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. 2024 Aug 19;34(2):e14308. doi: 10.1111/jsr.14308

Chronotype in relation to shift work: A cohort study among 37,731 female nurses

Linske de Bruijn 1, Nina E Berentzen 1, Roel C H Vermeulen 2, Jelle J Vlaanderen 2, Hans Kromhout 2, Flora E van Leeuwen 1, Michael Schaapveld 1,✉
PMCID: PMC11911045  PMID: 39160129

Summary

Chronotype may affect tolerance for circadian disruption induced by shift work. This study examines the association between chronotype, self‐reported sleep timing, shift type preference, and sleep problems among nurses, and studies chronotype stability over time. The study included 37,731 Dutch female nurses who completed a baseline (2011) and follow‐up questionnaire (2017), with information on shift work (e.g., job history, shift type preference [collected in 2017 only]), and sleep characteristics (e.g., chronotype, preferred sleep–wake time in a work‐free period [collected in 2017 only], and sleep problems between working days according to Medical Outcomes Study‐Sleep Problem Index II [MOS‐SPI‐II]). The association between chronotype and sleep timing was examined using (age‐adjusted) linear regression. Associations between chronotype and shift type preference and sleep problems (MOS‐SPI‐II >30) were examined using ordered logistic and Poisson regression, respectively. With later chronotype, midsleep time increased (definite evening vs. intermediate types [reference]: β = 55 min, 95% confidence interval [95% CI]: 54–55), the odds ratio (OR) for 1‐point increase in preference for night (2.68; 95% CI: 2.48–2.90) and evening shifts increased (OR 2.20; 95% CI: 2.03–2.38), while the odds for day (OR 0.17; 95% CI: 0.16–0.18) and morning shifts (OR 0.22; 95% CI: 0.21–0.24) decreased. Intermediate chronotype was associated with fewer sleep problems (median MOS‐SPI‐II = 27.2, p < 0.01), compared with definite morning (28.9) and evening types (31.7). This study shows that chronotype is associated with sleep–wake times in a work‐free period, shift type preference, and sleep problems in nurses. Future studies on the association of shift work‐induced circadian disruption and health outcomes should therefore consider chronotype as effect‐modifier.

Keywords: chronotype, circadian disruption, nurses, shift work tolerance, sleep quality, sleep–wake timing

1. INTRODUCTION

Working night shifts disrupts the circadian rhythm and affects sleep quality and health (Boivin et al., 2022; Wu et al., 2022). Being awake when the body is primed for sleep, may lead to increased blood pressure and glucose intolerance (Kervezee et al., 2020; Poggiogalle et al., 2018). Accordingly, human‐based studies found strong evidence that exposure to night shift work increases the risk of type 2 diabetes mellitus (Gan et al., 2015; Vetter, Devore, et al., 2015) and cardiovascular disease (Vetter et al., 2016; Vyas et al., 2012). Night shift work also affects sleep quality, showing lighter and shorter sleep in shift workers right after or during a period of night shift work, compared with day workers (Garde et al., 2020; Feng et al., 2021; van de Langenberg et al., 2023). Furthermore, exposure to artificial light at night has been shown to promote tumour growth in rodents (Schwimmer et al., 2014; Zubidat et al., 2018). Based on experimental studies, the International Agency for Research on Cancer (IARC) classified shift work that involves disruption of the circadian rhythm as “probable carcinogenic to humans (Group 2A)” (Ward et al., 2019).

Despite the negative impact of night shift work on the human body, night shift work is unavoidable for many professions. Yet, tolerance for night shift work may differ between shift workers as not every individual has the same diurnal preference (“chronotype”). Previous studies showed that evening types better tolerate circadian disturbances as a consequence of night shift work, compared with morning types (Ritonja et al., 2019). However, being an evening type has been associated with higher all‐cause mortality (Knutson & von Schantz, 2018), poorer mental health (Jones et al., 2019), and a higher prevalence of cardiometabolic risk factors (Merikanto et al., 2013; Yu et al., 2015). Hence, chronotype is thought to play a modifying role in the relationship between (night) shift work‐induced circadian disruption and health outcomes (Erren et al., 2017). However, existing epidemiological research has predominantly examined chronotype and sleep within cohorts involving a limited number of shift workers (Juda et al., 2013; Kervezee et al., 2021; Vetter, Fischer, et al., 2015).

