INTRODUCTION

The rise of flexible and diverse work schedules has become increasingly common in modern society. This change is driven by both the commercial sector, which promotes consumer engagement through services like dawn deliveries, and the public sector, which ensures the continuity of essential services such as emergency medical care, firefighting, and policing. Non-standard work schedules—defined as any schedule deviating from the traditional Monday-to-Friday, 8 a.m. or 9 a.m. to 5 p.m. or 6 p.m. routine—have become more prevalent in recent years. While flexible work arrangements can offer economic benefits for both employees and employers [1-4] and may enhance work-life balance [1,2,5], improve job performance, and reduce costs [2,3], they can also adversely affect employees’ health, social relationships, and overall quality of life [3,6]. The increasing prevalence and potential drawbacks of non-standard work schedules have thus emerged as an important public health issue.

Studies examining the influence of work schedules on health and health-related quality of life (HRQoL) have found that employees working non-standard schedules face physical ailments such as circadian disruption, cardiovascular conditions, gastrointestinal complaints, and obesity [2-4,6-11]. Disruptions to the circadian rhythm can lead to sleep deprivation and ensuing fatigue [3,8,11,12]. This sleep loss may further contribute to mental health issues, including psychological distress, depressive symptoms, and anxiety [3,4,6,7], ultimately resulting in behavioral changes and a decline in overall quality of life [3]. In addition, non-standard work schedules contribute to social desynchronization and isolation by disturbing stable social rhythms [4,5]. The misalignment with typical social hours can create work-life conflicts—such as work-leisure and work-family conflicts—that further detract from HRQoL [5,6,13,14]. Consequently, individuals with non-standard work schedules are at greater risk of experiencing sleep difficulties and declines in various aspects of HRQoL.

The negative effects of non-standard work schedules on sleep difficulty and other HRQoL outcomes may vary by gender due to biological, psychological, and social differences. Previous studies have suggested that women are more prone to sleep-related problems than men under similar conditions [8,10,12]. Women also tend to report subjective sleep deprivation more frequently than men, even when their objective sleep duration is longer [10]. Moreover, gender role differences in family responsibilities may lead to greater psychological strain from work-family conflicts and, consequently, stronger sleep-related effects in women compared to men [8,9]. The traditional preference for full-time employment among men —reinforced by the ‘male breadwinner’ notion—and women’s disproportionate responsibility for family care [2,5,15] may further contribute to the global trend of women working part-time (less than 35 hours per week) and under non-standard work schedules [2]. Therefore, the impacts of non-standard work schedules on sleep and other aspects of HRQoL are likely to differ significantly by gender.

Investigating the effects of non-standard work schedules on sleep and HRQoL is particularly relevant in Korea due to the country’s distinctive national, socio-cultural, and organizational characteristics [1,3,5,6,11,13,16]. Despite legally mandated reductions in working hours, Korea remains known for having the longest working hours among Organization for Economic Cooperation and Development member countries [13,17]. The implementation of flexible work systems is nationally enforced through the Labor Standards Act [18] and central government policies [19]. However, with caring responsibilities disproportionately shouldered by women [17] and labor market practices that are often unfavorable to them [17,20-22], the impact of non-standard work schedules on health [20,23], work-life balance [16,20-22], and work flexibility [17,20-22] likely varies considerably by gender. Research on how non-standard work schedules affect sleep and HRQoL remains limited, especially studies that account for gender differences. This study therefore aims to examine whether non-standard work schedules are related to sleep difficulty and other aspects of HRQoL in Korea, with special attention to gender differences.

METHODS

Data and Study Sample

The study used data from the 2019 and 2021 Korean National Health and Nutrition Examination Survey (KNHANES) Phase 8. KNHANES is a nationwide survey conducted by the Korea Disease Control and Prevention Agency (KDCA) since 1998 [24]. It is designed to generate national statistics on health status, health behaviors, and dietary and nutritional intake in accordance with the National Health Promotion Act [25]. A rolling sampling design was adopted to ensure consistent data collection across the phase [25]. Although Phase 8 includes data from 2019 through 2021, we excluded the 2020 data because it did not include Health-related Quality of Life Instrument with 8 items (HINT-8)—the source of our dependent variables [25]. The study focused on economically active individuals aged 19 or older, defined as those who worked for at least 1 hour for income or as unpaid family workers for at least 18 hours in the past week [25]. After excluding non-respondents, unknown responses, and incomplete data, the final sample comprised 6735 participants.

