Front. Psychol. Frontiers in Psychology Front. Psychol. 1664-1078 Frontiers Media S.A. 10.3389/fpsyg.2023.1133498 Psychology Original Research Changes in work/sleep patterns due to the COVID-19 pandemic are associated with psychological distress among Japanese workers Tondokoro Tsukumi 1 2 Nakata Akinori 2 3 Tateishi Seiichiro 4 Mafune Kosuke 5 Tsuji Mayumi 6 Ando Hajime 7 Odagami Kiminori 8 Matsugaki Ryutaro 9 Fujino Yoshihisa 2 * for the CORoNaWork project 1The Health Care Science Institute, Tokyo, Japan 2Department of Environmental Epidemiology, Institute of Industrial Ecological Sciences, University of Occupational and Environmental Health, Japan, Kitakyushu, Japan 3Graduate School of Public Health, International University of Health and Welfare, Tokyo, Japan 4Disaster Occupational Health Center, Institute of Industrial Ecological Sciences, University of Occupational and Environmental Health, Japan, Kitakyushu, Japan 5Department of Mental Health, Institute of Industrial Ecological Sciences, University of Occupational and Environmental Health, Japan, Kitakyushu, Japan 6Department of Environmental Health, School of Medicine, University of Occupational and Environmental Health, Japan, Kitakyushu, Japan 7Department of Work Systems and Health, Institute of Industrial Ecological Sciences, University of Occupational and Environmental Health, Japan, Kitakyushu, Japan 8Department of Occupational Health Practice and Management, Institute of Industrial Ecological Sciences, University of Occupational and Environmental Health, Japan, Kitakyushu, Japan 9Department of Preventive Medicine and Community Health, School of Medicine, University of Occupational and Environmental Health, Japan, Kitakyushu, Japan

Edited by: Bei Liu, China University of Mining and Technology, China

Reviewed by: Sara Marelli, San Raffaele Hospital (IRCCS), Italy; Aleksandra Gaworska-Krzeminska, Medical University of Gdansk, Poland

 *Correspondence: Yoshihisa Fujino, zenq@med.uoeh-u.ac.jp

This article was submitted to Organizational Psychology, a section of the journal Frontiers in Psychology

 14 03 2023 2023 14 1133498 29 12 2022 20 02 2023 Copyright © 2023 Tondokoro, Nakata, Tateishi, Mafune, Tsuji, Ando, Odagami, Matsugaki and Fujino. 2023 Tondokoro, Nakata, Tateishi, Mafune, Tsuji, Ando, Odagami, Matsugaki and Fujino

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

 Objectives

The coronavirus disease 2019 (COVID-19) pandemic has significantly impacted working life quality. This study investigated whether changes in work/sleep patterns due to the pandemic are related to poor psychological status among Japanese workers during the third wave of the COVID-19 pandemic (December 2020). We hypothesized that workers who experienced drastic changes in working hours and sleep duration would be at an increased risk of psychological distress.

 Methods

A cross-sectional self-administered Internet survey was conducted that included questions regarding socio-demographics, lifestyle, health, and occupational background and conditions. Multivariable logistic regression models were utilized to examine the association between psychological distress and a combination of changes in working hours and sleep duration.

 Results

Among 25,762 workers, decreased work hours and decreased sleep duration presented 2.59 times higher odds of psychological distress (95% confidence interval [CI] = 2.05–3.28) compared to those who had no changes in work hours combined with unchanged sleep duration (reference group). Increased work hours and decreased sleep duration were associated with 1.98 times higher odds of psychological distress (95% CI = 1.64–2.39).

 Conclusion

Our observations confirmed that decreased sleep duration could be a key factor for psychological distress, irrespective of working hours. Interestingly, workers with a combination of decreased work hours and sleep duration posed the highest risk of psychological distress. Decreased work hours accompanied by financial difficulties in the early stage of the pandemic may have caused decreased sleep duration, resulting in a high prevalence of psychological distress. Our study underlined the importance of sleep management in maintaining workers’ mental health, moreover, the need to consider situations and conditions of other daily tasks, such as work hours, for better sleep management.

 employees financial difficulties mental health online survey self-report sleep duration time-use work hours Introduction

The prolonged novel coronavirus disease 2019 (COVID-19) pandemic has drastically changed our daily lives. While it has brought about some positive changes, such as less commuting and more time spent with family, it has negatively impacted mental health (Bueno-Notivol et al., 2021; Cénat et al., 2021; COVID-19 Mental Disorders Collaborators, 2021; Nochaiwong et al., 2021; OECD, 2021; Tanaka and Okamoto, 2021). Globally, due to the COVID-19 pandemic, the prevalence of mental health issues, including psychological distress, was higher than prior to the pandemic (Cénat et al., 2021; Nochaiwong et al., 2021). The estimated pooled prevalence of psychological distress among the general population was up to 50.0% in the first year of the pandemic (Cénat et al., 2021; Nochaiwong et al., 2021). Due to such psychological distress, total suicide rates have increased in Japan by 16% between July and October 2020 (Tanaka and Okamoto, 2021).

During the first year of the pandemic, many workers experienced changes in working conditions, such as forced teleworking, changes in job demands, and income reduction (financial difficulties). Moreover, workers experienced changes in their time use, especially the time used for working and sleeping.

In terms of work hours, experience of changes in work hours varies by workers. Some workers, such as health care and research laboratory workers, experienced increases in work hours during the first year of pandemic (Ischia et al., 2022; Loibner et al., 2022). According to the white paper from the Ministry of Health, Labour and Welfare, Japan (2021), workers in the information and communication industry also experienced an increase in work hours in December 2020 compared to December 2019.

In contrast, other industries, such as aviation, had a dramatic drop in demand (OECD, 2020), and the workers experienced decreases in working hours. Remote work also changed working hours; a reduction of work hours by 26 min was observed during remote work (Hallman et al., 2021), while a private survey in Japan reported that more than half of remote workers experienced longer working hours than working in the office (Japanese Trade Union Confederation, 2020). Another study also concluded that the time spent at the workstation increased by 1.5 h during a typical day for remote workers (Awada et al., 2021).

