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29 The Big Sleep 94,95 Bigelow, Kathryn , Bigger Than Life , , The Conversation , , Coogan's Bluff , Cooper.
Table of contents
Eisenkraft , James M Berry. Jan Ehrenwerth and Dr. James B. Eisenkraft, offers expert, highly visual, practical guidance on the full range of delivery systems and technology used in practice today. It equips you with the objective, informed answers you need to ensure optimal patient safety. Consult this title on your favorite e-reader with intuitive search tools and adjustable font sizes. Elsevier eBooks provide instant portable access to your entire library, no matter what device you're using or where you're located.
Ensure patient safety with detailed advice on risk management and medicolegal implications of equipment use. Details on MIDUS, 44 , 45 as well as procedures in the collection of cortisol 46 and AL-related biomarkers 45 can be found in previous publications. Sleep was assessed using actigraphy, a well-validated objective method that estimates sleep duration and quality via wrist movements.
They were asked to wear the actigraph on the nondominant wrist for 7 consecutive days and nights, register BT and RT using the event marker and complete sleep diary everyday, and return the watch in a prepaid envelope. The IIM and IIV of sleep variables were modeled using a purpose-built and validated Bayesian framework, 49 , 50 using all available data and accounting for measurement error.
Saliva sampling was repeated across 4 consecutive days. Further details on the saliva sampling protocol have been described in previous reports. A previous publication provides further details on each biomarker and a validated bifactor model 35 which was used for modeling AL as well as seven system-specific indices, controlling for age and sex. A summary of this model is in the Supplementary Material. A number of covariates were considered based on factors related to sleep IIV see a systematic review 7 and common covariates assessed in relation to cortisol and AL.
The above candidate covariates were also tested for AL analyses, except daily smoking and cortisol specific medication were not included; AL relevant medications were included. The model included four random effects that were allowed to freely correlate: the intercept ie, cortisol at awakening , CAR, Diurnal Slope, and assessment day cortisol sampling was repeated for 4 days.
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Residuals were assessed and were approximately normally distributed, therefore untransformed cortisol values were used. AL and the seven system-specific factors based on resting biomarkers were analyzed using linear regression with clustered standard errors to account for some twins and siblings included in the MIDUS sample. Residuals for AL and the system-specific factors were assessed and were also approximately normally distributed.
Quadratic relationships were tested by entering the squared individual means and IIVs and were dropped if not statistically significant. The aforementioned candidate covariates were individually tested to assess whether each of them predicted the outcomes in the baseline models. Only candidate covariates that were statistically significantly related to the outcomes bivariately were included in the adjusted models.
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Data were analysed using R 53 and Mplus v7. All statistical significance, including that used in analyses for selecting covariates, was determined based on two-tailed p -value at. In this sample, the majority of the sample They were relatively healthy, most reporting having no Descriptive statistics of all variables included in the final models for the overall sample are shown in Table 1 cortisol, demographics, and covariates and Table 2 sleep variables. For example, for BT, most of the sample had a SD of 0. A total of cortisol samples participants had measures on cortisol and actigraphy, and among these, cortisol samples did not have missing data on any covariates and contributed to both unadjusted and adjusted models.
Final covariates included in adjusted cortisol models were: sex, age, race, education, presence of bed partner, smoking history, perceived stress, and the number of chronic major medical conditions. We also tested an IIV by any chronic major medical condition interaction to examine whether results differed in those with and without a chronic major medical condition. None of the interactions were significant, and these were dropped in the final analyses. Key findings on cortisol analyses are summarized in Table 3 , and full model results including results on covariates can be found in Supplemental Material 2 Tables.
Quadratic terms for both the IIM and IIV of all sleep variables were tested to be not statistically significant and were thus not included in the final models. The associations found in the unadjusted models were attenuated after controlling for covariates. Covariates adjusted for are: sex, age, race, education, presence of bed partner, smoking history, perceived stress, and the number of chronic major medical conditions. No IIM of sleep variables uniquely predicted cortisol trajectory.
Overall, in both the unadjusted and adjusted models, the IIV of sleep variables shared stronger associations with cortisol trajectories than their IIM counterparts. A total of participants had measures on AL biomarkers and actigraphy, and among these, did not have missing data on any covariates and contributed to both unadjusted and adjusted models.
Age and sex were adjusted in the factor scores of AL in all models. In the adjusted model, the following additional covariates were controlled for: race, smoking history, perceived stress, the number chronic major medical conditions, and AL relevant medications. We also tested an IIV by any chronic major medical condition interaction to examine whether results differed in those without and with a chronic major medical condition.
