6 | 0.402 | 3301 | (94.0) | 0.6 ± 17.7 | 0.497 |
| Yes | 184 | (4.9) | −0.5 ± 17.1 | 212 | (6.0) | −0.6±19.8 | |||
| Eating speed | No | 2443 | (87.5) | 0.5 ± 16.1 | 1.000 | 2865 | (92.4) | 1.0 ± 18.0 | 1.000 |
| Yes | 348 | (12.5) | 0.7 ± 16.7 | 234 | (7.6) | 0.3 ± 18.1 | |||
| Frequency of drinking | No | 2371 | (89.5) | 0.6 ± 16.3 | 0.165 | 3614 | (95.7) | 0.8 ± 18.0 | 1.000 |
| Yes | 277 | (10.5) | −1.2 ± 18.3 | 164 | (4.3) | 1.4 ± 18.3 | |||
| Alcohol consumption per day | No | 2722 | (87.3) | 1.3 ± 15.9 | 0.671 | 2044 | (84.4) | 0.6 ± 17.7 | 0.278 |
| Yes | 395 | (12.7) | 0.4 ± 17.4 | 379 | (15.6) | −0.5 ± 18.7 | |||
| Sleeping | No | 2888 | (84.5) | 1.1 ± 15.2 | 0.014 | 2267 | (81.9) | 1.0 ± 17.7 | 0.036 |
| Yes | 531 | (15.5) | −1.0 ± 18.5 | 500 | (18.1) | −0.9 ± 17.9 | |||
Note: SD: standard deviation; WHO-HPQ: World Health Organization Health and Work Performance Questionnaire; t-test; *P-value correction using the Benjamini–Hochberg method was conducted.
DownLoad:
CSV
| Men |
Women |
||||||||
| Improvement | n | (%) | Mean ± SD | P-value* | n | (%) | Mean ± SD | P-value* | |
| Current smoking | No | 1222 | (92.9) | 0.9 ± 16.9 | 0.111 | 265 | (89.8) | −0.5±18.4 | 1.000 |
| Yes | 94 | (7.1) | −2.0 ± 14.5 | 30 | (10.2) | −1.3±18.7 | |||
| Regular exercise | No | 3326 | (90.4) | 0.6 ± 16.4 | 0.953 | 3838 | (95.7) | 0.7±18.3 | 0.084 |
| Yes | 354 | (9.6) | 0.0 ± 15.9 | 172 | (4.3) | 3.2±17.2 | |||
| Physical activity | No | 3200 | (86.8) | 0.9 ± 16.3 | 0.085 | 3219 | (89.4) | 0.6±18.2 | 1.000 |
| Yes | 485 | (13.2) | −0.4 ± 15.2 | 381 | (10.6) | 1.0±18.5 | |||
| Walking speed | No | 2062 | (83.5) | 0.7 ± 16.5 | 1.000 | 2620 | (89.8) | 0.7±18.1 | 1.000 |
| Yes | 406 | (16.5) | 0.4 ± 16.2 | 298 | (10.2) | 0.7±20.9 | |||
| Eating late evening meals | No | 1622 | (76.5) | 0.0 ± 16.8 | 0.362 | 859 | (68.6) | 0.0±19.0 | 1.000 |
| Yes | 497 | (23.5) | 1.0 ± 17.1 | 394 | (31.4) | 0.9±18.9 | |||
| Eating snacks after dinner | No | 207 | (47.9) | 0.3 ± 17.6 | 1.000 | 398 | (55.3) | −0.2±18.4 | 0.106 |
| Yes | 225 | (52.1) | −0.2 ± 14.6 | 322 | (44.7) | 2.1±17.9 | |||
| Skipping breakfast | No | 916 | (81.6) | −0.3 ± 17.8 | 0.317 | 761 | (79.8) | 1.0±19.0 | 1.000 |
| Yes | 207 | (18.4) | 1.2 ± 16.4 | 193 | (20.2) | 1.7±20.5 | |||
| Eating speed | No | 1783 | (84.6) | 1.0 ± 16.1 | 1.000 | 1088 | (79.5) | −0.2±18.6 | 0.431 |
| Yes | 325 | (15.4) | 0.6 ± 16.5 | 280 | (20.5) | 1.2±18.3 | |||
| Frequency of drinking | No | 2003 | (89.0) | 1.1 ± 15.6 | 0.693 | 552 | (80.1) | −0.8±18.6 | 0.177 |
| Yes | 248 | (11.0) | 0.3 ± 16.5 | 137 | (19.9) | 1.9±19.1 | |||
| Alcohol consumption per day | No | 1277 | (71.7) | −0.1 ± 16.3 | 1.000 | 1568 | (76.7) | 0.7±18.5 | 0.766 |
| Yes | 505 | (28.3) | −0.4 ± 15.8 | 476 | (23.3) | 1.5±18.4 | |||
| Sleeping | No | 1031 | (69.7) | 0.7 ± 17.0 | 1.000 | 1272 | (74.8) | 0.0±19.0 | 0.015 |
| Yes | 449 | (30.3) | 0.2 ± 17.2 | 428 | (25.2) | 2.6±18.2 | |||
Note: SD: standard deviation; WHO-HPQ: World Health Organization Health and Work Performance Questionnaire; t-test; *P-value correction using the Benjamini–Hochberg method was conducted.
