Abstract
Social jetlag is a recent problem that is associated with a wide range of issues in the context of modern life. However, differences in the effects of social jetlag on sleep quality between young and middle-aged workers remain unclear. Accordingly, we aimed to examine the different effects of social jetlag on sleep quality in young (20–39 years) and middle-aged (40–59 years) workers from one factory. We included 106 male full-time workers (average age: 35.8 ± 11.5 years) who worked at the Kobe Factory of Fuji Electric Co., Ltd. Social jetlag was evaluated using the Munich ChronoType Questionnaire Japanese Version. Subjective sleep quality was assessed using the Pittsburgh Sleep Quality Index Japanese Version. Chronotype was determined using the Morningness–Eveningness Questionnaire Japanese Version (MEQ), while the health-related quality-of-life was evaluated using a revised version of the MOS 36-Item Short-Form Survey. Furthermore, we examined factors related to sleep quality in each age group using multiple regression analysis. Subjective sleep quality in the analysis set was poor; moreover, 39.4% of the participants had social jetlag for ≥ 1 h. Compared with middle-aged workers, young workers showed significantly longer and lower social jetlag and MEQ scores, respectively. Multiple regression analysis revealed that mental health and social jet lag were significantly associated with sleep quality in young participants. Contrastingly, social jetlag was not associated with sleep quality in middle-aged workers. Our findings demonstrate the importance of considering the effects of age-based factors on sleep quality.
Keywords: Social jetlag, Sleep quality, Chronotype, Circadian rhythm, Young workers, Middle-aged workers
Introduction
In humans, the circadian rhythm is controlled by the body’s internal clock and other various environmental factors. Since these rhythms control various cyclical biological behaviors, they are essential for maintaining overall good health. However, numerous factors in modern society disrupt the body’s inherent circadian rhythms. For example, some individuals engage in work-related nighttime activities, which often expose them to high-intensity illumination [1].
Social jetlag is a recent problem that is associated with a wide range of issues in the context of modern life. It can be described as a habitual mismatch between an individual’s internal circadian rhythm (their “chronotype”) and social schedule (e.g., place of employment or school) [2]. Social jetlag is associated with sleep debt [3] and often results from different sleeping rhythms on workdays and non-working days. For example, most individuals adjust to their social schedules (e.g., work or school) by waking earlier and sleeping less on workdays. However, they conform to their internal circadian rhythms on non-working days, when many activities begin later and they can sleep longer. This can negatively affect health. Specifically, social jetlag resulting from different sleeping habits on workdays/non-working days has been associated with several health conditions, including obesity, cardiovascular disease, and metabolic syndrome [4–6]. Furthermore, social jetlag is positively associated with the risk of developing mental health disorders, including depressive symptoms [7]. Other studies have shown that the “evening” chronotype is more common in young adults and may result in longer periods of social jetlag [8, 9]. Compared with middle-aged adults, young adults may show longer periods of social jetlag, with more pronounced effects. However, differences in the effects of social jetlag on sleep quality between young and middle-aged workers remain unclear. Accordingly, this study aimed to investigate differences in the effects of social jetlag on sleep quality between young and middle-aged workers from a single factory.
Materials and methods
Participants
This study was conducted between September 2019 and November 2020. The participants comprised 117 male full-time workers (regular work schedule: 8:30–17:15) at the Kobe Factory of Fuji Electric Co., Ltd (age: 20–59 years). To eliminate factors with considerable effects on social jetlag and sleep quality, we applied the following exclusion criteria: (1) a significantly disordered wake-sleep schedule due to shiftwork or other reasons, (2) a diagnosis of sleep disorders (e.g., sleep apnea syndrome), and (3) the use of regular sleep medication. This study was approved by the Ethics Committee of Kobe University (approved numbers: 2019-833). All participants provided written informed consent.
Social jetlag
Social jetlag was evaluated using the Munich ChronoType Questionnaire Japanese Version [10, 11]. Median sleep durations on workdays and non-working days were determined based on bedtimes and waking times. Social jetlag was determined as the absolute value after subtracting the median sleep duration on workdays from that on non-working days.
Background characteristics
We used questionnaires to obtain information regarding the participants’ background characteristics, including age, height, weight, time required to commute, working hours, type of work, sleep-related problems (y/n), and regular use of sleep medication (y/n).
