factors that disturb sleep in nursing home residents

Aging Clin. Exp. Res. 9: 207-213, 1997

Aging Clin. Exp. Res., Vol. 9, No. 3 207

ABSTRACT. The purpose of this cross-sectional survey was to examine subjective sleep quality, and identify factors that disturb sleep in cogni-tively intact nursing home (NH) residents. We in-terviewed 51 cognitively intact NH residents using a validated instrument, the Pittsburgh Sleep Qual-ity Index (PSQI). Questions regarding the environ-ment, symptoms suggestive of restless leg syn-drome, and periodic leg movements were also asked. Three individuals were unable to answer all the questions and, therefore, analyses were per-formed on 48 subjects. Seventy-three percent of the subjects were identified as “poor” sleepers based upon a total PSQI score >5. Factors that disturbed the residents’ sleep three or more times a week were: nocturia (71%), environment-related noise or light (38%), pain (33%), feeling too hot (6%) and leg cramps (6%). On multiple regression analysis, poor sleep correlated with depressive symptoms and comorbidity, not with age or gen-der. Poor sleep quality is common among cogni-tively intact NH residents. In our study, nocturia, environmental factors and pain were the most commonly perceived causes of sleep disturbance. Poor sleep independently correlated with depres-sive symptoms and comorbidity. These findings should be kept in mind when evaluating sleep complaints in the nursing home.(Aging Clin. Exp. Res. 9: 207-213, 1997)©1997, Editrice Kurtis

INTRODUCTION

Sleep disturbance increases with age (1, 2), and a variety of factors have been implicated in contribut-ing to this phenomenon. Surveys in community dwelling elderly have found a correlation between

poor sleep and female gender, anxiety, self-rated health (3), depressive symptoms, use of medications (1), nocturia (4), chronic pain, and somatic diseases (5). Such associations have not been extensively evaluated among residents of long-term care facili-ties. The prevalence of dementia in the nursing home (NH) setting, which is one factor that disturbs sleep, makes the evaluation of other potentially contributing factors difficult. Studies on subjective sleep quality in the NH setting showed that 45 to 75% of NH resi-dents complain of poor sleep (6, 7). Studies using objective methods, like actigraphy and sleep observa-tion, have demonstrated that sleep of NH residents is fragmented with frequent awakenings (8-10). Poor sleep in the NH is probably due to the interaction of multiple different factors, including age-related chang-es; medical, psychiatric and primary sleep disorders; medications, circadian rhythm disturbances and the environment.

Four studies have been published that examine subjective sleep quality in the NH (6, 7, 11, 12). In all of them, the investigators utilized a sleep question-naire or interview without established validity. Two studies did not rigorously assess the level of cognitive function of the participants (6, 7), whereas one other study included both cognitively impaired as well as cognitively intact subjects (12), making the validity of these results uncertain.

The purpose of this study was to determine the prevalence of sleep complaints in cognitively intact NH residents, and identify particular factors associ-ated with poor sleep. We used an established sleep questionnaire, the Pittsburgh Sleep Quality Index (PSQI), that has good internal consistency, validity and test-retest reliability (13, 14). Furthermore, we screened the subjects with a structured mental status questionnaire (15) to help assure the validity of their answers.

Factors that disturb sleep in nursing home residentsA. Gentili1,2,4, D.K. Weiner1,2,4, M. Kuchibhatla1, and J.D. Edinger3,5

1Center for the Study of Aging and Human Development, 2Department of Medicine and 3Department of Psychiatry, Duke University Medical Center, Durham, 4Geriatric Research, Education and Clinical Center and 5Psychology Service, Durham Veteran Affairs Medical Center, Durham, North Carolina, U.S.A.

Key words: Aged, nursing homes, sleep, sleep questionnaire.Correspondence: A. Gentili, M.D., Hunter Holmes McGuire VA Medical Center, Geriatrics and Extended Care (111T), 1201 Broad Rock Boulevard, Richmond, VA 23249, U.S.A.Received May 28, 1996; accepted in revised form December 19, 1996.