Key findings on AL analyses are summarized in Table 4 , and full model results including results on system specific outcomes not already accounted for by AL and findings on covariates can be found in Supplementary Tables 3 and 4. Quadratic terms for both the mean and IIV of all sleep variables were tested to be not statistically significant and were thus not included in the final models.
Both the unadjusted and adjusted models had age and sex adjusted. This study investigated the associations between sleep IIV and cortisol diurnal rhythm, as well as an index of multisystem physiological dysregulation ie, AL.
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Findings showed that after controlling for covariates, more variable sleep timing and duration was associated with flatter cortisol diurnal slope, over and above the effects of their respective mean values. More variable sleep quality was associated with higher multisystem physiological dysregulation; however, these associations were no longer significant after controlling for covariates. Therefore, in a sample of community-dwelling adults, there is evidence for higher sleep IIV to be associated with alterations in cortisol diurnal rhythm as a proximal outcome but not with higher multisystem physiological dysregulation as a distal outcome.
Findings on cortisol trajectory are consistent with the only other study on sleep IIV and cortisol, showing that in adolescents more variable sleep duration was associated with flatter diurnal slopes and lower levels of waking cortisol. Emerging evidence showed that flatter diurnal cortisol trajectories predicted mortality in breast 55 and lung 56 cancer. To put a 0. It is possible that cortisol, a biomarker with strong circadian influence, is more sensitive to disturbance to sleep duration and timing, compared to disturbance to sleep at the start ie, SOL or middle ie, WASO of the primary sleep period.
In both the unadjusted and adjusted models, the IIV of sleep variables shared much stronger associations with cortisol trajectories than their IIM counterparts; in the adjusted model, none of the sleep IIM variables made statistically significant contribution to cortisol trajectories.
Previous studies have linked more variable sleep patterns to more evening chronotype, 58 , 59 which is associated with later circadian phase, a risk factor for circadian misalignment. This may have contributed to the stronger associations between sleep IIV compared to IIM and diurnal cortisol trajectory, which is highly influenced by circadian processes. Based on models adjusted only for sex and age, more variable sleep was associated with higher AL as hypothesized.
Considered together with the findings that more variable sleep patterns are associated with a blunted cortisol rhythm, the findings are consistent with AL theory positing cortisol dysregulation as a primary mediator between repeated adaptation ie, adapting to changing sleep patterns and dysregulation across multiple physiological systems. As a distal outcome that is closely associated with overall health, AL is associated with many psychosocial factors in addition to sleep.
Indeed, several of the covariates included in the fully adjusted model eg, race, stress, chronic health conditions have previously been shown to be related to sleep IIV. Finally, although chronic conditions were included as a covariate when testing the relations between sleep IIV and AL, it may also be considered as an outcome of AL. Our findings that adjusting for age and sex, more variable sleep patterns were associated with higher AL provide evidence for an association, but its nature and causal directions require further research. Findings in this study need to be interpreted in light of a number of limitations.
Second, circadian phase was not assessed, and therefore, it was not possible to examine the role of circadian misalignment specifically. Third, the cross-sectional nature of the data preclude causal inference. It is also possible that a common cause eg, stress was underlying both variable sleep and elevated biomarkers. Finally, we recognize that not all findings would remain statistically significant using traditional methods of adjustment for multiple comparisons.
To assist interpretation of uncertainties, we presented confidence intervals in all findings. Despite these limitations, the study also had notable strengths. The unique combination of data collected in MIDUS allowed the linkage of objectively measure sleep IIM and IIV, diurnal rhythms of salivary cortisol, and multisystem physiological dysregulation measured by an expansive panel of biomarkers all in a large sample of adults.
To our knowledge, this is the first study that examined the associations between sleep IIV with diurnal cortisol rhythms in adults and the first study to assess the association between objectively measured sleep both IIM and IIV and multisystem physiological dysregulation. Rigorous methodologies are the core strengths of this study, these included 1 carefully and comprehensively measured physiological outcomes, 2 quantifying IIV using methods that are robust to missing data and measurement error, 3 accounting for important covariates, which included both the IIM of sleep variables, as well as a set of systematically selected covariates based on prior evidence, 4 taking into account multiple dimensions of sleep timing, duration, quality , and 5 the consideration of quadratic effects for both the IIM and IIV of sleep on the outcomes.
In conclusion, in a sample of community adults, more variable sleep timing and duration were associated with flatter diurnal cortisol trajectory, but the association between sleep IIV and multisystem physiological dysregulation appeared weak after accounting for covariates. The associations between sleep IIV and physiological dysregulation warrant further investigation.
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In addition to conducting new studies with a priori hypotheses, future studies could also examine existing data sets and incorporate IIV as a second dimension to the mean values when daily sleep is examined. Oxford University Press is a department of the University of Oxford. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide.
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