Abstract
Recent studies have challenged the traditional hypothesis that excessive environmental noise is central to the etiology of sleep disruption in the intensive care unit (ICU). We characterized potentially disruptive ICU noise stimuli and patient-care activities and determined their relative contributions to sleep disruption. Furthermore, we studied the effect of noise in isolation by placing healthy subjects in the ICU in both normal and noise-reduced locations. Seven mechanically ventilated patients and six healthy subjects were studied by continuous 24-hour polysomnography with time-synchronized environmental monitoring. Sound elevations occurred 36.5 ± 20.1 times per hour of sleep and were responsible for 20.9 ± 11.3% of total arousals and awakenings. Patient-care activities occurred 7.8 ± 4.2 times per hour of sleep and were responsible for 7.1 ± 4.4% of total arousals and awakenings. Healthy subjects slept relatively well in the typically loud ICU environment and experienced a quantitative, but not qualitative, improvement in sleep in a noise-reduced, single-patient ICU room. Our data indicate that noise and patient-care activities account for less than 30% of arousals and awakenings and suggest that other elements of the critically ill patient's environment or treatment should be investigated in the pathogenesis of ICU sleep disruption.
Previous studies have determined that acutely ill patients in the intensive care unit (ICU) suffer unique sleep disturbances (1–8). Sleep is fragmented by frequent arousals and awakenings, resulting in decreased or absent slow-wave and REM sleep. The circadian rhythm of sleep is distorted, with nearly half of the total sleep time occurring during the daytime. Although sleep disruption in ICU patients has been unequivocally confirmed, little is known about its etiology. However, potential sleep-disrupting factors such as noise, frequent patient-care activities, medication effects, acute and chronic illness, and dyssynchrony between the patient and the ventilator are hypothesized as being the causes of this syndrome.
Environmental stimuli, particularly noise, have been presumed to be the most disruptive factors in the ICU. Although numerous studies have documented excessive noise levels in the ICU (9–14), the link to sleep disruption has, until recently, been only indirectly established through simulated laboratory studies on healthy subjects (15–17) and correlations between noise levels and arousal frequencies in ICU patients (18). Studies on healthy subjects in sleep laboratories indicated that audiotape recordings of ICU noise disrupted sleep and that reducing noise, either through earplugs or on “quiet” control nights, improved sleep. However, a sleep laboratory cannot simulate the full auditory and visual experience of the ICU. In addition, the studies were only performed for an 8-hour nocturnal period rather than over 24 hours. Therefore, a more realistic evaluation of the effects of the ICU environment would be obtained by studying patients and healthy subjects in the ICU for at least a 24-hour period, with polysomnography (PSG) and time-synchronized recording of environmental variables to quantify their contribution to sleep disruption.
Two studies by Freedman and colleagues (8, 19) re-evaluated the contribution of noise to sleep disruption in ICU patients. A detailed questionnaire administered to patients after discharge from the ICU indicated that assessment of vital signs and phlebotomy were considered more disruptive than noise (19). This study had a large sample size and was thorough in its design and analysis but was limited by potential recall bias and the lack of objective measurement of sleep quality. The subsequent study (8), using PSG and time-synchronized recording of environmental noise, directly linked noise to arousals from sleep and determined that noise was responsible for only 15% of all arousals and awakenings. Although it was the first study to demonstrate that common noise elevations directly cause arousals in ICU patients, other environmental factors such as patient-care activities were not assessed. Furthermore, the extent to which different noise sources, such as alarms or conversation, contributed to sleep disruption was not documented.
Therefore, the relative contribution of noise and other components of the ICU environment to the pathogenesis of ICU sleep disruption are unknown. Because previous studies have not used PSG for objective sleep quality assessment, or have used simulated ICU environments or have not evaluated factors other than noise, the impact of these potential sleep disruptors on sleep continuity has been incompletely assessed. The objectives of our study were to (1) determine the prevalence of excessive noise and patient-care activities over a 24-hour period, (2) determine the relative impact of these factors on sleep continuity, (3) monitor healthy, unattended individuals in the actual ICU environment to examine, in isolation, the effect of noise on sleep quality as compared with that in critically ill patients, and (4) evaluate the effectiveness of a noise-reduction strategy by monitoring these healthy subjects in a single room in the ICU.
