Research in Gerontological Nursing

Research Brief 

Sleep Disturbance, Nocturnal Agitation Behaviors, and Medical Comorbidity in Older Adults With Dementia: Relationship to Reported Caregiver Burden

Suk-Sun Kim, PhD, RN, MSN; Kyeung Mi Oh, PhD, RN; Kathy Richards, PhD, RN, FAAN

Abstract

The purpose of this secondary analysis study was to determine whether care recipients’ nighttime sleep patterns, medical comorbidity, observed nocturnal agitation behaviors, and caregivers’ perceptions of nocturnal agitation behaviors in care recipients with dementia are associated with caregiver burden. Sixty care recipient–caregiver dyads, comprising older adults with geriatrician-diagnosed dementia living at home with caregivers, participated. Caregivers’ perceptions of the frequency of care recipients’ nocturnal agitation behaviors were associated with caregiver burden; however, objective, real-time data on the frequency of nocturnal agitation behaviors were not associated with burden. Care recipients’ increased minutes of wakefulness before falling asleep and severe cognitive impairment with musculoskeletal/integument and neurological comorbidities were associated with higher caregiver burden. These results suggest that targeted interventions to reduce sleep onset latency, medical comorbidity, and caregivers’ perception of frequency of nocturnal behaviors may reduce caregiver burden. [Res Gerontol Nurs. 2014; 7(5):206–214.]

Dr. Kim is Assistant Professor, Division of Nursing Science, College of Health Sciences, Ewha Womans University, Seoul, Korea; Dr. Oh is Assistant Professor, and Dr. Richards is Assistant Dean, Doctoral Division & Research Department, and Professor, School of Nursing, College of Health and Human Services, George Mason University, Fairfax, Virginia.

The authors have disclosed no potential conflicts of interest, financial or otherwise. This study was funded by the Department of Veterans Affairs (grant NRI01-077-1; K. Richards, Principal Investigator). The authors wish to acknowledge Corinne Lambert and Susan Jegley for their work on this project.

Address correspondence to Suk-Sun Kim, PhD, RN, MSN, Assistant Professor, Division of Nursing Science, College of Health Sciences, Ewha Womans University, 11-1 Daehyun-Dong, Seodaemoon-Ku, Seoul, Korea 120-750; e-mail: suksunkim@ewha.ac.kr. Questions related to the study data can be directed to Kathy Richards, PhD, RN, FAAN, Assistant Dean, Doctoral Division & Research Department, and Professor, School of Nursing, College of Health and Human Services, George Mason University, 4400 University Drive, Fairfax, VA 22030-4444; e-mail: kricha11@gmu.edu.

Received: November 14, 2013
Accepted: May 05, 2014
Posted Online: May 27, 2014

Abstract

The purpose of this secondary analysis study was to determine whether care recipients’ nighttime sleep patterns, medical comorbidity, observed nocturnal agitation behaviors, and caregivers’ perceptions of nocturnal agitation behaviors in care recipients with dementia are associated with caregiver burden. Sixty care recipient–caregiver dyads, comprising older adults with geriatrician-diagnosed dementia living at home with caregivers, participated. Caregivers’ perceptions of the frequency of care recipients’ nocturnal agitation behaviors were associated with caregiver burden; however, objective, real-time data on the frequency of nocturnal agitation behaviors were not associated with burden. Care recipients’ increased minutes of wakefulness before falling asleep and severe cognitive impairment with musculoskeletal/integument and neurological comorbidities were associated with higher caregiver burden. These results suggest that targeted interventions to reduce sleep onset latency, medical comorbidity, and caregivers’ perception of frequency of nocturnal behaviors may reduce caregiver burden. [Res Gerontol Nurs. 2014; 7(5):206–214.]

Dr. Kim is Assistant Professor, Division of Nursing Science, College of Health Sciences, Ewha Womans University, Seoul, Korea; Dr. Oh is Assistant Professor, and Dr. Richards is Assistant Dean, Doctoral Division & Research Department, and Professor, School of Nursing, College of Health and Human Services, George Mason University, Fairfax, Virginia.

