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      Shift work, sleep disturbances and social jetlag in healthcare workers

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          Sleep duration predicts cardiovascular outcomes: a systematic review and meta-analysis of prospective studies.

          Aims To assess the relationship between duration of sleep and morbidity and mortality from coronary heart disease (CHD), stroke, and total cardiovascular disease (CVD). Methods and results We performed a systematic search of publications using MEDLINE (1966-2009), EMBASE (from 1980), the Cochrane Library, and manual searches without language restrictions. Studies were included if they were prospective, follow-up >3 years, had duration of sleep at baseline, and incident cases of CHD, stroke, or CVD. Relative risks (RR) and 95% confidence interval (CI) were pooled using a random-effect model. Overall, 15 studies (24 cohort samples) included 474 684 male and female participants (follow-up 6.9-25 years), and 16 067 events (4169 for CHD, 3478 for stroke, and 8420 for total CVD). Sleep duration was assessed by questionnaire and incident cases through certification and event registers. Short duration of sleep was associated with a greater risk of developing or dying of CHD (RR 1.48, 95% CI 1.22-1.80, P < 0.0001), stroke (1.15, 1.00-1.31, P = 0.047), but not total CVD (1.03, 0.93-1.15, P = 0.52) with no evidence of publication bias (P = 0.95, P = 0.30, and P = 0.46, respectively). Long duration of sleep was also associated with a greater risk of CHD (1.38, 1.15-1.66, P = 0.0005), stroke (1.65, 1.45-1.87, P < 0.0001), and total CVD (1.41, 1.19-1.68, P < 0.0001) with no evidence of publication bias (P = 0.92, P = 0.96, and P = 0.79, respectively). Conclusion Both short and long duration of sleep are predictors, or markers, of cardiovascular outcomes.
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            Social jetlag: misalignment of biological and social time.

            Humans show large differences in the preferred timing of their sleep and activity. This so-called "chronotype" is largely regulated by the circadian clock. Both genetic variations in clock genes and environmental influences contribute to the distribution of chronotypes in a given population, ranging from extreme early types to extreme late types with the majority falling between these extremes. Social (e.g., school and work) schedules interfere considerably with individual sleep preferences in the majority of the population. Late chronotypes show the largest differences in sleep timing between work and free days leading to a considerable sleep debt on work days, for which they compensate on free days. The discrepancy between work and free days, between social and biological time, can be described as 'social jetlag.' Here, we explore how sleep quality and psychological wellbeing are associated with individual chronotype and/or social jetlag. A total of 501 volunteers filled out the Munich ChronoType Questionnaire (MCTQ) as well as additional questionnaires on: (i) sleep quality (SF-A), (ii) current psychological wellbeing (Basler Befindlichkeitsbogen), (iii) retrospective psychological wellbeing over the past week (POMS), and (iv) consumption of stimulants (e.g., caffeine, nicotine, and alcohol). Associations of chronotype, wellbeing, and stimulant consumption are strongest in teenagers and young adults up to age 25 yrs. The most striking correlation exists between chronotype and smoking, which is significantly higher in late chronotypes of all ages (except for those in retirement). We show these correlations are most probably a consequence of social jetlag, i.e., the discrepancies between social and biological timing rather than a simple association to different chronotypes. Our results strongly suggest that work (and school) schedules should be adapted to chronotype whenever possible.
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              Life between Clocks: Daily Temporal Patterns of Human Chronotypes

              Human behavior shows large interindividual variation in temporal organization. Extreme "larks" wake up when extreme "owls" fall asleep. These chronotypes are attributed to differences in the circadian clock, and in animals, the genetic basis of similar phenotypic differences is well established. To better understand the genetic basis of temporal organization in humans, the authors developed a questionnaire to document individual sleep times, self-reported light exposure, and self-assessed chronotype, considering work and free days separately. This report summarizes the results of 500 questionnaires completed in a pilot study individual sleep times show large differences between work and free days, except for extreme early types. During the workweek, late chronotypes accumulate considerable sleep debt, for which they compensate on free days by lengthening their sleep by several hours. For all chronotypes, the amount of time spent outdoors in broad daylight significantly affects the timing of sleep: Increased self-reported light exposure advances sleep. The timing of self-selected sleep is multifactorial, including genetic disposition, sleep debt accumulated on workdays, and light exposure. Thus, accurate assessment of genetic chronotypes has to incorporate all of these parameters. The dependence of human chronotype on light, that is, on the amplitude of the light:dark signal, follows the known characteristics of circadian systems in all other experimental organisms. Our results predict that the timing of sleep has changed during industrialization and that a majority of humans are sleep deprived during the workweek. The implications are far ranging concerning learning, memory, vigilance, performance, and quality of life.
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                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                Journal of Sleep Research
                J Sleep Res
                Wiley
                09621105
                August 2019
                August 2019
                December 05 2018
                : 28
                : 4
                : e12802
                Affiliations
                [1 ]Department of Public and Occupational Health; Amsterdam UMC; Vrije Universiteit Amsterdam; Amsterdam Public Health Research Institute; Amsterdam The Netherlands
                [2 ]Centre for Nutrition, Prevention and Health Services; National Institute for Public Health and the Environment; Bilthoven The Netherlands
                [3 ]Center for Health Protection; National Institute for Public Health and the Environment, Bilthoven; Bilthoven The Netherlands
                [4 ]Institute for Medical Psychology; Ludwig Maximilian University of Munich; Munich Germany
                Article
                10.1111/jsr.12802
                30520209
                80c69941-5248-4541-aa44-ad0a5c9dd4b8
                © 2018

                http://doi.wiley.com/10.1002/tdm_license_1.1

                http://onlinelibrary.wiley.com/termsAndConditions#vor

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