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Meditation is a way of relaxing the mind through techniques such as focusing and controlled breathing. People meditate to reduce stress and relieve a variety of physical ailments. Recent research showed meditation can also help people with diabetes. Meditation helps people achieve a more peaceful state of mind. It takes focus and practice to reach a peaceful meditative state of mind. No special equipment is required to meditate. Effective meditative techniques have been shown to reduce mental stress, hormone release and lower blood pressure. There are different types of meditation. Research presented at an American Diabetes Association Conference showed mindfulness meditation is beneficial for people with diabetes. Quiet contemplation helps you put aside worries about the past and future. The motions of breathing can bring you into the present moment. This helps you quiet the mind. Mindfulness meditation relieves anxiety and depression, which are common symptoms for people with diabetes. Mindfulness meditation can be done sitting on a chair or the floor. You can also lie on the floor. The object is to bring yourself into the present moment by focusing on your breathing. Always wear loose fitting comfortable clothing while meditating. Beginners can start for 10 minutes a day then work up to 20 minutes daily. Another type of meditation is transcendental meditation. This is typically done sitting on the floor. You focus on one word or phrase, which is called a “mantra.” Your mantra can be as simple as the word “one.” or ‘om ‘. Chant the word and keep breathing for 10 minutes each day. After a week, try 15 minutes daily then work up to 20 minutes everyday. This helps clear the mind of any thoughts. If you are unsure about how to meditate, there are books and CDs available to help you learn. There are web sites on meditation as well. Yoga instructors and community centers may also offer classes in meditation.

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Meditation is a Way of Relaxing the Mind Through Techniques Such as Focusing and Controlled Breathing

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Meditation is a way of relaxing the mind through techniques such as focusing and controlled breathing. People meditate to reduce stress and relieve a variety of physical ailments. Recent research showed meditation can also help people with diabetes. Meditation helps people achieve a more peaceful state of mind. It takes focus and practice to reach a peaceful meditative state of mind. No special equipment is required to meditate. Effective meditative techniques have been shown to reduce mental stress, hormone release and lower blood pressure. There are different types of meditation. Research presented at an American Diabetes Association Conference showed mindfulness meditation is beneficial for people with diabetes. Quiet contemplation helps you put aside worries about the past and future. The motions of breathing can bring you into the present moment. This helps you quiet the mind. Mindfulness meditation relieves anxiety and depression, which are common symptoms for people with diabetes. Mindfulness meditation can be done sitting on a chair or the floor. You can also lie on the floor. The object is to bring yourself into the present moment by focusing on your breathing. Always wear loose fitting comfortable clothing while meditating. Beginners can start for 10 minutes a day then work up to 20 minutes daily. Another type of meditation is transcendental meditation. This is typically done sitting on the floor. You focus on one word or phrase, which is called a mantra. Your mantra can be as simple as the word one. or om . Chant the word and keep breathing for 10 minutes each day. After a week, try 15 minutes daily then work up to 20 minutes everyday. This helps clear the mind of any thoughts. If you are unsure about how to meditate, there are books and CDs available to help you learn. There are web sites on meditation as well. Yoga instructors and community centers may also offer classes in meditation. Mindfulness meditation helps minimize stress levels and lowers blood pressure. It reduces levels of stress hormones including cortisol, norepinephrine and epinephrine. Hormones such as cortisol contribute toward weight gain and belly fat and insulin resistance. Excess weight increases the risk of high blood pressure and related heart problems. A study in the Archives of Internal Medicine showed transcendental meditation can help people with diabetes control their blood sugar levels. It can also lower blood pressure and reduce insulin resistance. Meditation is also used to relieve chronic pain. People with diabetes who have nerve pain or back pain are subject to increased stress and blood sugar levels. Reducing pain through meditation minimizes anxiety and related health problems as well as blood sugar readings. Useblood pressure monitorsor blood pressure machines before and after meditation sessions to gauge your progress. Have aglucose meterhandy to keep track of your blood sugar levels prior and subsequent to meditation. Give yourself a few weeks to master the techniques and start seeing real results.ABSTRACTTop of Form

