The later, the better: Evening chronotypes in Uruguayan shift workers are associated with longer sleep in an observational study

The later, the better: Evening chronotypes in Uruguayan shift workers are associated with longer sleep in an observational study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article The later, the better: Evening chronotypes in Uruguayan shift workers are associated with longer sleep in an observational study José Mathias Cosentino, Bettina Tassino, Ignacio Estevan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6172058/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Shift work disrupts sleep patterns and is linked to adverse health outcomes, yet the role of chronotype in modulating shift workers' sleep behavior remains underexplored. A field study was conducted with 85 shift workers from an industrial plant in Montevideo, Uruguay, operating under a slow, counterclockwise three-shift rotation system. The abbreviated Munich Chronotype Questionnaire was used to assess sleep timing, duration, and napping behavior across shifts, and chronotype. Sleep duration on workdays was significantly shorter during morning (4.9±0.8h) and night shifts (5.0±1.5h) compared to afternoon shifts (7.9±1.3h). Napping was more frequent during the morning (54.1%) and night shifts (52.9%), contributing to almost an additional hour of sleep to mitigate sleep debt. Evening-oriented workers demonstrated greater sleep flexibility and adaptability, sleeping longer on days off and during night and afternoon shifts: A 1-hour delay in chronotype associated with 7-25 minutes of additional sleep. Morning-oriented workers adapted better to advancing sleep during morning shifts and got more sleep. These results highlight the importance of considering individual chronotypes when designing shift schedules. Personalized shift schedules could enhance sleep health and mitigate the adverse effects of shift work. Biological sciences/Neuroscience/Circadian rhythms and sleep/Sleep Health sciences/Health care/Occupational health sleep napping occupational health shift work schedule eveningness Figures Figure 1 Figure 2 1. Introduction Many health inequalities among workers in different industries are associated with differences in work organization 1,2 , including the temporal organization of activities 3 . Numerous individuals worldwide are compelled to operate within non-standard working times (between 19:00 and 06:00), often with work schedules that include fixed night shifts or rotating shifts. Shift work is particularly pervasive in occupations characterized by continuous activities that are essential either due to the provision of services or the inherent nature of the production processes involved. Between 10% and 20% of the world’s working population works in shifts, reaching 21% in Europe and 38% in the US 4 . Most shift workers remain entrained to the natural light-dark cycle, even though their work schedules disrupt their activity-wake patterns 5 . In this sense, it is still unclear to what extent the negative health effects of shift work are mediated through circadian and social misalignment, as well as disturbed sleep 6 –10 . Shift workers face difficulties initiating and maintaining their sleep, reporting greater sleepiness and shorter sleep durations than daytime workers 11 , especially while working night and morning shifts 12,13 . The irregular, insufficient, and low-quality sleep associated with shift work has adverse health consequences and affects performance and safety at work 6 –10,14,15 . Ensuring adequate sleep duration –above 7 h in adults– is essential for health, physical development, quality of life, cognitive performance, and wellness 16 . In this regard, reduced sleep durations are linked to all-cause mortality and pathologies such as metabolic syndrome, cardiovascular disease, and osteoporosis 17 . Several characteristics, such as age, gender, and chronotype, have been identified as protective against shift work's negative effects 18 , including sleep and sleep disorders 19 . Chronotype is recognized for influencing individual adaptability to work in shifts 8,20 . Chronotype can be operationalized as the midpoint of sleep on free days based on the Munich Chronotype Questionnaire (MCTQ) 21 . Several studies have found that this measure influences sleep timing and duration across different work shifts 22 –25 . Overall, the flexibility in sleep behavior of late chronotypes enhances their tolerance for shift work 26 . Due to social pressures associated with work schedules, sleep debt tends to accumulate on workdays. Two main recovery or compensatory strategies are employed 27,28 : taking naps and increasing sleep duration on days off. Napping is common among shift workers to mitigate accumulated sleep debt and increase sleep duration 29 , and there is evidence that napping depends on the interaction between workers' chronotype and work shift 24 . Studies indicate that Uruguayan youngsters exhibit markedly late chronotypes 30,31 , that affect their sleep differently depending on the social clock 32 . In this study, we investigated three main aspects of the sleep patterns of a group of Uruguayan shift workers: a) the association between sleep timing and duration and shift work schedules, b) how chronotype influences this association, and c) differences in the use of compensatory strategies for sleep debt according to chronotype. 2. Results We present data from an effective sample of 85 shift workers (80% male) between 20 and 58 years old (mean age: 36.8 ± 8.3 years; mean number of years of shift work: 10.8 ± 8.0 years), from an industrial plant in Montevideo, Uruguay, operating under a slow, counterclockwise three-shift rotation system (morning, night, and afternoon). Chronotype, social jetlag, and sleep timing and duration were calculated from answers to the Munich Chronotype Questionnaire for shift workers (MCTQshift) according to Juda et al. 23 . The mean chronotype was 5:04 ± 1:30 and the mean social jetlag was 2.22 ± 1.07 h, -4.66 ± 2.23 h, and 0.12 ± 0.57 h, for morning, night, and afternoon shifts, respectively. We calculated the main sleep duration (SD) and the 24-hour sleep duration (the sum of main sleep and nap durations; SD/24) for each day type (workdays and days off) and shift. Additionally, we computed the daily averages over the workweek for both the SD (SDweek) and the SD/24 (SDweek/24). We found that the SD on workdays was 2-3 h shorter than the recommended 7 h in the morning and night shifts, but not in the afternoon shift, nor during days off. We also observed differences in sleep timing and duration according to the chronotype. The analysis of strategies to compensate for sleep debt (naps and extended sleep on days off) revealed two interesting results: 1) These strategies were predominantly employed during shifts with shorter SD (morning and night), increasing the SDweek/24 up to almost 7 h, and 2) Their use was modulated by chronotype, with evening-oriented workers adding the most hours of sleep. 2.1. Shift-based sleep patterns Table S1 and Figure 1 present mean sleep timing, duration, and nap frequency by day type and shift. To analyze the influence of work schedule on sleep timing and duration, we first compared the Beta coefficients for each day type in Table 1 (estimated means; dots in Figures 2a and 2b) to examine the influence of day type on each sleep variable. Secondly, we analyzed the impact of work shifts on SDweek. SDweek represents the weighted contribution of the SD (or the SD/24) in workdays and days off. It is compared between shifts in Table 2 (Beta coefficients for workweek type; dots in Figures 2c and 2d). The contribution of naps to daily sleep can be examined by comparing the SD and the SD/24 Beta coefficients for each day type in Table 1 or between the SDweek and the SDweek/24 Beta coefficients in Table 2. We found a significant effect of day type on sleep timing and duration (Table 1). Estimated sleep timing (both sleep onset and end) was similar between workdays during the afternoon shift and days off in the afternoon and night shifts. Sleep timing was the latest during the night shift and earliest during the morning shift, especially on workdays. Estimated SD was approximately 8 h on days off across all shifts and on workdays during the afternoon shift. However, it was about 3 h shorter on workdays during the morning and night shifts. As shown in Table S1, napping was more frequent on workdays during morning and night shifts (over 50%), but rare during the afternoon shift. On days off, naps occurred on almost 20% of days across all shifts. This napping pattern resulted in an SD/24 of nearly 6 h on workdays for both the morning and night shifts (1 h longer than the SD), approximately 8 h on workdays for the afternoon shift, and 8.5 h on days off across all shifts. When examining SDweek, the estimated sleep duration was 2 h shorter during the morning and night shifts than during the afternoon shift (Table 2). However, SDweek/24 during the morning and night shifts was almost 45 min longer than SDweek, nearing 7 h, and the differences in SDweek/24 between the morning and night shifts and the afternoon shift were reduced to 1.5 h (Table 2). 