The study was approved by the research ethics boards of both participating institutions. Patients or their substitute decision-makers provided written consent to participate in the protocol. Patients admitted to the Critical Care Unit of Sunnybrook and Women's College Health Sciences Centre were screened for eligibility according to previously published exclusion criteria for reliable PSG (7). Selection criteria were endotracheal intubation and anticipated mechanical ventilation for a further 24 hours. Healthy volunteers were recruited by advertising on the St. Michael's Hospital e-mail network and were excluded on the basis of previous medical history, sleep disorders, or exposure to the ICU environment.
Mechanically ventilated patients were studied in the Critical Care Unit of Sunnybrook and Women's College Health Sciences Centre, an 18-bed ICU, with beds arranged in a semicircular open-plan, separated by curtains and a separate row of 2-patient and 4-patient rooms. Healthy subjects were studied in the medical/surgical ICU of St. Michael's Hospital, a 24-bed ICU, with 19 beds arranged in a semicircular open-plan, separated by curtains, and 5 beds in enclosed single-patient rooms. Healthy subjects were randomized to spend a 24-hour period in one of two ICU locations—a single-patient, enclosed room or one of several beds in the open-plan ICU—and were then crossed-over to the other location at least 1 week later to avoid acclimatization. After each study, healthy subjects completed a questionnaire to rate sleep quality and the main sources of sleep disruption.
All subjects were monitored with continuous and attended 24-hour PSG using Sandman DOS 2.4 software (NPB-Melville, Ottawa, ON, Canada). Polysomnographs and arousals from sleep were scored according to standard criteria (20, 21). Arousals or awakenings caused by noise were defined as those occurring within 3 seconds of the termination of the noise (Figure 1)
Figure 1. Polysomnographic example of noise-induced arousal. Arrow indicates abrupt increase in noise (68 dB[A]) due to an alarm, followed by arousal from Stage 2 non-REM sleep. EOG, electrooculogram; EMG, electromyogram. C4 and O1 refer to the placement of electrodes over the central and occipital lobes, respectively.
[More] [Minimize]Each subject's immediate environment was continuously monitored using a sound meter and infrared camera synchronized to the PSG. In addition to background sound intensity, the magnitude, source, and disruptive effect of each abrupt sound elevation above 10 dB(A) (A-weighted decibel scale) was recorded. Each interaction between the patient and a member of the critical care team, sleep technician, or visitor that occurred during sleep was classified by type, and its effect on sleep continuity was documented.
All data are reported as mean ± standard deviation. Paired t tests were used to compare noise and sleep data between healthy subjects in the two ICU locations. Unpaired t tests were used to compare noise and sleep data between patients and healthy subjects. One-way analyses of variance with post hoc Bonferroni tests were used to compare arousal/awakening thresholds among sleep stages and the contribution of different noise sources to sleep disruption. Chi-square tests were used to compare relative proportions of noise-source sleep disruption between ICU locations. Statistical calculations were performed using SPSS 10.1 (SPSS, Chicago, IL).
In all, 3,443 patient-days (daily screening result for an individual patient) were screened, 1,048 (30.4%) of which were new, representing the first patient-day, and 2,395 (69.6%) of which were repeats, representing subsequent patient-days. A total of 1,296 (37.6%) patient-days passed screening criteria for reliable PSG. Of the excluded patient-days, 42.7% of exclusions were due to central nervous system injury, 21.7% to Glasgow Coma Scale ranking below 10, and 8.8% to morphine equivalent sedation above 10 μg/kg/hour. Other factors, such as general anesthesia in the preceding 24 hours (4.2%), drug overdose (0.9%), and anticipated death in the following 24 hours (0.5%), were less common reasons for exclusion.