Regarding changes in sleep duration, the total sleep time increased by 1.5 h in duration at the end of the quarantine/stay-at-home condition compared to the pre-pandemic period in many countries (AMHSI Research Team et al., 2020; Raman and Coogan, 2022). Several studies on the pandemic have also indicated that remote work was associated with longer sleep duration (Hallman et al., 2021; Raman and Coogan, 2022); 34 min longer sleep duration for workdays (Hallman et al., 2021).

The Whitehall II prospective cohort study concluded that long working hours were significantly associated with shortened sleep (Virtanen et al., 2009). Furthermore, working more than 55 h per week (vs. normal work hours of 35–40 h) was related to an adjusted odds ratio (OR) of 2.25 (95% confidence interval (CI) 1.62–3.12) for short sleep of <7 h. In the same study, repeated exposure to long working hours was also associated with shortened sleep duration (OR = 2.80, 95% CI 1.20–6.53) (Virtanen et al., 2009). Given that work hours are inversely related to sleep duration, a study investigated the combined effects of working hours and sleep duration on mental health (Nakata, 2011); employees who worked more than 10 h per day with short sleep duration (<6 h/day) were at the highest risk for experiencing depressive symptoms (OR = 2.69, 95% CI 1.67–4.33) compared to those with 6–8 h of work hours and more than 6 h of sleep per day.

However, the above findings are from a pre-pandemic situation; drastic changes in our work and life conditions, including work/sleep patterns, may have been caused by COVID-19, leading to poor mental health. These past studies considered ordinal work hours and sleep duration, but not societal changes posed by the COVID-19 pandemic.

Therefore, we sought to explore how changes in work/sleep patterns due to the pandemic have impacted workers’ psychological status. This study aimed to investigate the combined association of work hours and sleep duration on psychological distress among Japanese workers during the third wave of the COVID-19 pandemic in Japan. We hypothesized that workers with increased work hours and decreased sleep duration due to the pandemic would have an increased risk of psychological distress.

 Materials and methods Study design and population

This Internet-based survey targeted Japanese workers, including sectional employees, managers, and executive managers as well as temporary/contract employees, self-employed, small office/home office workers, but excluding part-time workers, homemakers, students, and the unemployed. We distributed a nationwide health survey to workers registered in the research company’s panel from December 20 to 26, 2020, during the third wave of the COVID-19 pandemic in Japan (the detailed procedure is presented in our protocol paper; Fujino et al., 2021). After excluding invalid responses containing inconsistent answers, short response time (≤360 s), extremely low body weight (<30 kg), short height (<140 cm) (n = 6,051), and those who were already diagnosed with depression (n = 1,274), a total of 25,762 respondents were submitted for the final analysis.

 Exposure measures

Participants were asked whether they had experienced changes in work hours and sleep duration due to the COVID-19 pandemic with the answering choice of increased, unchanged, or decreased. To investigate the combined association, we categorized work hours and sleep duration into nine work/sleep patterns: (i) increased work hours and increased sleep duration, (ii) increased work hours and unchanged sleep duration, (iii) increased work hours and decreased sleep duration, (iv) unchanged working hours and increased sleep duration, (v) unchanged working hours and unchanged sleep duration (reference group), (vi) unchanged work hours and decreased sleep duration, (vii) decreased work hours and increased sleep duration, (viii) decreased work hours and unchanged sleep duration, and (ix) decreased work hours and decreased sleep duration.

 Outcome measure: Psychological distress

Psychological distress was assessed using the Japanese version of the Kessler 6 (K6) (Kessler et al., 2002), which is a short screening scale for psychological distress (Furukawa et al., 2008). K6 includes six questions regarding psychological states during the past 30 days, with five answering choices (0 = none of the time to 4 = all the time) (Furukawa et al., 2008). The scores for the six questions were then summed, and the cutoff score for mild psychological distress was set at 5 or higher, as it was suggested to be the optimal cutoff score for the Japanese population (Kessler et al., 2002; Furukawa et al., 2008; Sakurai et al., 2011).

 Control variables

Control variables included socio-demographics (sex, age groups, marital status, educational level, living with a child or children under 12 years old [yes/no], residential area where the state of emergency was declared [yes/no], and subjective financial situation [very difficult/slightly difficult/average/comfortable/very comfortable]), lifestyle (smoking, drinking frequency, exercise frequency, time for house chores during the pandemic [increased/unchanged/decreased], and time spent with family during the pandemic [increased/unchanged/decreased]), health (body mass index, diseases currently being treated [yes/no]), and occupational background and conditions (type of industry, number of employees, one-way commuting time, teleworking preference, telework frequency, changes in job stress due to the COVID-19 pandemic [increased/unchanged/decreased], job control, and social support at work). Job control and social support at work were assessed using the job content questionnaire (Karasek et al., 1998) and divided into low/high groups by the median score of each: job control (64.0) and social support (22.0).

 Statistical analysis

Multivariable logistic regression models were utilized to investigate the association between psychological distress and combined changes in work hours and sleep duration. We first adjusted for sex and age groups (Model 1). In the next model (Model 2), we adjusted for marital status, educational level, living with a child or children under 12 years old, residential area where the state of emergency was declared, smoking, drinking frequency, exercise frequency, time for house chores, time with family, body mass index, and diseases currently being treated, in addition to Model 1. In Model 3, we additionally adjusted for occupational background and conditions, such as type of industry, number of employees, one-way commuting time, teleworking preference, telework frequency, job control, social support, and job stress during the pandemic. Finally, in Model 4, we further adjusted for the financial situation because work hours, sleep duration, and psychological states were more likely related to the financial situation during the pandemic among workers.

In addition to the above analyses, we recorded the financial situation into two groups (difficult/not difficult) and performed a multivariate logistic regression analysis to investigate the impact of psychological distress on changes in work hours and sleep duration, in tandem with the financial situation, resulting in the creation of 18 groups.

All analyses were performed using SPSS Version 25.0 (IBM Corp., Armonk, NY, United States). The level of significance was set at p < 0.05.