2.2. Chronotype modulates the relationship between shift and sleep pattern To explore the association between chronotype, sleep patterns, and work schedules, we analyzed the interaction between chronotype and day type (Table 1; Figure 2). The Beta coefficients (estimated slope) for the day type and chronotype interaction (for daily sleep parameters; Table 1) and for the workweek type and chronotype interaction (for SDweek and SDweek/24; Table 2) indicate the association between chronotype and sleep by shift. Furthermore, differences in the SD based on chronotype were observed when slopes for sleep onset and end became distant or nearby (larger vs. opposite values, respectively). The interaction was significant for the four daily sleep timing and duration variables (Table 1). Chronotype showed a significant negative association with sleep onset during the night shift workdays, with a 1-h delay in chronotype resulting in a ~15 min advance in sleep onset. However, chronotype had a significant positive association on the other days, with a 1-h delay in chronotype resulting in delays of 26 - 53 min. Regarding sleep end, most of the correlations were stronger than those for sleep onset, except for workdays during morning and night shifts, when the association was not significant. In all other day types, the association between chronotype and sleep end was positive, and the delay in sleep end associated with a 1-h delay in chronotype was between 54 and 63 min. As a result, on workdays during the morning shift the association between chronotype and main sleep duration was negative, with a reduction of more than 20 min for each 1-h delay in chronotype. Conversely, on all other day types, there was a positive association, with durations 21 - 25 min longer for each 1-h delay in chronotype (Figure 2a). On days off, evening-oriented workers reported longer SD across all work schedules, although this association was non-significant during the night shift (Figure 2b). When nap duration was taken into account, the association between chronotype and SD/24 remained similar for most of the day types. However, the association was negative and not significant for workdays during the morning shift. Chronotype was associated with SDweek only for the night and afternoon shifts (Table 2; Figures 2c and 2d). A delay of 1 h in chronotype was associated with approximately 25 min longer sleep in both SDweek and SDweek/24. The estimated SDweek was greater than 7 h for workers with chronotypes later than 07:33 in the night shift or 02:24 in the afternoon shift, while none of the observed chronotypes predicted a healthy sleep duration in the morning shift (Figure 2c). The estimated SDweek/24 was above 7 h for workers with chronotypes later than 07:39, 05:51, and 02:14 for morning, night, and afternoon shifts respectively (Figure 2d). 3. Discussion To the best of our knowledge, our study is the first to use a Spanish version of the MCTQshiftquestionnaire to assess chronotypes and sleep patterns in shift workers. Moreover, it is the first in Latin America to examine the impact of a slow and counterclockwise rotation system on workers’ sleep patterns and their adaptive strategies based on their chronotype. Sleep duration on workdays varied across shifts, with both morning and night shifts associated with the shortest durations. This pattern originated in the misalignment between the sleep opportunity window and the circadian clock imposed by the work schedule. Napping significantly increased daily sleep duration, especially on workdays during these more demanding morning and night shifts, resulting in an additional hour of sleep. Furthermore, extended SD on days off led to an almost one-hour increase in SDweek during both morning and night shift workweeks. Chronotype-based differences were found in the ability to cope with shifts in sleep opportunity and the amount of sleep recovery by naps and extended sleep on days off: a) Morning-oriented workers demonstrated a greater ability to advance sleep compared to days off and experienced longer sleep durations on workdays during the morning shift; b) Evening-oriented individuals coped better with daytime sleeping with longer sleep durations on all other workdays; c) Eveningness was associated with extended catch-up sleep, as suggested by longer naps on workdays during the morning shift and longer sleep durations on all days off. 3.1. Sleep on workdays Consistent with previous studies, both permanent or rotating morning and night shifts imposed constraints on sleep timing and duration by altering the available sleep window opportunity 11,12 . Our findings confirmed that workers were compelled to either delay their sleep onset during the night shift or curtail their sleep during the morning shift, resulting in a despaired SD of about 5 h. Similar reduced main SD during morning and night shifts have been documented in other studies on shift workers 12,13 . Laboratory studies have shown that daytime sleep during night shifts tends to be of shorter duration 33 . Additionally, the later the sleep onset, the more challenging it becomes to maintain sleep, with chronotype-related differences influencing this pattern 23 . On the other hand, work shifts that start very early in the morning are almost as challenging for sleep as night shifts for many workers since they fail to advance the sleep onset because of the wake maintenance zone 34 . Social and family commitments may also hinder the adjustment of sleep timing 35 . In Uruguay, late dinner times are common and have been shown to restrict sleep duration in adolescents 32 . This cultural delay may exacerbate difficulties in advancing sleep onset on the nights preceding a morning shift. 3.2. Sleep debt compensation Extended SD on days off is a common strategy to compensate for insufficient sleep on workdays 36,37 . Our results indicate that SD was longer on days off than workdays during morning and night shift workweeks. The main SD on days off was similar to that reported in German workers 23 and higher than those registered in other shift workers 24,38 . As a result, SDweek exceeded SD on workdays for both morning and night shifts but remained shorter than SDweek during afternoon shifts. Unexpectedly, SD on days off during morning and night shifts was similar to SD on both work days and days off during the afternoon shift, suggesting that sleep on days off remained constrained. Napping complements daily sleep and facilitates adaptation to shift work 8,39 . In our study, naps were more frequent on workdays during morning and night shifts, adding almost 1 h of sleep to the SD/24 and nearly 45 min to the SDweek/24. Napping patterns remained consistent across different shifts during days off. Overall, naps were common and helped to mitigate the disparities in SDweek/24 across distinct workweeks. Our results align with previous findings 22,38 . However, other studies have reported an even greater contribution of naps during night shifts 24 . 3.3. Chronotype-based differences in daily sleep Participants exhibited late chronotypes, later than values reported in other shift workers 24,38,40,41 . This pronounced eveningness aligns with previous records of extreme late chronotypes in Uruguayan and Argentinian populations 42 . Average social jetlag was similar to previous research for the morning and afternoon shifts but lower in the night shift 23 . Sleep timing and duration varied among workers with different chronotypes. Late chronotypes exhibited delayed sleep timing when sleeping at night (on morning and afternoon shift workdays, and all days off) compared to early chronotypes. Conversely, during daytime sleep on night shifts, late chronotypes went to bed earlier and woke up later than early chronotypes. Furthermore, late chronotypes slept longer than early chronotypes on most days, except for morning shift workdays. The positive association between eveningness and main SD in the night shift was also observed in previous studies 22 –25 . Evidence on SD in late chronotypes during the afternoon shift is mixed. Some studies support our finding of longer SD 23,24 , while others do not 22,43 . The restriction of sleep in late chronotypes during morning shifts due to early start times has been previously reported 23,24,43 , though this effect was not observed in fast-forward rotating shifts 22 . Additionally, late chronotypes tend to sleep longer and accumulate less sleep debt when assigned to night shifts compared to morning shifts 44 . The longer SD of late chronotypes on night shift workdays may be attributed to their better ability to sleep during the daytime 18 . However, daytime sleep is often challenging and susceptible to premature interruption 33 . This may also explain the longer sleep durations on days off for evening-oriented workers, even in shifts when they sleep longer than early chronotypes. Furthermore, during the morning shift, evening-oriented workers exhibited greater efficacy in compensating for sleep deficits by incorporating daytime naps. Our findings align with previous studies suggesting that individuals with late chronotypes exhibit more flexible sleep timing and a greater tolerance for shift work than early chronotypes 20,45 . Beyond the relevance of shift work reorganization for sleep health 13 , accounting for individual differences, such as chronotypes, which aid workers in adapting to shifts, and designing rosters that consider these traits, appears to be a promising approach 46 . Incorporating chronobiological insights into shift scheduling represents a promising direction for future research and workplace policies. 