Seven male patients (24 to 82 years) and six healthy male subjects (23 to 65 years) were recruited. The number of days from ICU admission to PSG ranged from 9 to 110. Five patients were admitted for respiratory insufficiency and two for multiple trauma. All patients were hemodynamically stable (systolic blood pressure > 90 mm Hg). Mean total Acute Physiology and Chronic Health Evaluation III score (22) was 31 ± 18 (range 7 to 61). Four patients were ventilated on pressure-support ventilation and three on assist-control ventilation (Table 1)
Age, yr | 56.7 ± 19.2 |
|---|---|
| M:F | 7:0 |
| Duration of stay, d | 48.3 ± 40.2 |
| APACHE III score | 31 ± 18 |
| Mean arterial BP, mm Hg | 77.8 ± 10.3 |
| Serum creatinine, μmol/L | 225 ± 131 |
| BUN, mmol/L | 5.0 ± 3.5 |
| Positive blood cultures, +:− | 5:2 |
| Inotropes, +:− | 1:6 |
| Benzodiazepines, +:− | 6:1 |
| Opioids, +:− | 4:3 |
| Hypnotic agents, +:− | 2:5 |
| Antidepressants, +:− | 1:6 |
| Butyrophenones/Phenothiazines, +:− | 2:5 |
Sleep diaries of the healthy subjects indicated that they maintained a regular nocturnal sleep schedule, with an estimated total sleep time of 7.3 ± 0.8 hours. Epworth Sleepiness Scale scores were normal (< 6) for all subjects (mean 2.6 ± 2.0) (23). All subjects were naive to PSG and the ICU environment and had no history of sleep disorders.
Noise levels were similar in the open ICU for patients and healthy subjects (Table 2)
Healthy Subjects (SMH) | ||||
|---|---|---|---|---|
| Patients (SWC)
Open ICU | Open ICU | Single Room† | ||
| Mean (night), dB | 53.9 ± 2.5 | 51.4 ± 2.8 | 43.2 ± 0.5 | |
| Mean (day), dB | 56.2 ± 2.2 | 55.6 ± 1.9 | 44.3 ± 0.7 | |
| Mean maximum (night), dB | 60.7 ± 2.3 | 61.1 ± 2.0 | 49.1 ± 1.0 | |
| Mean maximum (day), dB | 66.1 ± 1.6 | 67.1 ± 1.6 | 55.4 ± 1.0 | |
| Mean (wakefulness), dB | 56.2 ± 2.5 | 54.6 ± 1.6 | 44.7 ± 0.5 | |
| Mean (sleep), dB | 54.1 ± 3.2 | 52.2 ± 2.4 | 43.1 ± 0.4 | |
| Mean maximum (wakefulness), dB | 65.6 ± 2.4 | 65.4 ± 1.2 | 56.0 ± 2.5 | |
| Mean maximum (sleep), dB | 60.7 ± 2.9 | 61.6 ± 1.6 | 48.7 ± 1.0 | |
| Wake > 60 dB, % | 14.5 ± 11.5 | 8.4 ± 5.1 | 2.1 ± 0.5 | |
| Wake > 70 dB, % | 0.9 ± 0.7 | 0.3 ± 0.1 | 0.1 ± 0.1 | |
| Sleep > 60 dB, % | 4.9 ± 4.7 | 3.7 ± 1.4 | 0.02 ± 0.02 | |
| Sleep > 70 dB, % | 0.3 ± 0.2 | 0.1 ± 0.1 | None | |
| Sound peaks/hr of sleep, No. | 36.5 ± 20.1* | 72.0 ± 12.9 | 32.7 ± 8.9 | |
| Mean sound “peak”, dB | 67.1 ± 2.8 | 64.5 ± 1.7 | 53.7 ± 0.7 | |
| Mean increase of “peak”, dB | 14.5 ± 1.6 | 15.1 ± 0.6 | 12.1 ± 0.7 | |
| Peaks > 75 dB/hr wake, No. | 9.5 ± 6.8 | 4.6 ± 1.6 | 2.6 ± 1.2 | |
| Peaks > 75 dB/hr sleep, No. | 1.7 ± 1.5 | 1.5 ± 0.6 | 0.1 ± 0.1 | |
In comparison with healthy subjects in the open-plan ICU, there was a tendency toward poorer sleep quality in the patient group (Table 3)
Healthy Subjects (SMH) | ||||
|---|---|---|---|---|
| Patients (SWC)
Open ICU | Open ICU | Single Room | ||
| Total sleep time, hr | 6.2 ± 2.5 | 8.2 ± 1.4 | 9.5 ± 1.7† | |
| Night sleep, hr | 3.2 ± 2.6 | 5.6 ± 1.3 | 6.4 ± 1.1† | |
| Day sleep, hr | 3.0 ± 1.6 | 2.6 ± 1.1 | 3.1 ± 1.5 | |
| Day sleep, % | 54.4 ± 32.8 | 30.9 ± 12.3 | 32.0 ± 11.1 | |
| Stage 1, %TST | 19.0 ± 6.6 | 13.1 ± 2.4 | 11.0 ± 2.3 | |
| Stage 2, %TST | 64.0 ± 10.0 | 63.3 ± 5.5 | 63.3 ± 5.5 | |
| SWS, %TST | 2.7 ± 3.3* | 7.3 ± 2.7 | 7.0 ± 2.4 | |