Sleep-related indicators
Subjective sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI) Japanese Version [12, 13], with higher total scores indicating worse sleep disturbances (total score range 0–21 points; ≤ 5 points and ≥ 6 points indicate good and poor sleep quality, respectively).
Chronotypes were determined using the Morningness–Eveningness Questionnaire (MEQ) Japanese Version [10, 14], with scores of ≤ 41, 42–58, and ≥ 59 indicating the evening, intermediate, and morning types, respectively.
Health-related quality-of-life
Health-related quality-of-life (HRQOL) was evaluated using the MOS 36-Item Short-Form Survey (SF-36), which was originally developed to survey health status in the context of the Medical Outcomes Study [15]. The Japanese version was translated by Fukuhara et al. and its reliability and validity have been verified [16, 17]. This study used a revised version of the SF-36 known as the SF-36v2, which is a self-reported questionnaire that assesses HRQOL based on the following eight subscales: (1) physical functioning, (2) role-physical, (3) pain, (4) general health, (5) vitality, (6) social functioning, (7) role-emotional, and (8) mental health. Subsequently, we applied a factor analysis approach to obtain the following three summary scores [18]: physical component summary (a summary score for physical QOL aspects), mental component score (MCS; a summary score of mental QOL aspects), and role-social component summary score (RCS; a summary score of role-social QOL aspects).
Statistical analysis
Data are presented as mean ± standard deviation. Each variable was analyzed using the Shapiro–Wilk test (P < 0.05), with all quantitative variables, except for social jet lag, showing a normal distribution. Examination of the distribution of social jet lag using a histogram revealed no large distortion; therefore, all data were treated as normal distribution data. The two-sample t test was used for comparisons between the young group (20–29 and 30–39 years) and middle-aged group (40–49 and 50–59 years). We performed stepwise multiple regression analysis to examine factors related to sleep quality in each group (the young and middle-aged groups). We performed multiple regression analysis using variables significantly associated with sleep quality on univariate analysis among the potential confounders (p < 0.2). All statistical analyses were conducted using IBM SPSS Statistics for Windows, Version 25 (IBM Corp., Armonk, N.Y., USA). Statistical significance was set at P < 0.05.
Results
General characteristics
Among the 117 recruited participants, five were excluded for having one or more sleep disorders (e.g., sleep apnea syndrome) and six were excluded for regularly taking sleep medications. Accordingly, 106 participants (44, 21, 25, and 16 participants were aged 20–29, 30–39, 40–49, and 50–59 years, respectively) were included in the final analysis. Table 1 presents the general characteristics of the participants. The included participants showed poor subjective sleep quality. Specifically, the distribution of social jetlag was as follows: 63 participants with < 1 h, 25 participants with ≥ 1 h and < 2 h, 11 participants with ≥ 2 h and < 3 h, 3 participants with ≥ 3 h and < 4 h, and 2 participants with ≥ 4 h. Further, 39.4% of all participants had social jetlag measuring ≥ 1 h. The mean time required to commute was 40.9 ± 19.6 min while the mean working hours was 9:08 h ± 1 h; moreover, 72 (67.9%) participants were engaged in desk work.
Table 1.
