Sleep Pattern Differences Between Older Adult Dementia Caregivers and Older Adult Noncaregivers Using Objective and Subjective Measures Meredeth A

University of South Florida Scholar Commons

Nursing Faculty Publications College of Nursing

8-2008 Sleep Pattern Differences Between Older Adult Dementia Caregivers and Older Adult Noncaregivers Using Objective and Subjective Measures Meredeth A. Rowe University of Florida, [email protected]

Christina S. McCrae University of Florida, [email protected]

Judy M. Campbell University of Florida

Andrea Pe Benito University of Florida

Jing Q. Cheng University of Florida

Follow this and additional works at: https://scholarcommons.usf.edu/nur_facpub Part of the Nursing Commons

Scholar Commons Citation Rowe, Meredeth A.; McCrae, Christina S.; Campbell, Judy M.; Benito, Andrea Pe; and Cheng, Jing Q., "Sleep Pattern Differences Between Older Adult Dementia Caregivers and Older Adult Noncaregivers Using Objective and Subjective Measures" (2008). Nursing Faculty Publications. 33. https://scholarcommons.usf.edu/nur_facpub/33

This Article is brought to you for free and open access by the College of Nursing at Scholar Commons. It has been accepted for inclusion in Nursing Faculty Publications by an authorized administrator of Scholar Commons. For more information, please contact [email protected]. Scientific investigations Sleep Pattern Differences Between Older Adult Dementia Caregivers and Older Adult Noncaregivers Using Objective and Subjective Measures

Meredeth A. Rowe, R.N., Ph.D.; Christina S. McCrae, Ph.D.; Judy M. Campbell, A.R.N.P., M.S.N.; Andrea Pe Benito, R.N., B.S.N.; Jing Cheng, Ph.D.

University of Florida, Gainesville, FL

Study Objectives: Informal caregivers of persons with dementia of- tency, and wake after sleep onset. Caregivers’ sleep had greater night- ten complain about poor quality sleep; however, studies on caregivers to-night variability. have mixed results when examining sleep values. The purpose of this Conclusions: Caregivers consistently report poorer quality sleep and study was to describe the sleep patterns in a subset of dementia care- greater fatigue than noncaregivers. However, when sleep is measured givers who provide care during the night, and compare those patterns objectively and subjectively, a mixed picture emerges regarding sleep to noncaregiving adults. deficits. Thus sleep changes are caused by a multitude of factors af- Methods: Data from a study on dementia caregivers and from a study fecting sleep in a variety of ways. It is important for health care provid- of sleep in older adults were used. Both studies used objective and ers to assess sleep adequacy and depression in caregivers. subjective methods to measure sleep in the home setting over a 7-day Keywords: Dementia, Alzheimer’s disease, caregivers, sleep, night- period. Participants were over 60 years old and relatively healthy. time activity Results: Older dementia caregivers had worse objectively measured Citation: Rowe MA; McCrae CS; Campbell JM; Benito AP; Cheng J. sleep than noncaregiving older adults, characterized by fewer minutes Sleep pattern differences between older adult dementia caregivers asleep and longer time to fall asleep. For subjectively measured sleep, and older adult noncaregivers using objective and subjective mea- depressive symptoms were the only predictive factor, with depressed sures. J Clin Sleep Med 2008;4(4):362-369. participants reporting longer total sleep time, greater sleep onset la-

he majority of persons with dementia (PWD) are cared for quantity was considered normal because it was similar to age- Tin the home setting by informal caregivers, usually a fe- matched controls,3 while in other studies, poorer sleep was at- male relative. Researchers have reported a variety of changes in tributed to causes such as depression or caregiver burden rather caregiver sleep including changes in sleep quantity and quality.1 than caregiver status.1,12 Because the results are variable, however, research is needed One reason for inconsistent findings may be the sampling to determine whether there are factors that predict which care- strategy used in previous studies, namely, that all dementia givers are at risk for poor sleep. Accurate assessment and pre- caregivers were sampled regardless of the nighttime activity in vention is critical, because poor sleep in caregivers has been the PWD. Approximately 25% to 54% of PWD have significant associated with sympathoadrenal medullary arousal,2 changes changes in their sleep-wake cycle, causing awakenings at night. in immune function,3 poorer psychological state,4,5 and early in- These awakenings could have a significant impact on caregiver stitutional placement of the PWD.6,7 Additionally, interventions sleep quantity, since the PWD often requires supervision when could be better targeted to the factors associated with poor sleep up at night. This supervision is critical to prevent injuries, such in dementia caregivers. as falls13 or inappropriate actions, such as home exits,14 asso- While caregivers consistently report poorer sleep quality ciated with cognitive impairments typically seen in dementia. than noncaregivers,5,8-11 the picture is not as clear when sleep is Thus caregivers who supervise PWD with nighttime activity measured objectively or subjectively.1,4 In some studies, sleep are potentially most at risk for substantial sleep changes or disturbances. Secondly, inconsistent findings may be due to inad- equate measurement of sleep. Thus research on caregiver sleep, Disclosure Statement using commonly accepted sleep measurements (e.g., sleep dia- This was not an industry supported study. The authors have indicated no ries and actigraphy over a period of time), is needed to fill this 15 financial conflicts of interest. gap in knowledge. Caregivers of PWD may develop night-to-night sleep defi- Submitted for publication November, 2006 cits caused by the need to provide supervision or other caregiv- Accepted for publication March, 2008 ing tasks, and they may attempt to make up for this by sleep- Address correspondence to: Dr. Meredeth Rowe, University of Florida ing longer in subsequent nights. If, however, researchers only College of Nursing, PO Box 100187, Gainesville, FL 32610-0187; Tel: examine the week’s mean values of sleep and wake variables, (352) 273-6396; Fax: (352) 273-6536; E-mail: [email protected] values may appear normal, and the pattern of high variability