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The etiology of sleep disruption in patients in intensive care units (ICUs) is poorly understood, but is thought to be related to environmental stimuli, especially noise. We sampled 203 patients (121 males and 82 females) from different ICUs (cardiac [CCU], cardiac stepdown [CICU], medical [MICU], and surgical [SICU]) by questionnaire on the day of their discharge from the unit, to determine the perceived effect of environmental stimuli on sleep disturbances in the ICU. Perceived ICU sleep quality was significantly poorer than baseline sleep at home (p = 0.0001). Perceived sleep quality and daytime sleepiness did not change over the course of the patients' stays in the ICU, nor were there any significant differences (p > 0.05) in these parameters among respective units. Disruption from human interventions and diagnostic testing were perceived to be as disruptive to sleep as was environmental noise. In general, patients in the MICU appeared to be more susceptible to sleep disruptions from environmental factors than patients in the other ICUs. Our data show that: (1) poor sleep quality and daytime sleepiness are problems common to all types of ICUs, and affect a broad spectrum of patients; and (2) the environmental etiologies of sleep disruption in the ICU are multifactorial. Several polysomnographic studies have demonstrated sleep deprivation, sleep fragmentation, and altered sleep architecture in patients in intensive care units (ICUs) (1-8). Sleep in ICU patients has been characterized by a predominance of Stages 1 and 2 sleep, decreased or absent Stage 3 and 4 and rapid eye movement (REM) sleep, shortened periods of REM sleep, frequent arousals, and sleep fragmentation. Studies have also shown that 40 to 50% of the total sleep time in an ICU occurs during the day (2,6). Altered sleep patterns may not improve over the course of the patient's ICU stay (3), and may take several days to normalize after transfer of the patient to a general hospital ward (7). The mechanisms responsible for altered sleep/wake cycles are poorly understood. Studies examining the etiologies of sleep deprivation in the ICU have focused on environmental stimuli (e.g., increased noise and interruptions from human interventions) as the basis for arousals and sleep fragmentation (1,9-21). Altered light/dark cycles, which depend on window orientation and the ambient light, may make it difficult to achieve sleep, and may alter circadian rhythms (15,22). Frequent interruptions for diagnostic tests and routine patient care also may cause frequent arousals from sleep. Woods and colleagues (23) found that postcardiotomy patients had their sleep interrupted on an average of 59.5 times per night, with the greatest amount of uninterrupted sleep time being 43 min on postoperative Day 1. The environmental stimulus most often cited in the literature as disturbing sleep is noise (10,15,16). The U.S. Environmental Protection Agency (EPA) recommends that hospital noise levels not exceed 45 dB during the day and 35 dB at night (24). Several studies have shown that noise levels in the ICU are substantially higher than the EPA recommendations (12,15,16,25,26). Noise levels in the ICU range from 60 dB to 84 dB throughout a 24-h period (10,15). As references, a busy office has average noise levels of 70 dB and a pneumatic drill heard from 50 ft away has a noise level measured at 80 dB (16). Noise levels below 40 dB are generally required for a normal individual to fall asleep, and increases in noise intensity are known to cause arousals from sleep (9). Exposure of normal individuals in a sleep laboratory to nocturnal ICU noise levels results in decreased total sleep time, decreased total REM sleep time, and decreased sleep efficiency; and to increased REM sleep latency and an increased arousal index (number of arousals from sleep/hour of sleep) (19,20). However, polysomnographic studies of ICU patients have so far only indirectly linked noise to sleep disruption (1). The effect of environmental stimuli on sleep disruption in ICU patients has never been examined in a large-scale study. To gain a better understanding of the underlying mechanisms of altered sleep/wake patterns in ICU patients, and specifically to determine the effect of the ICU environment on sleep quality, we sampled a cohort of ICU patients. Our primary hypothesis was that noise would be the most disruptive environmental stimulus to subjective sleep quality. Secondary aims of the study were to determine: (1) whether sleep quality and daytime sleepiness changed over the course of a patient's ICU stay; (2) whether there were differences in sleep quality between ventilated and nonventilated patients; (3) whether there were differences in sleep quality between different types of ICUs; and (4) the relative roles of other environmental factors (human interventions, light) on perceived sleep disruption.METHODSTop of Form