To improve our understanding of the concept of circadian alignment in shift workers, this study investigates associations between chronotype, sleep problems, and (tolerance to) shift work in the Nightingale study, a Dutch observational cohort study comprising 59,947 female nurses (Pijpe et al., 2014). This study aims to examine the association between chronotype and self‐reported preferred sleep timing, preference for shift types, and sleep problems, and to examine the stability of chronotype over time.

2. METHODS

2.1. Study cohort and questionnaires

The Nightingale Study is a prospective cohort study set up to investigate the associations between occupational exposures and the risk of chronic diseases among nurses. The study design and study population were described in detail previously (Pijpe et al., 2014). In brief, the recruitment of participants for the Nightingale Study took place between October 2011 and February 2012. A total of 192,931 (former) Dutch female nurses were invited to participate. Of all invited nurses, 59,947 responded to the baseline questionnaire in 2011, of whom 63% (n = 37,731) also responded to the first follow‐up questionnaire in 2017. These questionnaires were used to collect data on demographics, medical history, lifestyle, job history, and occupational exposures and included a detailed shift work section. The follow‐up questionnaire asked about shift work performed after baseline and sleep behaviour. All participants gave written informed consent and the study procedures were approved by the Institutional Review Board of the Netherlands Cancer Institute (Nightingale Study, 2011, codes PTC10.2692 and PTC11.1972/JA).

2.2. Assessment of shift work status

The minimum frequency and duration for all shift work types was having worked at least one shift (i.e. one evening or night or morning) per month for at least 6 months. A night shift was defined as having worked at least 1 hour between midnight and 05:00, a morning shift was defined as a shift starting between 05:00 and 06:59, and an evening shift was defined as having worked at least 1 hour after 19:00 and the shift ending no later than midnight. Shifts worked in job types other than nursing were included, but shifts worked during nursing education were excluded. All participants were classified into “day work only”, “shift work without night shifts”, “shift work with night shifts”, “no work” based on whether the participant worked night shifts in the period from 2011 until 2017. The “day work only” group also included participants who performed shift work for less than 6 months per job title. The “no work” group included women who were retired, unemployed, or work disabled during the entire follow‐up period (2011–2017). Women who stopped working between 2011 and 2017 were included in one of the above categories based on the type of work they performed between 2011 and the date they stopped working.

2.3. Chronotype and sleep timing

In 2011 and 2017, chronotype was assessed using a single‐item question (“How would you describe yourself?” included in a sleep habits section) with six answer options: (1) definitely a morning type, (2) more a morning than an evening type, (3) no specific type (“intermediate type”), (4) more an evening than a morning type, (5) definitely an evening type and (6) I don't know. Responders who reported “I don't know” (n = 1183 in 2017) or responders with missing chronotypes (n = 2587) were excluded from analyses. Responders were classified as having a stable chronotype if they reported the exact same chronotype in 2011 and 2017. As sleep timing was only assessed in the 2017 questionnaire, sleep timing was only studied among responders to the follow‐up questionnaire (N = 37,731). Time of going to bed and time of awakening were assessed during a hypothetical work‐free period (“Approximately at what time would you go to sleep/wake‐up during a period when you would be completely free to plan your days?”) and were used as a proxy for the participant's biological night. Timing of midsleep (in decimal hours) was derived by calculating the midpoint between the reported time of going to bed and time of awakening and sleep duration was defined as the total number of hours between these two time points.

2.4. Shift type preference and sleep problems

As an indicator of tolerance to shift work, preference for various shift types (day, evening, night, morning) was assessed in 2017 using a 7‐point Likert scale (1 = least preferred to 7 = most preferred), even if a participant did not (or no longer) work in this particular shift type. The Medical Outcomes Study Sleep Problem Index II (MOS‐SPI‐II) (Spritzer & Hays, 2003) was used to assess sleep quality between two regular working days (business hours or day shifts) in the period of 4 weeks before filling out the questionnaire (2011 and 2017). A higher MOS‐SPI‐II score indicates poorer sleep quality and potentially higher degrees of circadian disruption. A MOS‐SPI‐II >30 was used as a cut‐off value indicating sleeping problems (Hays et al., 2005; Smith & Wegener, 2003).

2.5. Statistical analysis

Baseline characteristics were compared between responders to the follow‐up questionnaire (2017, n = 37,731) and participants who only responded to the baseline questionnaire (n = 22,213). Normality of the data was assessed by the Skewness and Kurtosis test.