Journal of Clinical Sleep Medicine, Vol. 4, No. 4, 2008 362 Caregiver Sleep Patterns in night-to-night sleep would be lost. The perception of sleep The noncaregiving older adult subsample was taken from adequacy may be affected by this inconsistent pattern of sleep a previous study on sleep in older adults who were relatively over the week. Hence, analysis of the intra-individual variabil- healthy, living in the community, and not diagnosed with sleep ity of night-to-night sleep may help explain why most caregiv- disorders other than insomnia; the study methods and sample ers complain of poor sleep quality even if sleep times are in a characteristics have been previously reported.16 Interested par- normal range. ticipants were prescreened by telephone. In-person screening The purposes of this study were threefold: interviews were then conducted to include participants who 1. To quantify sleep parameters in older adult caregivers met these criteria: (a) ≥ 60 years of age; (b) no self-report of of PWD with nighttime activity over a 7-day period in sleep disorders diagnoses other than insomnia (e.g., sleep ap- the natural caregiving setting (using both objective and nea, narcolepsy, restless leg syndrome); (c) no self-report of subjective sleep measurement methods) and to compare symptoms suggestive of other sleep disorders (e.g., heavy snor- these findings to a group of noncaregiving older adults. ing, gasping for breath, leg jerks, daytime sleep attacks); (d) no The following measurements and variables were used: severe psychiatric disorders (e.g., thought disorders, high levels

a. Actigraphy (objective): total sleep time (TSTo), of depressive symptomatology); (e) no cognitive impairment,

sleep onset latency (SOLo), wake after sleep onset defined as scoring in the impaired range on 3 or more subtests 18 (WASOo), and sleep efficiency (SEo). of the Cognistat ; (f) no regular use of psychotropic or other

b. Sleep diary (subjective): total sleep time (TSTs), medications (e.g., β-blockers) known to alter sleep; and (g) no

sleep onset latency (SOLs), wake after sleep onset medical conditions that affected the individual’s ability to be

(WASOs), sleep efficiency (SEs), and sleep quality completely independent in normal daily functions. The sample

(SQs) rating. size for the noncaregiving sample was 102. c. Daytime functioning: Epworth Sleepiness Scale The 2 subsamples were recruited throughout an entire calen- (ESS) and Fatigue Severity Scale (FSS). dar year from communities in North and Central Florida using 4. To examine the night-to-night variability in caregivers’ similar methods (solicitation through media advertisements, sleep patterns and to compare these with sleep patterns community groups, and flyers). In addition, the dementia care- of noncaregiving adults. giver subsample was recruited through dementia support group 5. To explore the correlation patterns of sleep measured leaders and announcements in the local chapter’s newsletter of subjectively and objectively between caregivers and the Alzheimer’s Association. Both studies received approval noncaregivers. from a research university’s Institutional Review Board.

Methods Measures

Design Ob j e c t i v e Sl e e p

This was a post-hoc descriptive, correlational study. Caregiver Actigraphy data were collected using the 2-channel Acti- data were taken from baseline measures of a study of dementia watch-L with an integral ambient light sensor (max 150,000 caregivers designed to test the effectiveness of a nighttime ac- lux) and an omnidirectional accelerometer with a sensitivity of tivity monitoring system in improving sleep in caregivers. Data ≥ 0.01 g-force17 worn on the participant’s nondominant wrist. for noncaregiving, relatively healthy, community-based older Integrated degree and speed of motion sensed were used to cal- adults were taken from a previously studied sample described culate “counts” or values of activity. This digitally integrated elsewhere.16 For both samples, sleep was measured objectively method of examining motion is recommended as the most ac- with actigraphy using the Mini Mitter Actiwatch-L17 and subjec- curate, reflecting intensity of movement as well as number of tively with self-report sleep diaries for 7 consecutive days of motions.17,19,20 We utilized a 30-sec recording epoch for both normal routine. Demographic, clinical, and questionnaire data samples in the study, which allows storage of 7.5 continuous were collected on Day 1, and subjective/objective sleep data col- 24-h periods of data. lection began that night. In our noncaregiving subsample, there were 7 days’ worth of data for all participants. In the caregiving sample, participants Participants were instructed to wear the watch for 7 consecutive days and nights. If the subject was not able/did not do this, data were used Informal caregivers ≥ 60 years of age providing direct care to if a minimum of 3 sleep periods were collected. This minimum a person with dementia with nighttime activity were recruited. was chosen because recommendations made by the American Inclusion criteria for the caregiver subsample included the fol- Association of Sleep Medicine indicate that actigraphic studies lowing: (a) ability to speak and read English; (b) not undergo- should collect ≥ 3 days/nights of data for adequate representa- ing active treatment for sleep disorders (e.g., using prescrip- tion of the subject’s sleep patterns.21 tion medications or sleep apnea treatments on a nightly basis); Data were analyzed using Actiware-Sleep v. 3.3.17 The me- (c) living with the care recipient; and (d) a Mini-Mental Status dium threshold/ sensitivity setting was used in both samples, Exam (MMSE) score > 27. In addition, the care recipient was where total activity values > 40 are necessary in order to score required to have an MMSE < 23, a medical diagnosis of demen- an epoch as wake. Since both total sleep and total wake param- tia, and nighttime awakenings as reported by the caregiver. The eters are important in older adults and caregivers, we chose sample size for the caregiver population was 31. the medium threshold algorithm. A study comparing poly-