3.4. Strengths and limitations Our study is the first to employ the MCTQshift to examine the impact of a slow, counterclockwise rotating shift system on sleep patterns within a Latin American context. It provides valuable insights into how regional and cultural factors, particularly the prevalence of late chronotypes in Uruguay, influence workers' sleep behavior. However, several limitations should be noted. The sample size may limit the generalizability of our findings to other populations or industries. Additionally, the use of self-reported data introduces the potential for recall bias or inaccuracies. Future studies could benefit from integrating objective measures such as actigraphy and dim light melatonin onset to enhance data reliability. Furthermore, assessing sleep quality would provide a more comprehensive understanding of sleep health. The cross-sectional design precludes causal inferences. Lastly, external factors, including social and family commitments, were not explored. 3.5. Concluding remarks The morning and night shifts proved to be the most demanding for workers' sleep, as shift workers typically achieved only 4 to 5 h of sleep. As the work shift overlaps with the usual sleep window, workers adjusted their sleep timing but were unable to reach the recommended 7 h. While additional sleep obtained through napping and on days off increased SD, average SD/24 and SDweek during morning and night shifts remained below 7 h. Individual characteristics, such as chronotype, influenced the impact of shift work. Specifically, eveningness was associated with longer SD across nearly all day types. Additionally, eveningness was associated with an increased utilization of compensatory strategies, such as extended sleep on days off and increased napping. 4. Methods 4.1. Data collection We conducted a field study among shift workers from an industrial plant in Montevideo, Uruguay. The company organized its activities into three shifts: morning (06:00 to 14:00), night (22:00 to 06:00), and afternoon (14:00 to 22:00). Participants' work schedules were organized in weeks, mostly comprising five workdays followed by two days off. The rotation system was slow and counterclockwise (morning - rest - night - rest - afternoon - rest - morning - …). The staff was divided into four rosters, with one roster working on each shift and the fourth resting. A total of 195 workers with a minimum of two years of experience working rotating shifts were invited to participate through union communications and plant tours led by a shop steward. Of these, 104 employees voluntarily agreed to participate. Data were collected from March to May 2021 with strong. All participants provided written informed consent and answers were processed anonymously. Ethics approval was obtained on December 23, 2020, from the Ethics Committee of the School of Psychology, Universidad de la República. All data collection was performed in accordance with relevant guidelines and regulations. 4.2. Sleep and chronotype assessment Participants were asked about gender, household composition, age, number of years of shift-working, and other characteristics by a sociodemographic questionnaire. Participants were also instructed to answer the questions from the abbreviated version of the MCTQshift 47 . The MCTQshift was developed to assess the sleep/wake behavior in shift workers, and the main difference between the original MCTQ and this version is that the set of questions for work and days off are queried for each shift 23,47 . The abbreviated version of the MCTQshift asks simple questions about sleep timing such as bedtime, wake-up time, use of alarms, and the use and duration of naps on all days (work or off), and shifts (morning, night, afternoon). 4.3. Data processing and statistical analysis From the answers given to the reduced version of the MCTQshift questionnaire, we calculated the following variables, according to Juda et al. 23 : Day-specific (a workday on morning, night, or afternoon shift workweek, or a day off on morning, night, or afternoon shift workweek) sleep onset, sleep end, main sleep duration (SD), and 24-hour basis sleep duration (main sleep and nap durations added together; SD/24). Daily averaged over the workweek main and 24-hour basis sleep duration in a given shift (SDweek or SDweek/24 in a morning, night, or afternoon shift workweek). Chronotype was assessed based on mid-sleep between two days off after working in the afternoon shift corrected for oversleeping. Social jetlag in every workweek. The data were analyzed using the R language in RStudio 48 . Data from 19 participants (18.2%) who relied on wake-up alarms on their days off were excluded. Generalized linear regressions were used with ID as a random effect to evaluate the influence of day type (six levels: workday or day off during morning, night, or afternoon shifts) or workweek type (three levels: morning, night, or afternoon shift) as well as chronotype on sleep variables. The chronotype was mean-centered before being included in the models. Age was included as a covariate. Post hoc pairwise comparisons were performed using Tukey's method. The calculated statistics were considered significant when p < 0.05. Descriptive statistics are presented as mean ± standard deviation or sample size (n) and percentage, and estimated values are presented as estimated mean ± standard error. Declarations 6. Acknowledgments The authors thank the workers who participated, as well as the union and the company that endorsed and promoted this research. Kent Dunlap read and made suggestions on an earlier version of this manuscript. 7. Author contributions All authors designed the experiment. Collecting and processing data, J.M.C.; data analysis and figure design, I.E.; writing the original draft, J.M.C.. All authors reviewed the manuscript. 8. Data availability All data reported in this paper will be shared by the corresponding author upon request. Any additional information required to reanalyze the data reported in this paper is available from the corresponding author upon request. 9. 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J., Matthews, R. W., Boivin, D. B. & Kantermann, T. Working Time Society consensus statements: Individual differences in shift work tolerance and recommendations for research and practice. Ind Health 57 , 201–212 (2019). Vetter, C., Fischer, D., Matera, J. L. & Roenneberg, T. Aligning Work and Circadian Time in Shift Workers Improves Sleep and Reduces Circadian Disruption. Current Biology 25 , 907–911 (2015). Juda, M., Vetter, C. & Roenneberg, T. The Munich ChronoType Questionnaire for Shift-Workers. J Biol Rhythms 28 , 130–140 (2013). RStudio Team. RStudio: Integrated development environment for R. RStudio, Inc. (2016). Tables Table 1. Influence of the interaction between day type and chronotype on sleep variables. Beta [95% CI] Morning shift Night shift Afternoon shift ANOVA Workday Day off Workday Day off Workday Day off Sleep onset (R 2 marginal/conditional = 0.90/0.92) Day type F(5,425)=1067.1, p<0.001 00:05 [23:53, 00:16] 1 00:37 [00:26, 00:49] 2 07:40 [07:28, 07:51] 5 01:29 [01:17, 01:40] 4 01:09 [00:58, 01:21] 3,4 01:05 [00:53, 01:16] 3 Chronotype * Day type F(5,425)=53.3, p<0.001 26 [18, 34] 2 37 [29, 45] 2,3 -16 [-24, -9] 1 53 [45, 61] 4 35 [27, 43] 2 51 [43, 59] 3,4 Sleep end (R 2 marginal/conditional = 0.85/0.88) Day type F(5,425)=526.5, p<0.001 04:59 [04:46, 05:13] 1 08:53 [08:40, 09:07] 2 12:44 [12:31, 12:58] 5 09:36 [09:23, 09:50] 4 09:06 [08:53, 09:20] 2,3 09:25 [09:11, 09:38] 3,4 Chronotype * Day type F(5,425)=39.5, p<0.001 4 [-6, 13] 1 54 [44, 63] 2 9 [-1, 18] 1 60 [51, 69] 2 56 [46, 65] 2 63 [53, 72] 2 Main sleep duration (R 2 marginal/conditional = 0.65/0.76) Day type F(5,425)=229.6, p<0.001 4.91 [4.65, 5.17] 1 8.27 [8.01, 8.53] 2 5.08 [4.82, 5.34] 1 8.13 [7.87, 8.39] 2 7.95 [7.7, 8.21] 2 8.33 [8.07, 8.59] 2 Chronotype * Day type F(5,425)=15.4, p<0.001 -22 [-33, -12] 1 16 [6, 27] 2,3 25 [14, 36] 3 7 [-4, 17] 2 21 [10, 31] 2,3 12 [1, 22] 2,3 24-hour basis sleep duration (R 2 marginal/conditional = 0.50/0.68) Day type F(5,425)=125.4, p<0.001 5.89 [5.61, 6.17] 1 8.58 [8.29, 8.86] 3 5.98 [5.7, 6.26] 1 8.46 [8.18, 8.75] 3 7.98 [7.69, 8.26] 2 8.57 [8.29, 8.85] 3 Chronotype * Day type F(5,425)=4.7, p<0.001 -3 [-14, 9] 1 18 [7, 30] 2 24 [12, 35] 2 8 [-4, 20] 1,2 22 [11, 34] 2 12 [1, 24] 1,2 Notes: Beta coefficients for day type are the estimated mean time (HH:MM) or duration (h), while Beta coefficients for the interaction between day type and chronotype are the estimated change in minutes when chronotype is delayed in 1h. The chronotype was mean-centered, ID was included as a random effect, and age was included as a covariate. 1<2<3<4<5 with p<0.05 using Tukey correction. CI: Confidence Interval. Table 2. Influence of the interaction between workweek type and chronotype on daily averaged over the workweek sleep durations (SDweek). Beta [95% CI] ANOVA Morning shift Night shift Afternoon shift Daily averaged over the workweek main sleep duration (R 2 marginal/conditional = 0.61/0.74) Workweek type F(2,170)=216.1, p<0.001 5.87 [5.67, 6.07] 5.95 [5.75, 6.16] 8.06 [7.86, 8.26] Chronotype * Workweek type F(2,170)=27.3, p<0.001 -11 [-19, -2] 1 20 [12, 29] 2 19 [10, 27] 2 Daily averaged over the workweek 24-hour sleep duration (R 2 marginal/conditional = 0.42/0.64) Workweek type F(2,170)=85.8, p<0.001 6.66 [6.43, 6.88] 6.69 [6.46, 6.92] 8.15 [7.92, 8.37] Chronotype * Workweek type F(2,170)=6.5, p=0.002 4 [-6, 13] 1 20 [10, 29] 2 20 [10, 29] 2 Notes: Beta coefficients for day type are the estimated mean duration (h), while Beta coefficients for the interaction between day type and chronotype are the estimated change in minutes when chronotype is delayed in 1h (the slope). The chronotype was mean-centered, ID was included as a random effect, and age was included as a covariate. 1<2 with p<0.05 using Tukey correction. CI: Confidence Interval. Additional Declarations No competing interests reported. Supplementary Files MSSRfinalSM.