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Site The study was conducted between July 1996 and April 1997 at the University of Pennsylvania Medical Center, and was approved by the Institutional Review Board of the university. Questionnaires were given to patients on the day of their discharge from four ICUs: (1) a 12-bed cardiac care unit (CCU); (2) an 18-bed cardiac intermediate care unit (CICU); (3) a 24-bed surgical intensive care unit (SICU); and (4) a 24-bed medical intensive care unit (MICU). Patients Patients were volunteers who received no remuneration for their participation. Patients scheduled for discharge from their respective ICU were selected to participate in the study. All patients except those who had insufficient cognitive function to allow them to cooperate were candidates for the study. Patients gave oral consent prior to their participation. Those patients unable to fill out the questionnaire because of muscle weakness or poor eyesight had the questionnaire read aloud to them and their verbal responses recorded. Questionnaire A questionnaire was developed that assessed the sleep quality of ICU patients and the factors that contributed to sleep disruption among these patients (Figure1). Patients evaluated their sleep quality on a scale of 1 to 10 (1 = poor, 10 = excellent) at home and in the ICU. Sleep quality over the duration of the patients' ICU stay was assessed with the same scale. Participants were asked to determine their degree of daytime sleepiness over the duration of their ICU stay on a scale of 1 to 10 (1 = unable to stay awake, 10 = fully alert and awake). The effect of environmental stimuli on sleep disruption was measured on a scale of 1 to 10 (1 = no disruption, 10 = significant disruption). The environmental stimuli that were evaluated included noise, light, nursing interventions (bathing, etc.), diagnostic tests (i.e., chest radiographs), evaluation of vital signs, blood sampling, and the administration of medications. Patients were also asked to assess the effects of different ICU noises on sleep disruption, using a scale of 1 (no disruption) to 10 (significant disruption). The ICU noises that were evaluated included telemetry alarms, ventilator sounds and ventilator alarms (for applicable patients), sounds of pulse oximetry, communications between staff members, intravenous pump alarms, suctioning sounds, doctor's beepers, and television and telephone sounds. This last section of the questionnaire was added after the questionnaire was pilot tested on the first 43 patients. Fig The questionnaire utilized in the study.[More] Statistical Analysis Demographics. A one-way analysis of variance (ANOVA) was used to determine differences in age and duration of stay among respective units. Chi-square analysis was used to determine differences in sexes and numbers of ventilated patients among units. Primary analysis. Pearson's correlation analysis was used to determine the relationship of patient age and duration of stay to the following factors: sleep quality, daytime sleepiness, environmental disruptive factors, and specific ICU noises. Spearman's correlation analysis was used to confirm the Pearson's analysis. Only the Pearson's correlation coefficients were reported if there was agreement between these latter two analyses. The square root of the number of days (instead of the actual number of days) a patient spent in the ICU was used in the calculation for duration of stay, to correct for the large range of values. Unpaired Student'sttests were used to compare differences between males and females and between ventilated and nonventilated patients in relation to sleep quality, daytime sleepiness, environmental disruptive factors, and specific ICU noises. A one-way ANOVA was used to compare differences between the respective units and these factors. Multivariate and univariate repeated-measures ANOVAs were used to test for significant systematic differences among perceived degrees of disruption by the various environmental activities examined in the study. First, a Wilks' lambda F-statistic was used to test the overall null hypothesis of equal mean values at = 0.05. When this hypothesis was rejected, a set of comparisons between each disruptive factor and the patient specific mean was examined. Thus, Type I error was controlled only at the level of the overall hypothesis. Factor analysis. Factor analysis was done to establish patterns of interrelationships and associations between variables, as well as to internally validate our questionnaire. An orthogonally rotated, four-factor analysis was used, since it confirmed a clear factor structure. The data on the disruptive effect of specific ICU noises on ICU sleep were not included in the factor analysis because only 160 patients completed this section of the questionnaire. The relative proportion of variance explained by each of the four factors in the analysis was computed as the sum of the squared factor loadings divided by the sum of the shared variance. Bivariate analysis with Factors 1 through 4 was done to determine associations between each of the factors and: age, duration of ICU stay, gender, ventilator status, and respective ICU.RESULTSTop of Form