| REM, %TST | 14.3 ± 9.8 | 16.4 ± 4.6 | 18.6 ± 3.0 | |
| Arousals per hr | 10.7 ± 5.9 | 9.3 ± 2.9 | 8.9 ± 2.7 | |
| Awakenings per hr | 10.9 ± 7.6 | 5.5 ± 1.2 | 4.6 ± 2.5 | |
| Arousals + awakenings per hr | 21.7 ± 7.6 | 14.8 ± 3.7 | 13.5 ± 4.0 | |
Figure 2. Twenty-four–hour hypnograms of each patient in the open intensive care unit. Stages 1, 2, 3, and 4, of non-REM sleep.
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Figure 3. Twenty-four–hour hypnograms of each healthy subject in the open intensive care unit. Stages 1, 2, 3, and 4 of non-REM sleep.
[More] [Minimize]Healthy subjects in the single room experienced significant improvements in total sleep time and night sleep compared with the open ICU (Table 3, Figure 4)
Figure 4. Twenty-four–hour hypnograms of each healthy subject in the single-room intensive care unit. Stages 1, 2, 3, and 4 of non-REM sleep.
[More] [Minimize]The majority of healthy subjects' total arousals and awakenings in the open ICU were caused by sound peaks (Table 4)
Healthy Subjects (SMH) | ||||
|---|---|---|---|---|
| Patients (SWC)
Open ICU | Open ICU | Single Room† | ||
| Arousals from sound, % | 17.5 ± 11.2* | 68.4 ± 11.1 | 47.1 ± 11.0 | |
| Awakenings from sound, % | 24.1 ± 15.2* | 57.9 ± 6.6 | 37.2 ± 15.0 | |
| Arousals and awakenings from peaks > 75 dB, % | 30.8 ± 17.9 | 38.2 ± 6.2 | ‡ | |
| Sound-arousals from alarms, % | 32.6 ± 35.3 | 22.8 ± 8.0 | 6.8 ± 9.5 | |
| Sound-arousals from talking, % | 42.4 ± 32.1 | 35.6 ± 6.4 | 6.0 ± 5.6 | |
| Sound-arousals from ICU activities, % | 25.1 ± 25.3 | 41.6 ± 6.1 | 87.1 ± 9.3 | |
| Sound-awakenings from alarms, % | 20.1 ± 16.3 | 17.9 ± 8.7 | 6.0 ± 7.3 | |
| Sound-awakenings from talking, % | 39.1 ± 25.4 | 39.2 ± 10.2 | 7.7 ± 4.0 | |
| Sound-awakenings from ICU activities, % | 41.0 ± 31.7 | 42.9 ± 11.3 | 86.4 ± 10.0 | |
| Spike intensity causing waking, dB | 69.8 ± 3.4* | 66.3 ± 1.6 | 56.1 ± 3.1 | |
| Spike intensity causing arousal, dB | 68.7 ± 2.6 | 65.3 ± 3.6 | 55.4 ± 2.3 | |
| Spike intensity with no effect, dB | 67.1 ± 2.8 | 64.4 ± 1.8 | 54.0 ± 0.6 | |
| Intensity increase causing waking, dB | 16.7 ± 3.4 | 15.5 ± 1.8 | 14.6 ± 2.7 | |
| Intensity increase causing arousal, dB | 16.4 ± 2.8 | 15.8 ± 3.2 | 12.3 ± 1.7 | |
| Intensity increase with no effect, dB | 14.0 ± 1.5 | 15.0 ± 0.7 | 12.5 ± 1.9 | |
In critically ill patients in the open ICU, no single noise source was predominantly responsible for sound-induced sleep disruption. In the healthy subjects in the open ICU, alarm noises were less disruptive than were conversation or staff activities. The proportions of different noise types responsible for sleep disruption were not significantly different between patients and healthy subjects in the open ICU. However, noise resulting from staff activities was the predominant source of sound-induced sleep disruption in the single room. The great majority of these arousals and awakenings (75.4 ± 14.4% and 81.9 ± 27.7%) was associated specifically with the opening of the main door of the ICU, which was in close proximity to the single room. Without the main-door effect on the single room, the arousal and awakening indices would have been 9.5 ± 2.9 events/hour, which would have been significantly different from the arousal and awakening indices in the open ICU (p < 0.05).