DownLoad:
CSV
| Men | Multiple regression analysis |
Sensitivity analysis |
||||||||
| n* | B | 95% CI | β | P-value** | Adjusted R2 | n* | B | SE | P-value** | |
| Current smoking | 3583 | −2.807 | (−5.82 to 0.21) | −0.026 | 0.093 | 0.281 | 4541 | −2.338 | 1.403 | 0.132 |
| Regular exercise | 1219 | −2.082 | (−3.83 to −0.34) | −0.058 | 0.024 | 0.288 | 1522 | −1.911 | 0.812 | 0.023 |
| Physical activity | 1214 | −0.351 | (−1.96 to 1.26) | −0.010 | 1.000 | 0.304 | 1482 | −0.584 | 0.742 | 0.948 |
| Walking speed | 2431 | −0.578 | (−1.95 to 0.80) | −0.014 | 0.901 | 0.293 | 2886 | −0.420 | 0.633 | 1.000 |
| Eating late evening meals | 2780 | −0.931 | (−2.22 to 0.36) | −0.024 | 0.247 | 0.281 | 3616 | −0.873 | 0.575 | 0.202 |
| Eating snacks after dinner | 4467 | −0.026 | (−1.78 to 1.73) | 0.000 | 1.000 | 0.285 | 5566 | −0.278 | 0.799 | 1.000 |
| Skipping breakfast | 3776 | −1.058 | (−3.07 to 0.95) | −0.015 | 0.555 | 0.277 | 4682 | −0.733 | 0.915 | 0.775 |
| Eating speed | 2791 | −0.045 | (−1.60 to 1.51) | −0.001 | 1.000 | 0.282 | 3289 | 0.295 | 0.744 | 1.000 |
| Frequency of drinking | 2648 | −2.372 | (−4.13 to −0.62) | −0.044 | 0.009 | 0.286 | 3623 | −2.359 | 0.816 | 0.004 |
| Alcohol consumption per day | 3117 | −0.585 | (−2.04 to 0.87) | −0.012 | 1.000 | 0.290 | 3570 | −0.375 | 0.710 | 1.000 |
| Sleeping | 3419 | −2.722 | (−3.96 to −1.49) | −0.063 | <0.001 | 0.307 | 4202 | −3.262 | 0.559 | <0.001 |
| Women | Multiple regression analysis |
Sensitivity analysis |
||||||||
| n* | B | 95% CI | β | P-value** | Adjusted R2 | n* | B | SE | P-value** | |
| Current smoking | 4172 | −0.329 | (−5.97 to 5.31) | −0.001 | 1.000 | 0.310 | 6586 | 0.565 | 2.468 | 1.000 |
| Regular exercise | 457 | −2.068 | (−5.19 to 1.05) | −0.055 | 0.354 | 0.263 | 707 | −1.128 | 1.239 | 0.665 |
| Physical activity | 867 | −0.045 | (−2.10 to 2.01) | −0.001 | 1.000 | 0.326 | 1243 | −1.119 | 0.873 | 0.275 |
| Walking speed | 1549 | −0.775 | (−2.65 to 1.10) | −0.018 | 1.000 | 0.284 | 2230 | −0.381 | 0.789 | 1.000 |
| Eating late evening meals | 3214 | −1.526 | (−3.04 to −0.01) | −0.029 | 0.059 | 0.328 | 5062 | −1.163 | 0.621 | 0.067 |
| Eating snacks after dinner | 3747 | −1.005 | (−2.87 to 0.86) | −0.014 | 0.639 | 0.311 | 5773 | −0.550 | 0.763 | 1.000 |
| Skipping breakfast | 3513 | −2.554 | (−4.64 to −0.47) | −0.034 | 0.018 | 0.306 | 5351 | −1.527 | 0.839 | 0.084 |
| Eating speed | 3099 | −1.498 | (−3.50 to 0.51) | −0.022 | 0.196 | 0.306 | 4557 | −0.492 | 0.845 | 1.000 |
| Frequency of drinking | 3778 | 0.526 | (−1.84 to 2.89) | 0.006 | 1.000 | 0.311 | 6119 | 0.102 | 1.099 | 1.000 |
| Alcohol consumption per day | 2423 | −1.147 | (−2.82 to 0.52) | −0.023 | 0.279 | 0.305 | 2909 | −0.781 | 0.803 | 0.520 |
| Sleeping | 2767 | −2.219 | (−3.67 to −0.77) | −0.048 | 0.003 | 0.305 | 4121 | −1.414 | 0.596 | 0.018 |
Note: WHO-HPQ: World Health Organization Health and Work Performance Questionnaire; *The number of subjects who had “good” habits in each lifestyle habits at baseline; **P-value correction using the Benjamini–Hochberg method was conducted; Adjusted variables: age, job position, department, diseases (hypertension, dyslipidemia, diabetes, cancer, mental illness, infectious diseases, musculoskeletal diseases, and oral diseases), lifestyle habits and WHO-HPQ score at baseline; Code of independent variables: No worsening, 0; worsening, 1.
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| Men | Multiple regression analysis |
Sensitivity analysis |
||||||||
| n* | B | 95% CI | β | P-value** | Adjusted R2 | n* | B | SE | P-value** | |
| Current smoking | 1316 | −1.143 | (−4.17 to 1.88) | −0.018 | 0.721 | 0.279 | 1637 | −1.505 | 1.364 | 0.371 |
| Regular exercise | 3680 | 0.235 | (−1.30 to 1.77) | 0.004 | 1.000 | 0.285 | 4597 | 0.607 | 0.698 | 0.604 |
| Physical activity | 3685 | −0.604 | (−1.93 to 0.72) | −0.013 | 0.510 | 0.277 | 4612 | −0.019 | 0.604 | 1.000 |
| Walking speed | 2468 | 0.438 | (−1.08 to 1.95) | 0.010 | 1.000 | 0.275 | 3226 | 0.860 | 0.691 | 0.260 |
| Eating late evening meals | 2119 | 0.672 | (−0.78 to 2.12) | 0.017 | 0.443 | 0.289 | 2508 | 0.434 | 0.669 | 0.946 |
| Eating snacks after dinner | 432 | 0.374 | (−2.32 to 3.07) | 0.012 | 1.000 | 0.263 | 555 | 0.583 | 1.202 | 1.000 |
| Skipping breakfast | 1123 | 1.472 | (−0.78 to 3.72) | 0.033 | 0.220 | 0.296 | 1389 | 1.731 | 1.034 | 0.104 |
| Eating speed | 2108 | 0.180 | (−1.45 to 1.81) | 0.004 | 1.000 | 0.284 | 2563 | −0.088 | 0.764 | 1.000 |
| Frequency of drinking | 2251 | −0.121 | (−1.91 to 1.67) | −0.002 | 1.000 | 0.277 | 2509 | 0.028 | 0.853 | 1.000 |
| Alcohol consumption per day | 1782 | −0.216 | (−1.69 to 1.26) | −0.006 | 1.000 | 0.265 | 2002 | 0.066 | 0.702 | 1.000 |
| Sleeping | 1480 | 1.305 | (−0.36 to 2.97) | 0.035 | 0.125 | 0.247 | 1925 | 1.692 | 0.751 | 0.024 |
| Women | Multiple regression analysis |
Sensitivity analysis |
||||||||
| n* | B | 95% CI | β | P-value** | Adjusted R2 | n* | B | SE | P-value** | |
| Current smoking | 295 | −2.581 | (−8.82 to 3.66) | −0.042 | 0.763 | 0.317 | 413 | −2.191 | 2.713 | 0.769 |
| Regular exercise | 4010 | 2.382 | (0.04 to 4.73) | 0.026 | 0.051 | 0.315 | 6101 | 2.140 | 0.974 | 0.031 |
| Physical activity | 3600 | −0.483 | (−2.12 to 1.15) | −0.008 | 1.000 | 0.306 | 5463 | 0.085 | 0.687 | 1.000 |
| Walking speed | 2918 | −0.022 | (−1.85 to 1.80) | 0.000 | 1.000 | 0.321 | 4568 | 0.888 | 0.744 | 0.320 |
| Eating late evening meals | 1253 | 1.174 | (−0.82 to 3.17) | 0.029 | 0.305 | 0.262 | 1749 | 1.461 | 0.846 | 0.103 |
| Eating snacks after dinner | 720 | 1.220 | (−1.10 to 3.54) | 0.033 | 0.416 | 0.294 | 1036 | 0.595 | 0.986 | 1.000 |
| Skipping breakfast | 954 | 0.474 | (−2.11 to 3.06) | 0.010 | 1.000 | 0.311 | 1413 | 0.506 | 1.054 | 1.000 |
| Eating speed | 1368 | 1.023 | (−1.04 to 3.09) | 0.022 | 0.522 | 0.306 | 1982 | 1.035 | 0.924 | 0.413 |
| Frequency of drinking | 689 | 0.770 | (−2.34 to 3.88) | 0.016 | 1.000 | 0.277 | 768 | −0.034 | 1.525 | 1.000 |
| Alcohol consumption per day | 2044 | 0.171 | (−1.44 to 1.78) | 0.004 | 1.000 | 0.312 | 2348 | 0.160 | 0.789 | 1.000 |
| Sleeping | 1700 | 2.225 | (0.47 to 3.98) | 0.051 | 0.013 | 0.313 | 2688 | 1.944 | 0.722 | 0.007 |
Note: WHO-HPQ, World Health Organization Health and Work Performance Questionnaire; *The number of subjects who had “poor” habits in each lifestyle habits at baseline; **P value correction using the Benjamini–Hochberg method was conducted; Adjusted variables: age, job position, department, diseases (hypertension, dyslipidemia, diabetes, cancer, mental illness, infectious diseases, musculoskeletal diseases, and oral diseases), lifestyle habits and WHO-HPQ score at baseline; Code of independent variables: No improvement, 0; improvement, 1.