SUBJECTS AND METHODSSampleThe study sample consisted of two groups of NH

residents: one from a university affiliated community NH (118 residents), and one from the Durham Vet-erans Affairs Medical Center (VAMC) Extended Care and Rehabilitation Center (90 long-term care resi-dents). We excluded subjects who: (a) were less than 60 years of age (N=16); (b) had length of stay (LOS) less than thirty days (N=7); (c) were not able to com-municate because of severe aphasia (N=8) or severe hearing loss (N=3); and (d) had cognitive impairment based on the Short Portable Mental Status Question-naire (SPMSQ, 15), defined as 2 incorrect answers out of 10 for white subjects with at least some high school education; one less error is allowed for sub-jects with education beyond high school and one more error is allowed for subjects with only a grade school education and for black subjects, using identi-cal education criteria (subjects who had cognitive im-pairment: N=118). From a total sample of 208 resi-dents there were 56 eligible subjects; five refused, so 51 were enrolled. All subjects gave their informed consent.

Instruments1) Comorbidity was measured using the Duke

University Severity of Illness (DUSOI) Scale, a scale that can be used to score comorbidity based on chart-recorded diagnoses. Each diagnosis is given a score from 0 to 4 based on three parameters: 1) treatabil-ity and prognosis; 2) complications; and 3) symp-toms. An overall DUSOI score is calculated using a formula that gives the greatest weight to the diag-noses with the highest severity score, and progres-sively smaller weights to the other diagnoses; this method limits the increase of the overall score by diagnoses which are causing the least comorbidity. The overall score is standardized on a scale from 0 to 1.0 where higher scores indicate poorer health. The reliability and validity of this scale were deter-mined for an outpatient adult population, with intra-rater reliability coefficient of 0.76, inter-rater reliabil-ity coefficient of 0.56, and coefficient of agreement between chart auditor and providers of 0.59 (16, 17).

2) Disability was measured using the Rapid Disabil-ity Rating Scale-2 (RDRS-2), a validated functional status scale that gives a total score, from 18 to 72. Elderly living in the community have scores averaging 21-22, whereas NH residents have an average score of 36 (18). In the study of Linn and Linn (18), the scale was completed by the patients’ nurses; they

rated 845 elderly men at the time of transfer from a general medical hospital to community NHs, and the scale correctly identified patients who would die within 6 months (72%). The scale also had good in-terobserver reliability with intraclass correlations ranging from r=0.62 to r=0.98.

3) Depressive symptoms were measured using the geriatric depression scale, short form (GDS-SF). The GDS was initially developed as a 30-item, yes/no questionnaire, and was shown to be reliable and valid among community-living elderly (19) and medi-cally ill elderly, both inpatient (20) and outpatient (21). Among NH residents, the GDS was more sensi-tive among cognitively intact residents (sensitivity 75%, specificity 75%), than cognitively impaired residents (sensitivity 47%, specificity 75%) (22). Sheikh and Yesavage developed a shorter form (GDS-SF), by selecting the 15 items that had the highest correlation with depressive symptoms (23). The two forms of the GDS are highly correlated (r=0.84). Among a group of older adults admitted to an inpatient geropsychiatric unit, Lesher and Berry-hill also found a high correlation between the two forms; a GDS-SF cut-off score > 5 had a sensitivity of 83%, a specificity of 69% and correctly diagnosed 78% of the patients (24). The GDS-SF was a more effective screening instrument than the Hamilton Depression Scale among elderly veterans admitted to a VAMC nursing home (25). We used the established cut-off score of > 5.

4) Self-rated health (SRH) was determined by ask-ing: “For your age would you say, in general, your health is: excellent, good, fair, poor or bad?” (26).