Journal of Clinical Sleep Medicine, Vol. 4, No. 4, 2008 363 MA Rowe, CS McCrae, JM Campbell et al somnography to the same brand of actigraphy found that the Table 1—Definitions of Actigraphic Sleep Variables values obtained using the medium threshold algorithm were in close agreement with polysomnography findings, and the Bedtime Set by researchers as the point at which total sleep time, sleep efficiency, and total wake times using there was a notable decrease in activ- actigraphy were not significantly different from polysomno- ity amplitude, a decrease in ambient light, 22 and/or the time recorded in a sleep diary. graphic values. Although the sample characteristics differed Out-of-Bed time Set by researchers as the point at which from this study, the researchers demonstrated that as detecting there was a notable increase in activity am- sleep accurately (sensitivity) increases, detecting wake ac- plitude, an increase in ambient light, and/ curately (specificity) decreases. Thus, the medium threshold or the time recorded in the sleep diary. algorithm was chosen, since we were interested in both sleep All remaining variables were calculated by Actiware-Sleep soft- and wake variables. ware, and definitions are taken from their instruction manual.17 In both samples, sleep diary recordings were used to begin Time in Bed Bed Time – Out-of-Bed time the determination of the Bedtime and Out-of-Bed analysis win- Sleep Start Calculated as the first 30-sec epoch in a dow. The nearest point corresponding to objective signs of bed- 10-min interval in which only one epoch is scored as wake. time and out-of-bed activity (change in activity and light) was Sleep End Calculated as the last 30-sec epoch with no used as either Bedtime or Out of Bed Time. If objective signs activity in the 10-min interval before Get differed (light changes occurred at a different point than activ- up Time. ity changes), then the objective sign closest to the sleep diary Total Sleep Time Amount of time scored as sleep between time was given precedence in the decision. A single researcher (TSTo) Sleep Start and Sleep End. in each study was responsible for establishing all Bedtime— Wake After Sleep Amount of time scored as wake between Out-of-Bed analysis windows to ensure they were set similarly Onset (WASOo) Sleep Start and Sleep End. across all participants in both samples. Both researchers were Sleep Efficiency (Total Sleep Time ÷ Time in Bed) x 100 (SE ) trained by the same senior researcher, who substantiated that o Sleep Onset Latency Time in minutes between Bedtime and at least 10% of each subsample’s analysis windows were set (SOL ) Sleep Start. correctly. o Within these analysis windows, the Actiware program scored Eight common daytime activities, such as watching television each epoch as sleep or wake using activity values from that ep- or driving, were scored with regard to how likely they were och and those surrounding it. The algorithm used to identify to induce sleep, ranging from 0 (would never doze) to 3 (high sleep or wake is as follows.23 chance of dozing). Possible summed scores ranged from 0-24 Total Activity Value for Epoch A = with higher scores indicating more daytime sleepiness. The

EA-4 (0.04) + EA-3 (0.04) + EA-2 (0.20) + EA-1 (0.20) + EA (2) + ESS has demonstrated adequate validity and reliability, and is a 25,26 EA+1 (0.20) + EA+2 (0.20) + EA+3 (0.04) + EA+4 (0.04) simple, inexpensive measure. The Cronbach α in this study

(Where EA = activity value for that epoch and EA +/− 1-4 = ac- for the caregiver subsample was 0.73; for the noncaregiving tivity values in adjacent epochs). If the Total Activity Value for subsample, it was 0.62. Epoch A exceeded the threshold value of 40, it was scored as Fa t i g u e Se v e r i t y Sc a l e (FSS). The FSS measures impact wake; values ≤ 40 were scored as sleep. of fatigue on functional outcomes using 9 items scored on a These values were used by Actiware to establish sleep start 7-point scale (1 = strongly disagree, 7 = strongly agree).27 Item and sleep end, using the 10-minute period where no more than scores are averaged to create a mean FSS score. The FSS has one epoch is scored as wake or sleep, respectively. Addition- high internal consistency, concurrent validity, good test-retest ally, the activity values were used to calculate sleep and wake reliability, is sensitive to change, and discriminates well.28,29 variables within the sleep interval. Table 1 provides definitions Because of an electronic coding error, one item was deleted of sleep variables selected for analyses in this study.17 from the caregiver subsample, and means for available items for both groups were reported. The Cronbach α for the noncare- Su b j e c t i v e Sl e e p giver sample was 0.91; the caregiver subsample was 0.88.

Sleep diary data, collected simultaneously with actigraphy De p r e ss i v e Sy m p t o m s data, required participants to complete a standard form each morning during the study period.24 Data were collected on bed- A different measure of depressive symptoms was used in time, sleep start, number of awakenings, minutes awake during each sample, thus z-scores were created for each subject for night, wake time, out-of-bed time, minutes spent napping the comparison between the 2 subsamples. The z-scores were cal- 24 previous day, and a sleep quality rating. The SQs rating was culated using previously published normative data from each established by asking participants to rate quality of the previous instrument (see below). Values reported herein represent indi- night’s sleep on a 5-point scale ranging from 1 (very poor) to 5 vidual participants’ scores minus the mean for established nor- (excellent). mative data, and then divided by the standard deviation for the subsample. Da y t i m e Fu n c t i o n i n g The Beck Depression Inventory-II (BDI-II) was used to measure depressive symptoms in the noncaregiving subsample.30,31 Ep w o r t h Sl ee p i n ess Sc a l e (ESS). This self-administered scale The BDI-II was developed to indicate presence and severity of was used to measure daytime sleepiness in both samples.25 depressive symptoms (over the past 2 weeks) consistent with

Journal of Clinical Sleep Medicine, Vol. 4, No. 4, 2008 364 Caregiver Sleep Patterns