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 19 Nov, 2025 Reviews received at journal 01 Sep, 2025 Reviewers agreed at journal 12 Aug, 2025 Reviewers agreed at journal 11 Aug, 2025 Reviewers agreed at journal 08 Aug, 2025 Editor invited by journal 03 Jul, 2025 Reviewers invited by journal 05 May, 2025 Editor assigned by journal 18 Mar, 2025 Submission checks completed at journal 17 Mar, 2025 First submitted to journal 17 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6172058","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":437490669,"identity":"60dc3671-63d5-4a18-b407-f446c73d2194","order_by":0,"name":"José Mathias Cosentino","email":"","orcid":"","institution":"Universidad de la República","correspondingAuthor":false,"prefix":"","firstName":"José","middleName":"Mathias","lastName":"Cosentino","suffix":""},{"id":437490670,"identity":"b4eb5898-70bd-4776-9a32-009071377433","order_by":1,"name":"Bettina Tassino","email":"","orcid":"","institution":"Universidad de la República","correspondingAuthor":false,"prefix":"","firstName":"Bettina","middleName":"","lastName":"Tassino","suffix":""},{"id":437490672,"identity":"7385fda4-5e32-43d8-9f01-772d570d9309","order_by":2,"name":"Ignacio Estevan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4UlEQVRIiWNgGAWjYBACNhiDH4gPgFkHCGthbAAxJBuI1cIA02IAV0lICx97+/MHH9vs8oxvtz888KOCQY7vRgKbNA8+h/GcMWyc2ZZcbHbnjMHBnjMMxpIEtUjkMDbznGFO3HYjh+EwYxtD4gaCWuSfP2z+c6Y+cfOM9AcgLfWEtUgwGDYzVBxO3CCRYADSkmBA2C85hjN7Ko4nzoD4RcJw5pmHzZZz8GiRbz/+4MMPg+rE/tntjz/8qLCR5zuefPDGGzxaEEACTjI2MOFzGLoWCGD8QZSWUTAKRsEoGCEAADEIURMLq4c/AAAAAElFTkSuQmCC","orcid":"","institution":"Universidad de la República","correspondingAuthor":true,"prefix":"","firstName":"Ignacio","middleName":"","lastName":"Estevan","suffix":""}],"badges":[],"createdAt":"2025-03-06 15:53:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6172058/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6172058/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":80029801,"identity":"d71159ce-3200-44ba-a9dd-b38d376a0c45","added_by":"auto","created_at":"2025-04-07 07:16:03","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":48845,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMain sleep pattern on workdays and days off during each shift.\u003c/strong\u003eColored rectangles indicate main sleep periods, extending from mean sleep onset to mean sleep end, while colored lines represent the standard deviations. Gray rectangles represent each 8-hour work period.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6172058/v1/fa5c9084d65cc00dfc6f4ae1.png"},{"id":80029807,"identity":"654b3505-7dd4-4c6c-954b-37f2d9bf899d","added_by":"auto","created_at":"2025-04-07 07:16:03","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":136025,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEstimated association between chronotype and sleep durations by work shift. \u003c/strong\u003eChronotype was assessed based on mid-sleep between two days off after working the night shift and corrected for oversleeping. Lines represent the predicted mean association and ribbons the 95% Confidence Interval. Continuous lines indicate slopes significantly different from zero (p\u0026lt;0.05). The dots indicate the estimated sleep value for the average chronotype. SD: Sleep duration; SDweek: Daily averaged over the workweek main sleep duration; SDweek24: Daily averaged over the workweek 24-hour sleep duration.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6172058/v1/cd81223a2348f34ee3df79b6.png"},{"id":80031798,"identity":"616c4aa7-4bf3-4ff6-b3f8-e95de5a53ff9","added_by":"auto","created_at":"2025-04-07 07:40:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1085880,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6172058/v1/e47c55e2-b116-4920-b191-66bd8edb59a6.pdf"},{"id":80029803,"identity":"7fa87311-941b-4615-b608-c9f1565e5b1c","added_by":"auto","created_at":"2025-04-07 07:16:03","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":9889,"visible":true,"origin":"","legend":"","description":"","filename":"MSSRfinalSM.docx","url":"https://assets-eu.researchsquare.com/files/rs-6172058/v1/b31d05a6bbfcdec5e5dcac21.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The later, the better: Evening chronotypes in Uruguayan shift workers are associated with longer sleep in an observational study","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eMany health inequalities among workers in different industries are associated with differences in work organization \u003csup\u003e1,2\u003c/sup\u003e, including the temporal organization of activities \u003csup\u003e3\u003c/sup\u003e. Numerous individuals worldwide are compelled to operate within non-standard working times (between 19:00 and 06:00), often with work schedules that include fixed night shifts or rotating shifts. Shift work is particularly pervasive in occupations characterized by continuous activities that are essential either due to the provision of services or the inherent nature of the production processes involved. Between 10% and 20% of the world’s working population works in shifts, reaching 21% in Europe and 38% in the US \u003csup\u003e4\u003c/sup\u003e. Most shift workers remain entrained to the natural light-dark cycle, even though their work schedules disrupt their activity-wake patterns \u003csup\u003e5\u003c/sup\u003e. In this sense, it is still unclear to what extent the negative health effects of shift work are mediated through circadian and social misalignment, as well as disturbed sleep \u003csup\u003e6\u003c/sup\u003e\u003csup\u003e–10\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eShift workers face difficulties initiating and maintaining their sleep, reporting greater sleepiness and shorter sleep durations than daytime workers \u003csup\u003e11\u003c/sup\u003e, especially while working night and morning shifts \u003csup\u003e12,13\u003c/sup\u003e. The irregular, insufficient, and low-quality sleep associated with shift work has adverse health consequences and affects performance and safety at work \u003csup\u003e6\u003c/sup\u003e\u003csup\u003e–10,14,15\u003c/sup\u003e. Ensuring adequate sleep duration –above 7 h in adults– is essential for health, physical development, quality of life, cognitive performance, and wellness\u003csup\u003e16\u003c/sup\u003e. In this regard, reduced sleep durations are linked to all-cause mortality and pathologies such as metabolic syndrome, cardiovascular disease, and osteoporosis \u003csup\u003e17\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSeveral characteristics, such as age, gender, and chronotype, have been identified as protective against shift work's negative effects \u003csup\u003e18\u003c/sup\u003e, including sleep and sleep disorders \u003csup\u003e19\u003c/sup\u003e. Chronotype is recognized for influencing individual adaptability to work in shifts \u003csup\u003e8,20\u003c/sup\u003e. Chronotype can be operationalized as the midpoint of sleep on free days based on the Munich Chronotype Questionnaire \u0026nbsp;(MCTQ) \u003csup\u003e21\u003c/sup\u003e. Several studies have found that this measure influences sleep timing and duration across different work shifts \u003csup\u003e22\u003c/sup\u003e\u003csup\u003e–25\u003c/sup\u003e. Overall, the flexibility in sleep behavior of late chronotypes enhances their tolerance for shift work \u003csup\u003e26\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDue to social pressures associated with work schedules, sleep debt tends to accumulate on workdays. Two main recovery or compensatory strategies are employed \u003csup\u003e27,28\u003c/sup\u003e: taking naps and increasing sleep duration on days off. Napping is common among shift workers to mitigate accumulated sleep debt and increase sleep duration \u003csup\u003e29\u003c/sup\u003e, and there is evidence that napping depends on the interaction between workers' chronotype and work shift \u003csup\u003e24\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eStudies indicate that Uruguayan youngsters exhibit markedly late chronotypes \u003csup\u003e30,31\u003c/sup\u003e, that affect their sleep differently depending on the social clock \u003csup\u003e32\u003c/sup\u003e. In this study, we investigated three main aspects of the sleep patterns of a group of Uruguayan shift workers: a) the association between sleep timing and duration and shift work schedules, b) how chronotype influences this association, and c) differences in the use of compensatory strategies for sleep debt according to chronotype.\u003c/p\u003e"},{"header":"2. Results","content":"\u003cp\u003eWe present data from an effective sample of 85 shift workers (80% male) between 20 and 58 years old (mean age: 36.8 \u0026plusmn; 8.3 years; mean number of years of shift work: 10.8 \u0026plusmn; 8.0 years), from an industrial plant in Montevideo, Uruguay, operating under a slow, counterclockwise three-shift rotation system (morning, night, and afternoon). Chronotype, social jetlag, and sleep timing and duration were calculated from answers to the Munich Chronotype Questionnaire for shift workers (MCTQshift) according to Juda et al. \u003csup\u003e23\u003c/sup\u003e. The mean chronotype was 5:04 \u0026plusmn; 1:30 and the mean social jetlag was 2.22 \u0026plusmn; 1.07 h, -4.66 \u0026plusmn; 2.23 h, and 0.12 \u0026plusmn; 0.57 h, for morning, night, and afternoon shifts, respectively. We calculated the main sleep duration (SD) and the 24-hour sleep duration (the sum of main sleep and nap durations; SD/24) for each day type (workdays and days off) and shift. Additionally, we computed the daily averages over the workweek for both the SD (SDweek) and the SD/24 (SDweek/24). We found that the SD on workdays was 2-3 h shorter than the recommended 7 h in the morning and night shifts, but not in the afternoon shift, nor during days off. We also observed differences in sleep timing and duration according to the chronotype. The analysis of strategies to compensate for sleep debt (naps and extended sleep on days off) revealed two interesting results: 1) These strategies were predominantly employed during shifts with shorter SD (morning and night), increasing the SDweek/24 up to almost 7 h, and 2) Their use was modulated by chronotype, with evening-oriented workers adding the most hours of sleep.