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The Journals of Gerontology Series A: Biological Sciences and Medical Sciences 62:55-61 (2007)
© 2007 The Gerontological Society of America

SPECIAL SECTION

Polysomnographic and Clinical Correlates of Behaviorally Observed Daytime Sleep in Nursing Home Residents

Yohannes W. Endeshaw, Joseph G. Ouslander, Jack F. Schnelle and Donald L. Bliwise

1 Division of Geriatrics and Gerontology, Department of Medicine, Emory University School of Medicine, Atlanta, Georgia.
2 Emory Center for Health in Aging, Atlanta, Georgia.
3 Birmingham/Atlanta VA GRECC, Atlanta, Georgia.
4 UCLA Borum Center for Gerontological Research, Los Angeles, California.
5 Department of Neurology, Emory University School of Medicine, Atlanta, Georgia.

Address correspondence to Yohannes Endeshaw, MD, MPH, 1841 Clifton Road NE, 5th Floor, Atlanta, GA 30329. E-mail: yendesh{at}emory.edu


   Abstract
Top
 Abstract
Methods
 Results
 Discussion
 References
 
Background. The causes of daytime sleepiness among nursing home residents have not been well recognized. This study examines clinical and polysomnographic factors that are associated with daytime sleepiness among nursing home residents.

Methods. One hundred seventy-four nursing home residents from eight nursing homes in Atlanta, Georgia, participated in the study. Demographic and clinical data were obtained from medical records and assessment of participants obtained by trained research staff. Daytime sleepiness was determined by behavioral sleep–wake observation performed every 15 minutes. Overnight polysomnography was performed in a subgroup of the sample.

Results. The mean ± standard deviation age was 83.4 ± 8.8 years, and 136 participants were women (78%). The mean percentage ± standard deviation of behavioral observations with sleep (BOS%) was 19.5 ± 13.3%. Participants who were able to ambulate independently had significantly lower BOS% (14.2 ± 9.6 vs 21.2 ± 6.0, p =.001). Mini-Mental State Examination score was negatively correlated with BOS% (rho = –.279, p =.001). Among 48 participants who had polysomnography, sleep latency, total sleep time, wake after sleep onset, and sleep efficiency were not associated with BOS%. There was a significant negative correlation between BOS% and percentage of time spent in rapid eye movement sleep (rho = –.367, p =.010). Linear regression analyses, with BOS% as the dependent variable, showed that percentage of time spent in rapid eye movement sleep was the only variable independently predicting BOS%.

Conclusion. Absence of association between BOS% and nocturnal sleep suggests that the causes of daytime sleepiness and nocturnal sleep problems may not be related. This finding may have important implications for interventions that aim to reduce daytime sleepiness among nursing home residents.

NURSING home residents spend a significant amount of the daytime asleep (1,2). Several factors have been reported to be associated with daytime sleepiness in this group of people. These factors include decreased daytime activity (3,4), decreased light exposure during the day, increased light and noise during the night, poor sleep hygiene (5–7), and fragmented sleep during the night due to sleep disorders, medications, and medical comorbidities (8,9). In ambulatory elderly populations, epidemiological studies have shown that daytime napping portends adverse outcomes such as stroke (10), myocardial infarct (10,11), cognitive decline, and mortality (12,13), and even among nursing home populations, excessive sleep during the daytime hours may be associated with reduced survival (14). Studies such as these suggest that napping in nursing home patients may be more than a curiosity or a response to the monotony of the institutional environment, although environmental influences on the extent of napping behavior in nursing homes have been amply demonstrated (15).

Interventional studies attempting to reduce daytime sleepiness in nursing home patients have focused on improving nocturnal sleep and altering the daytime environment by increasing daytime activity and light exposure, but such studies have not shown consistent results. For example, some studies of bright light have shown beneficial effects (16), whereas other studies have not (17,18). Studies that used both daytime and nighttime interventions have shown improved nocturnal sleep but not reduced daytime sleepiness (19). Hence, the problem of daytime sleepiness has been difficult to manage in nursing home residents. One reason for the lack of success in managing daytime sleepiness may be insufficient understanding of etiological factors associated with the problem. The objective of this study is to identify factors (including polysomnographically derived sleep variables) that may be related to daytime sleepiness among nursing home residents.