DSM-IV criteria for depression. Each item is rated on a 4-point participant. In this study, we used mixed effect models to ex- scale (0-3); the total possible score is 63, with higher scores in- amine the within-participant, night-to-night variance in care- dicating more severe depressive symptoms. Previous research- givers’ and noncaregivers’ sleep variables to determine if the ers have demonstrated internal consistency (0.92-0.93), test-re- night-to-night variance was different between the 2 groups. At test reliability (0.93), and also content, construct, and factorial the same time, we modeled the between-group (fixed effects) 32,34 validity. The BDI-II has also demonstrated no difference differences with TSTo&s, SOLo&s, WASOo&s as outcomes and according to age, sex, or ethnicity in later research.31,35,36 The with fixed covariates of age, education, depression z score, comparative normal group used in the initial psychometrics and and number of prescribed medications. As there was no sig- for creating our z-scores consisted of 120 predominately white nificant time trend in measurements of sleep variables, a time college students (mean age 19.58 years), who had a mean score trend was not included in these models as a fixed effect. The of 12.56 (SD = 9.93).30 mixed effect models are specified by the following expres-

The Center for Epidemiologic Studies Depression scale sions: yi j= xij’β + si +εij where i = 1, 2, …, n participants, j = 1, 37 (CES-D) was used to measure depressive mood in the care- 2, …, m occasions, yij is the measurement of a sleep variable giver subsample. Designed as a self-report measure of current of individual i on occasion j; xij is the vector of regressors, depressive symptomatology, the CES-D asks respondents to including group, age, education, depression and medication, rate how often they have had the 20 symptoms during the last and β is the corresponding vector of regression coefficients; 2 2 week, on a 4-point scale from “rarely or none of the time” to the random subject effect si~N(0, σ bg) and σ bg represents the 2 “most of the time.” Sum scores range from 0 to 60, with higher between-participants variance; and the errors εij ~ N(0, σ wg) 2 scores indicating more depressive symptoms. Three normative and σ wg are the within-participants night-to-night variance for samples and one patient sample were used to establish paramet- g = caregivers or noncaregivers group; that is, we allowed the rics, with the largest normative adult sample (N = 2514) dem- between-participants and within-participants variances to be onstrating a mean of 9.25 (SD = 8.58). The CES-D was found to different for 2 groups. Then we tested whether the variances have high internal consistency and adequate test-retest reliabil- were significantly different between 2 groups. ity, as well as demonstrated validity.37 In addition, the CES-D For each of the subsamples, objective sleep measures were has been used successfully to assess prevalence of symptoms compared with subjective sleep measures by group and by de- in the elderly and has demonstrated excellent sensitivity and pression score. Pearson r correlations and t-tests were used to specificity with regard to detecting a high level of depressive compare the groups. Descriptive statistics of under- and over- symptoms in older adults.38,39 In the original form, 4 items on reported comparisons of objective to subjective measures were the CES-D were worded in the positive direction to assess for reported. Because of the exploratory nature of this research positive affect, requiring reverse scoring of these items.37 Later question, the α level was set at 0.01. research called into question the validity of using the positive affect questions within the tool.40 Thus, in the current research, Results the 4 positively worded questions were changed to reflect negative affect. Descriptive Statistics Although the CES-D and BDI-II measure similar con- structs, they are not identical. The BDI-II measures severity In general, both noncaregiver (n = 102) and caregiver (n = of symptoms, while the CES-D assesses frequency of indica- 31) samples were predominantly female (64% and 74%, re- tors. However, in a study that compared the 2 measures in the spectively), white (96% and 97%, respectively), and currently same sample, adequate comparability was demonstrated with a married (72% and 84%, respectively). Ages ranged from 60 to Pearson r of 0.69 (p < 0.001), suggesting similar concepts are 89 years in the noncaregiver sample (M = 72.8, SD = 6.8) and measured.36 61 to 86 years in the caregivers (M = 70.7, SD = 7.8). There were no significant differences between groups with regard to Statistical Analysis age, gender, race, or marital status. The noncaregiver sample had a significantly greater level of education (40% with gradu- SPSS 14.0 and SAS 9.1 were used to analyze the data. De- ate education) than the caregiver sample (13% with graduate scriptive statistics, including an intra-individual coefficient of education) (χ 2=17.16, p = 0.002). variation (I-I CV) to describe night-to-night variation,41,42 were Two measures were collected to evaluate the effect of sleep used to compare the samples with regard to demographic and on daytime functioning. Sleepiness during the day (ESS) was clinical variables. Subjective and objective sleep variable 7-day significantly higher for caregivers than noncaregivers (M= means, as well as daytime functioning parameters of sleepiness 15.13, SD = 3.60 and M = 7.93, SD = 3.16, respectively; t = and fatigue, were compared using traditional descriptive statis- 10.62, p < 0.001). Fatigue severity (FSS) scores were not sig- tics and t-tests. nificantly different between groups. Repeated measurement analyses were performed using Since high levels of depressive symptoms and poorer health mixed effect models, which have become a primary method are both associated with changes in sleep, groups were com- for analysis of longitudinal data. Mixed effect models can pared on these variables. Caregivers had significantly higher efficiently handle unbalanced data and estimate both effects depressive symptom scores than noncaregivers, with z-scores of covariates and variance components simultaneously while of 0.50 ± 1.14 and −0.73 ± 0.48, respectively (t = 5.83, p < accounting for fixed effects of participant characteristics and 0.001). There were no significant differences in the number of for correlation between repeated measurements from the same medications, used as a proxy variable for health status in this

Journal of Clinical Sleep Medicine, Vol. 4, No. 4, 2008 365 MA Rowe, CS McCrae, JM Campbell et al Table 2—Differences in Objective and Subjective Sleep Variables by Group