\u003c/p\u003e\n\u003ch2\u003e2.1. Shift-based sleep patterns\u003c/h2\u003e\n\u003cp\u003eTable S1 and Figure 1 present mean sleep timing, duration, and nap frequency by day type and shift. To analyze the influence of work schedule on sleep timing and duration, we first compared the Beta coefficients for each day type in Table 1 (estimated means; dots in Figures 2a and 2b) to examine the influence of day type on each sleep variable. Secondly, we analyzed the impact of work shifts on SDweek. SDweek represents the weighted contribution of the SD (or the SD/24) in workdays and days off. It is compared between shifts in Table 2 (Beta coefficients for workweek type; dots in Figures 2c and 2d). The contribution of naps to daily sleep can be examined by comparing the SD and the SD/24 Beta coefficients for each day type in Table 1 or between the SDweek and the SDweek/24 Beta coefficients in Table 2.\u003c/p\u003e\n\u003cp\u003eWe found a significant effect of day type on sleep timing and duration (Table 1). Estimated sleep timing (both sleep onset and end) was similar between workdays during the afternoon shift and days off in the afternoon and night shifts. Sleep timing was the latest during the night shift and earliest during the morning shift, especially on workdays. Estimated SD was approximately 8 h on days off across all shifts and on workdays during the afternoon shift. However, it was about 3 h shorter on workdays during the morning and night shifts. As shown in Table S1, napping was more frequent on workdays during morning and night shifts (over 50%), but rare during the afternoon shift. On days off, naps occurred on almost 20% of days across all shifts. This napping pattern resulted in an SD/24 of nearly 6 h on workdays for both the morning and night shifts (1 h longer than the SD), approximately 8 h on workdays for the afternoon shift, and 8.5 h on days off across all shifts.\u003c/p\u003e\n\u003cp\u003eWhen examining SDweek, the estimated sleep duration was 2 h shorter during the morning and night shifts than during the afternoon shift (Table 2). However, SDweek/24 during the morning and night shifts was almost 45 min longer than SDweek, nearing 7 h, and the differences in SDweek/24 between the morning and night shifts and the afternoon shift were reduced to 1.5 h (Table 2).\u003c/p\u003e\n\u003ch2\u003e2.2.\u0026nbsp;Chronotype modulates the relationship between shift and sleep pattern\u003c/h2\u003e\n\u003cp\u003eTo explore the association between chronotype, sleep patterns, and work schedules, we analyzed the interaction between chronotype and day type (Table 1; Figure 2). The Beta coefficients (estimated slope) for the day type and chronotype interaction (for daily sleep parameters; Table 1) and for the workweek type and chronotype interaction (for SDweek and SDweek/24; Table 2) indicate the association between chronotype and sleep by shift. Furthermore, differences in the SD based on chronotype were observed when slopes for sleep onset and end became distant or nearby (larger vs. opposite values, respectively).\u003c/p\u003e\n\u003cp\u003eThe interaction was significant for the four daily sleep timing and duration variables (Table 1). Chronotype showed a significant negative association with sleep onset during the night shift workdays, with a 1-h delay in chronotype resulting in a ~15 min advance in sleep onset. However, chronotype had a significant positive association on the other days, with a 1-h delay in chronotype resulting in delays of 26 - 53 min. Regarding sleep end, most of the correlations were stronger than those for sleep onset, except for workdays during morning and night shifts, when the association was not significant. In all other day types, the association between chronotype and sleep end was positive, and the delay in sleep end associated with a 1-h delay in chronotype was between 54 and 63 min. As a result, on workdays during the morning shift the association between chronotype and main sleep duration was negative, with a reduction of more than 20 min for each 1-h delay in chronotype. Conversely, on all other day types, there was a positive association, with durations 21 - 25 min longer for each 1-h delay in chronotype (Figure 2a). On days off, evening-oriented workers reported longer SD across all work schedules, although this association was non-significant during the night shift (Figure 2b). When nap duration was taken into account, the association between chronotype and SD/24 remained similar for most of the day types. However, the association was negative and not significant for workdays during the morning shift.\u003c/p\u003e\n\u003cp\u003eChronotype was associated with SDweek only for the night and afternoon shifts (Table 2; Figures 2c and 2d). A delay of 1 h in chronotype was associated with approximately 25 min longer sleep in both SDweek and SDweek/24. The estimated SDweek was greater than 7 h for workers with chronotypes later than 07:33 in the night shift or 02:24 in the afternoon shift, while none of the observed chronotypes predicted a healthy sleep duration in the morning shift (Figure 2c). The estimated SDweek/24 was above 7 h for workers with chronotypes later than 07:39, 05:51, and 02:14 for morning, night, and afternoon shifts respectively (Figure 2d).\u003c/p\u003e"},{"header":"3. Discussion","content":"\u003cp\u003eTo the best of our knowledge, our study is the first to use a Spanish version of the MCTQshiftquestionnaire to assess chronotypes and sleep patterns in shift workers. Moreover, it is the first in Latin America to examine the impact of a slow and counterclockwise rotation system on workers\u0026rsquo; sleep patterns and their adaptive strategies based on their chronotype. Sleep duration on workdays varied across shifts, with both morning and night shifts associated with the shortest durations. This pattern originated in the misalignment between the sleep opportunity window and the circadian clock imposed by the work schedule. Napping significantly increased daily sleep duration, especially on workdays during these more demanding morning and night shifts, resulting in an additional hour of sleep. Furthermore, extended SD on days off led to an almost one-hour increase in SDweek during both morning and night shift workweeks. Chronotype-based differences were found in the ability to cope with shifts in sleep opportunity and the amount of sleep recovery by naps and extended sleep on days off: a) Morning-oriented workers demonstrated a greater ability to advance sleep compared to days off and experienced longer sleep durations on workdays during the morning shift; b) Evening-oriented individuals coped better with daytime sleeping with longer sleep durations on all other workdays; c) Eveningness was associated with extended catch-up sleep, as suggested by longer naps on workdays during the morning shift and longer sleep durations on all days off.\u003c/p\u003e\n\u003ch2\u003e3.1.\u0026nbsp; Sleep on workdays\u003c/h2\u003e\n\u003cp\u003eConsistent with previous studies, both permanent or rotating morning and night shifts imposed constraints on sleep timing and duration by altering the available sleep window opportunity \u003csup\u003e11,12\u003c/sup\u003e. Our findings confirmed that workers were compelled to either delay their sleep onset during the night shift or curtail their sleep during the morning shift, resulting in a despaired SD of about 5 h. Similar reduced main SD during morning and night shifts have been documented in other studies on shift workers \u003csup\u003e12,13\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eLaboratory studies have shown that daytime sleep during night shifts tends to be of shorter duration \u003csup\u003e33\u003c/sup\u003e. Additionally, the later the sleep onset, the more challenging it becomes to maintain sleep, with chronotype-related differences influencing this pattern \u003csup\u003e23\u003c/sup\u003e. On the other hand, work shifts that start very early in the morning are almost as challenging for sleep as night shifts for many workers since they fail to advance the sleep onset because of the wake maintenance zone \u003csup\u003e34\u003c/sup\u003e. Social and family commitments may also hinder the adjustment of sleep timing \u003csup\u003e35\u003c/sup\u003e. In Uruguay, late dinner times are common and have been shown to restrict sleep duration in adolescents \u003csup\u003e32\u003c/sup\u003e. This cultural delay may exacerbate difficulties in advancing sleep onset on the nights preceding a morning shift.\u003c/p\u003e\n\u003ch2\u003e3.2.\u0026nbsp;Sleep debt compensation\u003c/h2\u003e\n\u003cp\u003eExtended SD on days off is a common strategy to compensate for insufficient sleep on workdays \u003csup\u003e36,37\u003c/sup\u003e. Our results indicate that SD was longer on days off than workdays during morning and night shift workweeks.