The authors have disclosed no potential conflicts of interest, financial or otherwise. This study was funded by the Department of Veterans Affairs (grant NRI01-077-1; K. Richards, Principal Investigator). The authors wish to acknowledge Corinne Lambert and Susan Jegley for their work on this project.

Address correspondence to Suk-Sun Kim, PhD, RN, MSN, Assistant Professor, Division of Nursing Science, College of Health Sciences, Ewha Womans University, 11-1 Daehyun-Dong, Seodaemoon-Ku, Seoul, Korea 120-750; e-mail: suksunkim@ewha.ac.kr. Questions related to the study data can be directed to Kathy Richards, PhD, RN, FAAN, Assistant Dean, Doctoral Division & Research Department, and Professor, School of Nursing, College of Health and Human Services, George Mason University, 4400 University Drive, Fairfax, VA 22030-4444; e-mail: kricha11@gmu.edu.

Received: November 14, 2013
Accepted: May 05, 2014
Posted Online: May 27, 2014

Currently, an estimated 5.2 million adults older than 65 in the United States have Alzheimer’s disease (AD) (Alzheimer’s Association, 2014). As the American population ages, the prevalence of AD in older adults is expected to reach 7.1 million by 2025 and 11 to 16 million by 2050 (Alzheimer’s Association, 2014; Hebert, Scherr, Bienias, Bennett, & Evans, 2003). Approximately 80% of older adults with AD or other dementias are cared for in their homes by family caregivers. More than 15 million family caregivers provide 17.7 billion hours of care (Alzheimer’s Association, 2014). Individuals who care for older adults with dementia report distress that challenges their physical, psychosocial, financial, and spiritual lives, and is referred to as caregiver burden (Zarit, Reever, & Bach-Peterson, 1980). Because both caregivers and their care recipients interdependently affect and are affected by each other (Kim, Hayward, & Kang, 2013), several studies have demonstrated that caregiver burden influences the health of caregivers as well as care recipients (Kim, Chang, Rose, & Kim, 2012). Caregiver burden contributes to caregivers’ diminished mental health (e.g., anxiety, depression) (Butler, Turner, Kaye, Ruffin, & Downey, 2005; Cooper, Balamurali, & Livingston, 2007) and physical health (e.g., immune function problems, cardiovascular disease) (Aschbacher et al., 2008; Carretero, Garcés, Ródenas, & Sanjosé, 2009; Gallagher et al., 2008), which result in increased risk of premature mortality (Schulz & Beach, 1999). Moreover, caregiver burden has been associated with care recipients’ higher risk for functional decline (Vitaliano, Young, Russo, Romano, & Magana-Amato, 1993) and earlier institutionalization (Gaugler, Kane, & Newcomer, 2007).

Although numerous studies have reported that caregiver burden is significantly associated with care recipients’ cognitive and functional impairment and behavioral disturbance (Dunkin & Anderson-Hanley, 1998; Rymer et al., 2002), a paucity of studies have examined the association of care recipients’ sleep disturbance, nocturnal agitation behaviors, and comorbidity with caregiver burden. Care recipients’ sleep disturbance (e.g., nighttime awakenings, changes in sleep-wake patterns, reduced sleep duration) and nocturnal agitation behaviors (e.g., general restlessness, repetitious mannerisms, screaming, physical aggressiveness) are highly predictive of care recipients’ institutionalization (Ancoli-Israel & Vitiello, 2006; Hope, Keene, Gedling, Fairburn, & Jacoby, 1998; Pollak & Perlick, 1991) and may contribute to their becoming a caregiver burden. Identifying comorbidity clusters, such as sleep disturbance, nocturnal agitation behaviors, and medical conditions, and examining their interdependent effects on caregiver burden will allow more targeted and effective interventions to reduce caregiver burden and delay or prevent these adverse outcomes.