Measure Objective (actigraphy) Subjective (sleep diary) Non-caregiver Caregiver t-value p value Non-caregiver Caregiver t-value p value TST Mean 428.67 395.19 3.19 0.002 416.06 384.98 2.53 0.01 I-I CV 12.38 16.10 2.01 0.05 13.87 18.87 2.22 0.03 SE Mean 87.27 80.41 4.81 <0.001 85.22 77.39 3.88 <0.001 I-I CV 5.36 9.37 3.55 0.001 9.56 15.37 3.20 0.002 SOL Mean 12.52 22.81 2.87 0.007 24.44 30.64 1.44 0.15 I-I CV 102.18 89.05 1.42 0.16 65.71 58.45 1.11 0.27 WASO Mean 40.73 48.64 1.84 0.07 29.03 52.77 3.82 <0.001 I-I CV 37.04 43.81 1.64 0.10 87.85 74.13 1.17 0.24 SQ Mean ------3.59 3.07 4.31 <0.001 I-I CV ------21.23 25.37 1.57 0.12

TST = total sleep time; SE = sleep efficiency; SOL = sleep onset latency; WASO = wake after sleep onset; SQ = subjective sleep quality; I-I CV = Intra-Individual Coefficient of Variation (SD / Mean X 100).

study, indicating relatively similar health status (t = 0.64, p < ences in the I-I CV’s for SOLs, WASOs, or SQs, indicating that 0.53). night-to-night values for these variables were not different by

group. Both groups had relatively high values for SOLs and

Objective Sleep Measured with Actigraphy WASOs.

The univariate and bivariate analyses of sleep variables are Mixed Effect Models of Between Group and Within Subject displayed in Table 2. Caregivers had significantly lower TSTo Differences of Sleep Variables and SEo, and had longer SOLo. However, both groups had a substantial amount of WASO, and this variable did not signifi- In multivariate models controlling for age, education, de- cantly differ between groups. pression, and total number of medications, group differences

Not only did caregivers get less overall sleep with lower sleep suggesting worse sleep in caregivers were confirmed for TSTo efficiencies, they also had greater night-to-night variation with and SOLo (see Table 3). In contrast, the significant bivariate significantly higher TSTo and SEo I-I CV values. Compared to group differences for TSTs or WASOs were not confirmed; how- noncaregivers, higher I-I CV values for TSTo indicate that care- ever, depression was a significant predictor of these variables. givers had a wider night-to-night variation of minutes asleep Depression was also a significant covariate predicting SOLs. during the 7-night recording period. In contrast, noncaregivers’ In multivariate models controlling for age, education, de- lower TSTo I-I CV values indicated a more consistent amount pression and total number of medications, group differences of sleep each night. The same concept would apply to SEo I-I in intra-individual variability were similar to those seen in the

CV’s, with caregivers having greater night-to-night variability bivariate models for TSTs and revealed even greater statistically than noncaregivers. significant differentiation between the groups for SOLo, WA-

While caregivers had significantly longer SOLo times, the I-I SOo, SOLs, and WASOs, all in the direction of greater variability CV’s were not significantly different between caregivers and for the caregiver group. These findings may have reflected the noncaregivers. However, the high I-I CV’s indicated that SOL control over the covariates and/or the efficiency of the mixed was not consistent from night-to-night for both groups. effect models.

Subjective Report of Sleep using a Sleep Diary Associations Between Subjective and Objective Sleep Data as a Function of Caregiving Status Using bivariate analyses, caregivers had significantly lower TSTs, and SEs. In contrast to objective measures, caregiv- In order to explore the associations between subjective and ers also had significantly higher WASOs during the night than objective sleep measures for each group, we calculated Pear- noncaregivers (see Table 2). Unlike the actigraphic data, SOLs son r between the objective and subjective measure of each reported times did not differ between the 2 groups. Caregivers sleep variable (see Table 4, column 1) and used paired t-tests rated their sleep quality as significantly lower than noncaregiv- to determine if these values were significantly different (see ers (0.5 points on a 5-point scale). Table 4, column 2). In contrast to the noncaregivers, caregiv-

As with objective sleep measurement, the I-I CV’s for TSTs ers’ subjective and objective data generally showed higher and SEs were significantly greater for caregivers than noncare- correlations between objective and subjective sleep times, givers, indicating that caregivers reported more variable sleep and the objective and subjective times were not significantly times from night-to-night. There were not significant differ- different from each other. A difference score between partici-

Journal of Clinical Sleep Medicine, Vol. 4, No. 4, 2008 366 Caregiver Sleep Patterns Table 3—Fixed Effects Models of Sleep Variables

TSTo SOLo WASOo TSTs SOLs WASOs Predictor Variable Coefficient Coefficient Coefficient Coefficient Coefficient Coefficient (St. error) (St. error) (St. error) (St. error) (St. error) (St. error) Overall Intercept 393.47 (48.43) 13.49 (1.39) 23.78 (19.67) 426.98 (57.44) 41.03 (19.09) 69.23 (28.76) Age −0.35 (0.64) 0.09 (0.14) 0.52* (0.26) −0.85 (0.78) −0.22 (0.26) 0.03 (0.39) Educationa Some high school 54.07* (24.53) 0.54 (5.15) −0.24 (10.06) 33.87 (30.47) 12.91 (10.36) 7.73 (15.01) High school grad 32.37* (14.62) 5.53 (3.23) −1.00 (5.96) 0.24 (17.90) 16.75 (6.02) 8.07 (8.89) Some college 28.06* (11.75) 1.51 (2.57) −1.24 (4.79) 30.07 (14.78) 7.71 (4.90) −4.56 (7.35) College graduate 12.73* (11.37) 2.81 (2.43) −3.26 (4.65) 17.61 (14.11) 1.78 (4.78) −1.71 (7.01) Depression z-score −3.24 (8.11) −1.10 (1.92) −2.30 (3.28) −23.70** (8.49) 6.21* (2.80) 12.56** (4.18) Total medsb 0 8.81 (22.53) 0.94 (5.62) −14.58 (9.09) 8.50 (25.88) −6.08 (8.45) −28.99 (12.57) 1 6.32 (37.51) 1.69 (9.50) 2.63 (15.09) 39.86 (40.86) −8.06 (13.28) −28.77 (19.75) Groupc −35.57*** (14.79) 11.03** (4.16) 6.95 (5.88) −12.48 (17.73) −5.21 (5.70) 5.18 (8.66)