5) Sleep Interview: As there are no sleep question-naires or interviews specifically designed and vali-dated for use in NH subjects, sleep quality was deter-mined using the PSQI in interview format. The PSQI is a questionnaire that assesses sleep quality of the preceding one-month period. Nineteen questions generate seven “component scores”: subjective sleep quality; sleep latency (time to fall asleep); sleep dura-tion; habitual sleep efficiency (number of hours asleep divided by the number of hours in bed); sleep distur-bance (waking during sleep because of pain, nocturia or other reasons); use of sleeping medications; and daytime dysfunction. The sum of the component scores gives a global score. Each component score can range from 0 to 3, and the global score from 0 to 21. A higher score means more severe com-plaints. Buysse et al. (13) assessed the clinical and clinimetric properties of the PSQI in good and poor sleepers from a community population. A global score > 5 had a sensitivity of 89.6% and specificity of 86.5% in distinguishing good and poor sleepers.

A. Gentili, D.K. Weiner, M. Kuchibhatla, et al.

208 Aging Clin. Exp. Res., Vol. 9, No. 3

The Pearson correlation coefficients for the test-retest reliability varied from 0.46 (component sleep disturbance) to 0.85 (sleep efficiency). In cognitively intact NH residents, we demonstrated that the PSQI in interview format has comparable test-retest reliabil-ity, with intraclass correlation coefficients ranging form 0.45 (component daytime dysfunction) to 0.84 (component use of sleeping medications) (14). We also added questions regarding the environment, leg cramps, and symptoms suggestive of restless legs (feeling pins or needles or restlessness in the legs while trying to sleep) or periodic leg movements (jerk-ing or kicking the legs during sleep). If a subject complained of noise three or more times a week, we asked if she/he would be interested in trying ear-plugs. To maintain the validity of the questionnaire, the answers to these extra questions were not used to calculate the component and global scores.

Data collectionAll the data were collected by AG. At the time of

data collection, there were no a priori hypotheses. The charts of eligible subjects were reviewed to ab-stract demographic data including age, gender, race and length of stay in the NH, as well as data on medications, medical diagnoses and the information needed to complete the DUSOI. GDS-SF and PSQI were completed by interviewing the subjects, and the RDRS-2 by interviewing the resident’s nurse.

Statistical AnalysisStatistical analysis was done using SAS (SAS Insti-

tute, Inc., Cary, North Carolina). Descriptive statistics were computed to describe the sample characteristics. We used a two-tailed t test to compare PSQI scores by gender and site, and Pearson correlation coefficients to examine associations between the PSQI global score and the following variables: age, comorbidity (DUSOI), depressive symptoms (GDS-SF), self-rated health, disability (RDRS-2), number of medications, and diagnosis. In the multiple regression analysis we used PSQI as the dependent variable and age, gender, comorbidity and depressive symptoms as independent variables. To avoid using multiple variables that meas-ure the same domain, we did not include SRH in the multiple regression analysis, since we already had DUSOI to measure comorbidity and GDS-SF for de-pressive symptoms; in addition, in the univariate analysis SRH correlated with both comorbidity and depression.

RESULTSSample characteristics

Table 1 summarizes the characteristics of the total sample (N=48). Fifty-one subjects were enrolled, but three individuals were unable to answer all the ques-tions, therefore analysis was performed on 48 sub-jects. The mean age was 76.8 (SD = 8.8) years, 54% were male and 80% caucasian. The mean number of diagnoses was 4.5 (SD = 1.7), and the mean number of scheduled medications was 6.1 (SD = 2.8). Only 2 (4%) subjects were taking hypnotics, and 53% rated their health excellent or good. Fifteen (31%) subjects had a prior diagnosis of depression, and all of them were taking antidepressants; four other sub-jects were on an antidepressant, 3 for chronic pain

Sleep disturbances in the nursing home


Table 1 - Sample characteristics (N=48).

Variable Mean ± SD Range

Age 76.8±8.8 61-96

Length of stay (months) 30±68 1-438

Medications (No.) 6.1±2.8 3-9

Diagnosis (No.) 4.5±1.7 2-12

Disability (RDRS-2) 34.5±8.1 22-40

Comorbidity (DUSOI) 0.74±0.11 0.4-0.9

Variable No. (%)

Male gender 26 (54)

White race 43 (90)

Depressive symptoms (GDS-SF>5) 20 (42)

Most common diagnosis

Hypertension 23 (50)

Depression 15 (31)

Coronary artery disease 12 (25)

Stroke 11 (23)

Psychotropic medications

Benzodiazepines 1 (2)

Chloral Hydrate 1 (2)

Antipsychotics 2 (4)

Antidepressant 19 (40)

Self-Rated Health

Excellent 6 (13)

Good 19 (40)

Fair 13 (27)

Poor 7 (15)

Bad 3 (6)

and one for insomnia (Table 2). Twenty (42%) had depressive symptoms based on the GDS-SF; of these, only 9 had a prior diagnosis of depression and were on an antidepressant. Nineteen subjects had urinary incontinence based on the RDRS-2.