Researchers have emphasized using both caregivers’ reports and observations to assess behavioral and sleep disturbances. Because caregivers’ reports on care recipients’ behaviors and sleep disturbance were not congruent with objective measures in previous studies (Ancoli-Israel & Vitiello, 2006; Hadjistavropoulos, Taylor, Tuokko, & Beattie, 1994; Hoekert, van der Lek, Swaab, Kaufer, & Van Someren, 2006; Mangone et al., 1993; McCurry, Vitiello, Gibbons, Logsdon, & Teri, 2006), we used direct observational measures and polysomnography (PSG). To our knowledge, actual nocturnal agitation behaviors have not been observed or quantified in care recipients with dementia living at home to determine how they influence caregiver burden. Therefore, in this study, we conducted objective sleep evaluations for sleep disturbances with two nights of in-home, attended, portable PSG, and direct observations for nocturnal agitation behaviors during three nights at home in older adults with dementia. The specific aims of the study were to: (a) examine differences in care recipients’ nighttime sleep patterns, medical comorbidity, observed nocturnal agitation behaviors, and caregivers’ perceptions of nocturnal agitation behaviors in care recipients with dementia among three groups (little, mild, and moderate to severe burden); (b) examine bivariate associations of caregiver burden with care recipients’ nighttime sleep patterns, medical comorbidity, observed nocturnal agitation behaviors, and caregivers’ perceptions of nocturnal agitation behaviors in care recipients with dementia; and (c) identify significant predictors of caregiver burden.

Method

Study Design

This secondary analysis study used de-identified data from an observational, cross-sectional study on the nighttime sleep patterns of 60 older adults with dementia with nocturnal agitation behaviors, collected from 2003–2008. The original study was approved by an institutional review board, and participants consented to have their data used in secondary data analyses. Prior to data collection, participants provided informed consent.

Sample

Care recipients with dementia and their caregivers were recruited from two geriatric outpatient clinics at the Department of Veterans Affairs or the University of Arkansas for Medical Sciences. The inclusion criteria for care recipients were: (a) a dementia diagnosis made by a geriatrician; (b) English speaking and living in a private home with a family caregiver; (c) caregivers’ reports of one or more nocturnal agitation behaviors ≥3 times per week; (d) ambulatory; and (e) medically stable, unchanged medications within 30 days, and the absence of fever or other symptoms of acute illness. The inclusion criteria for family caregivers were: (a) living in a private home with and primarily caring for eligible care recipients; (b) being the care recipient’s spouse, child, or sibling; and (c) able to read and speak English. Seventy-four dyads of care recipients with dementia and their family caregivers consented and participated in the study; however, 60 dyads completed all data collection instruments and were included in this secondary analysis study.

Study Procedures and Measurements

The study procedures are reported in detail in a previous study (Rose et al., 2011). A brief description is provided below.

Caregivers’ Perceptions of Care Recipients’ Nocturnal Agitation Behaviors. After informed consent was obtained, caregivers completed the Cohen-Mansfield Agitation Inventory (CMAI) to measure their subjective perceptions of the nocturnal agitation behaviors of care recipients. Care recipients with dementia were included in this study only when a caregiver reported one or more nocturnal agitation behaviors ≥3 times per week. The CMAI consists of 29 items that are answered on a 7-point scale, ranging from 1 (never) to 7 (several times an hour) (Cohen-Mansfield, 1986). A total score is calculated by summing all scores of the 29 items. Scores range from 29 to 203; higher scores indicate a higher frequency of nocturnal agitation behaviors, such as pacing, screaming, biting, and general restlessness.

Care Recipients’ Sleep Patterns. Care recipients’ sleep patterns (i.e., sleep onset latency and sleep efficiency) were assessed during two nights using the Grass Portable Polysomnography Data Acquisition System®. While care recipients slept in their own beds at home during their habitual sleeping hours—with a bed partner if they usually slept with one—the PSG system and sleep technician remained in the hall outside the care recipient’s bedroom or in an adjoining room. Care recipients were continuously observed by the technician for their safety and the integrity of the monitoring devices using a small portable video camera. To assist with PSG recordings and for descriptive information, the technician noted every 5 minutes whether care recipients were awake or asleep. If the sleep technician was able to obtain a total of 4 hours or more of time in bed during the two PSG nights, three additional nights were scheduled for observing care recipients’ nocturnal agitation behaviors. However, if participants slept less than 4 hours in bed during the two PSG nights, they were excluded from the study.