 Results

Table 1 presents the characteristics of the study participants according to their psychological state. In this study, 9,827 participants (38.1%) had mild psychological distress. A higher proportion of younger (age 20–39 years) and unmarried participants fell into the groups with psychological distress in comparison to the group without psychological distress. Nearly half of the participants with psychological distress experienced difficulty or slight difficulty in relation to their financial situation, while nearly 30% in those without psychological distress. In the group with psychological distress, 46.3% of participants experienced increased job stress and 60.0% of them experienced low social support at work. The other characteristics were similar in both groups.

Characteristics of the study participants by psychological states (n = 25,762).

K6 Score < 5 K6 Score = > 5 (Mild psychological distress)
n = 15,935 n = 9,827
Mean age: 48.5 (SD 10.3) years Mean age: 44.6 (SD 10.5) years
n % n %
Sex, male 8,861 55.6 4,271 43.5
Age group
20–29 years 879 5.5 950 9.7
30–39 years 2,389 15.0 2,236 22.8
40–49 years 4,498 28.2 3,115 31.7
50–59 years 5,741 36.0 2,813 28.6
60 ≤years 2,428 15.2 713 7.3
Marital status
Married 9,687 60.8 4,762 48.5
Separated/divorced 1,550 9.7 1,127 11.5
Never married 4,698 29.5 3,938 40.1
Education
Junior high school 184 1.2 164 1.7
High school 4,094 25.7 2,514 25.6
Vocational school 2051 12.9 1,475 15.0
College 1,617 10.1 1,138 11.6
University 7,088 44.5 4,073 41.4
Graduate school 901 5.7 463 4.7
Living with a child/children aged <12 years old, yes 2,760 17.3 1,765 18.0
Residential area, areas where a state of emergency was declared 6,735 42.3 3,980 40.5
Financial situation
Very difficult 1,225 7.7 1,812 18.4
Slightly difficult 3,953 24.8 3,370 34.3
Average 8,389 52.6 3,909 39.8
Comfortable 2,012 12.6 650 6.6
Very comfortable 356 2.2 86 0.9
Smoking status
Non-smoker 8,437 52.9 5,520 56.2
Past smoker 3,374 21.2 1,821 18.5
Smoker 4,124 25.9 2,486 25.3
Drinking frequency
6–7 days/week 3,611 22.7 1,848 18.8
4–5 days/week 1,274 8.0 710 7.2
2–3 days/week 1,910 12.0 1,225 12.5
<1 day/week 2,637 16.5 1,732 17.6
Almost never 6,503 40.8 4,312 43.9
Exercise frequency
6–7 days/week 948 5.9 396 4.0
4–5 days/week 976 6.1 530 5.4
2–3 days/week 1,877 11.8 1,078 11.0
< 1 day/week 2,144 13.5 1,420 14.4
Almost never 9,990 62.7 6,403 65.2
Time for house chores
Increased 2,183 13.7 1,790 18.2
Unchanged 13,487 84.6 7,599 77.3
Decreased 265 1.7 438 4.5
Time with family
Increased 2,974 18.7 1,810 18.4
Unchanged 12,458 78.2 7,273 74.0
Decreased 503 3.2 744 7.6
Body mass index
0–18.49 1,672 10.5 1,388 14.1
18.5–24.9 10,919 68.5 6,577 66.9
25 < 3,344 21.0 1,862 18.9
Disease currently being treated, yes 4,713 29.6 3,523 35.9
Type of industry
Energy, materials, industrial machinery 602 3.8 320 3.3
Food 327 2.1 254 2.6
Beverages/tobacco products 99 0.6 35 0.4
Pharmaceuticals/medical supplies 242 1.5 141 1.4
Cosmetics/toiletries/sanitary products 83 0.5 70 0.7
Fashion and accessories 158 1.0 131 1.3
Precision machinery and office supplies 280 1.8 160 1.6
Home appliances/AV equipment 284 1.8 179 1.8
Automobiles and transportation equipment 499 3.1 366 3.7
Household goods 38 0.2 26 0.3
Hobby/sporting goods 46 0.3 28 0.3
Real estate and housing equipment 558 3.5 333 3.4
Information and communication 833 5.2 427 4.3
Distribution and retail 1,087 6.8 639 6.5
Finance/insurance 734 4.6 436 4.4
Transportation and leisure 408 2.6 226 2.3
Restaurant and other services 761 4.8 508 5.2
Public offices and organizations 1,156 7.3 609 6.2
Education, medical services, religion 2,574 16.2 1,717 17.5
Newspapers, magazines, television, radio, advertising, and other mass media 122 0.8 87 0.9
Market research 30 0.2 15 0.2
Other 5,014 31.5 3,120 31.7
Number of employees
1 (Freelance) 1,599 10.0 798 8.1
2–4 1,255 7.9 678 6.9
5–9 992 6.2 582 5.9
10–29 1,624 10.2 1,063 10.8
30–49 891 5.6 631 6.4
50–99 1,448 9.1 986 10.0
100–499 2,940 18.4 1,992 20.3
500–999 1,138 7.1 759 7.7
> 1,000 2,767 17.4 1,682 17.1
> 10,000 1,281 8.0 656 6.7
Commuting time
> 2 h 306 1.9 245 2.5
1–2 h(s) 2,075 13.0 1,398 14.2
30 min-1 h 4,267 26.8 2,831 28.8
< 30 min 6,781 42.6 3,932 40.0
Almost never 2,506 15.7 1,421 14.5
Telework preference
Willing to work from home 4,328 27.2 3,714 37.8
Neither 4,823 30.3 2,770 28.2
Willing to work at office 6,784 42.6 3,343 34.0
Telework frequency
> 4 days/week 1,672 10.5 920 9.4
> 2 days/week 888 5.6 503 5.1
> 1 day/week 529 3.3 306 3.1
> 1 day/month 355 2.2 239 2.4
Almost never 12,491 78.4 7,859 80.0
Job stress
Increased 2,922 18.3 4,551 46.3
Unchanged 12,399 77.8 4,955 50.4
Decreased 614 3.9 321 3.3
Social support
Low 6,630 41.6 5,893 60.0
High 9,305 58.4 3,934 40.0
Job control
Low 7,778 48.8 5,885 59.9
High 8,157 51.2 3,942 40.1

Table 2 highlights the characteristics of the study participants by nine groups of changes in work/sleep patterns due to the pandemic. In the group with decreased working hours and decreased sleep duration, more than 30% of participants had experienced extreme difficulty in relation to their financial situation. In the group with increased working hours and decreased sleep duration, 81.6% experienced increases in job stress due to the pandemic.