Variables

Explanatory variable

Our work schedule variable was defined using responses to the survey question, “Do you primarily work during the day (between 6 a.m. and 6 p.m.), or do you work during a different time period?” [25]. The responses were originally categorized as follows: day, evening (between 2 p.m. and midnight), night (between 9 p.m. and 8 a.m. the next day), regular day-and-night rotating shifts, 24-hour shifts, split shifts (working in 2 or more time periods in a day), and irregular rotating shifts [25]. Although non-standard work schedules can be categorized in various ways based on starting time, duration of work, rotation frequency, and shift rotation direction and speed [26], we combined the latter 4 categories into a single “other” category due to the study focus and sample size limitations. Thus, our work schedule variable consists of 3 indicators representing non-standard work schedules (evening, night, and other), with the standard day schedule serving as the reference category (Supplemental Material 1). For regression analyses, interaction terms between these indicator variables and gender were also included.

Outcome variable

The HINT-8 was developed using questions tailored for KNHANES, in response to growing demand for a Korean HRQoL measure and to overcome the EQ-5D-3L’s high ceiling effect [27]. Previous research has underscored the importance of sleep, vitality, and happiness in HRQoL among the Korean general population [28-30]. The HINT-8 consists of 8 items across 4 health domains: climbing stairs, pain, and vitality in the physical health domain; working in the social health domain; depression, memory, and sleep in the mental health domain; and happiness in the positive health domain [27]. Each item is rated on a 4-level ordinal scale: “having no difficulty,” “having some difficulty,” “having much difficulty,” and “not being able (i.e., having no ability)” [31]. Because only a small fraction (0.1 to 3.3%) reported “not being able,” we combined the “having much difficulty” and “not being able” categories to indicate “severe difficulty.”

In addition to analyzing the 8 individual items, we also examined the HINT-8 index—a composite measure ranging from 0.132 to 1.000, with higher values indicating better HRQoL [32]. In the 2019 KNHANES, the HINT-8 index was estimated at 0.76 for the elderly in Korea [33] and 0.84 for individuals aged 19 to 29 [34]. The index was calculated according to previous research [32], with the calculation formula provided in the Supplemental Material 2.

Covariates

Covariates included gender, age, marital status, personal income, education level, and occupation, as provided by KNHANES. Age was treated as a continuous variable. Marital status was included as a dichotomous variable indicating whether the respondent was currently married [25]. Personal income was divided into 4 quartiles: high, upper-middle, lower-middle, and low [25]. Education level was categorized as college or higher, high school, middle school, and elementary school or below [25]. Occupation was redefined into 6 categories: managers, professionals, and associates; office workers; service and sales workers; skilled agricultural, forestry and fishery workers; technicians, plant and machine operators, and assemblers; and elementary workers [25].

Statistical Analysis

A frequency analysis was performed to describe the characteristics of the study population. Differences in the “Sleep” item were examined across work schedules while accounting for gender differences. Ordinal logistic regression analysis was conducted for each item of the HINT-8, with predicted and incremental probabilities calculated accordingly. Additionally, a linear regression analysis was used to investigate associations between work schedules and the HINT-8 index. Subgroup analyses by gender were also undertaken. All statistical analyses were performed using Stata/SE version 18.0 (StataCorp., College Station, TX, USA).

Ethics Statement

This study obtained institutional review board (IRB) review exemption from the Seoul National University Hospital IRB (IRB No. E-2407-010-1548).

RESULTS

The general characteristics of the study subjects are presented in Table 1. The numbers and proportions across the 4 work schedule categories were as follows: 5693 (84.5%) for day, 560 (8.3%) for evening, 126 (1.9%) for night, and 356 (5.3%) for other. The total sample included 3270 women (48.6%). The gender composition varied significantly across work schedules, with women representing 62.7% of evening workers and 30.9% of night workers. The proportion of individuals in the lowest income category (“low”) was higher among evening (28.4%) and night (34.1%) workers compared to day workers (18.8%). The occupational composition also differed considerably by work schedule: night workers were more concentrated in service and sales (34.1%), elementary workers (26.2%), and technicians, plant and machine operators, and assemblers (19.1%), while they represented lower proportions in managers, professionals and associates (9.5%) and office workers (7.9%) compared to day workers (22.4 and 20.5%, respectively).