Observed Care Recipients’ Nocturnal Agitation Behaviors. Care recipients’ nocturnal agitation behaviors were observed either directly or by video camera using the CMAI modified for direct observation (Chrisman, Tabar, Whall, & Booth, 1991). After the care recipient had gone to bed, a research assistant (RA) observed and noted every 5 minutes whether the care recipient was behaviorally awake or asleep via a video camera placed in the bedroom and a monitor located in a hallway or room adjacent to the bedroom. Sleep was defined as a quiet state with eyes closed. The RA scored the frequency of specific nocturnal agitation behaviors during wake time at 5-minute intervals. The Agitation Behaviors Index (ABI; Rose et al., 2011) was calculated by summing numbers of episodes of all nocturnal agitation behaviors and dividing the total numbers by the exact hours of observation for each participant. Higher scores indicate more frequent nocturnal agitation behaviors. The RAs received a 1-week training course on identifying and scoring nocturnal agitation behaviors, taught by the investigators. Following this training, the RAs accurately identified nocturnal agitation behaviors at a high level of agreement (r > 0.90) with nurse experts and each other. During data collection, the RAs recoded randomly selected 2-hour segments of video data biannually with agreement, ranging from 0.80 to 0.95.

Care Recipients’ Comorbidities. Care recipients’ comorbidities were assessed using the modified Cumulative Illness Rating Scale for Geriatrics (CIRS-G) (Miller & Towers, 1991). In the original CIRS-G, diseases are categorized by 14 organ systems affected: (a) heart; (b) vascular; (c) hematopoietic; (d) respiratory; (e) eyes, ears, nose, and throat (ENT), and larynx; (f) upper gastrointestinal (GI); (g) lower GI; (h) liver; (i) renal; (j) genitourinary; (k) musculoskeletal/integument; (l) neurological; (m) endocrine/metabolic and breast; and (n) psychiatric illness. However, there are some issues with the original CIRS-G: categorizing dementia as a neurological or psychiatric illness is ambiguous (Miller & Towers, 1991). For the current study, another category was added—cognitive impairment—to separately measure the severity of dementia of care recipients who were diagnosed by a geriatrician as having dementia. The geriatric nurse practitioner rated the 15 categories on a 5-point scale from 0 (no problem), 1 (current mild or past significant problem), 2 (moderate disability), 3 (severe), to 4 (severe or life threatening) by reviewing patients’ medical charts and interviewing caregivers. The total score of the CIRS-G was calculated by adding all scores of the 15 categories, ranging from 0 to 60. In the current study, diseases that often clustered together were identified using 15 disease categories of the modified CIRS-G to examine the association between the identified comorbidity clusters and caregiver burden. The comorbidity cluster score was also calculated by adding all scores of the clustered disease categories. The process of identifying comorbidity clusters is discussed in the data analysis section.

Care Recipients’ Baseline Cognitive Function. The Mini-Mental State Examination (MMSE; Folstein, Folstein, & McHugh, 1975) was used to measure baseline cognitive function of the care recipients with dementia but was not the sole diagnostic criterion for the diagnosis of dementia. Scores range from 0 to 30, with scores >25 generally considered normal, whereas lower scores on the MMSE indicate more cognitive impairment.

Caregiver Burden. The outcome variable, caregiver burden, was assessed by the Zarit Burden Interview (ZBI) to measure caregiver’s perceived physical, financial, and psychosocial burden (Zarit et al., 1980; Zarit & Zarit, 1987). The total burden score is a sum of all 22 items using a 5-point scale ranging from 0 (never) to 4 (nearly always). Possible total scores range from 0 to 88, where the scores indicate little burden (<21), mild (21 to 40), moderate (41 to 60), and severe burden (61 to 88) (Zarit & Zarit, 1987).