Patient-care interactions occurred 7.8 ± 4.2 times/hour of sleep (Table 5)
Event Type | No. per hr of Sleep | Percentage Causing Disruption | Percentage of Total Disruption |
|---|---|---|---|
| Sound | 36.5 ± 20.1 | 11.7 ± 8.3 | 20.9 ± 11.3 |
| Family visits | 0.7 ± 0.7 | 38.6 ± 39.3 | 1.0 ± 1.3 |
| Resp/Physio | 0.4 ± 0.5 | 30.7 ± 32.6 | 0.5 ± 0.7 |
| Suctioning | 0.2 ± 0.8 | 62.5 ± 47.9 | 0.6 ± 0.8 |
| RN visits | 3.5 ± 1.8 | 21.7 ± 11.6 | 4.1 ± 3.5 |
| Assess vitals | 0.3 ± 0.4 | 51.4 ± 34.4 | 0.7 ± 0.9 |
| Mx admin. | 2.7 ± 3.1 | 49.4 ± 25.6 | 0.9 ± 1.0 |
| All medical care | 7.8 ± 4.2 | 17.7 ± 5.4 | 7.1 ± 4.4 |
| Apparatus/tech. | 1.1 ± 1.0 | 21.6 ± 26.3 | 1.4 ± 1.8 |
| Unidentifiable | — | — | 68.1 ± 9.7 |
Sleep quality in the open ICU was rated as significantly worse than in the single room (p < 0.05) and worse than at home (p < 0.05, Table 6)
Open ICU | Single Room | |
|---|---|---|
| Sleep quality—ICU | 5.5 ± 2.0 | 8.0 ± 1.7* |
| Sleep quality—home | 9.2 ± 0.8 | 9.2 ± 0.8 |
| Noise (all sources) | 7.6 ± 1.6 | 3.5 ± 2.5* |
| Staff conversation | 7.5 ± 3.2 | 1.3 ± 0.5* |
| Alarms | 6.2 ± 2.5 | 1.9 ± 2.1* |
| Suctioning noises | 4.5 ± 3.2 | 1.0 ± 0.0* |
| Telephone | 4.3 ± 2.3 | 1.3 ± 0.8* |
| Light | 4.3 ± 3.0 | 1.5 ± 1.2 |
| Pagers | 1.5 ± 1.2 | 1.0 ± 0.0 |
| Television | 3.2 ± 2.9 | 1.0 ± 0.0 |
This is the first study to systemically determine the contribution of the ICU environment to sleep disruption in both patients and healthy subjects and to estimate the effectiveness of a noise reduction strategy. Our findings demonstrate that although loud noise and frequent patient-care activities were prevalent in the ICU environment, they were responsible for only a small proportion of the observed sleep disruption. Healthy individuals slept relatively well in this potentially disruptive environment, and although noise accounted for a significant proportion of sleep disruption in this group, its extent was not pathologic. A quantitative improvement in sleep quality was observed as a result of noise reduction; however, there was no change in sleep architecture.