General characteristics of the participants
| Total population | Young group | Middle-aged group | p value | |
|---|---|---|---|---|
| n = 106 | n = 65 | n = 41 | ||
| Age | 35.8 ± 11.5 | 27.5 ± 4.9 | 48.9 ± 5.1 | |
| Body mass index (kg/m2) | 24.1 ± 3.8 | 23.5 ± 3.9 | 24.9 ± 3.5 | 0.069 |
| Morningness–Eveningness Questionnaire (MEQ) | 53.4 ± 9.1 | 50.3 ± 8.2 | 58.6 ± 8.3 | < 0.001** |
| Pittsburgh Sleep Quality Index (PSQI) | 5.7 ± 2.4 | 6.0 ± 2.6 | 5.3 ± 2.0 | 0.162 |
| Health-related quality-of-life (SF-36v2) | ||||
| Physical component summary score (PCS) | 53.0 ± 7.3 | 54.0 ± 6.5 | 51.6 ± 8.2 | 0.097 |
| Mental component summary (MCS) | 47.4 ± 8.7 | 46.3 ± 9.1 | 49.1 ± 7.8 | 0.118 |
| Role/Social component summary (RCS) | 52.6 ± 7.8 | 52.0 ± 8.5 | 53.6 ± 6.5 | 0.31 |
| Social jet lag (min) | 67.7 ± 66.3 | 85.7 ± 74.1 | 38.8 ± 36.1 | < 0.001** |
| < 1 h [number (percentage)] | 63 (60.6%) | |||
| ≧1 h, < 2 h [number (percentage)] | 25 (24.0%) | |||
| ≧2 h, < 3 h [number (percentage)] | 11 (10.6%) | |||
| ≧3 h, < 4 h [number (percentage)] | 3 (2.9%) | |||
| ≧4 h [number (percentage)] | 2 (1.9%) | |||
| Time required to commute (min) | 40.9 ± 19.6 | |||
| Working hours (hours) | 9:08 h ± 1 h | |||
| Type of work | ||||
| Desk work | 72 (67.9%) | |||
| Not desk work | 34 (32.1%) | |||
Normally distributed data are reported as mean ± standard deviation. Between-group comparisons were performed using the two-sample t test. *p < .05, **p < .01
Comparison of young and middle-aged workers
Table 1 also shows the results of the between-group comparisons. Social jetlag was significantly longer in the young group (85.7 ± 74.1 min) than in the middle-aged workers (38.8 ± 36.1 min) (p < 0.001). The mean MEQ scores in the young and middle-aged groups were 50.3 ± 8.2 and 58.6 ± 8.3, respectively, which indicated that the young group had a stronger tendency for the evening chronotype (p < 0.001). Table 2 shows the correlation between the PSQI score and each potential confounder in each group. The PSQI score was significantly associated with the MCS and RCS scores in the young and middle-aged groups, respectively.
Table 2.
Correlation between the PSQI score and each variable in the young and middle-aged groups
| Young group | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Age | BMI | MEQ | PCS | MCS | RCS | SJL (min) |
Working hours (h) | Type of worka | ||
| PSQI | Correlation coefficient | − .002 | .104 | − .202 | .031 | − .411 | − .055 | .194 | − .181 | − .065 |
| p value | .989 | .41 | .11† | .809 | .001†** | .666 | .124† | .149† | .609 | |
| Middle-aged group | ||||||||||
| Age | BMI | MEQ | PCS | MCS | RCS |
SJL (min) |
Working hours (h) | Type of worka | ||
| PSQI | Correlation coefficient | − .097 | − .102 | .045 | .201 | − .227 | − .315 | .188 | .166 | − .209 |
| p value | .546 | .525 | .781 | .207 | .153† | .045†* | .245 | .301 | .189† | |
Data are reported as Pearson’s correlation coefficient. Bivariate correlations for categorical variables were analyzed using the Spearman’s rank method (marked with a)
BMI body mass index, MEQ Morningness–Eveningness Questionnaire, PSQI Pittsburgh Sleep Quality Index, PCS physical component summary score, MCS mental component summary, RCS role/social component summary, SJL social jet lag
†p < 0.2, *p < .05, **p < .01
Factors associated with subjective sleep quality in the young and middle-aged workers
Table 3 shows the results of the multiple regression analysis of the potential explanatory factors for sleep quality. The independent variables in the young group were the MEQ score, MCS score, social jetlag (min), and working hours (h), while those in the middle-aged group were the MCS score, RCS score, and type of work (desk work/non-desk work) (Table 2). In the young workers, social jetlag was significantly associated with the PSQI score followed by the MCS score. Contrastingly, in the middle-aged workers, the RCS score remained independently associated with the PSQI score.
Table 3.