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Demographics A total of 203 patients completed the questionnaire (Table1). Overall, there were significantly more males than females in the study (p < 0.05), although there were no significant (p > 0.05) gender differences among ICUs. The MICU patients were significantly younger than patients in other units (p = 0.003). Otherwise, there were no age-dependent differences (p> 0.05) among the ICUs. The durations of stays in the CICU and MICU were longer than in other units (p = 0.02). The MICU had significantly more ventilated patients than did other units (p = 0.001). Table 1 View Larger Version DEMOGRAPHICSOFSTUDYPOPULATION* Primary Analysis Sleep quality in the ICU was perceived as significantly poorer than sleep at home (p = 0.0001) by all subjects (Figure2). Neither quality nor perceived daytime sleepiness changed significantly over the course of any patient's ICU stay (p > 0.05). There were no significant differences between genders or individual ICUs in perceived ICU sleep quality or daytime sleepiness, nor were there any significant correlations of perceived ICU sleep quality or daytime sleepiness with patient age or duration of ICU stay (p > 0.05). Perceived ICU sleep quality was not significantly different for ventilated and nonventilated patients (p > 0.05). These data indicate that poor sleep quality and sleep disruption are problems common to all types of ICUs and to many ICU patients. Fig Bar graph highlighting differences in perceived sleep quality between home and the ICU.[More] There were statistically significant differences between the individual environmental factors (p < 0.0001) in terms of their perceived degrees of ICU sleep disruption (Figure3). Checking of vital signs and phlebotomy were perceived as significantly more disruptive (p = 0.006) to sleep than was the mean of the other factors. Environmental light and medication administration were perceived as significantly less disruptive (p= 0.003) to sleep than was the mean of the other factors. Environmental noise, nursing interventions, and diagnostic tests were perceived as not significantly different (p > 0.05) in their disruptive effect on sleep than was the mean of the other disruptive factors. These data indicate that multiple environmental factors disturb sleep in the ICU, with noise being first among a number of important sleep-disruptive factors. However, there were no significant associations between individual environmental factors (noise, light, nursing interventions [bathing, etc.], diagnostic tests [i.e., chest radiography], recording of vital signs, blood sampling, and the administration of medications) and perceived sleep disruption in relation to patient age, gender, length of stay, ventilator status, or respective unit (p > 0.05). Fig Bar graph highlighting perceived sleep disruption from the ICU environment.[More] One hundred and sixty ICU patients completed the final section of the questionnaire, which evaluated the relationship between specific ICU noises and perceived sleep disruption. There were significant differences (p < 0.0001) among the specific ICU noises in terms of their perceived disruptive effect on sleep in the ICU. Communication between staff members (talking) and telemetry alarms were significantly more disruptive (p = 0.003) to sleep than was the mean of the other factors. Noises from telephones, television, and doctors' beepers were significantly less disruptive (p = 0.0001) to sleep than was the mean of the other factors. Sounds of pulse oximetry and intravenous pump alarms were perceived as not significantly different (p > 0.05) than the mean of the other disruptive factors (Figure4). There were no significant associations between patient age or duration of stay and sleep disruption by specific noises. There were no significant differences between the respective ICUs and perceived sleep disruption by specific noises (p > 0.05). Fig Bar graph highlighting perceived sleep disruption by specific environmental noises.[More] Factor Analysis Factor analysis was used to extract patterns of association from the data set and to reduce the large number of variables from the questionnaire to a smaller number of reference factors. Table2presents the factor loadings for the four-factor model after orthogonal rotation. The loadings refer to the correlation of each variable with each factor. A cutoff value of 0.40 was used to determine whether a particular variable was strongly associated with a specific factor. The loadings confirmed a definite factor structure; each of the questions had a large loading (strong association) on only one factor. The four factors were interpreted as follows: Factor 1, sleep disruption secondary to interruptions by hospital staff and diagnostic tests; Factor 2, ICU sleep quality; Factor 3, daytime sleepiness; and Factor 4, sleep disruption by environmental factors (light and noise). The total variance explained by the four-factor model was 54.9%. The proportions of the total variance that were explained by Factors 1 through 4 were 18.2%, 16.2%, 11.7%, and 8.8%, respectively. Table 2 View Larger Version ORTHOGONALLYROTATEDFACTORPATTERN. Factor analysis confirmed several findings from the primary analysis. None of the four factors showed significant (p> 0.05) associations with patient age or gender. The duration of stay was not (p > 0.05) associated with sleep disruptions caused by human intervention (Factor 1), sleep quality (Factor 2), or daytime sleepiness (Factor 3). A patient's ventilator status was not (p > 0.05) associated with perceived sleep disruptions caused by human intervention (Factor 1), noise/ light (Factor 4), or sleep quality (Factor 2). Also, neither sleep quality nor daytime sleepiness was (p > 0.05) associated with a patient's respective unit. Factor analysis allowed the detection of specific associations with respect to sleep quality, daytime sleepiness, and sleep disruption caused by the ICU environment that were not evident in the primary analysis. Daytime sleepiness (Factor 3) was significantly different in ventilated and nonventilated patients (p = 0.008), with ventilated patients experiencing more daytime sleepiness than nonventilated patients. Sleep disruption caused by environmental light and noise (Factor 4) showed a weak but significant positive correlation (r = 0.19; p= 0.006) with duration of ICU stay (i.e., patients who had longer stays in the ICU were more likely to have their sleep disturbed by environmental light and noise). Sleep disruptions caused by human interventions/diagnostic tests and environmental light/noise (Factors 1 and 4) were significantly different in the different units (p = 0.0365 and p = 0.0028, respectively) (Table3). Table 3 View Larger Version COMPARISONOFFACTORS1AND4ACROSSUNITS. Table3shows that patients in the MICU perceived interruptions caused by human interventions and diagnostic tests (Factor 1) to be significantly more disruptive to their sleep than did patients in the SICU and CICU (p = 0.012 and 0.016, respectively). There were no significant differences among the other groups with respect to Factor 1 (p > 0.05). MICU patients also perceived environmental light and noise (Factor 4) to be significantly more disruptive to their sleep than did patients in the SICU and CCU (p = 0.009 and 0.001, respectively). Patients in the CICU perceived environmental light and noise to be significantly more disruptive to their sleep than did patients in the CCU (p = 0.016), with a trend toward significance versus patients in the SICU (p = 0.06). There were no significant differences among the other groups with respect to Factors 1 and 4 (p > 0.05).DISCUSSIONTop of Form

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We subjectively evaluated sleep and the environmental factors disrupting sleep in 203 patients discharged from medical, surgical, and cardiac ICUs. Overall, perceived ICU sleep quality was poorer than sleep quality at home. Sleep quality and daytime sleepiness did not change over the course of the patients' ICU stays, and there were no significant differences in these parameters among the different units. Factor analysis showed that patients who had been mechanically ventilated during their ICU stay were significantly sleepier during the day than were nonventilated patients. Overall, sleep quality, daytime sleepiness, and perceived disruptions in sleep caused by environmental factors were not affected by the patients' age or gender. Although the primary analyses did not show significant differences between various environmental sources of sleep disruption, factor analysis showed that MICU patients perceived their sleep to be more disrupted by environmental factors than did patients in other units. Study Design and Limitations Our study design had several limitations. Because the study assessed sleep quality subjectively, we were unable to determine the patients' true sleep architecture and degree of sleep disruption caused by the various environmental stimuli. However, we believe that this investigation was an essential first step in understanding the factors perceived by ICU patients as disturbing sleep. We did not control for patients' severity of illness or medication use, both of which factors may have affected the results of the study. We also did not have a control group of hospitalized non-ICU patients for comparison, since our main objectives were to determine whether sleep quality was disturbed in an ICU setting and to determine the effect of ICU environmental stimuli (specifically noise) on perceived sleep disruption. Thus hospitalized non-ICU patients would not have constituted a suitable control group, since the environmental stimuli to which they are exposed differ from those to which the ICU patient is exposed. Future studies, however, should be performed with polysomnography to accurately quantify total sleep times and define sleep architecture. The demographics of our ICU patients were different across the ICUs in our study. There were significantly more males than females in the cohort. MICU patients were significantly younger than the patients in other units, and MICU and CICU patients had significantly longer durations of stay than did CCU or SICU patients. However, we do not believe that these demographic differences adversely affected our results, because patient age, sex, and duration of ICU stay as a whole were not significantly associated with differences in perceived