\u0026nbsp;The main SD on days off was similar to that reported in German workers \u003csup\u003e23\u003c/sup\u003e and higher than those registered in other shift workers \u003csup\u003e24,38\u003c/sup\u003e. As a result, \u0026nbsp;SDweek exceeded SD on workdays for both morning and night shifts but remained shorter than SDweek during afternoon shifts. Unexpectedly, SD on days off during morning and night shifts was similar to SD on both work days and days off during the afternoon shift, suggesting that sleep on days off remained constrained.\u003c/p\u003e\n\u003cp\u003eNapping complements daily sleep and facilitates adaptation to shift work \u003csup\u003e8,39\u003c/sup\u003e. In our study, naps were more frequent on workdays during morning and night shifts, adding almost 1 h of sleep to the SD/24 and nearly 45 min to the SDweek/24. Napping patterns remained consistent across different shifts during days off. Overall, naps were common and helped to mitigate the disparities in SDweek/24 across distinct workweeks. Our results align with previous findings \u003csup\u003e22,38\u003c/sup\u003e. However, other studies have reported an even greater contribution of naps during night shifts \u003csup\u003e24\u003c/sup\u003e.\u003c/p\u003e\n\u003ch2\u003e3.3.\u0026nbsp;Chronotype-based differences in daily sleep\u003c/h2\u003e\n\u003cp\u003eParticipants exhibited late chronotypes, later than values reported in other shift workers \u003csup\u003e24,38,40,41\u003c/sup\u003e. This pronounced eveningness aligns with previous records of extreme late chronotypes in Uruguayan and Argentinian populations \u003csup\u003e42\u003c/sup\u003e. Average social jetlag was\u0026nbsp;similar to previous research for the morning and afternoon shifts but lower in the night shift \u003csup\u003e23\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eSleep timing and duration varied among workers with different chronotypes. Late chronotypes exhibited delayed sleep timing when sleeping at night (on morning and afternoon shift workdays, and all days off) compared to early chronotypes. Conversely, during daytime sleep on night shifts, late chronotypes went to bed earlier and woke up later than early chronotypes. Furthermore, late chronotypes slept longer than early chronotypes on most days, except for morning shift workdays. The positive association between eveningness and main SD in the night shift was also observed in previous studies \u003csup\u003e22\u003c/sup\u003e\u003csup\u003e\u0026ndash;25\u003c/sup\u003e. Evidence on SD in late chronotypes during the afternoon shift is mixed. Some studies support our finding of longer SD \u003csup\u003e23,24\u003c/sup\u003e , while others do not \u003csup\u003e22,43\u003c/sup\u003e. The restriction of sleep in late chronotypes during morning shifts due to early start times has been previously reported \u003csup\u003e23,24,43\u003c/sup\u003e, though this effect was not observed in fast-forward rotating shifts \u003csup\u003e22\u003c/sup\u003e. Additionally, late chronotypes tend to sleep longer and accumulate less sleep debt when assigned to night shifts compared to morning shifts \u003csup\u003e44\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe longer SD of late chronotypes on night shift workdays may be attributed to their better ability to sleep during the daytime \u003csup\u003e18\u003c/sup\u003e. However, daytime sleep is often challenging and susceptible to premature interruption \u003csup\u003e33\u003c/sup\u003e. This may also explain the longer sleep durations on days off for evening-oriented workers, even in shifts when they sleep longer than early chronotypes. Furthermore, during the morning shift, evening-oriented workers exhibited greater efficacy in compensating for sleep deficits by incorporating daytime naps.\u003c/p\u003e\n\u003cp\u003eOur findings align with previous studies suggesting that individuals with late chronotypes exhibit more flexible sleep timing and a greater tolerance for shift work than early chronotypes \u003csup\u003e20,45\u003c/sup\u003e. Beyond the relevance of shift work reorganization for sleep health \u003csup\u003e13\u003c/sup\u003e, accounting for individual differences, such as chronotypes, which aid workers in adapting to shifts, and designing rosters that consider these traits, appears to be a promising approach \u003csup\u003e46\u003c/sup\u003e. Incorporating chronobiological insights into shift scheduling represents a promising direction for future research and workplace policies.\u003c/p\u003e\n\u003ch2\u003e3.4. Strengths and limitations\u003c/h2\u003e\n\u003cp\u003eOur study is the first to employ the MCTQshift to examine the impact of a slow, counterclockwise rotating shift system on sleep patterns within a Latin American context. It provides valuable insights into how regional and cultural factors, particularly the prevalence of late chronotypes in Uruguay, influence workers\u0026apos; sleep behavior. However, several limitations should be noted. \u0026nbsp;The sample size may limit the generalizability of our findings to other populations or industries. Additionally, the use of self-reported data introduces the potential for recall bias or inaccuracies. Future studies could benefit from integrating objective measures such as actigraphy and dim light melatonin onset to enhance data reliability. Furthermore, assessing sleep quality would provide a more comprehensive understanding of sleep health. The cross-sectional design precludes causal inferences. Lastly, external factors, including social and family commitments, were not explored.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e3.5. Concluding remarks\u003c/h2\u003e\n\u003cp\u003eThe morning and night shifts proved to be the most demanding for workers\u0026apos; sleep, as shift workers typically achieved only 4 to 5 h of sleep. As the work shift overlaps with the usual sleep window, workers adjusted their sleep timing but were unable to reach the recommended 7 h. While additional sleep obtained through napping and on days off increased SD, average SD/24 and SDweek during morning and night shifts remained below 7 h. Individual characteristics, such as chronotype, influenced the impact of shift work. Specifically, eveningness was associated with longer SD across nearly all day types. Additionally, eveningness was associated with an increased utilization of compensatory strategies, such as extended sleep on days off and increased napping.\u003c/p\u003e"},{"header":"4. Methods","content":"\u003ch2\u003e4.1.\u0026nbsp;Data collection\u003c/h2\u003e\n\u003cp\u003eWe conducted a field study among shift workers from an industrial plant in Montevideo, Uruguay. The company organized its activities into three shifts: morning (06:00 to 14:00), night (22:00 to 06:00), and afternoon (14:00 to 22:00). Participants\u0026apos; work schedules were organized in weeks, mostly comprising five workdays followed by two days off. The rotation system was slow and counterclockwise (morning - rest - night - rest - afternoon - rest - morning - \u0026hellip;). The staff was divided into four rosters, with one roster working on each shift and the fourth resting. A total of 195 workers with a minimum of two years of experience working rotating shifts were invited to participate through union communications and plant tours led by a shop steward. Of these, 104 employees voluntarily agreed to participate. Data were collected from March to May 2021 with strong. All participants provided written informed consent and answers were processed anonymously. Ethics approval was obtained on December 23, 2020, from the Ethics Committee of the School of Psychology, Universidad de la Rep\u0026uacute;blica. All data collection was performed in accordance with relevant guidelines and regulations.\u003c/p\u003e\n\u003ch2\u003e4.2.\u0026nbsp;Sleep and chronotype assessment\u003c/h2\u003e\n\u003cp\u003eParticipants were asked about gender, household composition, age, number of years of shift-working, and other characteristics by a sociodemographic questionnaire. Participants were also instructed to answer the questions from the abbreviated version of the MCTQshift \u003csup\u003e47\u003c/sup\u003e. The MCTQshift was developed to assess the sleep/wake behavior in shift workers, and the main difference between the original MCTQ and this version is that the set of questions for work and days off are queried for each shift \u003csup\u003e23,47\u003c/sup\u003e. The abbreviated version of the MCTQshift asks simple questions about sleep timing such as bedtime, wake-up time, use of alarms, and the use and duration of naps on all days (work or off), and shifts (morning, night, afternoon).\u003c/p\u003e\n\u003ch2\u003e4.3.\u0026nbsp;Data processing and statistical analysis\u003c/h2\u003e\n\u003cp\u003eFrom the answers given to the reduced version of the MCTQshift questionnaire, we calculated the following variables, according to Juda et al. \u003csup\u003e23\u003c/sup\u003e:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eDay-specific (a workday on morning, night, or afternoon shift workweek, or a day off on morning, night, or afternoon shift workweek) sleep onset, sleep end, main sleep duration (SD), and 24-hour basis sleep duration (main sleep and nap durations added together; SD/24).\u003c/li\u003e\n \u003cli\u003eDaily averaged over the workweek main and 24-hour basis sleep duration in a given shift (SDweek or SDweek/24 in a morning, night, or afternoon shift workweek).\u003c/li\u003e\n \u003cli\u003eChronotype was assessed based on mid-sleep between two days off after working in the afternoon shift corrected for oversleeping.