Multiple regression analysis in the young and middle-aged groups
| Young group | |||||
|---|---|---|---|---|---|
| Covariate | Coefficient (B) | SPRC | p value | 95% Confidence interval (B) | |
| Lower limit | Upper limit | ||||
| MCS score | − 0.127 | − 0.44 | < 0.001 | − 0.19 | − 0.06 |
| Social jet lag (min) | 0.009 | 0.252 | 0.033 | 0.001 | 0.20 |
| Middle-aged group | |||||
| 95% Confidence interval (B) | |||||
| Covariate | Coefficient (B) | SPRC | p value | Lower limit | Upper limit |
| RCS score | − 0.098 | − 0.315 | 0.045 | − 0.195 | − 0.002 |
Young workers: R2 = 0.23, ANOVA p < .001
Middle-aged workers: R2 = 0.09, ANOVA p = .0045
SPRC standardized partial regression coefficient, MCS mental component summary, RCS role/social component summary
Discussion
Our findings showed that compared with the middle-age group, the young group showed significantly longer social jetlag and significantly lower MEQ scores, indicating a higher tendency for the evening chronotype. Furthermore, the MCS score and social jetlag were significantly associated with sleep quality in young workers. Contrastingly, social jetlag was not associated with sleep quality in middle-aged workers; however, the RCS score showed the strongest association with sleep quality in middle-aged workers.
The MCS score was significantly associated with sleep quality in young workers. The MCS score is positively correlated with mental health [18–20]. Previous studies have reported an association between mental health and sleep quality in young people [21, 22], which is consistent with our findings. Furthermore, we observed a significant association between social jetlag and sleep quality in young workers.
Few studies have examined the age-based effects of social jetlag on sleep quality in individuals employed within the same regular work setting. To our knowledge, this is the first study to demonstrate an association of social jetlag with sleep quality in young, but not middle-aged, workers. Specifically, compared with middle-aged workers, young workers showed significantly longer social jetlag. Generally, young people require longer sleep times, which increases their sleep debt; accordingly, they may experience longer social jetlag [3]. Physiological differences in chronotype, including the increased tendency of eveningness among young workers, also affect the duration of social jetlag [8]. These factors jointly contributed to the association of sleep quality with social jetlag among young workers. Previous studies have demonstrated that delaying bedtime by 3 h on non-working days causes a phase shift delay in the melatonin rhythm, even when the individuals wake up at the same time and receive the same light exposure as on workdays [23]. Another study that eliminated sleep debt by delaying waking times by 3 h during non-working days reported delayed onset of melatonin secretion and increased fatigue/sleepiness on workdays [24]. Taken together, adequate focus should be placed on the occupational health issues with respect to social jetlag among young workers.
We found that mental health and social jetlag were significantly associated with sleep quality in young workers. Contrastingly, the RCS score showed the strongest association with sleep quality in middle-aged workers. This suggests that different factors affect sleep quality in young and middle-aged individuals. Generally, sleep quality in young workers may be improved by reducing the risk of social jetlag resulting from mismatches between the work schedules and individual chronotypes. Contrastingly, sleep quality in middle-aged workers may be improved by targeting factors other than social jetlag. The SF-36v2 defines “role function” as “the extent health impairs normal activities of daily living such as work, housework, and school” and “social function” as “the extent health impairs normal functional social activities such as visiting friends” [19, 20]. Middle-aged individuals not only experience physiological changes with increasing age but also find that their personal and social roles become more sophisticated and complex over the course of various life events. This increases the risk of impaired role/social functioning through physical and psychological factors, which leads to reduced sleep quality. However, there is a need for further research to determine factors that affect sleep quality in middle-aged workers.
Limitations
This study has several limitations. First, our small sample size did not allow consideration of all potential confounders of sleep quality; accordingly, future large-scale studies are warranted. Second, social jetlag is influenced by working hours, which impedes the generalizability of our findings. Therefore, future studies on workers from other companies and different occupations are warranted. Third, although we observed a significant association between sleep quality and role-social health in middle-aged participants, the underlying reasons remain unclear. Further research on the relationship between role-social health and sleep quality in middle-aged workers is warranted.
Conclusions
Social jetlag and MEQ scores were significantly longer and lower in young workers than in middle-aged workers. Furthermore, the MCS score and social jetlag were significantly associated with sleep quality in young workers. Contrastingly, the RCS score showed the strongest association with sleep quality in middle-aged workers. Accordingly, it is important to consider the effects of age-based factors on sleep quality.
Acknowledgements
We thank all volunteers for participating in this study.
Funding
The authors received no financial support for the research, authorship, and/or publication of this article.
Declarations
Conflict of interest
The authors report no conflicts of interest.
Ethical approval
This study was approved by the Ethics Committee of Kobe University (approved numbers: 2019-833). All participants were informed about the nature of this study and provided written informed consent.
Footnotes
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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