Characteristics of the study participants by changes in work/sleep patterns.

Work hours Increased Unchanged Decreased
Sleep duration Increasedn = 347Mean age: 42.5 (SD 11.3) years Unchangedn = 1,049 Mean age: 44.6 (SD 10.2) years Decreasedn = 692Mean age: 45.1 (SD 10.1) years Increasedn = 1,202 Mean age: 43.5 (SD 11.0) years Unchangedn = 18,185 Mean age: 47.6 (SD 10.5) years Decreasedn = 1,624 Mean age: 45.8 (SD 10.5) years Increasedn = 601 Mean age: 43.7 (SD 11.1) years Unchangedn = 1,625Mean age: 48.4 (SD 10.1) years Decreasedn = 437Mean age: 46.6 (SD 10.8) years
n % n % n % n % n % n % n % n % n %
Sex, male 140 40.3 486 46.3 312 45.1 425 35.4 9,795 53.9 648 39.9 229 38.1 892 54.9 205 46.9
Age group
20–29 years 55 15.9 95 9.1 64 9.2 150 12.5 1,140 6.3 127 7.8 79 13.1 81 5.0 38 8.7
30–39 years 93 26.8 223 21.3 129 18.6 314 26.1 3,043 16.7 327 20.1 148 24.6 268 16.5 80 18.3
40–49 years 85 24.5 357 34.0 231 33.4 349 29.0 5,317 29.2 533 32.8 173 28.8 438 27.0 130 29.7
50–59 years 92 26.5 309 29.5 232 33.5 294 24.5 6,246 34.3 487 30.0 143 23.8 610 37.5 141 32.3
60 = < years 22 6.3 65 6.2 36 5.2 95 7.9 2,439 13.4 150 9.2 58 9.7 228 14.0 48 11.0
Marital status
Married 179 51.6 582 55.5 365 52.7 606 50.4 10,499 57.7 861 53.0 270 44.9 878 54.0 209 47.8
Separated/divorced 27 7.8 104 9.9 69 10.0 119 9.9 1,854 10.2 232 14.3 50 8.3 170 10.5 52 11.9
Never married 141 40.6 363 34.6 258 37.3 477 39.7 5,832 32.1 531 32.7 281 46.8 577 35.5 176 40.3
Education
Junior high school 4 1.2 11 1.0 15 2.2 10 0.8 253 1.4 16 1.0 6 1.0 23 1.4 10 2.3
High school 46 13.3 192 18.3 162 23.4 251 20.9 4,847 26.7 463 28.5 116 19.3 418 25.7 113 25.9
Vocational school 31 8.9 136 13.0 100 14.5 156 13.0 2,478 13.6 264 16.3 72 12.0 223 13.7 66 15.1
College 25 7.2 115 11.0 78 11.3 140 11.6 1,889 10.4 211 13.0 65 10.8 173 10.6 59 13.5
University 208 59.9 501 47.8 289 41.8 567 47.2 7,802 42.9 608 37.4 312 51.9 709 43.6 165 37.8
Graduate school 33 9.5 94 9.0 48 6.9 78 6.5 916 5.0 62 3.8 30 5.0 79 4.9 24 5.5
Living with a child/children aged <12 years old, yes 66 19.0 212 20.2 140 20.2 201 16.7 3,151 17.3 356 21.9 84 14.0 235 14.5 80 18.3
Residential area, areas where a state of emergency was declared 209 60.2 487 46.4 301 43.5 645 53.7 7,123 39.2 640 39.4 363 60.4 737 45.4 210 48.1
Financial situation
Very difficult 43 12.4 107 10.2 150 21.7 101 8.4 1,847 10.2 308 19.0 79 13.1 268 16.5 134 30.7
Slightly difficult 85 24.5 283 27.0 209 30.2 331 27.5 4,985 27.4 582 35.8 177 29.5 533 32.8 138 31.6
Average 147 42.4 501 47.8 259 37.4 576 47.9 9,180 50.5 626 38.5 245 40.8 637 39.2 127 29.1
Comfortable 59 17.0 145 13.8 68 9.8 155 12.9 1,858 10.2 93 5.7 89 14.8 162 10.0 33 7.6
Very comfortable 13 3.7 13 1.2 6 0.9 39 3.2 315 1.7 15 0.9 11 1.8 25 1.5 5 1.1
Smoking status
Non-smoker 213 61.4 603 57.5 394 56.9 729 60.6 9,759 53.7 908 55.9 341 56.7 796 49.0 214 49.0
Past smoker 57 16.4 190 18.1 121 17.5 222 18.5 3,729 20.5 292 18.0 125 20.8 376 23.1 83 19.0
Smoker 77 22.2 256 24.4 177 25.6 251 20.9 4,697 25.8 424 26.1 135 22.5 453 27.9 140 32.0
Drinking frequency
6–7 days/week 69 19.9 203 19.4 101 14.6 196 16.3 4,011 22.1 277 17.1 119 19.8 392 24.1 91 20.8
4–5 days/week 37 10.7 84 8.0 45 6.5 99 8.2 1,383 7.6 118 7.3 59 9.8 128 7.9 31 7.1