\u003c/li\u003e\n \u003cli\u003eSocial jetlag in every workweek.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe data were analyzed using the R language in RStudio \u003csup\u003e48\u003c/sup\u003e. Data from 19 participants (18.2%) who relied on wake-up alarms on their days off were excluded. Generalized linear regressions were used with ID as a random effect to evaluate the influence of day type (six levels: workday or day off during morning, night, or afternoon shifts) or workweek type (three levels: morning, night, or afternoon shift) as well as chronotype on sleep variables. The chronotype was mean-centered before being included in the models. Age was included as a covariate. Post hoc pairwise comparisons were performed using Tukey\u0026apos;s method. The calculated statistics were considered significant when p \u0026lt; 0.05. Descriptive statistics are presented as mean \u0026plusmn; standard deviation or sample size (n) and percentage, and estimated values are presented as estimated mean \u0026plusmn; standard error.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e6. \u0026nbsp; Acknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank the workers who participated, as well as the union and the company that endorsed and promoted this research. Kent Dunlap read and made suggestions on an earlier version of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e7. \u0026nbsp; Author contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors designed the experiment. Collecting and processing data, J.M.C.; data analysis and figure design, I.E.; writing the original draft, J.M.C.. All authors reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e8. \u0026nbsp; Data availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data reported in this paper will be shared by the corresponding author upon request. Any additional information required to reanalyze the data reported in this paper is available from the corresponding author upon request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e9. \u0026nbsp; Competing interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eClougherty, J. E., Souza, K. \u0026amp; Cullen, M. R. Work and its role in shaping the social gradient in health. \u003cem\u003eAnnals of the New York Academy of Sciences\u003c/em\u003e \u003cstrong\u003e1186\u003c/strong\u003e, 102\u0026ndash;124 (2010).\u003c/li\u003e\n\u003cli\u003eLandsbergis, P. A., Grzywacz, J. G. \u0026amp; LaMontagne, A. D. 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The Importance of Chronotype in Shift Work Research. 193 (2010).\u003c/li\u003e\n\u003cli\u003eLeung, M. \u003cem\u003eet al.\u003c/em\u003e Shift Work, Chronotype, and Melatonin Patterns among Female Hospital Employees on Day and Night Shifts. \u003cem\u003eCancer Epidemiology, Biomarkers \u0026amp; Prevention\u003c/em\u003e \u003cstrong\u003e25\u003c/strong\u003e, 830\u0026ndash;838 (2016).\u003c/li\u003e\n\u003cli\u003eTassino, B. \u0026amp; Leone, M. J. Night owls of Rio de la Plata region: Real-life scenarios to understand the biological clock. \u003cem\u003eNeuroscience\u003c/em\u003e (2025) doi:10.1016/j.neuroscience.2025.02.022.\u003c/li\u003e\n\u003cli\u003evan de Ven, H. A. \u003cem\u003eet al.\u003c/em\u003e Sleep and need for recovery in shift workers: do chronotype and age matter? \u003cem\u003eErgonomics\u003c/em\u003e \u003cstrong\u003e59\u003c/strong\u003e, 310\u0026ndash;324 (2016).\u003c/li\u003e\n\u003cli\u003eFischer, D., Roenneberg, T. \u0026amp; Vetter, C. Chronotype-specific Sleep in Two Versus Four Consecutive Shifts. \u003cem\u003eJournal of Biological Rhythms\u003c/em\u003e \u003cstrong\u003e36\u003c/strong\u003e, 395\u0026ndash;409 (2021).\u003c/li\u003e\n\u003cli\u003eRitonja, J., Aronson, K. J., Matthews, R. W., Boivin, D. B. \u0026amp; Kantermann, T. Working Time Society consensus statements: Individual differences in shift work tolerance and recommendations for research and practice. \u003cem\u003eInd Health\u003c/em\u003e \u003cstrong\u003e57\u003c/strong\u003e, 201\u0026ndash;212 (2019).\u003c/li\u003e\n\u003cli\u003eVetter, C., Fischer, D., Matera, J. L. \u0026amp; Roenneberg, T. Aligning Work and Circadian Time in Shift Workers Improves Sleep and Reduces Circadian Disruption. \u003cem\u003eCurrent Biology\u003c/em\u003e \u003cstrong\u003e25\u003c/strong\u003e, 907\u0026ndash;911 (2015).\u003c/li\u003e\n\u003cli\u003eJuda, M., Vetter, C. \u0026amp; Roenneberg, T. The Munich ChronoType Questionnaire for Shift-Workers. \u003cem\u003eJ Biol Rhythms\u003c/em\u003e \u003cstrong\u003e28\u003c/strong\u003e, 130\u0026ndash;140 (2013).\u003c/li\u003e\n\u003cli\u003eRStudio Team. RStudio: Integrated development environment for R. RStudio, Inc. (2016).\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Influence of the interaction between day type and chronotype on sleep variables.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"910\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 702px;\"\u003e\n \u003cp\u003eBeta [95% CI]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eMorning shift\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eNight shift\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eAfternoon shift\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003eANOVA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003eWorkday\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003eDay off\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003eWorkday\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003eDay off\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003eWorkday\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003eDay off\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\" valign=\"top\" style=\"width: 910px;\"\u003e\n \u003cp\u003eSleep onset (R\u003csup\u003e2\u003c/sup\u003e marginal/conditional = 0.90/0.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eDay type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003eF(5,425)=1067.1, p\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e00:05\u003cbr\u003e[23:53, 00:16]\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e00:37\u003cbr\u003e[00:26, 00:49]\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e07:40\u003cbr\u003e[07:28, 07:51]\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e01:29\u003cbr\u003e[01:17, 01:40]\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e01:09\u003cbr\u003e[00:58, 01:21]\u003csup\u003e3,4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e01:05\u003cbr\u003e[00:53, 01:16]\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eChronotype * Day type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003eF(5,425)=53.3, p\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e26\u003cbr\u003e[18, 34]\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e37\u003cbr\u003e[29, 45]\u003csup\u003e2,3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e-16\u003cbr\u003e[-24, -9]\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e53\u003cbr\u003e[45, 61]\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e35\u003cbr\u003e[27, 43]\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e51\u003cbr\u003e[43, 59]\u003csup\u003e3,4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\" valign=\"top\" style=\"width: 910px;\"\u003e\n \u003cp\u003eSleep end (R\u003csup\u003e2\u003c/sup\u003e marginal/conditional = 0.85/0.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eDay type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003eF(5,425)=526.5, p\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e04:59\u003cbr\u003e[04:46, 05:13]\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e08:53\u003cbr\u003e[08:40, 09:07]\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e12:44\u003cbr\u003e[12:31, 12:58]\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e09:36\u003cbr\u003e[09:23, 09:50]\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e09:06\u003cbr\u003e[08:53, 09:20]\u003csup\u003e2,3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e09:25\u003cbr\u003e[09:11, 09:38]\u003csup\u003e3,4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eChronotype * Day type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003eF(5,425)=39.5, p\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e4\u003cbr\u003e[-6, 13]\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e54\u003cbr\u003e[44, 63]\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e9\u003cbr\u003e[-1, 18]\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e60\u003cbr\u003e[51, 69]\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e56\u003cbr\u003e[46, 65]\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e63\u003cbr\u003e[53, 72]\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\" valign=\"top\" style=\"width: 910px;\"\u003e\n \u003cp\u003eMain sleep duration (R\u003csup\u003e2\u003c/sup\u003e marginal/conditional = 0.65/0.