   METHODS
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 Abstract
 Methods
Results
 Discussion
 References
 
Study participants were derived from a randomized clinical trial, the results of which have been published elsewhere (20). Nine nursing homes in Atlanta, Georgia, were approached to participate in the study; eight facilities agreed. These eight nursing homes were the recruitment sites of participants for the study. Participants residing in these eight facilities were screened based on the study criteria, and those who met the criteria were eligible for the study (Figure 1). The study inclusion criteria were: (i) being ≥65 years old; (ii) not having Medicare reimbursement (i.e., short-stay); (iii) not being terminally ill with life expectancy < 3 months; (iv) not being bed-bound (i.e., able to be assisted out of bed for periods of time each day); and (v) having a stable medical, neurological, or psychiatric condition. Informed consent was obtained from the participants or from the responsible party when the participant could not consent himself or herself. The study was approved by the Emory University Institutional Review Board. The data used in this study represented baseline data collected in the clinical trial mentioned above and were derived from completed initial assessments on 230 individuals, of which 174 had sufficient observational sleep–wake data (Figure 1).


Figure 01
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  		Figure 1. Flow chart showing subject recruitment

 
Research staff was trained specifically to collect demographic and medical information for the study. Cognitive assessment was made using the Folstein Mini-Mental State Examination (MMSE) (21), a 30-item rating scale in which higher scores signify more intact function. Scores below 24 on the MMSE represent dementia. Mood was assessed with the 15-item version of the Geriatric Depression Scale (GDS) (22). The GDS is a self-reported scale that inquires about depressive symptoms without reference to somatic items associated with depression. It has seen extensive use in elderly populations, including those with dementia residing in nursing homes. A higher score indicates a more depressed mood. Disease burden was assessed with the Charlson Comorbidity Index (CCI) (23), which is a weighted index of comorbid diseases, completed by the trained research assistant. Information related to demographics, diagnoses, medications, and ambulation status was derived from a review of medical records. Table 1 summarizes the demographic and clinical characteristics of the study participants.


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  		Table 1. Selected Demographic and Clinical Characteristics of Study Participants.

 
Behavioral observations of sleep–wake followed methods described previously (1). These observations were made by trained research staff for 60 seconds every 15 minutes from the hours of 8:00 AM–8:00 PM on three consecutive days. A given observation was considered to represent sleep when characterized by eye closure and absence of purposeful body movement during the 60-second window of observation. Behavioral observations with sleep (BOS) was the primary variable used in the data analyses and was defined as the proportion of such observations which were considered sleep (calculated as a percentage of total observations) out of 144 possible observations per participant (i.e., 12 hours x 4 observations per hour x 3 days).

Polysomnography (PSG) was performed in the participants' rooms by using a SensorMedics (Yorba Linda, CA) Somno/Star 4100 digital acquisition system. Participants were selected for PSG by the PSG technician on the basis of whether the participants would tolerate the procedure (having the monitoring equipment for several hours). Participants were prepared (skin cleaning, electrode application) after dinner. Recordings were initiated immediately prior to "lights out," which was individualized for each participant in each facility. The duration of this recording, which lasted until each participant's customary morning awakening time ("lights on"), constituted the total recording time (TRT). PSG was scored according to the standard scoring criteria (24) by a registered PSG technologist (RPSGT) who did not have access to clinical or behavioral observation data. Because of difficulty discriminating nonrapid eye movement (NREM) sleep stages among patients with neurodegenerative disease (25), we analyzed only rapid eye movement (REM) and total NREM sleep. Other PSG variables examined included TRT (minutes), total sleep time (TST) (minutes), wake after sleep onset (WASO) (minutes), and sleep efficiency (SE) (TST divided by TRT; %). Apnea–hypopnea index (AHI) was calculated as the number of apneas and hypopneas divided by TST in hours. Hypopnea was defined as decrease in airflow amplitude by ≥50% associated with arousals or oxygen desaturation by ≥3% (26). Periodic leg movements were not recorded.

We defined an adequate amount of BOS data to be at least 90% of all observations over the 3 days of observation. A total of 174 participants demonstrated <10% missing data. Of these 174 participants, overnight PSG was attempted in 61 participants and completed successfully in 50 participants (82%). Participants who successfully underwent PSG had higher CCI values (indicating higher disease burden [3.5 ± 2.7 vs 2.6 ± 1.6, p =.028]) and were less likely to ambulate without assistance (19% vs 29%, p =.056) than participants who did not undergo PSG. There were no differences in other demographic and clinical characteristics between the two groups (Table 2). Twenty-five participants (50%) had PSG during the period of behavioral observation whereas, because of technician and equipment availability, the remaining 25 participants (50%) were studied outside the period of behavioral observation.


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  		Table 2. Clinical Characteristics of Participants Who Underwent Successful Polysomnography (PSG) Recordings and Those in Whom PSG Recording Was Unsuccessful or Not Attempted.