Data Analysis

An exploratory data analysis was used to describe the total sample and screen the data for accuracy of response, outliers, and missing data. An exploratory factor analysis was undertaken to identify disease clusters and examine their relationships to caregiver burden. For the factor analysis, a principal components analysis using varimax rotation was used to extract factors. There were seven factors with eigenvalues >1.0 in the original unrotated matrix. After examining the seven factors, it was found that the two-factor results were more interpretable and a principal components analysis was re-performed for only six disease group items: upper GI, lower GI, renal, musculoskeletal/integument, neurological, and cognitive impairments. The two-factor solution explained 49.6% of the variance. The large first factor explained 29.1% of the variance. Item communalities ranged from 0.26 to 0.64. Items were considered associated with factors if they had loading of 0.4 or higher. Using this criterion, high loadings were found for three items on Factor I and three items on Factor II. The researchers interpreted Factor I as GI (both upper and lower), renal comorbidity cluster and factor II as cognitive, musculoskeletal/integument, neurological comorbidity cluster.

Descriptive statistics were used to assess the demographic characteristics for care recipients and caregivers, caregiver burden, and comorbidities. Chi-square analyses, one-way analysis of variance (ANOVA), and correlations were used to examine bivariate associations of caregiver burden with other variables. Additionally, simultaneous multiple regression analysis was performed to identify significant predictors of caregiver burden. Statistical assumptions of multiple regression analysis were examined, and multicollinearity of the independent variable was checked. As Jewell (2004) indicated, all independent variables with at least p < 0.3 in bivariate analyses were included as covariates for the multiple regression model. PASW Statistics 18 was used for all analyses. For all analyses, the alpha coefficient was set at 0.05.

Results

Characteristics of the 60 care recipient–caregiver dyads are presented in Table 1. More than three quarters (76.7%) of the caregivers were women, and 66.7% were spouses, 26.7% were children, and 6.7% were other relatives of the care recipients. Seventeen (28.3%) had lower scores on the caregiver burden indicating little caregiver burden; 26 (43.3%) had mild burden; and 17 (28.3%) had moderate or severe burden. Mean caregiver burden was 31.2 (SD = 15.7, range = 2 to 71). Caregiver burden scores were used to classify the caregivers into three groups: little (<21), mild (21 to 40), and moderate (41 to 60) to severe burden (61 to 88). Because only two caregivers’ burden was scored as severe, the moderate and severe burden groups were merged into one group.

Caregiver and Care Recipient Characteristics and Descriptive Statistics of Study Variables by Level of Caregiver Burden (N = 60)

Table 1:

Caregiver and Care Recipient Characteristics and Descriptive Statistics of Study Variables by Level of Caregiver Burden (N = 60)

The 60 care recipients comprised 41 (68.3%) men and 19 (31.7%) women. Fifty-two (87%) were White and 13% were African American. Participants’ ages ranged from 66 to 89 (mean = 79, SD = 6 years). Care recipients’ sleep patterns showed a mean sleep efficiency of 68.3% (SD = 17.3%) and mean sleep onset latency of 27.1 minutes (SD = 24.5 minutes). Mean ABI was 3 (SD = 3.3, range = 0 to 14.9). Mean CMAI score was 69 (SD = 28.7, range = 36 to 199). Of the 15 items of the CIRS-G, the most prevalent disease among participants was cognitive disease (2.14, range = 1 to 4) followed by heart (1.66, range = 0 to 3), musculoskeletal/integument (1.51, range = 0 to 3), upper GI tract (1.39, range = 0 to 3), psychiatric (1.24, range = 0 to 3), ENT (1.15, range = 0 to 3), and neurological (1, range = 0 to 3). The severity means of the other diseases including vascular, hematopoietic, respiratory, lower GI, liver, renal, genitourinary, and endocrine disease were <1.