2–3 days/week 51 14.7 128 12.2 84 12.1 164 13.6 2,193 12.1 186 11.5 80 13.3 205 12.6 44 10.1
< 1 day/week 69 19.9 203 19.4 126 18.2 233 19.4 2,958 16.3 286 17.6 125 20.8 300 18.5 69 15.8
Almost never 121 34.9 431 41.1 336 48.6 510 42.4 7,640 42.0 757 46.6 218 36.3 600 36.9 202 46.2
Exercise frequency
6–7 days/week 36 10.4 50 4.8 31 4.5 83 6.9 938 5.2 70 4.3 33 5.5 87 5.4 16 3.7
4–5 days/week 29 8.4 66 6.3 32 4.6 86 7.2 1,039 5.7 84 5.2 41 6.8 105 6.5 24 5.5
2–3 days/week 74 21.3 132 12.6 69 10.0 169 14.1 2,013 11.1 160 9.9 87 14.5 204 12.6 47 10.8
< 1 day/week 46 13.3 160 15.3 117 16.9 198 16.5 2,401 13.2 219 13.5 115 19.1 241 14.8 67 15.3
Almost never 162 46.7 641 61.1 443 64.0 666 55.4 11,794 64.9 1,091 67.2 325 54.1 988 60.8 283 64.8
Time for house chores
Increased 206 59.4 256 24.4 181 26.2 484 40.3 1,574 8.7 407 25.1 331 55.1 387 23.8 147 33.6
Unchanged 126 36.3 744 70.9 345 49.9 686 57.1 16,474 90.6 1,086 66.9 248 41.3 1,194 73.5 183 41.9
Decreased 15 4.3 49 4.7 166 24.0 32 2.7 137 0.8 131 8.1 22 3.7 44 2.7 107 24.5
Time with family
Increased 205 59.1 294 28.0 142 20.5 530 44.1 2,282 12.5 361 22.2 329 54.7 523 32.2 118 27.0
Unchanged 111 32.0 668 63.7 371 53.6 592 49.3 15,513 85.3 1,098 67.6 216 35.9 985 60.6 177 40.5
Decreased 31 8.9 87 8.3 179 25.9 80 6.7 390 2.1 165 10.2 56 9.3 117 7.2 142 32.5
Body mass index
0–18.49 40 11.5 128 12.2 95 13.7 164 13.6 2,091 11.5 212 13.1 55 9.2 208 12.8 67 15.3
18.5–24.9 235 67.7 713 68.0 452 65.3 817 68.0 12,450 68.5 1,021 62.9 448 74.5 1,078 66.3 282 64.5
25 < 72 20.7 208 19.8 145 21.0 221 18.4 3,644 20.0 391 24.1 98 16.3 339 20.9 88 20.1
Disease currently being treated, yes 108 31.1 361 34.4 285 41.2 369 30.7 5,579 30.7 627 38.6 185 30.8 541 33.3 181 41.4
Type of industry
Energy, materials, industrial machinery 15 4.3 30 2.9 18 2.6 32 2.7 658 3.6 60 3.7 15 2.5 72 4.4 22 5.0
Food 4 1.2 23 2.2 17 2.5 17 1.4 394 2.2 41 2.5 18 3.0 49 3.0 18 4.1
Beverages/tobacco products 2 0.6 7 0.7 1 0.1 7 0.6 101 0.6 5 0.3 3 0.5 6 0.4 2 0.5
Pharmaceuticals/medical supplies 6 1.7 16 1.5 13 1.9 24 2.0 264 1.5 25 1.5 8 1.3 23 1.4 4 0.9
Cosmetics/toiletries/sanitary products 4 1.2 6 0.6 8 1.2 10 0.8 96 0.5 8 0.5 5 0.8 10 0.6 6 1.4
Fashion and accessories 3 0.9 12 1.1 8 1.2 20 1.7 173 1.0 16 1.0 13 2.2 36 2.2 8 1.8
Precision machinery and office supplies 8 2.3 20 1.9 7 1.0 21 1.7 314 1.7 20 1.2 6 1.0 35 2.2 9 2.1
Home appliances/AV equipment 10 2.9 24 2.3 10 1.4 24 2.0 329 1.8 19 1.2 8 1.3 29 1.8 10 2.3
Automobiles and transportation equipment 16 4.6 31 3.0 19 2.7 38 3.2 554 3.0 59 3.6 21 3.5 107 6.6 20 4.6
Household goods 0 0.0 4 0.4 2 0.3 3 0.2 44 0.2 6 0.4 0 0.0 4 0.2 1 0.2
Hobby/sporting goods 4 1.2 2 0.2 0 0.0 3 0.2 56 0.3 2 0.1 1 0.2 3 0.2 3 0.7
Real estate and housing equipment 2 0.6 28 2.7 23 3.3 36 3.0 662 3.6 56 3.4 11 1.8 56 3.4 17 3.9
Information and communication 39 11.2 71 6.8 34 4.9 105 8.7 823 4.5 71 4.4 40 6.7 64 3.9 13 3.0
Distribution and retail 27 7.8 55 5.2 40 5.8 85 7.1 1,258 6.9 110 6.8 40 6.7 90 5.5 21 4.8
Finance/insurance 19 5.5 53 5.1 36 5.2 69 5.7 808 4.4 74 4.6 25 4.2 66 4.1 20 4.6
Transportation and leisure 14 4.0 17 1.6 12 1.7 38 3.2 380 2.1 35 2.2 52 8.7 71 4.4 15 3.4
Restaurant and other services 12 3.5 45 4.3 32 4.6 46 3.8 827 4.5 80 4.9 63 10.5 133 8.2 31 7.1