Table 2 presents the responses to the sleep item of the HINT-8 by work schedule and gender. Workers on the day schedule were more likely to report no difficulty with sleep than those on non-standard work schedules. Severe sleep difficulty was more common among individuals with evening (14.6%) and night (15.9%) schedules than among those on the day schedule (8.2%). In addition, men generally reported no difficulty with sleep more frequently than women. The gender disparity was most notable under the night schedule, where 25.6% of women reported severe difficulty compared to 11.5% of men. This 14.1 percentage point (%p) difference under the night schedule was considerably larger than the 4.6%p difference observed under the day schedule.

Table 3 summarizes the predicted and incremental probabilities of reporting severe difficulty for each HINT-8 item, as estimated by ordinal logistic regression. For the sleep item, the probability of reporting severe sleep difficulty increased under both the evening and night work schedules compared to the day schedule, for both men and women, with a greater incremental probability observed under the night schedule than the evening schedule. The largest incremental probability for the sleep item occurred among women on the night schedule, who experienced a 12.2%p increase from the 9.6% predicted for the day schedule. This suggests that the risk of severe sleep difficulty more than doubles for women working on the night schedule compared to those on the day schedule. Although the incremental probabilities for severe sleep difficulty were relatively modest for men (3.0%p for evening and 3.6%p for night), these increases are notable given a baseline predicted probability of 6.9%. Notably, women on the night schedule were 2.3%p more likely to report severe difficulty with working, an increase from 4.7% under the day schedule. In addition to sleep and work difficulties, the vitality and happiness items of the HINT-8 were also adversely affected by the night schedule, with incremental probabilities that were now greater in men than in women. Interestingly, difficulty with vitality decreased in the “other” category, in contrast to the evening or night schedules. The remaining 4 HINT-8 items did not exhibit substantial or consistent effects from non-standard work schedules.

Table 4 presents the results of the linear regression analysis using the HINT-8 index as a composite measure. The coefficients for the work schedule variables and their interaction terms with gender were small and statistically insignificant. This pattern remained unchanged in separate analyses by gender (Supplemental Materials 3 and 4).

DISCUSSION

This study found that non-standard work schedules are associated with a higher likelihood of sleep disturbance compared to standard daytime work schedules. In particular, the negative impact of night work on sleep is more pronounced among women than among men. Moreover, the working, happiness, and vitality items of the HINT-8 were negatively associated with the night work schedule, whereas the other HINT-8 items and the overall HINT-8 index did not show similar patterns.

Our key finding—namely, that non-standard work schedules correlate with sleep difficulty—aligns with a large body of previous research [3,12]. Non-standard work arrangements disrupt the sleep-wake cycle, leading to insomnia and increased sleepiness [35]. Even when individuals maintain a similar time in bed, abnormal sleep-wake patterns can severely compromise sleep quality. Additionally, conflicts between daytime obligations and available opportunities for rest may prevent individuals with non-standard schedules from obtaining the same amount of sleep as those with standard schedules. This dynamic may help explain the gender difference observed in our study, although other mechanisms—physiological and psychological, for instance—cannot be ruled out [36].

Beyond sleep, our item-level analysis of the HINT-8 indicates that 3 additional aspects of HRQoL are adversely affected by non-standard work schedules. The increased probability of severe work difficulty among women on the night schedule suggests that such arrangements may impair work performance. This finding is consistent with studies indicating that non-standard work schedules can reduce alertness and increase the risk of workplace accidents [37]. Our results also suggest that both happiness and vitality are negatively influenced by a night work schedule. These associations may be partially mediated by sleep disturbances but might also stem from social isolation and work-family conflict, which often accompany non-standard work arrangements. In contrast, the other 4 HINT-8 items (climbing stairs, pain, depression, and memory) did not show substantial or consistent negative effects. Taken together, our item-level analysis implies that non-standard work schedules predominantly affect sleep and broader HRQoL elements (working, happiness, and vitality), while items related to specific activities, bodily symptoms, or severe mental and cognitive conditions remain largely unaffected. This inconsistency may also help explain the absence of a clear association between non-standard work schedules and the composite HINT-8 index.