PSQI scores and subjective sleep variablesThe mean global PSQI score was 8.1 (SD = 3.5,

range 1-16). Seventy-three percent of the subjects had a PSQI global score > 5 consistent with poor sleep. The highest component score was sleep effi-ciency (mean 1.8), followed by sleep disturbances (mean 1.5). The subjective estimates of the sleep variables showed a large intersubject variability. On average, the subjects reported a bedtime of 21:00 (SD = 47 min, range 18:00-24:00), took 35 minutes (SD = 51, range 2-300 min) to fall asleep, slept 6 hours and 43 minutes (SD = 100 min, range 3-10 hours), and slept 71% (range 40-100%) of the time spent in bed each night.

Sleep disturbancesThe most common causes of sleep disturbances

three or more times a week were: 1) Nocturia, 33 (69%) subjects (18 males and 15

females); of these, 8 had congestive heart failure (4 males and 4 females), and 12 were on diuretics (6 males and 6 females).

2) Noise or light, 18 (38%) subjects; of these, 17 were disturbed by noise, made by other residents (N=5), by the nurses (N=4), or both (N=8).

3) Pain, 16 (33%) subjects.Other factors like temperature, shortness of

breath, leg cramps, bad dreams, symptoms sugges-tive of restless legs and periodic limb movements were much less frequent (Table 3). The residents disturbed by the nurses complained about being awakened for diaper change (7 out of 19 incontinent residents), or by the nurses giving medications, talk-ing loudly or making noise with the medication cart. Six subjects were disturbed by the nurses’ flashlights or the light in the hallway. Only 3 out of 17 subjects frequently disturbed by noise were interested in trying earplugs. Of the 16 patients with frequent pain, none were taking non-steroidal anti-inflammatory drugs or acetaminophen regularly; two were on scheduled opioid analgesics and one on aspirin.

T-test and correlationsThere was no significant difference in PSQI scores

by gender (mean PSQI score in men 8±3.4, in women 8±3.8, p=1) or site (mean PSQI score in community NH 8.2±3.3, in VAMC NH 7.8±3.9, p=0.689). Pearson correlations showed that the PSQI global score correlated with depressive symp-toms (r=0.40, p=0.004), comorbidity (r=0.43, p=0.002) and self-rated health (r=0.32, p=0.026), but not with age, disability, number of diagnoses, or number of medications (Table 4). On multiple regres-sion analysis, significant predictors of poor sleep were comorbidity (p=0.032) and depressive symp-toms (p=0.044), but not age or gender (r2=0.26, adjusted r2=0.19, Table 5).

DISCUSSION

Although sleep disturbance has been cited as an important problem in older adults and in demented elders in particular, sleep complaints have not been carefully examined among residents of long-term



Table 2 - GDS-SF scores in subjects on anti-depressants (N=19).

Prior diagnosis of depression On anti-depressant for pain On anti-depressant for sleep

GDS-SF > 5 9 1 -

GDS-SF 5 6 2 1

Total 15 3 1

Table 3 - Subjective sleep disturbances.