Public offices and organizations 17 4.9 79 7.5 50 7.2 66 5.5 1,364 7.5 88 5.4 29 4.8 59 3.6 13 3.0
Education, medical services, religion 54 15.6 235 22.4 173 25.0 202 16.8 3,039 16.7 332 20.4 44 7.3 161 9.9 51 11.7
Newspapers, magazines, television, radio, advertising, and other mass media 6 1.7 12 1.1 8 1.2 12 1.0 126 0.7 11 0.7 7 1.2 15 0.9 12 2.7
Market research 0 0.0 3 0.3 0 0.0 0 0.0 38 0.2 2 0.1 0 0.0 2 0.1 0 0.0
Other 85 24.5 276 26.3 181 26.2 344 28.6 5,877 32.3 504 31.0 192 31.9 534 32.9 141 32.3
Number of employees
1 (Freelance) 37 10.7 82 7.8 41 5.9 95 7.9 1,648 9.1 110 6.8 74 12.3 254 15.6 56 12.8
2–4 8 2.3 50 4.8 47 6.8 71 5.9 1,450 8.0 108 6.7 35 5.8 134 8.2 30 6.9
5–9 16 4.6 38 3.6 41 5.9 71 5.9 1,164 6.4 97 6.0 33 5.5 90 5.5 24 5.5
10–29 21 6.1 82 7.8 74 10.7 114 9.5 1,991 10.9 174 10.7 53 8.8 141 8.7 37 8.5
30–49 16 4.6 53 5.1 42 6.1 67 5.6 1,097 6.0 112 6.9 26 4.3 86 5.3 23 5.3
50–99 20 5.8 89 8.5 56 8.1 109 9.1 1,746 9.6 182 11.2 55 9.2 140 8.6 37 8.5
100–499 64 18.4 196 18.7 132 19.1 215 17.9 3,510 19.3 347 21.4 95 15.8 287 17.7 86 19.7
500–999 42 12.1 105 10.0 72 10.4 112 9.3 1,259 6.9 114 7.0 46 7.7 104 6.4 43 9.8
> 1,000 73 21.0 234 22.3 132 19.1 221 18.4 3,071 16.9 287 17.7 123 20.5 242 14.9 66 15.1
> 10,000 50 14.4 120 11.4 55 7.9 127 10.6 1,249 6.9 93 5.7 61 10.1 147 9.0 35 8.0
Commuting time
> 2 h 34 9.8 31 3.0 28 4.0 32 2.7 337 1.9 23 1.4 22 3.7 29 1.8 15 3.4
1–2 h(s) 78 22.5 172 16.4 119 17.2 212 17.6 2,257 12.4 247 15.2 89 14.8 243 15.0 56 12.8
30 min-1 h 87 25.1 321 30.6 193 27.9 339 28.2 4,895 26.9 476 29.3 191 31.8 472 29.0 124 28.4
< 30 min 84 24.2 386 36.8 281 40.6 416 34.6 7,889 43.4 670 41.3 192 31.9 643 39.6 152 34.8
Almost never 64 18.4 139 13.3 71 10.3 203 16.9 2,807 15.4 208 12.8 107 17.8 238 14.6 90 20.6
Telework preference
Willing to work from home 213 61.4 462 44.0 266 38.4 560 46.6 5,009 27.5 545 33.6 273 45.4 548 33.7 166 38.0
Neither 59 17.0 255 24.3 175 25.3 287 23.9 5,656 31.1 474 29.2 147 24.5 434 26.7 106 24.3
Willing to work at office 75 21.6 332 31.6 251 36.3 355 29.5 7,520 41.4 605 37.3 181 30.1 643 39.6 165 37.8
Telework frequency
> 4 days/week 117 33.7 161 15.3 65 9.4 234 19.5 1,551 8.5 102 6.3 124 20.6 193 11.9 45 10.3
> 2 days/week 55 15.9 123 11.7 38 5.5 142 11.8 748 4.1 47 2.9 65 10.8 127 7.8 46 10.5
> 1 day/week 24 6.9 63 6.0 36 5.2 48 4.0 486 2.7 40 2.5 34 5.7 84 5.2 20 4.6
> 1 day/month 11 3.2 41 3.9 19 2.7 20 1.7 375 2.1 38 2.3 23 3.8 50 3.1 17 3.9
Almost never 140 40.3 661 63.0 534 77.2 758 63.1 15,025 82.6 1,397 86.0 355 59.1 1,171 72.1 309 70.7
Job stress
Increased 191 55.0 584 55.7 565 81.6 361 30.0 3,732 20.5 1,010 62.2 193 32.1 587 36.1 250 57.2
Unchanged 117 33.7 423 40.3 110 15.9 700 58.2 14,182 78.0 581 35.8 242 40.3 885 54.5 114 26.1
Decreased 39 11.2 42 4.0 17 2.5 141 11.7 271 1.5 33 2.0 166 27.6 153 9.4 73 16.7
Social support
Low 171 49.3 556 53.0 439 63.4 506 42.1 8,618 47.4 943 58.1 270 44.9 783 48.2 237 54.2
High 176 50.7 493 47.0 253 36.6 696 57.9 9,567 52.6 681 41.9 331 55.1 842 51.8 200 45.8
Job control
Low 184 53.0 525 50.0 345 49.9 602 50.1 9,715 53.4 959 59.1 294 48.9 810 49.8 229 52.4
High 163 47.0 524 50.0 347 50.1 600 49.9 8,470 46.6 665 40.9 307 51.1 815 50.2 208 47.6