The observation that not all HINT-8 items were associated with non-standard work schedules merits further discussion. First, although non-standard work schedules affect sleep and subsequently influence health and HRQoL across various domains, more severe negative effects may not be easily detectable in survey studies like ours. One possible explanation is that individuals with serious health problems might be driven away from work altogether, or specifically from non-standard work arrangements. The well-known healthy worker effect refers to the phenomenon whereby workers are generally healthier than non-workers, as those in poor health are more likely to leave the workforce—an adjustment on the extensive margin [38]. Additionally, a similar adjustment on the intensive margin may occur if less healthy individuals switch from non-standard to standard work schedules or avoid non-standard schedules while remaining in the workforce. Second, if this reasoning holds, our findings may capture only early signs of health deterioration associated with non-standard work schedules rather than their full, long-term impact. Third, the specific associations of non-standard work schedules with only certain HINT-8 items (namely, sleep, working, vitality, and happiness) may actually support a causal interpretation; had all 8 items been negatively affected, one might question whether individuals on non-standard schedules are inherently different from those on standard schedules.

This discussion leads to an important methodological limitation: selection bias and residual confounding could mean that the observed associations between non-standard work schedules and sleep, as well as other HRQoL domains, may not necessarily reflect causal relationships. Two additional limitations concern the operational definitions used for both the outcome variables and the primary explanatory variable. Using a single self-reported measure of sleep difficulty may not fully capture the multidimensional nature of sleep disturbances; a more comprehensive set of sleep-related variables and objective measures would ideally be employed. Moreover, our operational definition of work schedule focused on comparing 2 current non-standard schedules (evening and night) with a day schedule, thereby overlooking substantial heterogeneity within each category regarding duration and frequency. Furthermore, evening or night work schedules might be transient and partially overlap with the “other” work schedule category, which we defined by combining various other non-standard arrangements such as rotating shifts. This concern is partly mitigated by our finding of clear differences between night work and the “other” category—indeed, the vitality item performed even better in the “other” category than in the standard schedule. Finally, although the initial 2-year KNHANES sample was relatively large, limiting our focus to current workers and considering 4 work schedule indicators along with gender substantially reduced the sample size for certain intersections.

Despite these limitations, our study sheds light on the sleep and broader health-related consequences of non-standard work schedules, with a particular focus on gender differences. In light of the widespread move toward flexible work hours, our findings have significant implications for research, practice, and policy. Although non-standard work schedules may be preferred by some workers and employers for economic reasons—and are essential in critical public sectors—their negative impacts on health and quality of life are often less visible and, therefore, overlooked. Moreover, such adverse effects may compromise workplace safety and performance, as demonstrated by the increased work difficulty observed among night-working women. Given the extensive range of health, quality-of-life, and work performance impacts associated with non-standard work schedules, as well as the vulnerability of workers under these schedules, it is imperative to monitor and further explore their short-term and long-term consequences, including examining gender and other subgroup differences. Future research should refine measures of work schedules and improve study designs—potentially using longitudinal data from large samples—to better isolate the causal effects of non-standard work schedules on sleep, health, and quality of life. Public health practice and policy should acknowledge non-standard work schedules as an emerging public health challenge and focus on developing effective interventions to mitigate their negative impacts on worker health, well-being, and workplace safety and performance. Context-specific interventions, which consider national, institutional, and socio-cultural factors, should be comprehensively adopted [1,5,6]. In particular, widespread implementation of employee-oriented working hours could help support work-life balance [2,9]. Employers should further assist workers on flexible schedules with organizational interventions—such as adjusting shift rotation direction and speed to facilitate rest and circadian resynchronization—in order to alleviate the adverse effects of circadian disruption [5,6,11,12,26]. Furthermore, if the long-term economic burden of health deterioration due to non-standard work schedules extends beyond individual employers to society at large, this negative externality raises critical public policy questions about whether non-standard work arrangements should incur greater social costs and whether non-essential, though convenient, services reliant on such schedules should be reconsidered.

In conclusion, non-standard work schedules are associated with increased sleep difficulty, particularly among women, and negatively affect several other aspects of HRQoL, such as vitality, happiness, and work performance. Given their rising prevalence and their adverse impact on workers’ sleep, health, and well-being, as well as on workplace safety and performance, non-standard work schedules should be recognized as an important public health concern.

Supplemental Materials

Notes

Conflict of Interest

The authors have no conflicts of interest associated with the material presented in this paper.

Funding

None.

Acknowledgements

Earlier results of this study were presented at the 2023 Annual Fall Conference of the Korean Society for Preventive Medicine.

Author Contributions

Conceptualization: Park S, Do YK. Data curation: Park S, Do YK. Formal analysis: Park S, Do YK. Funding acquisition: None. Methodology: Park S, Do YK. Writing – original draft: Park S. Writing – review & editing: Park S, Park JS, Lee MH, Do YK.

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Figure & Data

References

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