We verified whether self‐reported chronotype as reported in 2017 was associated with preferred sleep timing (bedtime, midsleep, wake‐up time) using linear regression analysis adjusted for age (years), using intermediate types as the reference group. Adjusted R‐squared (R2) was used to measure the proportion of variance in sleep timing which could be explained from chronotype (at follow‐up) and age. Breakpoints within the association of age and midsleep were assessed using piecewise linear regression.

The association between chronotype and shift type preference (7‐point Likert scale, 1 = least to 7 = most preferred) was examined using ordered logistic regression adjusted for age and recent shift work status (in the following categories: “day work only”, “shift work without night shifts”, “shift work with night shifts”, “no work” in 2011–2017). The association between chronotype and sleep problems (MOS‐SPI‐II >30) at follow‐up was assessed using a Poisson regression model with robust standard errors (adjusted for age, recent shift work status, and use of sleep medication between two working days in the past 4 weeks (never/rarely vs. sometimes/often/always). In all these analyses, participants who reported to have an intermediate chronotype were used as reference. Poisson regression analysis was performed to assess the effect of age on the change in chronotype. Age (per 10 years) at baseline (2011) was used as independent variable. We categorised participants based on the absolute change between the reported chronotypes in 2011 and 2017 (ranging from 0 to 5 steps) and included this as outcome variable in the analysis. We adjusted for chronotype at baseline and menopausal status (premenopausal in 2017, shifted from pre‐ to postmenopausal between 2011 and 2017, postmenopausal in 2011). For all analyses, a p‐value of <0.05 was considered statistically significant. All statistical analyses were conducted using Stata Statistical Software (Release 15).

3. RESULTS

The study included 37,731 Dutch female nurses who completed a baseline questionnaire in 2011 and a follow‐up questionnaire in 2017. The median age in 2017 was 55.7 years (interquartile range [IQR]: 47.4–61.8; Table 1). Compared with responders who only completed the baseline questionnaire (n = 22,213), responders who also completed the follow‐up questionnaire were older at baseline (median age: 46.8 vs. 49.8 years). This difference in age resulted in differences in various other characteristics, e.g., cumulative lifetime night shift work duration and the distribution of menopausal status (Table S1).

TABLE 1.

General, shift work, and sleep characteristics of nurses for different chronotypes.