The three groups created, based on their caregiver burden scores, were compared to the MMSE, sleep patterns, nocturnal agitation behaviors, and the previously described comorbidity disease cluster. ANOVA indicated that MMSE (F[2,56] = 4.78, p = 0.012); caregivers’ self-report of nocturnal agitation behaviors (F[2,57] = 4.65, p = 0.006); and the cognitive, musculoskeletal/integument, neurological comorbidity cluster (F[2,57] = 3.18, p = 0.049) were significantly different among groups. However, there were no significant differences among groups with respect to sleep onset latency, sleep efficiency, RAs’ observation of nocturnal agitation behaviors, and the GI, renal comorbidity cluster.

Correlations Predictors and Caregiver Burden

Results from the correlation analyses are shown in Table 2. Caregivers’ self-report of care recipients’ nocturnal agitation behaviors (CMAI) was significantly positively associated with caregiver burden (r = 0.425, p < 0.01). Unexpectedly, RAs’ observation of nocturnal agitation behaviors, care recipients’ sleep onset latency, and the cognitive, musculoskeletal/integument, neurological comorbidity cluster were not significantly related to caregiver burden.

Pearson’s Correlation of Study Variables on Caregiver Burden

Table 2:

Pearson’s Correlation of Study Variables on Caregiver Burden

Multiple Regression Models

The results of the simultaneous multiple regression using caregiver burden as the dependent variable and four predictors in the model are shown in Table 3. The overall model with all four predictors was statistically significant and explained 29.6% of the variance in caregiver burden (F[4, 54] = 5.687, p = 0.001). Caregivers’ self-report of care recipients’ nocturnal agitation behaviors (b = 0.195, p = 0.004, ß = 0.355) was a persistently significant predictor of caregiver burden in a multivariable analysis. On the other hand, the two variables, care recipients’ sleep onset latency (b = 0.164, p = 0.048, ß = 0.252) and the cognitive, musculoskeletal/integument, neurological comorbidity cluster (b = 3.329, p = 0.021, ß = 0.285) significantly predicted caregiver burden in this regression model, although the two variables were not significantly related to caregiver burden in bivariate analyses. The RAs’ observations of care recipients’ nocturnal agitation behavior were unrelated to caregiver burden in both bivariate and multivariable analyses. A post hoc power analysis indicated that the power to detect obtained effects at the 0.05 level was 0.98 for the overall regression to predict caregiver burden.

Multiple Linear Regression Model for Caregiver Burden

Table 3:

Multiple Linear Regression Model for Caregiver Burden

Discussion

We examined the associations of caregiver burden with care recipients’ nighttime sleep patterns, medical comorbidity, observed nocturnal agitation behaviors, and caregivers’ perceptions of nocturnal agitation behaviors in care recipients with dementia. We found that care recipients’ sleep onset latency; cognitive, musculoskeletal/integument, neurological comorbidity cluster; and caregivers’ perceptions of care recipients’ nocturnal agitation behaviors were significantly associated with caregiver burden in the multiple regression models. However, the RAs’ observation of care recipients’ nocturnal agitation behavior was not significantly related to caregiver burden. These findings are important as they are derived from the only research (to our knowledge) that has used both direct observations of three nights of nocturnal agitation behaviors by RAs and caregiver reports of nocturnal agitation behaviors in care recipients with dementia to examine the influence of these two variables on caregiver burden. We also used in-home, attended, portable PSG, which is considered the gold standard for assessing objective sleep patterns during two nights.

Although caregivers in this study cared for individuals with dementia, nocturnal agitation behaviors, sleep disturbance, and several comorbidities, 28.3% had little burden, the majority (43.3%) had mild caregiver burden, and 28.3% had moderate or severe burden. Mean caregiver burden was 31.2 (SD = 15.7), which is similar to previous studies of caregivers who cared for individuals with AD and related dementias who had neuropsychiatric symptoms (29.2, SD = 18) (Allegri et al., 2006) or behavioral disturbance (32.5, SD = 15.8) (Rymer et al., 2002). In comparison, this mean is higher than those reported by caregivers of patients with advanced cancer receiving palliative care: 18.3 (SD = 11.6) (Grunfeld et al., 2004) and 18.5 (SD = 11) (Higginson & Gao, 2008).