Factor Prevalence

not past month <1/week 1-2/week 3/week No. (%) No. (%) No. (%) No. (%)

Nocturia 6 (12) 2 (4) 7 (15) 33 (69)

Noise and/or light 17 (35) 3 (6) 10 (21) 18 (38)

Pain 19 (40) 4 (8) 9 (19) 16 (33)

Shortness of breath 40 (83) 2 (4) 4 (8) 2 (4)

Feeling too hot 42 (88) 1 (2) 2 (4) 3 (6)

Feeling too cold 37 (77) 7 (15) 4 (8) 0 (0)

Leg cramps 41 (86) 2 (4) 2 (4) 3 (6)

Restless legs 41 (86) 3 (6) 2 (4) 2 (4)

Leg movements 44 (92) 1 (2) 0 (0) 3 (6)

Bad dreams 42 (88) 3 (6) 1 (2) 2 (4)

care facilities. As a step toward understanding this complex issue, we examined the prevalence of poor sleep in cognitively intact NH residents of two such institutions, and identified associated factors. We found that 73% of the subjects had a PSQI>5, con-sistent with poor sleep. These results are comparable to those reported by Middlekop et al., who found a 75% prevalence of poor sleep in the NH (7). Two other studies found a prevalence of poor sleep in the NH of 45% (6) and 13% (12). These latter studies used questionnaires that had not previously been validated, thus the lower prevalence may relate to ineffective case finding. Further, one study included cognitively impaired residents and utilized nursing staff as surrogates for those residents who were un-able to complete the questionnaire (12).This strategy may have also led to ineffective case finding, as the nurses might have under-rated residents’ sleep distur-bances.

The PSQI has seven components that address dif-ferent aspects of sleep. The highest mean score was sleep efficiency followed by sleep disturbances. The subjects reported a mean sleep efficiency of only 71%, that is, on average, they slept only 71% of the time they were in bed, with an early bedtime (mean 21:00) and long sleep latency (mean 35 min). Not uncommonly, the sleep-wake cycle of older subjects does not coincide with the institution’s schedule and,

therefore, residents are sometimes put in bed before they are ready to go to sleep (27). Sleep efficiency is also decreased because of frequent awakenings. The most commonly perceived causes of sleep distur-bances were nocturia, environmental factors and pain. Similarly, Middelkop et al. found that among 32 residents of a NH in the Netherlands, the factors that interfered with their sleep included nocturia (36%), pain (10%) and noise or room-mates (10%). Age-matched independent living controls had a simi-lar prevalence of pain and nocturia, but were not disturbed by noise (7).

Pain is a common complaint among NH residents, with the most common cause being musculoskeletal disorders (28). Because of a variety of barriers that exist in the NH, pain is often inadequately treated in this setting (29). In our study, out of 16 subjects fre-quently disturbed by pain only 3 were on scheduled medications for pain. By providing better analgesia, sleep disturbances might be somewhat curtailed.

Schnelle et al. studied incontinent and mostly cog-nitively impaired NH residents. They measured sleep, body movements, light and noise with a bedside monitor. Fifty percent of waking episodes of 4 min-utes or longer were associated with environmental factors, including nursing staff changing diapers, giv-ing medications, talking and noise from other NH residents (30, 31). The authors had few suggestions on how to decrease sleep disturbance and at the same time prevent skin irritation of incontinent NH residents. They recommended more individualized nighttime care practices, such as aides doing hourly rounds and providing care only if the patients are awake, at high risk for pressure sores or still asleep at the third hourly check (30). They also suggested stressing the importance of residents’ sleep to the nurses and aides, emphasizing that they should try to be less noisy and use quieter carts (31).

Our study is complementary to that of Schnelle et al., as we demonstrated that cognitively intact and mostly continent NH residents are also frequently disturbed by environmental factors. Incontinence care practices were probably not a major factor in our study, as only 7 out of 19 incontinent residents com-plained about being awakened by the nurses. Resi-dents reported being disturbed by the nurses giving medications, talking loudly, making noise with the medication cart or using flashlights, and/or by other residents. This is in agreement with the findings of Schnelle et al. who used objective methods to meas-ure noise and light. In addition to the ideas of these investigators, we suggest that medications should be scheduled during daytime hours as much as possible, and that flashlights with dimmer yellow lights should



Table 4 - Correlation between PSQI and different variables.

Variable r p-value

Comorbidity (DUSOI) 0.43 0.002

Depression (GDS) 0.40 0.004

Self-rated health 0.32 0.026

Disability (RDRS-2) 0.21 0.149

No. of Medications 0.22 0.135

Age -0.10 0.472

No. of Diagnoses 0.06 0.646

Table 5 - Regression Analysis.