 
Statistical Analysis
The relationship between BOS and demographic, clinical, and PSG characteristics were determined using independent t tests, chi-square tests, and Spearman's correlation as appropriate. Multiple linear regression was performed with BOS as the dependent variable and sex, age, and variables that showed a significant relationship with BOS on bivariate analysis as the independent variables.


   RESULTS
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 Abstract
 Methods
 Results
Discussion
 References
 
Gender and Racial Differences
With the exception of women being older than men, gender differences were generally unremarkable (Table 1). Analyses of racial differences showed that African Americans were younger than Caucasians (81.3 ± 10.2 vs 84.5 ± 8.1 years, p =.033), and had increased CCI scores (3.8 ± 2.0 vs 2.5 ± 1.9, p =.001), indicating greater disease burden. There were no other significant differences between the two racial groups for any variable in Table 1.

Behavioral Sleep–Wake Observations
The mean ± standard deviation of the number of observations per patient was 135 ± 8, which accounted for 94% of the total possible observations (maximum 144 per participant). The mean ± standard deviation BOS% was 19.3 ± 13.5%, averaged for each patient across the 3 days (Figure 2). Correlations between BOS% on days 1 and 2, days 1 and 3, and days 2 and 3 were.505 (p <.0001),.441 (p <.0001), and.530 (p <.0001), respectively, suggesting some stability in this measure over the 3 days of observation. Figure 3 illustrates the 3-day average BOS values by time of day (8:00 AM–8:00 PM). Sleep was least likely between the hours of 8:00 AM and 9:00 AM, 12:30 PM and 1:30 PM, and 5:30 PM and 6:30 PM.


Figure 02
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  		Figure 2. Frequency distribution of mean percentages of behavioral observations at which participants were asleep

 

Figure 03
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  		Figure 3. Percent of behavioral observations at which participants were asleep (8:00 AM–8:00 PM) over a 3-day period

 
There were no significant relationships between BOS% and age, racial group, CCI value, or GDS score. Men had marginally higher BOS relative to women (23.1 ± 16.4 vs 18.3 ± 12.5, p =.099). MMSE score was negatively correlated with BOS% (rho = –.279, p =.001), indicating that participants with lower mental status had greater daytime sleepiness. Participants who were able to walk independently had significantly lower BOS% (14.2 ± 9.6 vs 21.2 ± 6%, p =.001).

One hundred twelve participants (64%) used scheduled medications that had their primary effect on the central nervous system (psychotropic drugs). These medications included antidepressants in 72 participants (41%; tricyclic antidepressants in 4 participants and selective serotonin reuptake inhibitors and other newer antidepressants in 68 participants), antipsychotics in 43 participants (25%; atypical antipsychotics in 40 participants and other antipsychotics in 3 participants), sedative hypnotics in 18 participants (10%; medium- and long-acting benzodiazepines in 15 participants and trazadone in 3 participants), antiepileptics in 27 participants (16%), and cholinesterase inhibitors in 16 participants (9%). There was no significant difference in BOS between those participants taking and not taking these medications.

PSG Sleep Characteristics
PSG recordings for two participants were excluded from analysis because their TST was less than 10 minutes. Table 3 shows demographic, clinical, and sleep characteristics of participants who had their PSG during the sleep–wake observation period and outside that period. Participants who had their PSG recordings during the sleep–wake observation period had increased TRT, which resulted in increased TST. Differences in other variables were unremarkable.


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  		Table 3. Clinical and Sleep Characteristics of Participants by Time of Polysomnography (PSG) Recordings.

 
Overall, there was no significant difference in any PSG-derived variable between men and women. African Americans spent more time in bed (518.6 vs 481.2 minutes, p =.047), had higher WASO time (182.4 vs 113.5 minutes, p =.027), and lower SE (54.8% vs 67.4%, p =.050) than did Caucasians.

There was no significant correlation between GDS score and PSG-derived sleep variables. Higher CCI score was associated with higher WASO (rho =.305, p =.039), and showed marginal correlation with lower TST (rho = –.257, p =.085) and lower SE (rho = –.241, p =.107). These relationships grew slightly stronger after controlling for use of sedatives or hypnotic drugs, with partial correlation values of.310 (p =.038); –.321 (p =.032), and –.284 (p =.059) corresponding to correlations between CCI value and WASO, TST and SE, respectively, implying that sedatives or hypnotic drugs may have had some sleep-promoting effect in these participants. There were no significant associations between MMSE and PSG-derived variables except for REM%, which showed a marginal positive correlation (rho =.286, p =.063).