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This study examined the relationship between worsening or improvement of 11 lifestyle habits (current smoking, regular exercise, physical activity, walking speed, eating late evening meals, eating snacks after dinner, skipping breakfast, eating speed, frequency of drinking, alcohol consumption per day, and sleeping) and presenteeism change over 1 year by sex. Since the WHO-HPQ score of the study subjects at baseline (60.5 ± 17.1) was similar to that of Japanese workers aged 20 to 60 years (60.5 ± 17.8) [33], the presenteeism in this study population may be generalized to Japanese workers of the same age group.
The key results of this study, which were common across multiple regression analyses and sensitivity analyses, are as follows. First, worsening of sleeping for both sexes, and of regular exercise and frequency of drinking for men, were associated with increased presenteeism. Second, improvement of sleeping for women was associated with reduced presenteeism.
First, regarding worsening of lifestyle habits, the results of both multiple regression analyses and sensitivity analyses showed that worsening of sleeping was associated with increased presenteeism in both sexes, thus this association is considered to be robust. This is consistent with previous findings [17] and supports the first hypothesis (i.e., that worsening of sleeping habits is associated with increased presenteeism). Our finding suggests that to prevent the decline in employees' work performance, maintaining sufficient sleep is vital, regardless of sex.
However, other lifestyle habits associated with increased presenteeism differed by sex. For men, worsening of regular exercise and frequency of drinking were associated with increased presenteeism in the results of both multiple regression analyses and sensitivity analyses; thus, these associations are considered to be robust. The former is similar to our cross-sectional study findings that the exercise habits were associated with presenteeism, especially among men [15], whereas the latter is not. This indicates that while there is no relationship between alcohol consumption and presenteeism at the cross-sectional time point, an increased frequency of alcohol consumption leads to increased presenteeism in men. In a national survey in Japan, men drank more frequently than women [19], and the proportion of those who worsened the frequency of drinking was also higher than that of women in our study. In addition, the risk of alcohol-related presenteeism was higher among men [34]. This may be because that the worsening frequency of drinking was associated with increased presenteeism only for men and suggests that it is important for male employees to avoid increasing their drinking frequency to maintain their work performance.
On the other hand, for women, as the result of the multiple regression analyses, the worsening of skipping breakfast was associated with increased presenteeism. Skipping breakfast is known to decrease intellectual work capacity [35] and be a factor of dysmenorrhea or menstrual cramps [36],[37], which are associated with presenteeism in women [38]. Thus, it is possible that the worsening of skipping breakfast was associated with increased presenteeism in women. However, this association was not observed in the result of the sensitivity analysis, which is consistent with a previous study that found no relationship between the worsening of dietary habits and presenteeism change in Japanese employees [17]. Therefore, further validation studies are needed in this regard.
Second, regarding improvement of lifestyle habits, the results of both multiple regression analyses and sensitivity analyses showed that improved sleeping was associated with reduced presenteeism only for women; thus, this association is considered to be robust. On the other hand, only the result of the sensitivity analysis showed that improvement of sleeping for men, and of regular exercise and sleeping for women, were also associated with reduced presenteeism. The former suggested that changes in sleeping may be key to changes in work performance for both sexes, and the latter is consistent with a previous study of U.S. corporate employees, which reported that improvement of physical inactivity was related to reduced presenteeism [16] and supported the second hypothesis (i.e., that improvement of exercise habits is associated with reduced presenteeism). However, these need to be explored through further validation studies and other population surveys.
Our previous cross-sectional study that addressed 11 lifestyle habits showed that insufficient sleep, lack of regular exercise, and eating late evening meals in both sexes, slow walking speed, smoking, and skipping breakfast in men, and fast eating speed in women were related to presenteeism [15]. However, a more detailed examination in this longitudinal study provided new evidence that the relationship between changes in these lifestyle habits and presenteeism change reflected worsening rather than improvement, especially among men; this suggests that worsening of lifestyle habits is particularly likely to lead to increased presenteeism. Therefore, to prevent a decline in work performance, it should be vital to preserve sufficient sleep in both sexes, and regular exercise and a low frequency of drinking for men. On the other hand, to improve work performance, women's lack of sleep must be improved.
Currently, Japanese companies provide health guidance to employees under the Specific Health Guidance to improve poor lifestyle habits for those at high risk of developing lifestyle-related diseases [3]. Moreover, companies have developed initiatives to improve dietary habits through environmental improvement and app provision, as well as to improve exercise habits through the organization of sporting events and partnerships with sports clubs outside the workplace under “H&PM” [4]. Although these are behavior change initiatives targeting individuals with poor lifestyle habits, our findings suggest the importance of maintaining good habits of sleeping, exercise, and drinking as a measures against presenteeism. Therefore, in addition to lifestyle modification efforts targeting individuals, the development of a comfortable psychosocial work environment is considered important. This is because workplace environmental factors such as long working hours and occupational stress are related to sleep quality [39],[40], exercise habits [41], and drinking habits [42]. Companies should provide an appropriate work environment, including adequate working hours and workloads, so that employees can maintain healthy lifestyles.
This study had several limitations. First, since the study participants were employees of a Japanese company, our study had a selection bias. In addition, the magnitude of associations between lifestyle habits and presenteeism were small and the results of the sensitivity analyses showed some different from the main results. Therefore, the generalization of our study findings to the entire Japanese employee populations was difficult, and more precise validation studies and further investigations in other populations are warranted. Second, because this study was conducted over 1 year, the long-term effects of lifestyle improvement and continuity, and the causal relationship between lifestyle habits changes and changes in presenteeism are unknown. Therefore, it is necessary to carefully determine the causal relationship between lifestyle habits changes and changes in presenteeism through long-term follow-ups. Finally, because the data on lifestyle habits in this study were collected using questionnaires from specific health examination checkups, reporting biases could not be ruled out. Further verification using a wider range of objectives and survey data is necessary.
Despite the above limitations, to the best of our knowledge, this is the first study to examine the relationship between changes in both directions (worsening or improvement) of multiple lifestyle habits and presenteeism change by sex using 11 lifestyle items obtained from the specific health checkup. The results of this study suggested that for maintaining work performance, preserving sufficient sleep in both sexes, and regular exercise and frequency of drinking in men are critical. On the other hand, to improve work performance, improving sleeping in women is essential. This indicates that the target populations, as well as effective interventions of lifestyle habits, excluding sleeping, to either maintain or improve work performance, differ by sex. The findings of this study may be useful in considering lifestyle interventions for employees to maintain and improve work performance.
This study examined the relationship between changes in lifestyle habits (worsening/improvement) and presenteeism change among Japanese employees by sex. The results showed that worsening of sleeping, regular exercise, and frequency of drinking for men, while worsening of sleeping for women, were associated with increased presenteeism. On the other hand, no improvement in lifestyle habits for men, and improvement of sleeping for women, were associated with reduced presenteeism. These findings suggest that maintaining good lifestyle habits of sleeping for both sexes, and exercise and drinking for men, may be beneficial in maintaining work performance, while improving poor sleeping may be beneficial for women to improve work performance. Therefore, a supportive work environment should be enhanced to help employees maintain good lifestyle habits. However, further validation studies and investigations in other populations are needed.
The authors declare they have not used Artificial Intelligence (AI) tools in the creation of this article.