Dependent variable: PSQI

Independent Variables p-value

DUSOI 0.032

GDS 0.044

Gender 0.842

Age 0.885

be used so as to be less disruptive to the residents’ sleep. It is probably more difficult to decrease the noise made by other residents, particularly those who are cognitively impaired. A recent preliminary study on the use of environmental “white noise” for agi-tated NH residents has shown that mountain stream and gentle ocean audiotapes, used with headphones, can decrease the frequency of verbal agitation (32). Further studies are needed to determine whether this intervention is also effective during nighttime hours, and whether it can improve the sleep of non-agitated residents. Earplugs are unlikely to be very helpful in the NH, as only 3 out of 17 subjects in our study, who were frequently disturbed by noise, were inter-ested in trying them.

A major strength of our study was the use of a validated instrument, the PSQI. This questionnaire has the advantage of yielding a simple global score, which allowed us to use multiple regression analysis to control for potential confounders, which was not done in previous studies (6, 7, 11 ,12). Poor sleep independently correlated with depression and comor-bidity; this is not surprising as insomnia is a common manifestation of depression. Similarly, among com-munity living elderly, depressed mood has been shown to correlate with poor sleep even when age, gender, and health status are simultaneously control-led (33). While 20 (42%) subjects had depressive symptoms according to the GDS-SF, only 9 of these had a diagnosis of depression and were on antide-pressants. The GDS-SF does not ask questions about sleep, but the fact that it correlated with sleep com-plaints suggests that better recognition and treatment of depression may improve sleep in these subjects. A prospective intervention study is needed in order to definitively explore this question.

Several epidemiological studies have shown that comorbid conditions have a great impact on sleep in old age. Poor sleep has been related to poor self-rated health in general (3, 34-36) and to specific chronic medical and psychiatric conditions including depression (35, 36), dementia (36, 37), nocturia (4), peptic ulcer disease (34), chronic obstructive lung disease (5), cardiovascular disease (5, 38), obesity (5), Parkinson’s disease (39), diabetes mellitus (5, 38) and those illnesses that cause chronic pain (5, 40). Symp-toms from comorbid conditions cause frequent awak-enings (39, 41); the associated inactivity and bed rest can disrupt the sleep-wake cycle (37).

In addition to its strengths, our study has some limitations. First, these results can be generalized only to cognitively intact NH residents. Second, it might be argued that the sample was not homogene-ous, as about half of the subjects were from a com-

munity NH, with a predominantly female population and about half from a VAMC NH with mainly male residents. Still, the quality of sleep was comparable among men and women and among residents of the two NH; in addition, we had the advantage of having a relatively equivalent number of men and women. Third, we measured comorbidity using the DUSOI which was created for outpatients and not NH resi-dents; however, its construct validity in our popula-tion is supported by the high scores which would be expected in this population with multiple medical problems. Fourth, although sleep apnea and peri-odic leg movements are prevalent among NH resi-dents (9), we were not able to detect such primary sleep disorders with our subjective sleep measure (PSQI). Some subjects might have awakened because of sleep apnea or leg movements, but mistakenly at-tributed such awakening to pain or the need to uri-nate. As this is a cross sectional study, we can only say that certain factors were associated with poor sleep, but cannot make assumptions on the direction of the relationship.

In conclusion, we found that most cognitively in-tact NH residents report poor sleep. The most com-monly perceived causes of sleep disturbance were nocturia, noise and pain. Predictors of poor sleep were comorbidity and depression, not age or gender. To determine whether there is a cause-effect relation-ship between these factors and poor sleep, there is a need for prospective studies to determine whether correction of treatable causes of sleep disturbance can improve quality of sleep in NH residents.

ACKNOWLEDGEMENTSThis study was supported, in part, by the National Institute of

Health, National Institute on Aging, Claude D. Pepper Older Americans Independence Center, Grant # 5 P60AG 11268 (DKW and MK) and by an NIA Academic Award, Grant # K08AG00643 (to D.K. Weiner).

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