Overview Credits Quotes Comments. Conon Fraser Director, Writer, Editor. David H Fowler Producer. Dale Pomeroy Camera. See all 8 credits. At one of their largest meeting houses [in Ruatoki] plans for establishing a pine forest are discussed: the affairs of the future dealt with in the manner of the past.
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Subjects also reported number of minutes needed to get out of bed after waking up on days-off. Self-reported on a scale from 0 to 6 as in the MCTQ. Typical schedule variables. Based on the typical schedule, midsleep time and sleep duration for work days was determined from Day F for night-shifters and from the transition to Day F from Day E for day-shifters see Figure S1. Midsleep time and sleep duration for free days was determined from Day B for both shift types.
Total Sleep Duration for the entire work schedule was also quantified. Sleep debt is defined as midsleep on Free Day B minus sleep debt 0. For night-shift schedules, sleep strategies were defined as described in the main text. Phenotypes outcome variables from the survey responses were analyzed using SPSS version For outcome variables derived from the typical schedule part of the survey, some subjects had completed schedules for both day- and night-shift.
To avoid duplicate genetic data for these subjects, the entire dataset was analyzed two ways: first, with all shifts included together un-stratified and second, stratified for shift-type during the analysis of these variables. The strength of this stratification approach is that it allowed comparison of associations during different shift environments; however, the weakness of this strategy is that the overall sample size was decreased, resulting in a loss of power.
Therefore, it is likely that some associations may not have been detected in our analysis. Moreover, we used the Generalized Multifactor Dimensionality Reduction GMDR method  to evaluate potential multi-locus interactions that predict each phenotype after adjusting for significant covariates. Nurses were asked to rate how well adapted they felt to their current or past work schedule, and were given several examples see Figure S1.
Data for shift and age categories determined by being younger or older than the median age of 36 are summarized in Table 1 for minutes to get out of bed, adaptation to current schedule, caffeine consumption, alcohol consumption, and qualitative self-reported chronotype. C Histogram of self-reported chronotype for night-shift nurses black bars and day-shift nurses white bars.
D Adaptation to the current shift was significantly predicted by self-reported chronotype for night-shift nurses black circles and day-shift nurses white circles. Sample responses are represented in Figure 2A—E. Self-reported sleep times accurately reflect and are significantly correlated with ambulatory monitoring such as actigraphy  ,  , . This age dependency for sleep duration was not evident in day-shift workers.
Nurses were instructed to shade the time boxes for any time in which they would sleep, including any naps for only the schedules they had actually experienced. Some nurses had experienced both night-shift or day-shift and they completed both work-week schedules, but most nurses completed only one of the schedules. Each column is one h period beginning at am, and each box represents min.
Gray shaded area refers to the night-shift work schedule pm to am for the typical night-shift schedule at Vanderbilt Hospital. Red shaded areas represent typical responses for sleep time in the surveys. These responses were categorized into five strategy types: A Night Stay: Continued to sleep regularly in the daytime on or off shift; B Nap Proxy NP : On days off, they nap longer than one hour on at least four out of the five days off during the time in which they would normally be asleep when working night-shift; C Switch Sleepers SS : Switch from nights to days by using a strictly enforced schedule, but they do not give up any sleep in order to do so i.
The observation that sleep duration is the same between night-shift and day-shift nurses, but that adaptation is poorer for night-shift nurses implies that the phasing of sleep relative to the underlying and disrupted circadian oscillator is important for optimal adaptation. Therefore, it may be that adaptation to night shift is affected by not only how much sleep the nurses get, but when they sleep relative to their work episode. Here, we distinguished five different sleep strategies that the Vanderbilt Hospital nurses chose for days-off represented in Figure 2A—E.
Three strategies distinguished those who switched completely from nights to days on days-off. The response rate and chronotype distribution for each strategy is depicted in Figure 3A. In our sample, the sleep deprivation commonly occurred just before the first work day, which could impair performance and alertness on the job. While strategy types did not differ with regard to sex or schedule rotation, the strategy types did differ in terms of nurses' age, years of experience, children in the home, and chronotype see Table 3 for descriptive and inferential statistics.
Generally, Incomplete Switchers had the latest chronotypes, while the Sleep Switchers and No Sleep nurses had significantly earlier chronotypes compared to the Incomplete Switchers see Figure 3 , panel A.
Shift Work in Nurses: Contribution of Phenotypes and Genotypes to Adaptation
In addition, older and more experienced nurses were more likely to choose the No Sleep strategy or Night Stay strategies, while nurses with children at home were less likely to be an Incomplete switcher and more likely to choose the No Sleep strategy. See also Table 2 for sleep strategy descriptives. In order to quantify which strategy was associated with the best adaptation for shift work, we selected the nurses who were currently working night-shift and performed a multivariate analysis of variance MANOVA using multiple variables that could be indicative of adaptation.
Further univariate post hoc analysis revealed that nurses adopting the No Sleep strategy resulted in significantly lower adaptation levels 5. In addition to the social factors described above, we investigated whether the environment of shift work could combine with genetic variation to potentially influence the adaptation of nurses to their schedules for review, see . The specific clock gene variants studied here were previously identified to be common polymorphisms in clock genes using a candidate gene approach  see Methods S1. While the overall contribution of rare versus common polymorphisms to trait variation is unknown for the traits of interest in the current study, a candidate gene approach looking for predictive common variation was performed based on the key advantages of this tactic such as high power, ability to generalize, and previous successes of such an approach.
First, alcohol consumption was significantly associated with the NPAS2. Interestingly, two SNPs were associated with a decreased likelihood to doze. However, these two SNPs did not synergistically interact to predict likelihood to doze see Table 3. E Using age and children at home as covariates, total duration of sleep for the work-cycle was significantly associated with PER2. Graph depicts the percentage of nurses within each genotype with sleep durations that fell above or below the mean for all nurses This association was only significant for day-shift nurses.
F Off-shift sleep phase corrected for sleep debt as in  was significantly associated with an NPAS2. All panels: numbers in parentheses below each bar indicate the number of cases with that genotype. The significant single locus, genotype-phenotype associations presented thus far were detected from the un-stratified dataset and did not take into account shift-type. However, it is clear that environment not only contributes to but also interacts with genetics to influence behavior for review, see . In the case of this study, day- vs. For example, the total duration of sleep for the entire schedule was more likely to be above the mean for GA heterozygote nurses on day-shift than GG homozygotes at the PER2.
The NPAS2. In general, sleep phase on free days was earlier for day-shift and later for night-shift Table 2. For CT or CC genotypes, the percentage of late chronotype nurses was higher for night-shift than for day-shift, but the percentage of late-chronotype TT homozygotes did not increase on night-shift compared to day-shift.
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