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Figures(6) / Tables(1)
Namfon Samsalee, Rungsinee Sothornvit. Physicochemical, functional properties and antioxidant activity of protein extract from spent coffee grounds using ultrasonic-assisted extraction[J]. AIMS Agriculture and Food, 2021, 6(3): 864-878. doi: 10.3934/agrfood.2021052
| All (n = 9366) | |||
| n (%) or Mean ± SD | |||
| Attributes | |||
| Sex | Men | 4899 | (52.3) |
| Women | 4467 | (47.7) | |
| Age | 43.1 | ±11.9 | |
| 21–29 y | 1736 | (18.5) | |
| 30–39 y | 1897 | (20.3) | |
| 40–49 y | 2560 | (27.3) | |
| 50–59 y | 2372 | (25.3) | |
| 60–69 y | 801 | (8.6) | |
| Department | Sales | 4831 | (51.5) |
| Customer service | 3405 | (36.4) | |
| Administration | 1130 | (12.1) | |
| Job position | Non-manager | 7700 | (82.2) |
| Manager | 1666 | (17.8) | |
| Lifestyle habits | |||
| Current smoking (Yes) | 1611 | (17.2) | |
| Regular exercise (No) | 7690 | (82.1) | |
| Physical activity (No) | 7285 | (77.8) | |
| Walking speed (No) | 5386 | (57.5) | |
| Eating late evening meals (Yes) | 3372 | (36.0) | |
| Eating snacks after dinner (Yes) | 1152 | (12.3) | |
| Skipping breakfast (Yes) | 2077 | (22.2) | |
| Eating speed (quicker) | 3476 | (37.1) | |
| Frequency of drinking (Everyday) | 2940 | (31.4) | |
| Alcohol consumption per day (Heavy) | 3826 | (40.8) | |
| Sleeping (No) | 3180 | (34.0) | |
| Diseases | |||
| Hypertension | 1120 | (12.0) | |
| Dyslipidemia | 1378 | (14.7) | |
| Diabetes | 569 | (6.1) | |
| Cancer | 241 | (2.6) | |
| Mental illness | 610 | (6.5) | |
| Infectious diseases | 5062 | (54.0) | |
| Musculoskeletal diseases | 2309 | (24.7) | |
| Oral diseases | 4735 | (50.6) | |
| WHO-HPQ score | 60.5 | ±17.1 | |
Note: SD: standard deviation; WHO-HPQ: World Health Organization Health and Work Performance Questionnaire.
DownLoad:
CSV
| Men |
Women |
||||||||
| Worsening | n | (%) | Mean ± SD | P-value* | n | (%) | Mean ± SD | P-value* | |
| Current smoking | No | 3503 | (97.8) | 0.7 ± 15.9 | 0.091 | 4144 | (99.3) | 0.8 ± 18.2 | 1.000 |
| Yes | 80 | (2.2) | −2.4 ± 17.9 | 28 | (0.7) | −0.7 ± 14.1 | |||
| Regular exercise | No | 923 | (75.7) | 1.5 ± 15.5 | 0.132 | 311 | (68.1) | 0.0 ± 17.2 | 0.617 |
| Yes | 296 | (24.3) | −0.2 ± 15.6 | 146 | (31.9) | −1.4 ± 18.5 | |||
| Physical activity | No | 778 | (64.1) | 0.4 ± 16.1 | 1.000 | 529 | (61.0) | 0.8 ± 18.2 | 1.000 |
| Yes | 436 | (35.9) | 0.5 ± 15.9 | 338 | (39.0) | 0.9 ± 17.7 | |||
| Walking speed | No | 1977 | (81.3) | 0.7 ± 15.6 | 1.000 | 1224 | (79.0) | 0.4 ± 17.2 | 0.836 |
| Yes | 454 | (18.7) | 0.6 ± 16.7 | 325 | (21.0) | 1.1 ± 19.7 | |||
| Eating late evening meals | No | 2241 | (80.6) | 1.2 ± 15.1 | 0.289 | 2786 | (86.7) | 0.8 ± 17.6 | 1.000 |
| Yes | 539 | (19.4) | 0.1 ± 17.3 | 428 | (13.3) | 0.6 ± 19.4 | |||
| Eating snacks after dinner | No | 4216 | (94.4) | 0.7 ± 16.1 | 1.000 | 3472 | (92.7) | 0.6 ± 18.2 | 1.000 |
| Yes | 251 | (5.6) | 0.9 ± 16.1 | 275 | (7.3) | 1.0 ± 18.1 | |||
| Skipping breakfast | No | 3592 | (95.1) | 1.0 ± 15.6 | 0.402 | 3301 | (94.0) | 0.6 ± 17.7 | 0.497 |
| Yes | 184 | (4.9) | −0.5 ± 17.1 | 212 | (6.0) | −0.6±19.8 | |||
| Eating speed | No | 2443 | (87.5) | 0.5 ± 16.1 | 1.000 | 2865 | (92.4) | 1.0 ± 18.0 | 1.000 |
| Yes | 348 | (12.5) | 0.7 ± 16.7 | 234 | (7.6) | 0.3 ± 18.1 | |||
| Frequency of drinking | No | 2371 | (89.5) | 0.6 ± 16.3 | 0.165 | 3614 | (95.7) | 0.8 ± 18.0 | 1.000 |
| Yes | 277 | (10.5) | −1.2 ± 18.3 | 164 | (4.3) | 1.4 ± 18.3 | |||
| Alcohol consumption per day | No | 2722 | (87.3) | 1.3 ± 15.9 | 0.671 | 2044 | (84.4) | 0.6 ± 17.7 | 0.278 |
| Yes | 395 | (12.7) | 0.4 ± 17.4 | 379 | (15.6) | −0.5 ± 18.7 | |||
| Sleeping | No | 2888 | (84.5) | 1.1 ± 15.2 | 0.014 | 2267 | (81.9) | 1.0 ± 17.7 | 0.036 |
| Yes | 531 | (15.5) | −1.0 ± 18.5 | 500 | (18.1) | −0.9 ± 17.9 | |||
Note: SD: standard deviation; WHO-HPQ: World Health Organization Health and Work Performance Questionnaire; t-test; *P-value correction using the Benjamini–Hochberg method was conducted.