TST = total sleep time; SOL = sleep onset latency; WASO = wake after sleep onset; o = objective; s = subjective. St. error = standard error. Total meds = total number of prescribed medications. *p < 0.05; **p < 0.01; ***p < 0.001 Referent category—acompleted graduate education; b2 or more medications; cnoncaregivers. pants’ 7-day actigraphic and diary-based means for each vari- wake time during the night was anticipated to be particularly able was calculated to determine which score was higher (see problematic for the caregivers, because the present sample

Table 4, column 3). For both groups, SOLs values were gener- included only caregivers who reported nighttime activity in ally greater than SOLo; for WASO, it was more common for the PWD. Thus factors that ordinarily contribute to disturbed noncaregivers, but not caregivers, to have sleep diary values sleep (stress, anxiety, poor health) were expected to combine below actigraphy values. with awakenings to provide care for PWD, producing greater Since the depressive symptoms variable was a significant wake time in caregivers. The present findings do not provide covariate for subjective sleep measures, we examined whether strong support for this argument. It is possible that although participants with high levels of depressive symptoms tended to there is not greater time awake, awakenings occur erratically have a different comparison of subjective (sleep diary) to objec- during the night since night awakenings in PWD differ signif- tive (actigraphy) values (see Table 4, column 4). We used par- icantly from night-to-night.43 The unpredictable awakenings ticipants’ original depression scores to determine whether they may occur during deeper sleep, resulting in a perception of exceeded the established cutoff score for that scale (CES-D ≥15 poorer sleep without a concomitant increase in WASO. Un- or BDI ≥10), indicating significant depressive symptomatology. fortunately, data about the nature of night awakenings were We then created a dichotomized variable with that data. Despite not collected in either study; future research that collects such substantial differences in depression in caregiver and noncare- information is needed. giver groups, analyses of discrepancies between actigraphic and Interestingly, the observed differences in objectively mea- diary reported measures (defined as cases with sleep diary > ac- sured sleep were not duplicated when sleep was measured sub- tigraphy for depressed versus nondepressed groups) showed no jectively and the models were adjusted for level of depressive consistent differences as a function of mood. Thus, sleep diary symptoms. This result may not seem surprising, given the high vs. actigraphic discrepancy appeared more likely to be related levels of depressive symptoms in the caregiving sample and the to caregiving status than depression. well-established relationship between depression and poor sleep (both subjective and objective).15 These discrepant findings for Discussion subjective versus objective sleep measures and caregivers versus noncaregivers are consistent with a recent review highlight- Older caregivers of PWD with nighttime activity reported ing the complex, multifactorial nature of caregiver sleep.1 The poorer quality sleep than noncaregiving older adults and rated review supports the idea that changes in caregiver sleep do not their sleep as fair. Caregivers also reported greater levels of match well with other common areas of sleep research, such as daytime sleepiness than noncaregivers. These findings on self- insomnia or sleep apnea. Additional research is needed to fur- report of poorer sleep quality in caregivers have been consistent ther understand the unique alterations in sleep that account for from study to study.8-10 poorer sleep rating and higher levels of fatigue. When sleep was measured objectively, and after adjusting Caregiver sleep was also more variable from night to night, for depressive symptoms, age, health condition, and educa- likely indicating that poor nights of sleep are mixed with good tion, caregivers took longer to fall asleep and had less total nights of sleep. Since, on average, caregivers had only 6.5 h of sleep than noncaregivers. Although not significant, there was sleep/night, there would be some nights caregivers did not get also a trend towards caregivers spending more time awake even this amount. Since this irregular sleep pattern also promotes during the night than noncaregivers. This latter finding was the perception of poor sleep, all efforts should be made to man- somewhat surprising. Of all the sleep variables examined, age nighttime awakenings in the PWD to improve caregiver

Journal of Clinical Sleep Medicine, Vol. 4, No. 4, 2008 367 MA Rowe, CS McCrae, JM Campbell et al Table 4—Associations of Objective and Subjective Sleep Values by Caregiver Status

Measure r-value Paired t-test % reporting % depressed reporting Noncaregivers (n = 102) Sbj. to Obj. Sbj./Obj. mean Sbj.>Obj. Sbj.>Obj. TST 0.49*** 2.84** 44.1 40.0 SOL 0.29** 5.90*** 78.4 86.7 WASO 0.28** 3.69*** 25.5 26.7 Caregivers (n = 31) TST 0.46 0.92 51.6 33.3 SOL 0.68*** 2.52 67.7 66.7 WASO 0.59** 0.05 45.2 50.0