Self‐reported chronotype at follow‐up (2017)
Participant characteristics at follow‐up (unless differently specified) Total Definite morning type More morning than evening type Intermediate type More evening than morning type Definite evening type Type unknown or missing
N (%) 37,731 (100%) 5908 (15.7%) 8318 (22.0%) 8759 (23.2%) 7872 (20.9%) 3104 (8.2%) 3770 (10.0%)
Age (years) a 55.7 (47.4–61.8) 56.5 (49.9–62.2) 55.0 (46.6–61.1) 56.3 (48.0–62.5) 54.9 (45.3–61.2) 57.2 (49.1–63.2) 54.8 (45.9–61.3)
Menopausal status
Premenopausal 11,737 (31.1%) 1580 (26.7%) 2832 (34.0%) 2676 (30.6%) 2805 (35.6%) 865 (27.9%) 979 (26.0%)
Postmenopausal 24,735 (65.6%) 4242 (71.8%) 5348 (64.3%) 5968 (68.1%) 4958 (63.0%) 2197 (70.8%) 2022 (53.6%)
Missing 1259 (3.3%) 86 (1.5%) 138 (1.7%) 115 (1.3%) 109 (1.4%) 42 (1.4%) 769 (20.4%)
Recent shift work status (2011–2017)
Day work only 12,383 (32.8%) 2567 (43.4%) 3347 (40.2%) 3053 (34.9%) 2310 (29.3%) 716 (23.1%) 390 (10.3%)
Shift work without night shifts 7544 (20.0%) 1155 (19.5%) 1688 (20.3%) 1818 (20.8%) 1651 (21.0%) 570 (18.4%) 662 (17.6%)
Shift work with night shifts 11,679 (31.0%) 1314 (22.2%) 2195 (26.4%) 2621 (29.9%) 3009 (38.2%) 1369 (44.1%) 1171 (31.1%)
No work 3815 (10.1%) 672 (11.4%) 798 (9.6%) 960 (11.0%) 693 (8.8%) 375 (12.1%) 317 (8.4%)
Missing 2310 (6.1%) 200 (3.4%) 290 (3.5%) 307 (3.5%) 209 (2.7%) 74 (2.4%) 1230 (32.6%)
Current shift work status (2017)
Day work only 10,560 (28.0%) 2209 (37.4%) 2898 (34.8%) 2590 (29.6%) 1966 (25.0%) 588 (18.9%) 309 (8.2%)
Shift work without night shifts 5851 (15.5%) 928 (15.7%) 1390 (16.7%) 1501 (17.1%) 1386 (17.6%) 437 (14.1%) 209 (5.5%)
Shift work with night shifts 8497 (22.5%) 937 (15.9%) 1689 (20.3%) 2015 (23.0%) 2370 (30.1%) 1066 (34.3%) 420 (11.1%)
No work 7373 (19.5%) 1276 (21.6%) 1570 (18.9%) 1856 (21.2%) 1454 (18.5%) 774 (24.9%) 443 (11.8%)
Missing 5450 (14.4%) 558 (9.4%) 771 (9.3%) 797 (9.1%) 696 (8.8%) 239 (7.7%) 2389 (63.4%)
Preferred bedtime (hh:mm) b 23:07 (00:45) 22:37 (00:34) 22:49 (00:30) 23:09 (00:34) 23:27 (00:40) 00:08 (00:55) 22:57 (00:38)
Preferred midsleep time (hh:mm) b 03:41 (00:39) 03:08 (00:31) 03:24 (00:26) 03:40 (00:27) 04:02 (00:32) 04:36 (00:45) 03:37 (00:30)
Preferred wake‐up time (hh:mm) b 08:14 (00:48) 07:39 (00:42) 08:00 (00:37) 08:11 (00:39) 08:38 (00:42) 09:03 (00:51) 08:17 (00:42)
Preferred sleep duration (hours) b 9.1 (0.8) 9.0 (0.8) 9.2 (0.7) 9.0 (0.8) 9.2 (0.9) 8.9 (1.0) 9.3 (0.9)
Sleep quality (MOS‐SPI‐II)ᵃ 29.4 (20.6–40.0) 28.9 (20.0–39.4) 29.4 (20.6–38.9) 27.2 (20.0–37.8) 31.1 (22.2–40.6) 31.7 (22.2–43.3) 33.9 (24.4–44.4)
Sleep problems (% MOS‐SPI‐II >30) 15,279 (40.5%) 2447 (41.4%) 3588 (43.1%) 3362 (38.4%) 3745 (47.6%) 1498 (48.3%) 639 (16.9%)
Sleep medication use (% yes) 1280 (3.4%) 203 (3.4%) 268 (3.2%) 281 (3.2%) 280 (3.6%) 182 (5.9%) 66 (1.8%)
Shift type preference (1 = least to 7 = most preferred) b
Day shift 5.6 (1.6) 6.3 (1.2) 6.1 (1.3) 5.8 (1.5) 5.1 (1.7) 4.0 (2.0) 5.3 (1.7)
Evening shift 4.4 (1.7) 3.7 (1.7) 4.0 (1.6) 4.5 (1.7) 4.9 (1.6) 5.4 (1.6) 4.3 (1.7)
Night shift 2.5 (1.9) 2.1 (1.7) 2.1 (1.6) 2.6 (1.9) 2.8 (2.0) 3.7 (2.3) 2.8 (2.0)
Morning shift 3.4 (2.0) 4.6 (2.1) 3.9 (2.0) 3.5 (2.0) 2.6 (1.7) 2.0 (1.4) 3.2 (1.9)
Self‐reported chronotype at baseline (2011)
Definite morning type 4923 (13.0%) 3032 (51.3%) 1222 (14.7%) 275 (3.1%) 45 (0.6%) 6 (0.2%) 343 (9.1%)
More morning than evening type 8836 (23.4%) 1952 (33.0%) 4328 (52.0%) 1563 (17.8%) 283 (3.6%) 14 (0.5%) 696 (18.5%)
Intermediate type 9493 (25.2%) 494 (8.4%) 1792 (21.5%) 4472 (51.1%) 1299 (16.5%) 136 (4.4%) 1300 (34.5%)
More evening than morning type 8174 (21.7%) 111 (1.9%) 470 (5.7%) 1675 (19.1%) 4348 (55.2%) 857 (27.6%) 713 (18.9%)
Definite evening type 4023 (10.7%) 19 (0.3%) 42 (0.5%) 222 (2.5%) 1469 (18.7%) 1954 (63.0%) 317 (8.4%)
Missing 2282 (6.0%) 300 (5.1%) 464 (5.6%) 552 (6.3%) 428 (5.4%) 137 (4.4%) 401 (10.6%)
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Abbreviations: BQ, baseline questionnaire; FQ, follow‐up questionnaire; MOS‐SPI‐II, Medical Outcomes Study Sleep Problem Index II.

a

Data are presented as median (IQR).

b

Data are presented as mean (SD).