DownLoad:
CSV
| Men |
Women |
||||||||
| Improvement | n | (%) | Mean ± SD | P-value* | n | (%) | Mean ± SD | P-value* | |
| Current smoking | No | 1222 | (92.9) | 0.9 ± 16.9 | 0.111 | 265 | (89.8) | −0.5±18.4 | 1.000 |
| Yes | 94 | (7.1) | −2.0 ± 14.5 | 30 | (10.2) | −1.3±18.7 | |||
| Regular exercise | No | 3326 | (90.4) | 0.6 ± 16.4 | 0.953 | 3838 | (95.7) | 0.7±18.3 | 0.084 |
| Yes | 354 | (9.6) | 0.0 ± 15.9 | 172 | (4.3) | 3.2±17.2 | |||
| Physical activity | No | 3200 | (86.8) | 0.9 ± 16.3 | 0.085 | 3219 | (89.4) | 0.6±18.2 | 1.000 |
| Yes | 485 | (13.2) | −0.4 ± 15.2 | 381 | (10.6) | 1.0±18.5 | |||
| Walking speed | No | 2062 | (83.5) | 0.7 ± 16.5 | 1.000 | 2620 | (89.8) | 0.7±18.1 | 1.000 |
| Yes | 406 | (16.5) | 0.4 ± 16.2 | 298 | (10.2) | 0.7±20.9 | |||
| Eating late evening meals | No | 1622 | (76.5) | 0.0 ± 16.8 | 0.362 | 859 | (68.6) | 0.0±19.0 | 1.000 |
| Yes | 497 | (23.5) | 1.0 ± 17.1 | 394 | (31.4) | 0.9±18.9 | |||
| Eating snacks after dinner | No | 207 | (47.9) | 0.3 ± 17.6 | 1.000 | 398 | (55.3) | −0.2±18.4 | 0.106 |
| Yes | 225 | (52.1) | −0.2 ± 14.6 | 322 | (44.7) | 2.1±17.9 | |||
| Skipping breakfast | No | 916 | (81.6) | −0.3 ± 17.8 | 0.317 | 761 | (79.8) | 1.0±19.0 | 1.000 |
| Yes | 207 | (18.4) | 1.2 ± 16.4 | 193 | (20.2) | 1.7±20.5 | |||
| Eating speed | No | 1783 | (84.6) | 1.0 ± 16.1 | 1.000 | 1088 | (79.5) | −0.2±18.6 | 0.431 |
| Yes | 325 | (15.4) | 0.6 ± 16.5 | 280 | (20.5) | 1.2±18.3 | |||
| Frequency of drinking | No | 2003 | (89.0) | 1.1 ± 15.6 | 0.693 | 552 | (80.1) | −0.8±18.6 | 0.177 |
| Yes | 248 | (11.0) | 0.3 ± 16.5 | 137 | (19.9) | 1.9±19.1 | |||
| Alcohol consumption per day | No | 1277 | (71.7) | −0.1 ± 16.3 | 1.000 | 1568 | (76.7) | 0.7±18.5 | 0.766 |
| Yes | 505 | (28.3) | −0.4 ± 15.8 | 476 | (23.3) | 1.5±18.4 | |||
| Sleeping | No | 1031 | (69.7) | 0.7 ± 17.0 | 1.000 | 1272 | (74.8) | 0.0±19.0 | 0.015 |
| Yes | 449 | (30.3) | 0.2 ± 17.2 | 428 | (25.2) | 2.6±18.2 | |||
Note: SD: standard deviation; WHO-HPQ: World Health Organization Health and Work Performance Questionnaire; t-test; *P-value correction using the Benjamini–Hochberg method was conducted.
DownLoad:
CSV
| Men | Multiple regression analysis |
Sensitivity analysis |
||||||||
| n* | B | 95% CI | β | P-value** | Adjusted R2 | n* | B | SE | P-value** | |
| Current smoking | 3583 | −2.807 | (−5.82 to 0.21) | −0.026 | 0.093 | 0.281 | 4541 | −2.338 | 1.403 | 0.132 |
| Regular exercise | 1219 | −2.082 | (−3.83 to −0.34) | −0.058 | 0.024 | 0.288 | 1522 | −1.911 | 0.812 | 0.023 |
| Physical activity | 1214 | −0.351 | (−1.96 to 1.26) | −0.010 | 1.000 | 0.304 | 1482 | −0.584 | 0.742 | 0.948 |
| Walking speed | 2431 | −0.578 | (−1.95 to 0.80) | −0.014 | 0.901 | 0.293 | 2886 | −0.420 | 0.633 | 1.000 |
| Eating late evening meals | 2780 | −0.931 | (−2.22 to 0.36) | −0.024 | 0.247 | 0.281 | 3616 | −0.873 | 0.575 | 0.202 |
| Eating snacks after dinner | 4467 | −0.026 | (−1.78 to 1.73) | 0.000 | 1.000 | 0.285 | 5566 | −0.278 | 0.799 | 1.000 |
| Skipping breakfast | 3776 | −1.058 | (−3.07 to 0.95) | −0.015 | 0.555 | 0.277 | 4682 | −0.733 | 0.915 | 0.775 |
| Eating speed | 2791 | −0.045 | (−1.60 to 1.51) | −0.001 | 1.000 | 0.282 | 3289 | 0.295 | 0.744 | 1.000 |
| Frequency of drinking | 2648 | −2.372 | (−4.13 to −0.62) | −0.044 | 0.009 | 0.286 | 3623 | −2.359 | 0.816 | 0.004 |
| Alcohol consumption per day | 3117 | −0.585 | (−2.04 to 0.87) | −0.012 | 1.000 | 0.290 | 3570 | −0.375 | 0.710 | 1.000 |
| Sleeping | 3419 | −2.722 | (−3.96 to −1.49) | −0.063 | <0.001 | 0.307 | 4202 | −3.262 | 0.559 | <0.001 |
| Women | Multiple regression analysis |
Sensitivity analysis |
||||||||
| n* | B | 95% CI | β | P-value** | Adjusted R2 | n* | B | SE | P-value** | |
| Current smoking | 4172 | −0.329 | (−5.97 to 5.31) | −0.001 | 1.000 | 0.310 | 6586 | 0.565 | 2.468 | 1.000 |
| Regular exercise | 457 | −2.068 | (−5.19 to 1.05) | −0.055 | 0.354 | 0.263 | 707 | −1.128 | 1.239 | 0.665 |
| Physical activity | 867 | −0.045 | (−2.10 to 2.01) | −0.001 | 1.000 | 0.326 | 1243 | −1.119 | 0.873 | 0.275 |
| Walking speed | 1549 | −0.775 | (−2.65 to 1.10) | −0.018 | 1.000 | 0.284 | 2230 | −0.381 | 0.789 | 1.000 |
| Eating late evening meals | 3214 | −1.526 | (−3.04 to −0.01) | −0.029 | 0.059 | 0.328 | 5062 | −1.163 | 0.621 | 0.067 |
| Eating snacks after dinner | 3747 | −1.005 | (−2.87 to 0.86) | −0.014 | 0.639 | 0.311 | 5773 | −0.550 | 0.763 | 1.000 |
| Skipping breakfast | 3513 | −2.554 | (−4.64 to −0.47) | −0.034 | 0.018 | 0.306 | 5351 | −1.527 | 0.839 | 0.084 |
| Eating speed | 3099 | −1.498 | (−3.50 to 0.51) | −0.022 | 0.196 | 0.306 | 4557 | −0.492 | 0.845 | 1.000 |
| Frequency of drinking | 3778 | 0.526 | (−1.84 to 2.89) | 0.006 | 1.000 | 0.311 | 6119 | 0.102 | 1.099 | 1.000 |
| Alcohol consumption per day | 2423 | −1.147 | (−2.82 to 0.52) | −0.023 | 0.279 | 0.305 | 2909 | −0.781 | 0.803 | 0.520 |
| Sleeping | 2767 | −2.219 | (−3.67 to −0.77) | −0.048 | 0.003 | 0.305 | 4121 | −1.414 | 0.596 | 0.018 |
Note: WHO-HPQ: World Health Organization Health and Work Performance Questionnaire; *The number of subjects who had “good” habits in each lifestyle habits at baseline; **P-value correction using the Benjamini–Hochberg method was conducted; Adjusted variables: age, job position, department, diseases (hypertension, dyslipidemia, diabetes, cancer, mental illness, infectious diseases, musculoskeletal diseases, and oral diseases), lifestyle habits and WHO-HPQ score at baseline; Code of independent variables: No worsening, 0; worsening, 1.