Independent associations between psychological distress and work hours and sleep duration, respectively, as estimated by stepwise logistic regression models, are denoted in Table 3. In Model 4, participants with increased working hours had significantly higher odds of experiencing psychological distress (odds ratio [OR] = 1.15, 95% confidence interval [CI] = 1.03–1.28) compared to those with unchanged work hours, while decreased work hours showed an insignificant association. Regarding sleep duration, those who experienced decreased sleep duration showed OR of 1.97 (95% CI = 1.79–2.18) compared to unchanged sleep duration.

Independent association between psychological distress and changes in work hours and sleep duration, respectively.

n Crude OR 95% CI Model 1 Model 2 Model 3 Model 4
Case/Exposed OR 95% CI OR 95% CI OR 95% CI OR 95% CI
Work hours
Increased 1163/2088 2.26c 2.06–2.47 2.10c 1.92–2.30 1.56c 1.41–1.72 1.12a 1.01–1.25 1.15a 1.03–1.28
Unchanged 7522/21011 Reference Reference Reference Reference Reference
Decreased 1142/2663 1.35c 1.24–1.46 1.35c 1.24–1.46 1.15b 1.05–1.26 1.05 0.96–1.16 0.99 0.90–1.09
Sleep duration
Increased 871/2150 1.31c 1.20–1.43 1.14 1.04–1.25 1.10 1.00–1.22 1.05 0.94–1.17 1.06 0.95–1.18
Unchanged 7139/20859 Reference Reference Reference Reference Reference
Decreased 1817/2753 3.73c 3.43–4.06 3.59c 3.30–3.91 3.14c 2.86–3.43 2.11c 1.91–2.32 1.97c 1.79–2.18

Model 1: adjusted for sex and age group. Model 2: Model 1 + adjusted for marital status, educational level, living with a child/children under 12 years old, residential area, smoking, drinking, exercise, time for house chores (increased/unchanged/decreased), time with family (increased/unchanged/decreased), body mass index, disease currently being treated (yes/no), and sleep duration for analysis for work hours. Model 3: Model 2 + adjusted for type of industry, number of employees, commuting time, teleworking preference, telework frequency, job control, social support, and job stress, and work hours for analysis of sleep duration. Model 4: Model 3 + financial situation. ap < 0.05, bp < 0.01, cp < 0.001.

Table 4 presents associations between psychological distress and changes in work/sleep combined patterns by stepwise logistic regression models. Notably, participants had more risk of experiencing psychological distress when sleep duration decreased. Those who reported decreases in work and sleep duration presented the highest odds of experiencing psychological distress (OR = 2.59, 95% CI = 2.05–3.28) among all other groups. This group had a drop of OR from 2.93 in Model 3 to 2.59 in Model 4, which was additionally adjusted for the financial situation. Those who experienced increased work hours and decreased sleep duration had higher odds of experiencing psychological distress (OR = 1.98, 95% CI = 1.64–2.39) compared to those who reported no changes in work hours and sleep duration (reference group).

Association between psychological distress and changes in work/sleep patterns.

Work hours Sleep duration n Crude OR 95% CI Model 1 Model 2 Model 3 Model 4
Case/Exposed OR 95% CI OR 95% CI OR 95% CI OR 95% CI
Increased Increased 169/347 1.92c 1.55–2.37 1.66c 1.34–2.06 1.71c 1.37–2.14 1.21 0.95–1.53 1.21 0.95–1.54
Unchanged 512/1049 1.92c 1.70–2.18 1.77c 1.56–2.01 1.73c 1.52–1.97 1.21b 1.06–1.39 1.24b 1.08–1.43
Decreased 482/692 4.63c 3.93–5.46 4.39c 3.72–5.19 3.73c 3.13–4.45 2.05c 1.70–2.47 1.98c 1.64–2.39
Unchanged Increased 467/1202 1.28c 1.14–1.45 1.11 0.98–1.25 1.11 0.97–1.26 1.08 0.94–1.23 1.08 0.94–1.24
Unchanged 6028/18185 Reference Reference Reference Reference Reference
Decreased 1027/1624 3.47c 3.12–3.86 3.31c 2.98–3.69 3.09c 2.77–3.45 2.08c 1.85–2.34 1.94c 1.72–2.18
Decreased Increased 235/601 1.30b 1.10–1.53 1.14 0.96–1.35 1.10 0.92–1.31 1.05 0.87–1.27 1.01 0.83–1.22
Unchanged 599/1625 1.18b 1.06–1.31 1.21c 1.09–1.35 1.18b 1.05–1.31 1.01 0.90–1.13 0.94 0.83–1.06
Decreased 308/437 4.82c 3.91–5.93 4.82c 3.90–5.95 3.87c 3.11–4.82 2.93c 2.32–3.69 2.59c 2.05–3.28

Model 1: adjusted for sex and age group. Model 2: Model 1 + adjusted for marital status, educational level, living with a child/children under 12 years old (yes/no), residential area, smoking, drinking frequency, exercise frequency, time for house chores (increased/unchanged/decreased), time with family (increased/unchanged/decreased), body mass index, and disease currently being treated (yes/no). Model 3: Model 2 + adjusted for type of industry, number of employees, commuting time, teleworking preference, telework frequency, job control, social support, and job stress (increased/unchanged/decreased). Model 4: Model 3 + financial situation. ap < 0.05. bp < 0.01. cp < 0.001.

Additionally, our supplementary analysis (Supplementary Table S1) indicated that those who experienced decreased work hours and decreased sleep duration coupled with financial difficulties had an OR of 5.88 (95% CI = 4.29–8.06) compared to those who experienced unchanged work hours and unchanged sleep duration coupled with no financial difficulties (reference group), which was the highest among all other groups.

 Discussion

This study investigated the association between changes in work/sleep patterns and psychological distress among Japanese workers during the COVID-19 pandemic. To our knowledge, this is the first study that specifically examined the combined effect of changes in work hours and sleep duration from pre-pandemic to in-pandemic periods on psychological distress. This study primarily questioned whether changes in work hours and sleep duration would pose a higher risk of psychological distress compared to when the work hours and sleep duration remained unchanged. We found an increased risk of psychological distress among those who had decreased sleep duration, irrespective of changes in work hours. Notably, workers with decreased work hours and decreased sleep duration had the highest odds of psychological distress (OR = 2.59, 95% CI = 2.05–3.28), followed by workers with increased work hours and decreased sleep duration (OR = 1.98, 95% CI = 1.64–2.39). This finding outlines that reduced sleep duration is a major factor related to increased psychological distress.

Moreover, we found that the risk of psychological distress was the highest among those with a combination of decreased work hours and sleep duration in the early stages of the pandemic, which was contrary to our expectations. A possible key factor that led to this result was financial impact of the pandemic on this decreased work hours and decreased sleep duration group. We found that more than 30% of participants in this group reported experiencing extreme difficulty in relation to their financial situation (Table 2). This group also experienced a drop of OR from 2.93 to 2.59 when adjusted for the financial situation in the final model in Table 4. Additionally, in our sub-analysis, we found a higher risk of psychological distress (OR = 5.88, 95% CI = 4.29–8.06) among those with decreased work hours and decreased sleep duration coupled with financial difficulties compared to unchanged work hours and unchanged sleep duration coupled with no financial difficulties (Supplementary Table S1). Previous studies have also supported our hypothesis; people experiencing financial difficulties had higher rates of psychological distress during the COVID-19 crisis (Griffiths et al., 2021; Nagasu et al., 2021; OECD, 2021; Ettman et al., 2022; Sekścińska et al., 2022).