We found that caregivers’ perceptions of care recipients’ nighttime agitation behaviors rather than direct observations of care recipients’ nighttime agitation behaviors by RAs was a significant predictor of caregiver burden. In other words, caregivers who perceived more frequent care recipient nocturnal agitation behaviors experienced a higher level of burden regardless of the actual frequency of nocturnal agitation behaviors. In addition, there was no significant correlation between caregivers’ perception and direct observations of care recipients’ nighttime agitation behaviors. These findings expand on those of previous studies in which investigators found that caregivers’ reports on care recipients’ functional impairments and sleep disturbances were not congruent with objective measures, and caregivers’ reports of care recipients’ functional impairments—rather than their actual impairments—was the best predictor of caregiver burden (Ancoli-Israel & Vitiello 2006; Hadjistavropoulos et al., 1994; Hoekert et al., 2006; Mangone et al., 1993; McCurry et al., 2006). This finding may be due in part to the lack of sensitivity of the caregiver self-report scale to accurately detect the frequency of care recipients’ nocturnal agitation behaviors. It is possible that caregivers’ perceptions of care recipients’ nocturnal agitation behaviors are related to the frequency of care recipients’ daytime agitation behaviors and other factors of caregiver daytime burden.

We also found that two variables—care recipients’ sleep onset latency (minutes awake before falling asleep) and cognitive, musculoskeletal/integument, neurological comorbidity cluster—significantly predicted caregiver burden in the regression model, but were not significantly related to caregiver burden in bivariate analyses. These findings indicate that caregivers had a higher level of caregiving burden when they cared for individuals with dementia who had both difficulty falling asleep at night and musculoskeletal and neurological diseases. One possible explanation for this result is that care recipients with dementia as well as physical pain or neurobiological changes associated with sleep/wake regulation, which interfere with their caregivers’ sleep, increase caregiver fatigue and burden (Beaudreau et al., 2008; Rose et al., 2011). Consistent with this idea, prior studies have reported that care recipients’ sleep disturbance and nocturnal disruptive behaviors were negatively related to caregiver sleep quality, and in turn, poor sleep quality in caregivers and frequent nocturnal disruptions in care recipients were associated with a higher level of caregiver burden and depression (Castro et al., 2009; Creese, Bédard, Brazil, & Chambers, 2008; Lee, 2008). Another potential explanation is that if care recipients have multiple chronic illnesses, particularly cognitive, musculoskeletal/integument, neurological comorbidity cluster, caregivers spend long hours per day providing assistance to care recipients, which may lead to higher burden.

Limitations

This study has one main limitation. Both care recipient and caregiver characteristics were found to significantly relate to caregiver burden in previous studies (Butler et al., 2005; Dunkin & Anderson-Hanley, 1998; Kim et al., 2012). Because we used secondary data analysis in this study, we had little information on caregiver characteristics.

Implications for Practice and Future Research

The findings of the current study have several implications for the clinical care of individuals with dementia and their caregivers. Health care professionals treating sleep problems and nocturnal behavioral disturbance should consider that caregivers may not always be reliable reporters of care recipients’ symptoms; therefore, caregiver reports should be interpreted carefully (McCurry et al., 2006). Several researchers suggested the use of both caregivers’ reports and observations to measure patients’ sleep and nocturnal agitation behaviors (Creese et al., 2008; Hoekert et al., 2006; McCurry et al., 2006). To develop appropriate and effective treatment for care recipients’ nocturnal disturbance, accurate objective measurements should be used to detect sleep disturbance and nocturnal behavioral disturbance in care recipients with dementia. Also, understanding the role of the perception of caregiver burden in the caregiving situation might help clinicians and researchers better interpret caregivers’ reports of care recipients’ sleep disturbance and nocturnal behavioral disturbance.