DownLoad:
CSV
| Men | Multiple regression analysis |
Sensitivity analysis |
||||||||
| n* | B | 95% CI | β | P-value** | Adjusted R2 | n* | B | SE | P-value** | |
| Current smoking | 1316 | −1.143 | (−4.17 to 1.88) | −0.018 | 0.721 | 0.279 | 1637 | −1.505 | 1.364 | 0.371 |
| Regular exercise | 3680 | 0.235 | (−1.30 to 1.77) | 0.004 | 1.000 | 0.285 | 4597 | 0.607 | 0.698 | 0.604 |
| Physical activity | 3685 | −0.604 | (−1.93 to 0.72) | −0.013 | 0.510 | 0.277 | 4612 | −0.019 | 0.604 | 1.000 |
| Walking speed | 2468 | 0.438 | (−1.08 to 1.95) | 0.010 | 1.000 | 0.275 | 3226 | 0.860 | 0.691 | 0.260 |
| Eating late evening meals | 2119 | 0.672 | (−0.78 to 2.12) | 0.017 | 0.443 | 0.289 | 2508 | 0.434 | 0.669 | 0.946 |
| Eating snacks after dinner | 432 | 0.374 | (−2.32 to 3.07) | 0.012 | 1.000 | 0.263 | 555 | 0.583 | 1.202 | 1.000 |
| Skipping breakfast | 1123 | 1.472 | (−0.78 to 3.72) | 0.033 | 0.220 | 0.296 | 1389 | 1.731 | 1.034 | 0.104 |
| Eating speed | 2108 | 0.180 | (−1.45 to 1.81) | 0.004 | 1.000 | 0.284 | 2563 | −0.088 | 0.764 | 1.000 |
| Frequency of drinking | 2251 | −0.121 | (−1.91 to 1.67) | −0.002 | 1.000 | 0.277 | 2509 | 0.028 | 0.853 | 1.000 |
| Alcohol consumption per day | 1782 | −0.216 | (−1.69 to 1.26) | −0.006 | 1.000 | 0.265 | 2002 | 0.066 | 0.702 | 1.000 |
| Sleeping | 1480 | 1.305 | (−0.36 to 2.97) | 0.035 | 0.125 | 0.247 | 1925 | 1.692 | 0.751 | 0.024 |
| Women | Multiple regression analysis |
Sensitivity analysis |
||||||||
| n* | B | 95% CI | β | P-value** | Adjusted R2 | n* | B | SE | P-value** | |
| Current smoking | 295 | −2.581 | (−8.82 to 3.66) | −0.042 | 0.763 | 0.317 | 413 | −2.191 | 2.713 | 0.769 |
| Regular exercise | 4010 | 2.382 | (0.04 to 4.73) | 0.026 | 0.051 | 0.315 | 6101 | 2.140 | 0.974 | 0.031 |
| Physical activity | 3600 | −0.483 | (−2.12 to 1.15) | −0.008 | 1.000 | 0.306 | 5463 | 0.085 | 0.687 | 1.000 |
| Walking speed | 2918 | −0.022 | (−1.85 to 1.80) | 0.000 | 1.000 | 0.321 | 4568 | 0.888 | 0.744 | 0.320 |
| Eating late evening meals | 1253 | 1.174 | (−0.82 to 3.17) | 0.029 | 0.305 | 0.262 | 1749 | 1.461 | 0.846 | 0.103 |
| Eating snacks after dinner | 720 | 1.220 | (−1.10 to 3.54) | 0.033 | 0.416 | 0.294 | 1036 | 0.595 | 0.986 | 1.000 |
| Skipping breakfast | 954 | 0.474 | (−2.11 to 3.06) | 0.010 | 1.000 | 0.311 | 1413 | 0.506 | 1.054 | 1.000 |
| Eating speed | 1368 | 1.023 | (−1.04 to 3.09) | 0.022 | 0.522 | 0.306 | 1982 | 1.035 | 0.924 | 0.413 |
| Frequency of drinking | 689 | 0.770 | (−2.34 to 3.88) | 0.016 | 1.000 | 0.277 | 768 | −0.034 | 1.525 | 1.000 |
| Alcohol consumption per day | 2044 | 0.171 | (−1.44 to 1.78) | 0.004 | 1.000 | 0.312 | 2348 | 0.160 | 0.789 | 1.000 |
| Sleeping | 1700 | 2.225 | (0.47 to 3.98) | 0.051 | 0.013 | 0.313 | 2688 | 1.944 | 0.722 | 0.007 |
Note: WHO-HPQ, World Health Organization Health and Work Performance Questionnaire; *The number of subjects who had “poor” habits in each lifestyle habits at baseline; **P value correction using the Benjamini–Hochberg method was conducted; Adjusted variables: age, job position, department, diseases (hypertension, dyslipidemia, diabetes, cancer, mental illness, infectious diseases, musculoskeletal diseases, and oral diseases), lifestyle habits and WHO-HPQ score at baseline; Code of independent variables: No improvement, 0; improvement, 1.
DownLoad:
CSV
| All (n = 9366) | |||
| n (%) or Mean ± SD | |||
| Attributes | |||
| Sex | Men | 4899 | (52.3) |
| Women | 4467 | (47.7) | |
| Age | 43.1 | ±11.9 | |
| 21–29 y | 1736 | (18.5) | |
| 30–39 y | 1897 | (20.3) | |
| 40–49 y | 2560 | (27.3) | |
| 50–59 y | 2372 | (25.3) | |
| 60–69 y | 801 | (8.6) | |
| Department | Sales | 4831 | (51.5) |
| Customer service | 3405 | (36.4) | |
| Administration | 1130 | (12.1) | |
| Job position | Non-manager | 7700 | (82.2) |
| Manager | 1666 | (17.8) | |
| Lifestyle habits | |||
| Current smoking (Yes) | 1611 | (17.2) | |
| Regular exercise (No) | 7690 | (82.1) | |
| Physical activity (No) | 7285 | (77.8) | |
| Walking speed (No) | 5386 | (57.5) | |
| Eating late evening meals (Yes) | 3372 | (36.0) | |
| Eating snacks after dinner (Yes) | 1152 | (12.3) | |
| Skipping breakfast (Yes) | 2077 | (22.2) | |
| Eating speed (quicker) | 3476 | (37.1) | |
| Frequency of drinking (Everyday) | 2940 | (31.4) | |
| Alcohol consumption per day (Heavy) | 3826 | (40.8) | |
| Sleeping (No) | 3180 | (34.0) | |
| Diseases | |||
| Hypertension | 1120 | (12.0) | |
| Dyslipidemia | 1378 | (14.7) | |
| Diabetes | 569 | (6.1) | |
| Cancer | 241 | (2.6) | |
| Mental illness | 610 | (6.5) | |
| Infectious diseases | 5062 | (54.0) | |
| Musculoskeletal diseases | 2309 | (24.7) | |
| Oral diseases | 4735 | (50.6) | |
| WHO-HPQ score | 60.5 | ±17.1 | |
| Men |
Women |