Sbj. = sleep diary; Obj. = actigraphy; TST = total sleep time; SOL = sleep onset latency; WASO = wake after sleep onset. Alpha level = 0.01, **p < 0.01; ***p < 0.001 sleep. Particular efforts are needed to reduce night-to-night sleep the best intervention strategies for improving caregiver sleep. It variability (due more to night supervision of PWD than an in- will be important for clinicians and researchers to understand ability to sleep). For example, newly developed bed monitoring tendencies of the different types of sleep measurements,46 par- devices that alert caregivers when care recipients leave the bed ticularly for non-normative populations such as caregivers. For may reduce the level of arousal and tendency to “sleep with an them, objective measures appeared to be less influenced by open ear.”44 Such devices will not prevent care recipients from depression, while subjective sleep measures were significantly receiving needed care or reduce the actual amount of time spent influenced by depression. Finally, the present study illustrates providing care. Instead, this reduction in sleep-interfering arousal the need for greater attention to the level of analysis applied to should allow caregivers to gain deeper, more restorative sleep caregiver sleep data. Given findings that reflect a high degree during those portions of the night when care is not required. of night-to-night variability in caregiver sleep, greater focus on In general, sleep patterns as measured by sleep diaries were intra-individual variability is warranted. not highly correlated with sleep data collected objectively, particularly in noncaregiving older adults who complained of Acknowledgments poor sleep. This finding was not surprising as other research efforts have also experienced difficulty finding concordance The research was conducted at the University of Florida. between subjective and objective measures of sleep.45 Sleep The caregiver study was supported by funds received from a diaries and actigraphy were more highly correlated for care- grant awarded by the National Institutes of Nursing Research givers than they were for noncaregivers, however, and this (2R42NR004952-02A2; Rowe, PI). The older adult study was has important implications. Sleep diaries are widely used in supported by funds received from the University of Florida clinical research and practice. Although they are generally Colleges of Liberal Arts and Sciences, and Nursing. considered easy to administer (requiring ~5 min/day), caregivers may consider diaries burdensome. Thus a potential References benefit of the close association between actigraphy and sleep 1. McCurry SM, Logsdon RG, Teri L, Vitiello MV. Sleep distur- diaries is that actigraphy may serve as a suitable substitute for bances in caregivers of persons with dementia: Contributing fac- diaries in this population. Wearing an actigraph requires little tors and treatment implications. Sleep Med Rev 2007;11:143-53. effort and may be solely used to obtain daily sleep information 2. Mausbach BT, Ancoli-Israel S, von Kanel R, et al. Sleep distur- without placing additional burden on caregivers. One caveat bance, norepinephrine, and D-dimer are all related in elderly care- to this suggestion is that sleep diary/actigraphy comparisons givers of people with Alzheimer disease. Sleep 2006;29:1347-52. conducted herein were based on weekly means of the sleep 3. von Kanel R, Dimsdale JE, Ancoli-Israel S, et al. Poor sleep is variables. As results indicate, caregiver sleep is highly vari- associated with higher plasma proinflammatory cytokine inter- able from night to night, so future research comparing sleep leukin-6 and procoagulant marker fibrin D-dimer in older care- diary and actigraphy on a daily basis is warranted before any givers of people with Alzheimer’s disease. J Am Geriatr Soc 2006;54:431-7. definitive suggestions about the utility of actigraphy as a sub- 4. Willette-Murphy K, Todero C, Yeaworth R. Mental health and stitute can be made. Further investigation is also needed to sleep of older wife caregivers for spouses with Alzheimer’s disease determine the best algorithm threshold (medium or high) in and related disorders. Issues Ment Health Nurs 2006;27:837-52. the older adult population. Additionally, information could be 5. Brummett BH, Babyak MA, Siegler IC, et al. Associations among collected to differentiate whether the caregiver is waking as a perceptions of social support, negative affect, and quality of sleep result of the caregiver role or for personal needs. This would in caregivers and noncaregivers. Health Psychol 2006;25:220-5. help differentiate actual sleep disruptions due to caregiving, 6. Hope T, Keene J, Gedling K, Fairburn C, Jacoby R. Predictors of which could not be accomplished in the present study. institutionalization for people with dementia living at home with In conclusion, we found that caregivers’ sleep patterns are a carer. Int J Geriatr Psychiatry 1998;13:682-90. 7. Heyman A, Peterson B, Fillenbaum G, Pieper C. Predictors of significantly worse than noncaregivers’ in some domains. In time until institutionalization of patients with Alzheimer’s disease: other areas, poorer sleep was related to depression rather than The CERAD experience, part XVII. Neurology 1997;48:1304-9. caregiver status. These results highlight the complex, multi- 8. Teel CS, Press AN. Fatigue among elders in caregiving and non- factorial nature of caregivers’ sleep patterns and the need for caregiving roles. West J Nurs Res 1999;21:498-514; discussion additional research to elucidate those factors and to determine 514-20.

Journal of Clinical Sleep Medicine, Vol. 4, No. 4, 2008 368 Caregiver Sleep Patterns