Of all responders to the follow‐up questionnaire, 16% reported being a “definite morning” type, 23% reported being an intermediate type, and 8% considered themselves as a “definite evening type” (Table 1). While 44% of the definite evening types worked night shifts in the period from 2011 to 2017, among definite morning types only 22% worked night shifts.

The average preferred bedtime was 23:07 and on average participants awoke at 08:14, resulting in an average sleep duration of 9.1 hours (Table 1). We found that each 10 year increase in age was associated with an 8 minutes later bedtime (p < 0.01, Table 2), while participants woke up 5 minutes earlier (p < 0.01), and consequently sleep duration decreased with advancing age (Figure 1a). Breakpoint analysis showed that midsleep time slightly increased with increasing age until age 48.4 years, after which midsleep no longer increased (Figure 1c). Age‐adjusted linear regression analyses showed that later chronotype was statistically significantly associated with later bedtime (definite evening type vs. intermediate type; β = 0:58 h, 95% CI = 0:58–0:59), later midsleep time (β = 0:55 h, 95% CI = 0:54–0:56), and later time of awakening (β = 0:52 h, 95% CI = 0:50–0:54). On average, the difference in midsleep time between definite morning and definite evening types was 1.5 h.

TABLE 2.

Associations between preferred sleep timing, chronotype, and age.

Participant characteristics Preferred sleep timing at follow‐up (hh:mm)
Bedtime β (95% CI) Midsleep time β (95% CI) Wake‐up time β (95% CI)
Age
Age of 50 23:04 (23:01–23:06)* 03:38 (03:36–03:41)* 08:13 (08:10–08:16)*
10 year increase 0:08 (0:08–0:09)* 0:01 (0:01–0:02)* −0:05 (−0:05 to−0:04)*
R 2 0.04 0.003 0.01
Chronotype at follow‐up (2017) a
Definite morning type −0:33 (−0:34 to −0:32)* −0:33 (−0:34 to −0:32)* −0:33 (−0:34 to −0:31)*
More morning than evening type −0:20 (−0:21 to −0:18)* −0:16 (−0:17 to −0:15)* −0:13 (−0:14 to −0:12)*
Intermediate type (ref.) 22:19 (22:17–22:21) 03:27 (03:25–03:29) 08:34 (08:32–08:37)
More evening than morning type 0:19 (0:18–0:20)* 0:22 (0:21–0:23)* 0:25 (0:24–0:26)*
Definite evening type 0:58 (0:56–0:59)* 0:55 (0:54–0:56)* 0:52 (0:50–0:54)*
Adjusted R 2 0.37 0.40 0.28
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a

Adjusted for age (in years).

*

p < 0.01.

FIGURE 1.

FIGURE 1

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The relationship between age and (a) sleep duration, (b) bedtime, (c) midsleep, and (d) wake‐up time in a free period for different chronotypes.

The median sleep problem score in 2017 was 28.9 (IQR = 19.4, Table 1) for definite morning types, 27.2 (IQR = 17.8) for intermediate types, and 31.7 (IQR = 21.1) for definite evening types. Compared with intermediate types, the incidence rate ratio (IRR) for having sleep problems (MOS‐SPI‐II >30) was 1.11 (95% CI = 1.07–1.15; Table 3) for definite morning types, and 1.25 (95% CI = 1.20–1.30) for definite evening types. On average, participants reported lower preference scores for night shifts (mean = 2.5, SD = 1.9) compared with morning (mean = 3.4, SD = 2.0) or day shifts (mean = 5.6, SD = 1.6). Except for definite evening types, who preferred evening shifts most, all chronotypes preferred day shifts the most. Among women who reported a very low preference score for night shifts (≤2, n = 21,955/35,050 [63%]), 20% had to work in night shifts in 2011–2017. Compared with intermediate types, the odds of reporting 1 point higher on the Likert scale for preference of day shifts was 2.17 (95% CI = 2.02–2.32; Table S2) times higher in definite morning types and 0.17 (95% CI = 0.16–0.18) times lower in definite evening types. For night shifts, definite morning types had an odds ratio of 0.58 (95% CI = 0.54–0.62) and definite evening types had an odds ratio of 2.20 (95% CI = 2.03–2.38) compared with intermediate types. Overall, with later chronotype the preference for night and evening shifts increased, while the preference for day and morning shifts decreased (Figure S1).