As has been reported previously in nursing home residents (9,27), the presence of sleep apnea was substantial, with a mean AHI of 18 ± 22 per hour of sleep and 43% of the participants having AHI ≥ 15 per hour of sleep. There was no significant correlation between AHI and the other sleep variables listed in Table 3.

Among the 48 residents successfully undergoing PSG, 29 participants (60%) received one or more psychotropic drugs. Of these participants, 11 (21%) were on antipsychotics, 20 (42%) antidepressants, and 6 (13%) sedatives or hypnotic drugs. There were no significant differences in PSG-derived sleep variables between those participants who were taking and not taking antidepressant drugs. Participants taking sedative-hypnotics had significantly lower WASO and higher SE (64 ± 76 vs 138 ± 88 minutes, p =.056 and 82.0 ± 17.5% vs 62.0 ± 21.5%, p =.033, respectively), whereas participants taking antipsychotics agents had marginally higher SE (72.7 ± 21.5% vs 62.1 ± 18.4%, p =.160). We were not able to determine the effect of cholinesterase inhibitors on sleep characteristics because only one participant who had PSG data used this medication class.

Relationship Among BOS, PSG, and Clinical Characteristics
Relationships between BOS% and sleep latency, TST, WASO, SE, and AHI were not statistically significant. There was a significant negative correlation between BOS and REM% (rho = –.367 p =.010). Multiple linear regression analysis, performed with BOS% as the dependent variable and variables significantly associated on bivariate analysis (MMSE, ambulatory status, and REM%) or having high face validity (age, gender, CCI) as the independent variables, showed that REM sleep was the only variable associated with BOS% (Table 4). This finding indicates that greater levels of observed daytime sleep were associated with lower amounts of nighttime REM sleep.


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  		Table 4. Linear Regression Coefficients Showing the Relationship Between Behavioral Observation With Sleep and Selected Variables (Dependent Variable: Behavioral Observation With Sleep; Adjusted R2 =.123).

 

   DISCUSSION
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 Abstract
 Methods
 Results
 Discussion
References
 
On average, these nursing home residents were observed asleep in 19% of their daytime observations, from which one could infer a total of about 2.3 hours of sleep during the daytime hours of 8:00 AM–8:00 PM. This is consistent with previous studies that have reported considerable daytime sleepiness in such populations (1,4). In our study, the tendency for daytime sleep was relatively stable, with at least modest reliability across days and a consistent pattern during the day. Mid-morning and mid-afternoon naps were common, as were periods of relative alertness coinciding with meal times, a finding noted by others as well (2). But even during these meal times, episodes of sleep were observed. Contrary to widely held belief and previous reports, we did not find significant association between PSG measures of nocturnal sleep (TST, SE) and BOS. This finding suggests that etiological factors involved in BOS may be independent of the amount of nocturnal sleep. The absence of relationship may partly explain the lack of effect on BOS of previous interventions aimed at improving nocturnal sleep.

As noted in other studies, higher levels of daytime sleepiness were associated with more severe dementia. This finding has been noted with actigraphy (2), behavioral observations (9), and 24-hour PSG (28,29). Perhaps less well appreciated in previous studies was the relationship between ability to ambulate and BOS. These data can be interpreted either as indicating that residents who remain in bed (be it from limited functioning or by volition) are more likely to sleep, or that inferred physical activity among nursing home residents is vital to prevent excessive daytime sleeping. A treatment program involving both enhanced daytime physical activity and decreased daytime sleep was reported to have some beneficial effects on sleep in nursing home residents (19).

PSG, performed in only a subset of the residents in this study, provided a number of interesting findings. First, the mean polysomnographically defined SE reported in this population (65%) was much lower than those reported by wrist actigraphy in nursing home populations (30). Although actigraphy has received some validation in demented, institutionalized populations (30), it has also been reported to overestimate TST and SE (31). A second noteworthy point involves medications. Although BOS% showed no relationship to overall psychotropic drug use, several classes of medications appeared to be associated with better PSG-defined sleep in this sample of nursing home residents. Additionally, correlations between higher CCI scores (suggesting greater disease burden) and PSG measures of more severe sleep disturbance were enhanced when the effects of sedatives or hypnotic drugs were removed statistically, suggesting that such medications may have had some beneficial effects on nocturnal sleep. The value of sedatives or hypnotic drugs in the nursing home setting has been viewed negatively, and substantial evidence suggests that psychotropics have been associated with falls, hip fracture, delirium, and cognitive decline in this population (32–34). In contrast, it is also possible that the effects of being unable to sleep at night (leading to arising from bed) may represent the critical exposure for falls for which sedatives or hypnotic drug use represents only a proxy (35,36). In light of these findings, a reexamination of the judicious use of sedatives or hypnotic drugs may be in order.