Numerous studies have examined noise levels in the ICU, and all have concluded that they exceed Environmental Protection Agency recommendations for hospitals, which are less than 45 dB(A) during the daytime and greater than 35 dB(A) at night (24). Rather, mean sound levels are usually in the 55–65 dB(A) range, with sound peaks greater than 80 dB(A) (8–14). In contrast to previous studies, we quantified all sound peaks that increased by more than 10 dB(A) (which represents a doubling of sound intensity) because the change in noise may be as important to the pathogenesis of sleep disruption as the actual decibel level achieved (8). We observed 37 and 72 sound peaks/hour of sleep in the open ICUs of patients and healthy subjects, respectively, which indicated frequent noise spiking. Consequently, we conclude that our open ICUs are sources of excessive noise that may cause sleep disruption, which is consistent with the previous literature.
Many studies have demonstrated unequivocal sleep disruption in critically ill patients, characterized by extreme sleep fragmentation, an overrepresentation of Stage 1 and Stage 2 non-REM sleep, reduced or absent slow-wave sleep and REM sleep, and circadian rhythm abnormalities (1–8). We observed similar findings in our patient cohort. Healthy subjects in the open ICU experienced a modest decrease in the proportion of slow-wave sleep and REM sleep and a concomitant increase in the proportion of Stage 1 and Stage 2 non-REM sleep, perhaps partly due to unrestricted daytime napping, as approximately 30% of sleep occurred during the day. The frequency of arousals and awakenings was within normal limits (25), supporting the more recent finding (8) that noise, which was the only known environmental factor intruding on the sleep of the healthy subjects, is not a significant source of sleep disruption.
Only 20% of arousals and awakenings in our ICU patient cohort were identifiably due to noise peaks, demonstrating that, in contradiction of traditional hypotheses, environmental noise is not responsible for the majority of ICU sleep disruption instances. Although noise peaks occurred frequently, only 12% of these peaks, and 35% of peaks greater than 75 dB(A), resulted in an arousal or awakening. Noise was responsible for the majority of sleep disruption in healthy subjects, likely because other potential sources of sleep disruption such as patient-care activities and mechanical ventilation did not exist. Previous work on arousal responses to acoustic stimulation in healthy individuals observed that 35% of sound spikes of 85 dB intensity resulted in an arousal (26), which is similar to our results with a threshold of 75 dB. These investigators observed sleep stage–dependent changes in arousal frequency for a fixed sound intensity, whereas we observed no significant differences in the peak sound intensity that caused sleep disruption across sleep stages and between patients and healthy subjects.
This is also the first study to directly quantify the effect of patient-care activities on sleep continuity. In the first study to characterize sleep disruption in the ICU by PSG, Hilton (1) monitored patient-care activities. However, she did not directly determine their contribution to sleep disruption with PSG synchronization. In our study, patient-care activities (which included nursing visits, assessment of vital signs, and administering medications) occurred approximately 8 times per hour of sleep, which is in contrast with the occurrence of noise spikes 37 times per hour of sleep. Approximately 20% of patient-care activities resulted in an arousal or an awakening, which accounted for only 7% of observed sleep disruption. Therefore, patient-care activities, although frequent, were not a predominant source of sleep disruption in ICU patients.
We used a questionnaire to subjectively assess the relative contribution of different sleep-disrupting factors (19). In contrast to the findings of Freedman and coworkers, our healthy subjects perceived noise as highly disruptive to sleep in the open ICU, presumably because the more disruptive factors from their study (phlebotomy, assessment of vital signs) are not relevant to healthy subjects. As with Freedman and colleagues, however, our healthy subjects perceived that sleep in the ICU was worse than at home. We observed that conversation and alarms were perceived to be the most disruptive noises, whereas noise from televisions, telephones, and pagers were rated as the least disruptive. Subject perceptions did not match the objective PSG–environmental data. For example, alarms were rated second to conversation in terms of sleep disruption, yet they were responsible for only 20–25% of arousals and awakenings. We suspect that subjects may subconsciously bias their observations on the basis of the perceived degree of irritation from these noises while awake; clearly, the transient nature of arousals and awakenings makes it difficult for the subject to recall the event and its cause.