TABLE 3.

Chronotype in relation to sleep problems using Poisson regression analysis.

Chronotype Having sleep problems in 2017 (MOS‐SPI‐II >30)
IRR (95% CI) a n MOS‐SPI‐II > 30/total
Definite morning type 1.11 (1.07–1.15)* 2447/5341
More morning than evening type 1.11 (1.07–1.15)* 3588/7699
Intermediate type 1.00 [ref.] 3362/8016
More evening than morning type 1.22 (1.18–1.26)* 3745/7219
Definite evening type 1.25 (1.20–1.30)* 1498/2814
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Note: Cut‐off value for sleep problems: MOS‐SPI‐II >30; n = 5577 excluded due to missing MOS‐SPI‐II.

Abbreviations: IRR, Incidence rate ratio; MOS‐SPI‐II, Medical Outcomes Study Sleep Problem Index II.

a

Poisson regression model with robust standard errors was adjusted for age at follow‐up, recent shift work status in 2011–2017 (“day work only”, “shift work without night shifts”, “shift work with night shifts”, “no work”), and sleep medication use (“never/rarely”, “sometimes/often/always”).

*

p < 0.01.

Among all participants who reported their chronotype in 2011 and 2017 (n = 32,080), 57% reported the same chronotype, while 43% reported different chronotypes (Figure 2). Among the participants who reported a different chronotype in 2017 compared with 2011 (n = 13,946), 85% (n = 11,829) shifted one step on the five‐step scale, 13% (n = 1880) shifted two steps, and <2% shifted >2 steps. Among them, 8246 (59%) shifted towards morningness and 5700 (41%) towards eveningness. With each 10 year increase in age (with a maximum age of 71 at the second measurement), the probability of a one‐step change in chronotype decreased with 6% (IRR: 0.94; 95% CI: 0.91–0.96).

FIGURE 2.

FIGURE 2

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Changes in self‐reported chronotype between 2011 and 2017 among 32,080 women.

4. DISCUSSION

In this large cohort study among (former) female nurses, we showed that single‐item chronotype was associated with preferred sleep–wake timing, and both the preference for different shift types and the occurrence of sleep problems strongly differed according to chronotype. Chronotype remained relatively stable over the 6 year observation period. Although earlier studies showed the impact of chronotype on sleep timing and quality, they did not focus specifically on shift workers (Merikanto et al., 2012; Roenneberg et al., 2007) or their sample size was small (Juda et al., 2013; Kervezee et al., 2021; Vetter, Fischer, et al., 2015). Our study suggests that shift work tolerance and levels of circadian disruption depend on chronotype, highlighting the importance of including chronotype as a potential effect modifier in future studies examining associations between shift work and health outcomes.

The distribution of chronotypes among our participants corresponds well with findings in two general population studies in the United Kingdom and Germany (Jones et al., 2019; Roenneberg et al., 2003), showing that the largest proportion considered themselves intermediate types, while there was a slightly larger proportion of morning types than evening types. Our study assessed chronotype using a self‐reported single‐item question, which correlates well with validated chronotype questionnaires (Horne & Östberg, 1976; Putilov et al., 2021; Roenneberg et al., 2003) such as the Horne‐Östberg Questionnaire (r = 0.74) (Turco et al., 2015) and the Munich Chronotype Questionnaire (r = −0.80) (Roenneberg et al., 2007). We showed that single‐item chronotype was associated with midsleep time, with an average difference of 1.5 h in midsleep time between definite morning and definite evening types. In a previous study, we showed that single‐item chronotype is associated with dim light melatonin onset, the gold standard for estimating the endogenous circadian phase (de Bruijn et al., 2022).