A third finding of interest derived from PSG involves the relationship between lower MMSE and lower REM%, a result that has been shown in higher functioning, noninstitutionalized Alzheimer's disease (AD) patients (37), but to the best of our knowledge, never before in nursing home residents. This finding is compatible with the dual role of acetylcholine in the regulation of both cognition and REM sleep (38,39). Although cholinesterase inhibitors have been shown to increase REM sleep in healthy young and elderly adults (40), only one study reported such an effect in AD patients (41). Because so few of our nursing home residents were receiving such medications, we were unable to detect whether such an effect might be operating in our patients. Furthermore, the type of dementia in the nursing home residents in this study was not determined. In addition to dementia of Alzheimer's type, participants with vascular dementia and dementia with Lewy bodies may also have been included. This complicates any interpretation of cholinergic system activity.

Perhaps even more intriguing than the relationship between REM% and cognition, however, was the fact that REM% was uniquely and independently associated with our behavioral measure of daytime sleep, BOS%. Lower REM% was associated with higher BOS%. This was a fairly robust relationship, being sustained in a multivariate model, and was unanticipated. Several explanations are possible. Studies have not only suggested that acetylcholine is critical for production of REM sleep (42), but also that cholinergic systems are vital for maintenance of sustained wakefulness (43) and probably operate through both basal forebrain circuits and projections from the mesencephalic reticular formation (44). Another explanation, essentially more complementary than competing, involves REM sleep as a marker for integrity of the biological clock. Converging evidence suggests that the biological clock, which may fundamentally have alerting properties (45), may deteriorate in dementing illness (46). Because the presence of nocturnal REM sleep is tied tightly to the integrity of the clock (47), a reduction in REM% could be interpreted here as a index of degeneration of the circadian timing system; the increase in daytime sleep would be thus viewed as a component of this same chronobiologic reorganization. A corollary prediction relevant to the latter interpretation is that our patients might have been expected to show relatively higher levels of REM sleep during their daytime naps. We were unable to test this hypothesis because we did not perform daytime PSG in this study. However, there is at least one case of a neuropathologically confirmed AD patient who showed abundant REM sleep in a series of daytime naps throughout the course of his illness (48); this finding is broadly consistent with the aforementioned explanation.

There are limitations of this study. In every second patient, overnight PSG was not performed on a night during the 3-day observation window. This might otherwise represent an interpretive confound if there was a change in the nursing home environment or a major change in the patient's clinical condition during the interval between PSG and behavioral measurements. However, such factors were minimized in our study, because we did not study residents who were acutely ill or who recently relocated or entered the facility (mean duration in facility was 46.1 ± 32.2 months prior to study). The lack of simultaneous measurement may have resulted in either an underestimation or overestimation of the point values observed (i.e., the effect is unpredictable).

Another limitation is that participants in the study were recruited from different nursing homes. It remains possible that some unmeasured characteristics of a particular nursing home may have had some effect on the variables under study.

Conclusion
The results of this study suggest that etiological factors associated with increased daytime sleepiness in these nursing home participants may be different from those associated with nighttime sleep disturbance. This finding is in contrast to commonly held opinion that incriminates poor nighttime sleep as the cause of daytime sleepiness, and has important implications for future interventions that attempt to improve daytime sleepiness, as well as nocturnal sleep, in the nursing home population.


   Acknowledgments
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 Methods
 Results
 Discussion
 References
 
This work was supported by the National Institutes of Health grants (NIH/NCRR K12 RR017643-03, K23 AG 025963-02, AG-17430, AG-10643, and AG-020269), Emory Center for Health in Aging, and the Birmingham-Atlanta VA GRECC.

We thank Zobair Nagamia, MD, Melissa Mohr, MPH, and Catherine Adelman, BSc, for their assistance in data collection and management. We also thank the nursing homes and nursing home residents who participated in the study.


   Footnotes
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 Results
 Discussion
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Decision Editor: Luigi Ferrucci, MD, PhD

Received April 22, 2005

Accepted February 7, 2006


   References
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 Results
 Discussion
 References
 

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S. Lesage and S. M. Scharf
Beyond the Usual Suspects: Approaching Sleep in Elderly People
J. Gerontol. A Biol. Sci. Med. Sci., January 1, 2007; 62(1): 53 - 54.
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