The increased risk of psychological distress, with increased work hours and decreased sleep duration, was in line with a past cross-sectional study in an ordinal pre-pandemic work situation (Nakata, 2011), which presented longer work hours of 10+ h per day with short sleep duration (<6 h/day) to have 2.69 times higher odds of having depressive symptoms compared to normal working hours (6–8 h/day with more than 6 h/day of sleep).

In the early stages of the pandemic, many workers experienced unstable working conditions, such as decreased quantitative job demands, decreased work hours, and reduced income. Despite this situation, the provision of job-related services and support (e.g., in-person job consultations and career seminars) was limited to reduced human contact, resulting in workers having poorer access to job-related services and support. According to statistics from the Ministry of Health, Labour and Welfare, Japan, the number of new job applications in public employment service centers in Japan dropped by approximately 10,000 from 2019 to 2020 (Ministry of Health Labour and Welfare, Japan, 2022). Such unstable working conditions and the limited provision of job-related services and support during the pandemic are more likely to lead to financial difficulties for several workers.

A previous occupational cohort study revealed strong associations between financial difficulties and sleep problems such as shortened sleep duration (Lallukka et al., 2012). In addition to financial difficulties and decreased sleep duration, uncertainty over the prolonged pandemic may have increased psychological distress among workers. Moreover, fewer socializing opportunities, limited access to mental health services and support, and fewer opportunities to seek help during the pandemic (Campion et al., 2020; Moreno et al., 2020) might have heightened the risk of psychological distress.

The strengths of this study are as follows. First, we assessed changes in work/sleep patterns among Japanese workers from pre-pandemic to in-pandemic periods, unlike previous studies that cross-sectionally assessed work/sleep patterns on typical workdays. Second, we employed self-reports to evaluate sudden changes in work/sleep patterns due to the pandemic. Most previous studies objectively assessed changes in working hours and sleep duration by the number of hours. However, evaluating the sudden changes caused by COVID-19 with numbers by self-report would lead to potential biases, that is, a recall bias in the number of hours before the pandemic. Additionally, sufficient working hours and sleep duration vary among individuals. Third, we included both changes in work hours and sleep duration due to the COVID-19 pandemic to examine the acute impact on psychological distress. Combining changes in work hours and sleep duration would reflect the time use of workers because they spend a large amount of time working and sleeping during the day.

However, this study had several limitations. First, although many workers experienced increased sleep duration in the early stages of the pandemic, our study focused on workers with decreased sleep duration. Second, this study utilizes an online closed survey, meaning that participants were recruited from the survey company’s panel, which may have led to selection bias. Third, we were unable to confirm the participants’ pre-pandemic psychological state. They may have already developed psychological distress prior to the pandemic. Fourth, we were unable to identify whether participants experienced decreased sleep duration because of the stressful situation from the pandemic, or whether they simply could not set aside time for sleep because they had other tasks to do. Finally, there might be other co-factors, such as napping, side jobs, and time-use on free days, which may have impacted psychological distress among Japanese workers.

 Conclusion

This study confirmed that decreased sleep duration could be a key factor in psychological distress, irrespective of working hours. Moreover, contrary to our expectations, workers with a combination of decreased work hours and sleep duration posed the highest risk of psychological distress. Decreased work hours accompanied by financial difficulties in the early stage of the pandemic may have caused decreased sleep duration, resulting in a high prevalence of psychological distress. Our study underlined the importance of sleep management in maintaining workers’ mental health as well as the need to consider situations and conditions of other daily tasks, such as work hours, for better sleep management.

 Data availability statement

The datasets presented in this article are not readily available because it is not open to the public. Requests to access the datasets should be directed to YF, zenq@med.uoeh-u.ac.jp.

 Ethics statement

The studies involving human participants were reviewed and approved by the Ethics Committee of the University of Occupational and Environmental Health, Japan and the Ethics Committee of the International University of Health and Welfare. The patients/participants provided their written informed consent to participate in this study.

 Author contributions

TT contributed to data analysis and writing. YF, ST, KM, MT, HA, KO, and RM contributed to the research design and data collection and provided useful suggestions during the analysis. AN supervised the analysis and drafting of the manuscript and reviewed the manuscript for intellectual content. All authors have contributed to the manuscript and approved the submitted version.

 Funding

This study was supported and partly funded by a research grant from the University of Occupational and Environmental Health, Japan; the Japanese Ministry of Health, Labour and Welfare (H30-josei-ippan-002, H30-roudou-ippan-007, 19JA1004, 20JA1006, 210301-1, 19H01763, and 20HB1004); Anshin Zaidan; the Collabo-Health Study Group; Hitachi Systems, Ltd.; scholarship donations from Chugai Pharmaceutical Co., Ltd.; and The Health Care Science Institute. The funder was not involved in the study design, collection, analysis, data interpretation, writing of this article, or the decision to submit it for publication.

 Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

 Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

We thank the participants for their time and effort in participating in this study. We also thank our colleagues in AN’s laboratory and members of the CORoNaWork Project for their valuable discussions and comments. The current members of the CORoNaWork Project, in alphabetical order, are as follows: Hajime Ando, Hisashi Eguchi, Yoshihisa Fujino (present chairperson of the study group), Ayako Hino, Yu Igarashi, Tomohiro Ishimaru, Mami Kuwamura, Kosuke Mafune, Shinya Matsuda, Ryutaro Matsugaki, Keiji Muramatsu, Koji Mori, Tomohisa Nagata, Akira Ogami, Makoto Okawara, Seiichiro Tateishi, and Mayumi Tsuji. All members are affiliated with the University of Occupational and Environmental Health, Japan. We thank Editage (www.editage.com) for English language editing.

 Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyg.2023.1133498/full#supplementary-material

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