||||||||
| Worsening | n | (%) | Mean ± SD | P-value* | n | (%) | Mean ± SD | P-value* | |
| Current smoking | No | 3503 | (97.8) | 0.7 ± 15.9 | 0.091 | 4144 | (99.3) | 0.8 ± 18.2 | 1.000 |
| Yes | 80 | (2.2) | −2.4 ± 17.9 | 28 | (0.7) | −0.7 ± 14.1 | |||
| Regular exercise | No | 923 | (75.7) | 1.5 ± 15.5 | 0.132 | 311 | (68.1) | 0.0 ± 17.2 | 0.617 |
| Yes | 296 | (24.3) | −0.2 ± 15.6 | 146 | (31.9) | −1.4 ± 18.5 | |||
| Physical activity | No | 778 | (64.1) | 0.4 ± 16.1 | 1.000 | 529 | (61.0) | 0.8 ± 18.2 | 1.000 |
| Yes | 436 | (35.9) | 0.5 ± 15.9 | 338 | (39.0) | 0.9 ± 17.7 | |||
| Walking speed | No | 1977 | (81.3) | 0.7 ± 15.6 | 1.000 | 1224 | (79.0) | 0.4 ± 17.2 | 0.836 |
| Yes | 454 | (18.7) | 0.6 ± 16.7 | 325 | (21.0) | 1.1 ± 19.7 | |||
| Eating late evening meals | No | 2241 | (80.6) | 1.2 ± 15.1 | 0.289 | 2786 | (86.7) | 0.8 ± 17.6 | 1.000 |
| Yes | 539 | (19.4) | 0.1 ± 17.3 | 428 | (13.3) | 0.6 ± 19.4 | |||
| Eating snacks after dinner | No | 4216 | (94.4) | 0.7 ± 16.1 | 1.000 | 3472 | (92.7) | 0.6 ± 18.2 | 1.000 |
| Yes | 251 | (5.6) | 0.9 ± 16.1 | 275 | (7.3) | 1.0 ± 18.1 | |||
| Skipping breakfast | No | 3592 | (95.1) | 1.0 ± 15.6 | 0.402 | 3301 | (94.0) | 0.6 ± 17.7 | 0.497 |
| Yes | 184 | (4.9) | −0.5 ± 17.1 | 212 | (6.0) | −0.6±19.8 | |||
| Eating speed | No | 2443 | (87.5) | 0.5 ± 16.1 | 1.000 | 2865 | (92.4) | 1.0 ± 18.0 | 1.000 |
| Yes | 348 | (12.5) | 0.7 ± 16.7 | 234 | (7.6) | 0.3 ± 18.1 | |||
| Frequency of drinking | No | 2371 | (89.5) | 0.6 ± 16.3 | 0.165 | 3614 | (95.7) | 0.8 ± 18.0 | 1.000 |
| Yes | 277 | (10.5) | −1.2 ± 18.3 | 164 | (4.3) | 1.4 ± 18.3 | |||
| Alcohol consumption per day | No | 2722 | (87.3) | 1.3 ± 15.9 | 0.671 | 2044 | (84.4) | 0.6 ± 17.7 | 0.278 |
| Yes | 395 | (12.7) | 0.4 ± 17.4 | 379 | (15.6) | −0.5 ± 18.7 | |||
| Sleeping | No | 2888 | (84.5) | 1.1 ± 15.2 | 0.014 | 2267 | (81.9) | 1.0 ± 17.7 | 0.036 |
| Yes | 531 | (15.5) | −1.0 ± 18.5 | 500 | (18.1) | −0.9 ± 17.9 | |||
| Men |
Women |
||||||||
| Improvement | n | (%) | Mean ± SD | P-value* | n | (%) | Mean ± SD | P-value* | |
| Current smoking | No | 1222 | (92.9) | 0.9 ± 16.9 | 0.111 | 265 | (89.8) | −0.5±18.4 | 1.000 |
| Yes | 94 | (7.1) | −2.0 ± 14.5 | 30 | (10.2) | −1.3±18.7 | |||
| Regular exercise | No | 3326 | (90.4) | 0.6 ± 16.4 | 0.953 | 3838 | (95.7) | 0.7±18.3 | 0.084 |
| Yes | 354 | (9.6) | 0.0 ± 15.9 | 172 | (4.3) | 3.2±17.2 | |||
| Physical activity | No | 3200 | (86.8) | 0.9 ± 16.3 | 0.085 | 3219 | (89.4) | 0.6±18.2 | 1.000 |
| Yes | 485 | (13.2) | −0.4 ± 15.2 | 381 | (10.6) | 1.0±18.5 | |||
| Walking speed | No | 2062 | (83.5) | 0.7 ± 16.5 | 1.000 | 2620 | (89.8) | 0.7±18.1 | 1.000 |
| Yes | 406 | (16.5) | 0.4 ± 16.2 | 298 | (10.2) | 0.7±20.9 | |||
| Eating late evening meals | No | 1622 | (76.5) | 0.0 ± 16.8 | 0.362 | 859 | (68.6) | 0.0±19.0 | 1.000 |
| Yes | 497 | (23.5) | 1.0 ± 17.1 | 394 | (31.4) | 0.9±18.9 | |||
| Eating snacks after dinner | No | 207 | (47.9) | 0.3 ± 17.6 | 1.000 | 398 | (55.3) | −0.2±18.4 | 0.106 |
| Yes | 225 | (52.1) | −0.2 ± 14.6 | 322 | (44.7) | 2.1±17.9 | |||
| Skipping breakfast | No | 916 | (81.6) | −0.3 ± 17.8 | 0.317 | 761 | (79.8) | 1.0±19.0 | 1.000 |
| Yes | 207 | (18.4) | 1.2 ± 16.4 | 193 | (20.2) | 1.7±20.5 | |||
| Eating speed | No | 1783 | (84.6) | 1.0 ± 16.1 | 1.000 | 1088 | (79.5) | −0.2±18.6 | 0.431 |
| Yes | 325 | (15.4) | 0.6 ± 16.5 | 280 | (20.5) | 1.2±18.3 | |||
| Frequency of drinking | No | 2003 | (89.0) | 1.1 ± 15.6 | 0.693 | 552 | (80.1) | −0.8±18.6 | 0.177 |
| Yes | 248 | (11.0) | 0.3 ± 16.5 | 137 | (19.9) | 1.9±19.1 | |||
| Alcohol consumption per day | No | 1277 | (71.7) | −0.1 ± 16.3 | 1.000 | 1568 | (76.7) | 0.7±18.5 | 0.766 |
| Yes | 505 | (28.3) | −0.4 ± 15.8 | 476 | (23.3) | 1.5±18.4 | |||
| Sleeping | No | 1031 | (69.7) | 0.7 ± 17.0 | 1.000 | 1272 | (74.8) | 0.0±19.0 | 0.015 |
| Yes | 449 | (30.3) | 0.2 ± 17.2 | 428 | (25.2) | 2.6±18.2 | |||
| Men | Multiple regression analysis |
Sensitivity analysis |
||||||||
| n* | B | 95% CI | β | P-value** | Adjusted R2 | n* | B | SE | P-value** | |
| Current smoking | 3583 | −2.807 | (−5.82 to 0.21) | −0.026 | 0.093 | 0.281 | 4541 | −2.338 | 1.403 | 0.132 |
| Regular exercise | 1219 | −2.082 | (−3.83 to −0.34) | −0.058 | 0.024 | 0.288 | 1522 | −1.911 | 0.812 | 0.023 |
| Physical activity | 1214 | −0.351 | (−1.96 to 1.26) | −0.010 | 1.000 | 0.304 | 1482 | −0.584 | 0.742 | 0.948 |
| Walking speed | 2431 | −0.578 | (−1.95 to 0.80) | −0.014 | 0.901 | 0.293 | 2886 | −0.420 | 0.633 | 1.000 |
| Eating late evening meals | 2780 | −0.931 | (−2.22 to 0.36) | −0.024 | 0.247 | 0.281 | 3616 | −0.873 | 0.575 | 0.202 |
| Eating snacks after dinner | 4467 | −0.026 | (−1.78 to 1.73) | 0.000 | 1.000 | 0.285 | 5566 | −0.278 | 0.799 | 1.000 |