9. Wilcox S, King AC. Sleep complaints in older women who 28. Dittner AJ, Wessely SC, Brown RG. The assessment of fatigue: are family caregivers. J Gerontol B Psychol Sci Soc Sci a practical guide for clinicians and researchers. J Psychosom Res 1999;54:189-98. 2004;56:157-70. 10. Carter PA, Acton GJ. Personality and coping: predictors of de- 29. Winstead-Fry P. Psychometric assessment of four fatigue pression and sleep problems among caregivers of individuals who scales with a sample of rural cancer patients. J Nurs Meas have cancer. J Gerontol Nurs 2006;3:245-53. 1998;6:111-22. 11. McCurry SM, Vitiello MV, Gibbons LE, Logsdon RG, Teri L. 30. Beck AT, Steer RA, Brown GK. Manual for the Beck Depression Factors associated with caregiver reports of sleep disturbances in Inventory--Second Edition. San Antonio, TX: Psychological Cor- persons with dementia. Am J Geriatr Psychiatry 2006;14:112-20. poration; 1996. 12. Kochar J, Fredman L, Stone KL, Cauley JA. Sleep problems 31. Steer RA, Rissmiller DJ, Beck AT. Use of the Beck Depression in elderly women caregivers depend on the level of depressive Inventory-II with depressed geriatric inpatients. Behav Res Ther symptoms: results of the Caregiver - Study of Osteoporotic Frac- 2000;38:311-8. tures. J Am Geriatr Soc 2007;55:2003-9. 32. Arnau RC, Meagher MW, Norris MP, Bramson R. Psychometric 13. Rowe MA, Fehrenbach N. Injuries sustained by communi- evaluation of the Beck Depression Inventory-II with primary care ty-dwelling individuals with dementia. Clin Nurs Res May medical patients. Health Psychol 2001;20:112-9. 2004;13:98-110, discussion 111-6. 33. Steer RA, Ball R, Ranieri WF, Beck AT. Dimensions of the Beck 14. Rowe M. Increased nighttime activity in persons with dementia: Depression Inventory-II in clinically depressed outpatients. J Clin A report on the impact on the caregiver. Paper presented at: UF Psychol 1999;55:117-28. Center for Research on Women’s Health, 2001; Gainesville, FL. 34. Steer RA, Clark DA, Beck AT, Ranieri WF. Common and specific 15. Buysse DJ. Insomnia, depression and aging. Assessing sleep dimensions of self-reported anxiety and depression: the BDI-II and mood interactions in older adults. Geriatrics 2004;59:47-51; versus the BDI-IA. Behav Res Ther 1999;37:183-90. quiz 52. 35. Gary FA, Yarandi HN. Depression among southern rural African- 16. McCrae CS, Rowe MA, Tierney CG, Dautovich ND, Definis AL, American Women: A factor analysis of the Beck Depression In- McNamara JP. Sleep complaints, subjective and objective sleep ventory-II. Nurs Res 2004;53:251-9. patterns, health, psychological adjustment, and daytime function- 36. Kojima M, Furukawa TA, Takahashi H, Kawai M, Nagaya T, ing in community-dwelling older adults. J Gerontol B Psychol Sci Tokudome S. Cross-cultural validation of the Beck Depression Soc Sci 2005;60:182-9 Inventory-II in Japan. Psychiatry Res 2002;110:291-9. 17. MiniMitter Co. I. Actiwatch 16/ Actiwatch 64/ Actiwatch-L/ Ac- 37. Radloff LS. The CES-D scale: A self-report depression scale tiwatch-Score Instruction Manual. Bend, Oregon: MiniMitter Co, for research in the general population. Appl Psychol Meas Inc; 2001. 1977;1:385-401. 18. Mueller J, Kiernan R, Langston JW. Manual for Cognistat. Fairfax, 38. Beekman AT, Deeg DJ, Van Limbeek J, Braam AW, De Vries MZ, Ca.: The Northern California Neurobehavioral Group, Inc; 2001. Van Tilburg W. Criterion validity of the Center for Epidemiologic 19. Ancoli-Israel S, Cole R, Alessi C, Chambers M, Moorcroft W, Studies Depression scale (CES-D): results from a community- Pollak CP. The role of actigraphy in the study of sleep and circa- based sample of older subjects in The Netherlands. Psychol Med dian rhythms. Sleep 2003;26:342-92. 1997;27:231-5. 20. Gorny SW, Allen RP. What is an activity count? A comparison 39. Callahan CM, Hui SL, Nienaber NA, Musick BS, Tierney WM. of different methodologies used in wrist actigraphy. 12th Annual Longitudinal study of depression and health services use among Meeting of the Association of Professional Sleep Societies. Or- elderly primary care patients. J Am Geriatr Soc 1994;42:833-8. lando, FL; 1999. 40. Schroevers MJ, Sanderman R, van Sonderen E, Ranchor AV. The 21. Littner M, Kushida CA, Anderson WM, et al. Practice param- evaluation of the Center for Epidemiologic Studies Depression eters for the role of actigraphy in the study of sleep and circadian (CES-D) scale: Depressed and Positive Affect in cancer patients rhythms: an update for 2002. Sleep 2003;26:337-41. and healthy reference subjects. Qual Life Res 2000;9:1015-29. 22. Kushida CA, Chang A, Gadkary C, Guilleminault C, Carrillo O, 41. Milross MA, Piper AJ, Norman M, et al. Night-to-night variabil- Dement WC. Comparison of actigraphic, polysomnographic, and ity in sleep in cystic fibrosis. Sleep Med 2002;3:213-9. subjective assessment of sleep parameters in sleep-disordered pa- 42. Sforza E, Haba-Rubio J. Night-to-night variability in periodic leg tients. Sleep Med 2001;2:389-96. movements in patients with restless legs syndrome. Sleep Med 23. Oakley NR. Validation with polysomnography of the Sleepwatch 2005;6:259-67. sleep/wake scoring algorithm used by the Actiwatch activity 43. Rowe M. Description of nighttime activity in community- monitoring system. Bend, Oregon: Mini Mitter Co, Inc;1997. dwelling individuals with dementia and the impact on the care- 24. Lichstein KL, Riedel BW, Means MK. Psychological treatment giver. Internet J Adv Nurs Pract 1999;3 http://www.icaap.org/ of late life insomnia. In: Schulz R, Maddox G, Lawton MP, eds. iuicode?88.83.82.82. Annual Review of Gerontology and Geriatrics: Vol. 18. Focus 44. Rowe MA, Lane S, Phipps C. CareWatch: a home monitoring on intervention research with older adults. New York: Springer; system for use in homes of persons with cognitive impairment. 1999:74-110. Top Geriatr Rehabil 2007;23:3-8. 25. Johns MW. A new method for measuring daytime sleepiness: the 45. Sivertsen B, Omvik S, Havik OE, et al. A comparison of actig- Epworth sleepiness scale. Sleep 1991;14:540-5. raphy and polysomnography in older adults treated for chronic 26. Johns MW. Reliability and factor analysis of the Epworth Sleepi- primary insomnia. Sleep 2006;29:1353-8. ness Scale. Sleep 1992;15:376-81. 46. Vallieres A, Morin CM. Actigraphy in the assessment of insom- 27. Krupp LB, LaRocca NG, Muir-Nash J, Steinberg AD. The fatigue nia. Sleep 2003;26:902-6. severity scale. Application to patients with multiple sclerosis and systemic lupus erythematosus. Arch Neurol 1989;46:1121-3.

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