sleep quality, daytime sleepiness, or sleep disruptions caused by environmental stimuli. Because the present study was the first instance of application of our questionnaire to a large ICU population, the test-retest reliability and validity of the questionnaire are unknown, and will require further study. Our questionnaire appears to be internally valid, on the basis of the groupings in the factor analysis. Assessment of the validity of our questionnaire for assessing sleep quality and the etiologies responsible for sleep disruption in the ICU will require future objective (polysomnographic) studies in conjunction with the questionnaire. Recall bias is another potentially confounding problem for questionnaire studies in general. This did not appear to be a major problem with our questionnaire, however, since the duration of ICU stay was not associated with changes in sleep quality, daytime sleepiness, or perceived environmental disruptions of sleep. Because no large studies have objectively evaluated the accuracy of patient recollections of their ICU stays, and it is clinically apparent that many ICU patients who undergo heavy sedation do not remember much of their ICU stay, our results may have been subject to patient recall bias, especially by those patients with longer durations of ICU stay and in those patients who were heavily sedated for a portion of their ICU stay. Additionally, there may have been a recruitment bias that potentially prevents application of the results of our study to the entire ICU population. Although all ICU patients were eligible to participate in the study, those patients who were not discharged from their respective ICUs because of increased severity of illness were not included in the study. Therefore, our results cannot be generalized to critically ill ICU patients who die before their ICU discharge. Sleep Quality and Daytime Sleepiness in the ICU Our finding that patients perceive their sleep quality to be worse in the ICU than at home is supported by previous investigations (1-8). Studies of sleep in ICU patients indicate that their sleep is abnormal, as demonstrated by a predominance of Stages 1 and 2 sleep, decreased or absent Stages 3 and 4 and REM sleep, shortened periods of REM sleep, frequent arousals, and sleep fragmentation (1-8). Our results also demonstrate that perceived sleep quality and daytime sleepiness did not improve over the course of our patients' ICU stays. These results are consistent with those of previous polysomnographic studies, which showed that 40% to 50% of the total sleep time in an ICU occurred during the day, and that altered sleep patterns may not improve over the course of a patient's ICU stay, and may take several days to normalize after transfer of the patient to a general hospital ward (2,3,6,7). Although daytime sleepiness appeared to be a problem common to all of the types of ICUs investigated in our study, factor analysis showed that patients who had been mechanically ventilated during their ICU stay perceived that they were significantly more sleepy during the day than were nonventilated patients. This finding may be explained by the typically greater sedation of mechanically ventilated than of nonventilated patients, and their greater severity of illness than that of nonventilated patients. Further studies will be necessary to determine whether sedation and severity of illness are correlated with sleep disruption in the ICU. Interestingly, there were no differences among any of the individual ICUs in perceived sleep quality, nor were there any associations between sleep quality and patient age, gender, duration of stay, or ventilator status. This suggests that poor sleep quality and disrupted sleep are problems common to all ICUs and to many ICU patients. It also suggests that factors disturbing sleep are generic to all ICUs. Sleep Disruption and the ICU Environment Our findings support the assertion that environmental causes of sleep disruption in the ICU are multifactorial. Our data indicate that human interventions and diagnostic testing appear to be as important to disrupting sleep as is environmental noise. Our findings do not support our primary hypothesis that ICU noise is the most disruptive environmental stimulus to sleep for most ICU patients. In fact, the effect of specific ICU noises on sleep disruption appears to be low, as shown by the low mean sleep-disruptive scores in our study. This finding contradicts much of the current literature, which suggests that noise is the major ICU factor responsible for sleep disruption (1,6,10,12,13,15-18,21,27-29). Why was noise not perceived as the most sleep-disruptive factor in the ICU? A number of possible explanations exist for this finding. First, noise in the ICU setting may not be as disruptive to sleep as is currently thought. Patients in intensive care may adapt quickly to environmental noise. Previous studies have shown that patients habituate to sound by increasing their arousal threshold with time, with some individuals being able to increase their arousal threshold for noise to more than 80 dB (30,31). Second, noise may have caused arousals but not awakenings during