| Skipping breakfast | 3776 | −1.058 | (−3.07 to 0.95) | −0.015 | 0.555 | 0.277 | 4682 | −0.733 | 0.915 | 0.775 |
| Eating speed | 2791 | −0.045 | (−1.60 to 1.51) | −0.001 | 1.000 | 0.282 | 3289 | 0.295 | 0.744 | 1.000 |
| Frequency of drinking | 2648 | −2.372 | (−4.13 to −0.62) | −0.044 | 0.009 | 0.286 | 3623 | −2.359 | 0.816 | 0.004 |
| Alcohol consumption per day | 3117 | −0.585 | (−2.04 to 0.87) | −0.012 | 1.000 | 0.290 | 3570 | −0.375 | 0.710 | 1.000 |
| Sleeping | 3419 | −2.722 | (−3.96 to −1.49) | −0.063 | <0.001 | 0.307 | 4202 | −3.262 | 0.559 | <0.001 |
| Women | Multiple regression analysis |
Sensitivity analysis |
||||||||
| n* | B | 95% CI | β | P-value** | Adjusted R2 | n* | B | SE | P-value** | |
| Current smoking | 4172 | −0.329 | (−5.97 to 5.31) | −0.001 | 1.000 | 0.310 | 6586 | 0.565 | 2.468 | 1.000 |
| Regular exercise | 457 | −2.068 | (−5.19 to 1.05) | −0.055 | 0.354 | 0.263 | 707 | −1.128 | 1.239 | 0.665 |
| Physical activity | 867 | −0.045 | (−2.10 to 2.01) | −0.001 | 1.000 | 0.326 | 1243 | −1.119 | 0.873 | 0.275 |
| Walking speed | 1549 | −0.775 | (−2.65 to 1.10) | −0.018 | 1.000 | 0.284 | 2230 | −0.381 | 0.789 | 1.000 |
| Eating late evening meals | 3214 | −1.526 | (−3.04 to −0.01) | −0.029 | 0.059 | 0.328 | 5062 | −1.163 | 0.621 | 0.067 |
| Eating snacks after dinner | 3747 | −1.005 | (−2.87 to 0.86) | −0.014 | 0.639 | 0.311 | 5773 | −0.550 | 0.763 | 1.000 |
| Skipping breakfast | 3513 | −2.554 | (−4.64 to −0.47) | −0.034 | 0.018 | 0.306 | 5351 | −1.527 | 0.839 | 0.084 |
| Eating speed | 3099 | −1.498 | (−3.50 to 0.51) | −0.022 | 0.196 | 0.306 | 4557 | −0.492 | 0.845 | 1.000 |
| Frequency of drinking | 3778 | 0.526 | (−1.84 to 2.89) | 0.006 | 1.000 | 0.311 | 6119 | 0.102 | 1.099 | 1.000 |
| Alcohol consumption per day | 2423 | −1.147 | (−2.82 to 0.52) | −0.023 | 0.279 | 0.305 | 2909 | −0.781 | 0.803 | 0.520 |
| Sleeping | 2767 | −2.219 | (−3.67 to −0.77) | −0.048 | 0.003 | 0.305 | 4121 | −1.414 | 0.596 | 0.018 |
| Men | Multiple regression analysis |
Sensitivity analysis |
||||||||
| n* | B | 95% CI | β | P-value** | Adjusted R2 | n* | B | SE | P-value** | |
| Current smoking | 1316 | −1.143 | (−4.17 to 1.88) | −0.018 | 0.721 | 0.279 | 1637 | −1.505 | 1.364 | 0.371 |
| Regular exercise | 3680 | 0.235 | (−1.30 to 1.77) | 0.004 | 1.000 | 0.285 | 4597 | 0.607 | 0.698 | 0.604 |
| Physical activity | 3685 | −0.604 | (−1.93 to 0.72) | −0.013 | 0.510 | 0.277 | 4612 | −0.019 | 0.604 | 1.000 |
| Walking speed | 2468 | 0.438 | (−1.08 to 1.95) | 0.010 | 1.000 | 0.275 | 3226 | 0.860 | 0.691 | 0.260 |
| Eating late evening meals | 2119 | 0.672 | (−0.78 to 2.12) | 0.017 | 0.443 | 0.289 | 2508 | 0.434 | 0.669 | 0.946 |
| Eating snacks after dinner | 432 | 0.374 | (−2.32 to 3.07) | 0.012 | 1.000 | 0.263 | 555 | 0.583 | 1.202 | 1.000 |
| Skipping breakfast | 1123 | 1.472 | (−0.78 to 3.72) | 0.033 | 0.220 | 0.296 | 1389 | 1.731 | 1.034 | 0.104 |
| Eating speed | 2108 | 0.180 | (−1.45 to 1.81) | 0.004 | 1.000 | 0.284 | 2563 | −0.088 | 0.764 | 1.000 |
| Frequency of drinking | 2251 | −0.121 | (−1.91 to 1.67) | −0.002 | 1.000 | 0.277 | 2509 | 0.028 | 0.853 | 1.000 |
| Alcohol consumption per day | 1782 | −0.216 | (−1.69 to 1.26) | −0.006 | 1.000 | 0.265 | 2002 | 0.066 | 0.702 | 1.000 |
| Sleeping | 1480 | 1.305 | (−0.36 to 2.97) | 0.035 | 0.125 | 0.247 | 1925 | 1.692 | 0.751 | 0.024 |
| Women | Multiple regression analysis |
Sensitivity analysis |
||||||||
| n* | B | 95% CI | β | P-value** | Adjusted R2 | n* | B | SE | P-value** | |
| Current smoking | 295 | −2.581 | (−8.82 to 3.66) | −0.042 | 0.763 | 0.317 | 413 | −2.191 | 2.713 | 0.769 |
| Regular exercise | 4010 | 2.382 | (0.04 to 4.73) | 0.026 | 0.051 | 0.315 | 6101 | 2.140 | 0.974 | 0.031 |
| Physical activity | 3600 | −0.483 | (−2.12 to 1.15) | −0.008 | 1.000 | 0.306 | 5463 | 0.085 | 0.687 | 1.000 |
| Walking speed | 2918 | −0.022 | (−1.85 to 1.80) | 0.000 | 1.000 | 0.321 | 4568 | 0.888 | 0.744 | 0.320 |
| Eating late evening meals | 1253 | 1.174 | (−0.82 to 3.17) | 0.029 | 0.305 | 0.262 | 1749 | 1.461 | 0.846 | 0.103 |
| Eating snacks after dinner | 720 | 1.220 | (−1.10 to 3.54) | 0.033 | 0.416 | 0.294 | 1036 | 0.595 | 0.986 | 1.000 |
| Skipping breakfast | 954 | 0.474 | (−2.11 to 3.06) | 0.010 | 1.000 | 0.311 | 1413 | 0.506 | 1.054 | 1.000 |
| Eating speed | 1368 | 1.023 | (−1.04 to 3.09) | 0.022 | 0.522 | 0.306 | 1982 | 1.035 | 0.924 | 0.413 |
| Frequency of drinking | 689 | 0.770 | (−2.34 to 3.88) | 0.016 | 1.000 | 0.277 | 768 | −0.034 | 1.525 | 1.000 |
| Alcohol consumption per day | 2044 | 0.171 | (−1.44 to 1.78) | 0.004 | 1.000 | 0.312 | 2348 | 0.160 | 0.789 | 1.000 |
| Sleeping | 1700 | 2.225 | (0.47 to 3.98) | 0.051 | 0.013 | 0.313 | 2688 | 1.944 | 0.722 | 0.007 |