In accordance with previous studies, we found that increasing age was associated with a reduced sleep duration (Juda et al., 2013; Schuster et al., 2019). In general, with ageing, individuals tend to experience sleepiness earlier in the evening and awakening earlier in the morning (Fischer et al., 2017; Roenneberg et al., 2004; Wright Jr & Frey, 2008). Although we observed that participants awoke earlier with advancing age, we also found later bedtime, resulting in slightly increased midsleep times with advancing age. We observed a plateau in the trend of increasing midsleep timing with increasing age around the age of 48, which is close to the average age of onset of menopause (te Velde & Pearson, 2002). Previous studies showed that women are more likely to be morning types than evening types only before menopause (Roenneberg et al., 2003; Tonetti et al., 2008). This finding may be explained by the occurrence of major life events in younger adults (e.g. new job, having children), which may affect sleep timing (Lenneis et al., 2021; Leonhard & Randler, 2009). Furthermore, the current study showed that chronotype remained relatively stable over a time period of 6 years, with slightly more stability with increasing age, showing that chronotype indeed is a fairly stable construct (Roenneberg et al., 2019). Less than 1% of the nurses who reported their chronotype in 2011 and 2017 showed a considerable change of more than two steps in chronotype.

We observed lower levels of sleep problems in intermediate chronotypes, potentially indicating less circadian disruption compared with extremer (earlier/later) types. This finding is consistent with a study in industrial workers, which found a poorer sleep quality in more extreme chronotypes (early and late) compared with intermediate chronotypes (Casjens et al., 2022). In addition, our study found a clear association between chronotype and the preference for day, morning, evening, and night shifts. These results reflect those of smaller studies showing that evening types that worked evening shifts had higher job satisfaction compared with intermediate types (Amini et al., 2021; Ingre et al., 2012). Furthermore, our findings support the hypothesis that circadian disruption and related health issues may be caused by shift types other than night shifts alone (Erren & Morfeld, 2014; Ganesan et al., 2019; Vetter et al., 2012).

The strengths of this study are the availability of a wide range of shift work exposures and a large number of participants, with a good representation of the Dutch nursing workforce. In addition, we collected preference scores for shift types other than night shifts (e.g., morning, evening, and day shifts), which may be equally relevant to the concept of circadian disruption. A limitation of our study was the reliance on self‐reported sleep timing. We tried to diminish any potential effect of (current or recent) shift work exposure on the reported sleep timing, by assessing hypothetical sleep timing in a work‐free period as a proxy for the biological night. Sleep latency (the time between going to bed and actually falling asleep) was not assessed separately, which may explain the longer average sleep duration on a hypothetical work‐free period (9.1 h) in this cohort compared with other shift work cohorts reporting circa 8 hours of sleep for an average night (Vanttola et al., 2022; Vetter, Fischer, et al., 2015). Yet, the average midsleep times of our study population were comparable to those of a study on Canadian female hospital employees who used actigraphy wearables to measure sleep timing (Korsiak et al., 2018).

5. CONCLUSION

This large‐scale epidemiological study among nurses shows that chronotype is associated with preferred sleep timing, shift type preference as a possible indicator of shift work tolerance, and sleep problem score as a possible indicator of circadian disruption. Our results suggest that shift workers with more extreme chronotypes may experience a higher degree of circadian disruption. A strong aversion to shift work extends beyond night shifts alone. Depending on an individual's chronotype, both evening and morning shifts may also be experienced as unpleasant. Future studies on shift work‐induced circadian disruption in relation to health outcomes should therefore consider chronotype as potential effect‐modifier, and should not be limited to exposure to night shift work alone.

FUNDING INFORMATION

This study was financially supported by a grant from the Dutch Cancer Society (Grant No. 2019–12560). The funder did not play any part in designing the study protocol, data analyses, data interpretation, or manuscript preparation.

CONFLICT OF INTEREST STATEMENT

The authors declare no conflict of interests.

Supporting information

DATA S1 Supporting Information.

JSR-34-e14308-s001.docx (89.9KB, docx)

ACKNOWLEDGEMENTS

We thank all participants of the Nightingale Study for their valuable contribution to this study.

de Bruijn, L. , Berentzen, N. E. , Vermeulen, R. C. H. , Vlaanderen, J. J. , Kromhout, H. , van Leeuwen, F. E. , & Schaapveld, M. (2025). Chronotype in relation to shift work: A cohort study among 37,731 female nurses. Journal of Sleep Research, 34(2), e14308. 10.1111/jsr.14308

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

DATA S1 Supporting Information.

JSR-34-e14308-s001.docx (89.9KB, docx)

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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