Two previous studies have attempted to reduce noise in the ICU (27, 28). Walder and coworkers (27) were partially successful in reducing noise and light levels, and Kahn and colleagues (28), through a detailed and comprehensive behavior modification program, significantly reduced mean peak noise levels and the number of sound peaks less than 80 dB(A). However, neither of these studies assessed the impact of these changes on sleep quality. Two overnight studies on healthy subjects by Topf and coworkers (15, 16) and one by Wallace and colleagues (17) simulated an ICU environment in the sleep laboratory by using audiotape-recorded ICU noise. Reduction of noise, either by stopping the audiotape or the use of earplugs, was associated with improved sleep quality. However, these studies were not performed for 24 hours, and the sleep laboratory cannot simulate the full auditory and visual experience of the ICU. In addition to being performed completely in the ICU, our study objectively assessed sleep quality in both the loud and noise-reduced environments. Although there was a quantitative improvement in sleep in the single room, sleep architecture was nearly identical and arousal frequencies were normal in both locations. Mean and mean maximum sound intensities were significantly reduced in the single room, as were the number of extremely loud (>75 dB[A]) sound peaks and the mean peak decibel level; however, the frequency of sound spikes remained elevated, essentially the same as that experienced by our patient cohort. This may be one explanation for a lack of improvement in sleep continuity despite a reduction in overall sound intensity and may suggest that the frequency and nature of sound peaks are important contributors to noise-induced sleep disruption. The single room was chosen as a realistic location for noise reduction because it is physically isolated from the main open ICU. Any ICU noise registered in the single room originated from “leakage” from the main ICU. In our case, this was particularly evident with respect to the main door of the ICU, which was responsible for a disproportionate amount of sleep disruption. However, when sleep disruption caused by the main door was excluded, the arousal and awakening indices of subjects in the single room were significantly lower than the arousal and awakening indices of subjects in the open ICU. Despite the effect of the main door, total sleep time and nocturnal sleep time improved significantly in the single room, perhaps because the noise-reduced location is more conducive to a return to sleep after an arousal or awakening. Alternatively, the comparative lack of visual and other distractions in the single room may have also played a role. Reductions in the frequency of irritating noises, noise intensity, and visual distractions, in addition to a longer total sleep time, may have contributed to the subjective rating of improved sleep quality in the single room despite no improvement in arousal and awakening indices.
Our study has a number of limitations, which should be noted. First, our sample sizes were small, which limited the power of our statistical analyses. Nevertheless, our adherence to strict inclusion and exclusion criteria enabled us to avoid a wide heterogeneity in our data, which resulted in relatively consistent PSG findings despite the fact that we recruited patients and healthy subjects over a wide age range. Second, the design of the study did not allow healthy subjects to adapt to the ICU environment and habituate to noise intensity, which may have occurred in our patients admitted to the ICU for prolonged periods. However, habituation to noise among our healthy individuals would be expected to reduce the prevalence of noise-related sleep disruption even further. Therefore, our finding that excessive noise did not pathologically disrupt the sleep of healthy subjects, without the opportunity to habituate to noise, coupled with our observations in ICU patients, strengthens our suggestion that noise is not a major sleep disrupter in the ICU.
Given that the elements of the ICU environment assessed in this study, namely sound and patient-care activities, together accounted for less than 30% of observed sleep disruption, a significant proportion of sleep disruption in mechanically ventilated ICU patients remains unexplained. One possibility is dyssynchrony between the patient and the ventilator. Preliminary data (29) suggest that this may be dependent on the mode of ventilation, but this requires further study. If patient–ventilator dyssynchrony is determined to be a major source of sleep disruption, this may be corrected by altering the mode of mechanical ventilation with the hope and expectation that improvement of sleep quality will ultimately benefit the clinical outcome of critically ill patients.
The authors are indebted to Dr. Hans Kunov and Dr. Taha Jaffer at the Institute for Biomaterial and Biomedical Engineering and the Edwards S. Rogers Department of Electrical and Computer Engineering, University of Toronto, who designed and built the apparatus for time-synchronization of PSG and environmental variables, and the Canadian Intensive Care Foundation and the St. Michael's Hospital Foundation for financial support of this work.
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