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eDay type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003eF(5,425)=229.6, p\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e4.91\u003cbr\u003e[4.65, 5.17]\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e8.27\u003cbr\u003e[8.01, 8.53]\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e5.08\u003cbr\u003e[4.82, 5.34]\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e8.13\u003cbr\u003e[7.87, 8.39]\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e7.95\u003cbr\u003e[7.7, 8.21]\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e8.33\u003cbr\u003e[8.07, 8.59]\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eChronotype * Day type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003eF(5,425)=15.4, p\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e-22\u003cbr\u003e[-33, -12]\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e16\u003cbr\u003e[6, 27]\u003csup\u003e2,3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e25\u003cbr\u003e[14, 36]\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e7\u003cbr\u003e[-4, 17]\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e21\u003cbr\u003e[10, 31]\u003csup\u003e2,3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e12\u003cbr\u003e[1, 22]\u003csup\u003e2,3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\" valign=\"top\" style=\"width: 910px;\"\u003e\n \u003cp\u003e24-hour basis sleep duration (R\u003csup\u003e2\u003c/sup\u003e marginal/conditional = 0.50/0.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eDay type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003eF(5,425)=125.4, p\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e5.89\u003cbr\u003e[5.61, 6.17]\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e8.58\u003cbr\u003e[8.29, 8.86]\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e5.98\u003cbr\u003e[5.7, 6.26]\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e8.46\u003cbr\u003e[8.18, 8.75]\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e7.98\u003cbr\u003e[7.69, 8.26]\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e8.57\u003cbr\u003e[8.29, 8.85]\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eChronotype * Day type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003eF(5,425)=4.7, p\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e-3\u003cbr\u003e[-14, 9]\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e18\u003cbr\u003e[7, 30]\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e24\u003cbr\u003e[12, 35]\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e8\u003cbr\u003e[-4, 20]\u003csup\u003e1,2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e22\u003cbr\u003e[11, 34]\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e12\u003cbr\u003e[1, 24]\u003csup\u003e1,2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\" valign=\"top\" style=\"width: 910px;\"\u003e\n \u003cp\u003eNotes: Beta coefficients for day type are the estimated mean time (HH:MM) or duration (h), while Beta coefficients for the interaction between day type and chronotype are the estimated change in minutes when chronotype is delayed in 1h. The chronotype was mean-centered, ID was included as a random effect, and age was included as a covariate. 1\u0026lt;2\u0026lt;3\u0026lt;4\u0026lt;5 with p\u0026lt;0.05 using Tukey correction.\u003c/p\u003e\n \u003cp\u003eCI: Confidence Interval.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Influence of the interaction between workweek type and chronotype on daily averaged over the workweek sleep durations (SDweek).\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"930\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 627px;\"\u003e\n \u003cp\u003eBeta [95% CI]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eANOVA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 209px;\"\u003e\n \u003cp\u003eMorning shift\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 209px;\"\u003e\n \u003cp\u003eNight shift\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 209px;\"\u003e\n \u003cp\u003eAfternoon shift\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 930px;\"\u003e\n \u003cp\u003eDaily averaged over the workweek main sleep duration (R\u003csup\u003e2\u003c/sup\u003e marginal/conditional = 0.61/0.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eWorkweek type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eF(2,170)=216.1, p\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 209px;\"\u003e\n \u003cp\u003e5.87\u003cbr\u003e\u0026nbsp;[5.67, 6.07]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 209px;\"\u003e\n \u003cp\u003e5.95\u003cbr\u003e\u0026nbsp;[5.75, 6.16]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 209px;\"\u003e\n \u003cp\u003e8.06\u003cbr\u003e\u0026nbsp;[7.86, 8.26]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eChronotype * Workweek type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eF(2,170)=27.3, p\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 209px;\"\u003e\n \u003cp\u003e-11\u003cbr\u003e[-19, -2]\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 209px;\"\u003e\n \u003cp\u003e20\u003cbr\u003e[12, 29]\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 209px;\"\u003e\n \u003cp\u003e19\u003cbr\u003e[10, 27]\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 930px;\"\u003e\n \u003cp\u003eDaily averaged over the workweek 24-hour sleep duration (R\u003csup\u003e2\u003c/sup\u003e marginal/conditional = 0.42/0.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eWorkweek type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eF(2,170)=85.8, p\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 209px;\"\u003e\n \u003cp\u003e6.66\u003cbr\u003e\u0026nbsp;[6.43, 6.88]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 209px;\"\u003e\n \u003cp\u003e6.69\u003cbr\u003e\u0026nbsp;[6.46, 6.92]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 209px;\"\u003e\n \u003cp\u003e8.15\u003cbr\u003e\u0026nbsp;[7.92, 8.37]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eChronotype * Workweek type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eF(2,170)=6.5, p=0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 209px;\"\u003e\n \u003cp\u003e4\u003cbr\u003e[-6, 13]\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 209px;\"\u003e\n \u003cp\u003e20\u003cbr\u003e[10, 29]\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 209px;\"\u003e\n \u003cp\u003e20\u003cbr\u003e[10, 29]\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 930px;\"\u003e\n \u003cp\u003eNotes: Beta coefficients for day type are the estimated mean duration (h), while Beta coefficients for the interaction between day type and chronotype are the estimated change in minutes when chronotype is delayed in 1h (the slope). The chronotype was mean-centered, ID was included as a random effect, and age was included as a covariate. 1\u0026lt;2 with p\u0026lt;0.05 using Tukey correction.\u003cbr\u003e\u0026nbsp;CI: Confidence Interval.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"sleep, napping, occupational health, shift work schedule, eveningness","lastPublishedDoi":"10.21203/rs.3.rs-6172058/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6172058/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eShift work disrupts sleep patterns and is linked to adverse health outcomes, yet the role of chronotype in modulating shift workers' sleep behavior remains underexplored. A field study was conducted with 85 shift workers from an industrial plant in Montevideo, Uruguay, operating under a slow, counterclockwise three-shift rotation system. The abbreviated Munich Chronotype Questionnaire was used to assess sleep timing, duration, and napping behavior across shifts, and chronotype. Sleep duration on workdays was significantly shorter during morning (4.9±0.8h) and night shifts (5.0±1.5h) compared to afternoon shifts (7.9±1.3h). Napping was more frequent during the morning (54.1%) and night shifts (52.9%), contributing to almost an additional hour of sleep to mitigate sleep debt. Evening-oriented workers demonstrated greater sleep flexibility and adaptability, sleeping longer on days off and during night and afternoon shifts: A 1-hour delay in chronotype associated with 7-25 minutes of additional sleep. Morning-oriented workers adapted better to advancing sleep during morning shifts and got more sleep. These results highlight the importance of considering individual chronotypes when designing shift schedules. Personalized shift schedules could enhance sleep health and mitigate the adverse effects of shift work.\u003c/p\u003e","manuscriptTitle":"The later, the better: Evening chronotypes in Uruguayan shift workers are associated with longer sleep in an observational study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-07 07:15:58","doi":"10.21203/rs.3.rs-6172058/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-20T03:58:06+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-01T09:15:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"337444796758015558133218518773319421553","date":"2025-08-12T08:05:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"25486896552482930185547111142260503575","date":"2025-08-11T11:29:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"121018397059329863348611510996486470272","date":"2025-08-08T15:14:50+00:00","index":"hide","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-03T09:24:02+00:00","index":"","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-05T19:38:53+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-18T12:46:51+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-17T18:37:47+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-03-17T18:36:41+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b7f38bc8-b743-4f08-965c-1f404084767f","owner":[],"postedDate":"April 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":46584507,"name":"Biological sciences/Neuroscience/Circadian rhythms and sleep/Sleep"},{"id":46584509,"name":"Health sciences/Health care/Occupational health"}],"tags":[],"updatedAt":"2026-04-09T08:38:49+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-07 07:15:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6172058","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6172058","identity":"rs-6172058","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}