sleep among our patients, leading to sleep fragmentation and poor sleep quality. Patients may not have been able to recall the etiology of their fragmented sleep because they were never fully awakened from sleep. Third, noise may have caused awakenings during delta sleep, when it is more difficult for individuals to recall the etiology of an awakening. This last explanation is an unlikely one, since the current literature indicates that ICU patients show decreased or absent delta sleep (2,6). Our results support the postulate that interruptions of sleep in the ICU are multifactorial, and that those caused by human interventions and diagnostic tests are important. This finding is consistent with the results described earlier in a study done by Woods and colleagues (23), who found that postcardiotomy patients had their sleep interrupted by human interventions (recording of vital signs, phlebotomy, etc.) on an average of 59.5 times per night, with the greatest amount of uninterrupted sleep time being 43 min on postoperative Day 1. The number of nocturnal awakenings decreased and the uninterrupted potential sleep time increased on subsequent hospital days. Meyer and coworkers monitored sleep interruptions in ICU patients over a 24-h period and showed that interruptions caused by human interventions occurred at least hourly throughout the day and night (15). Future research should focus on methods for decreasing frequent interruptions of sleep in ICU patients, especially at night, to allow them to achieve longer periods of consolidated sleep. Patient characteristics such as age, gender, or ventilator status were not related to perceived sleep quality or disruptions caused by any specific environmental stimulus in our study. That older age was not associated with poorer sleep quality is not necessarily consistent with findings reported in the literature. Typically, older individuals are more likely to be aroused from sleep than are younger individuals (30,31), yet the mean age of our patients was 58.6 15.4 yr. We may not have sampled enough younger patients (age < 40 yr) to show a significant association between age, sleep quality, and sleep disruptions. One would also assume that patients who were mechanically ventilated and thus exposed to the additional noise of ventilators and alarms would perceive the ICU environment, and specifically noise, as more disruptive to their sleep than would nonventilated patients. This was not the case in our study. Our results suggest that all ICU patients, and not specific subgroups, are at risk for sleep disturbances. Our results support the assertion that sleep disruption in the ICU is multifactorial, and suggest that other, nonenvironmental factors may be responsible for causing sleep disruptions in the ICU patient population. In general, the mean disruption scores for the environmental factors and specific ICU noises causing sleep disruption in our study were relatively low, although the perceived sleep quality among the vast majority of patients was poorer than that of sleep at home. It is clear that the ICU environment is not solely responsible for disturbed sleepwake patterns. Several nonenvironmental factors, such as medications (32), pain, fever (9,33), and a patient's underlying chronic disease may have adverse effects on sleep quality (34,35). Studies with surgical ICU patients that have decreased the number of environmental interruptions of sleep have not demonstrated improvement in the patients' altered sleep patterns. Other studies have shown continued alterations in sleep duration and other sleep parameters when patients were transferred from the ICU to a hospital ward (3). It is possible that the severity of illness or underlying disease states contributed to the perceived differences in sleep quality in our study. Future studies should examine the effects of relationships between disease severity, age, medications, and time in the ICU on disruptions of sleepwake patterns in the ICU, although this may prove difficult, given the number of potentially confounding factors.CONCLUSIONSTop of Form

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Our results show that good-quality sleep may be difficult to achieve in patients under intensive care. Poor sleep quality and sleep disruptions are problems common to many ICU patients, and environmental factors disturbing sleep are generic to all types of ICUs. Although environmental noise is an important contributor to sleep disruption in ICU patients, it is only one of a series of important factors. Among other environmental factors responsible for sleep disruptions in ICU patients, interruptions caused by human interventions and diagnostic testing appear to be as important as environmental noise. Objective studies are required to accurately determine the impact of the ICU environment on sleep disruption in patients under intensive care. Future studies aimed at improving sleep in ICU patients should focus on decreasing the number of interruptions caused by human interventions, in addition to means for alleviating environmental noise.

Read More:http://www.atsjournals.org/doi/full/10.1164/ajrccm.159.4.9806141#.VP88KfnF87Y