It has also been suggested that better management of care recipients’ sleep disturbance and nocturnal behavioral disturbance may reduce caregiver burden. Using behavioral management techniques to train caregivers how to manage the behavioral problems of patients with dementia and psychoeducation to teach them how to change their interactions with these individuals was successful in alleviating their neuropsychiatric symptoms (Livingston, Johnston, Katona, Paton, & Lyketsos, 2005). Selwood, Johnston, Katona, Lyketsos, and Livingston (2007) found that individual behavioral management techniques were effective in decreasing caregiver depression and recommended that individual behavioral management techniques combined with caregiver sleep and stress management techniques, problem solving, and education may reduce caregiver depression and burden. Moreover, future research on treating sleep disturbance in patients receiving care for dementia should consider the effects of their cognitive, musculoskeletal/integument, neurological comorbidity cluster on sleep. Thus, treatments targeting the cognitive, musculoskeletal/integument, neurological comorbidity cluster may be more efficacious than those focusing on treating each disease separately.

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Caregiver and Care Recipient Characteristics and Descriptive Statistics of Study Variables by Level of Caregiver Burden (N = 60)

Caregiver Burden (ZBI)
Characteristic/VariableTotal (N = 60)Little (<21) (n = 17)Mild (21 to 40) (n = 26)Moderate to Severe (41 to 88) (n = 17)F/χ2p Value (ANOVA)
Caregivers
Sex, n (%)2.460.65a
  Female46 (76.7)131815
  Male11 (18.3)362
  Missing3 (5)120
Relationships, n (%)6.200.80a
  Spouse40 (66.7)121711
  Child16 (26.7)376
  Other relative4 (6.7)220
Caregiver burdenb31.2 (15.7)
Care Recipients
Sex, n (%)0.180.91a
  Male41 (68.3)121712
  Female19 (31.7)595
Age (years)79 (6)78.7 (6.8)79.7 (5.9)78.2 (5.6)0.350.71
MMSEc20.0 (7.3)24.1 (5.6)19.1 (7.6)17.1 (6.6)4.780.012*
Nighttime sleep patterns
  Sleep onset latency (minutes)27.1 (24.5)18.9 (9.6)31.0 (29.1)29.4 (26.4)1.370.26
  Sleep efficiency (%)68.3 (17.3)70.2 (15.9)67.0 (19.2)68.5 (16.5)0.160.85
Nocturnal agitation behaviors
  RA observation of nocturnal agitation behaviors (ABI)3.0 (3.3)1.9 (2)2.9 (3)4.2 (4.5)2.000.15
  Caregivers’ self-report of nocturnal agitation behaviors (CMAI)d69.0 (28.7)54.5 (17)67.8 (20.6)85.1 (39.7)4.650.006**
Comorbidity
  CIRS-G total scoree14.2 (4.3)12.5 (2.9)14.9 (5.1)14.7 (3.9)1.780.18
Disease cluster
  Gastrointestinal, renal2.4 (1.5)2.0 (1.4)2.8 (1.6)2.1 (1.5)1.920.16
  Cognitive, musculoskeletal/integument, neurological4.6 (1.4)4.1 (1)4.6 (1.6)5.2 (1.2)3.180.049*

Pearson’s Correlation of Study Variables on Caregiver Burden

VariableCaregiver Burden1234
1 Sleep onset latency0.20810.341**0.029–0.229
2 RA observation of nocturnal agitation behaviors (ABI)0.2030.341**10.214–0.160
3 Caregivers’ self-report of nocturnal agitation behaviors (CMAI)0.425**0.0290.21410.122
4 Cognitive, musculoskeletal/integument, neurological comorbidity cluster0.243–0.229–0.1600.1221

Multiple Linear Regression Model for Caregiver Burden

Predictor VariablebSEßtp Value
Constant–2.6677.973–0.3340.739
Sleep onset latency0.1640.0810.2522.0250.048*
RA observation of nocturnal agitation behaviors (ABI)0.4140.5940.0870.6960.489
Caregivers’ self-report of nocturnal agitation behaviors (CMAI)0.1950.0650.3552.990.004**
Cognitive, musculoskeletal/integument, neurological comorbidity cluster3.3291.3600.2852.3740.021*

10.3928/19404921-20140512-01

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