Research in Gerontological Nursing

Empirical Research 

The Association Between Physiological Sources of Pain and Sleep Quality in Older Adults With and Without Dementia

Crystal-Rae D. Evans, PhD, RN; Christine R. Kovach, PhD, RN, FAAN, FGSA

Abstract

The association of musculoskeletal pain, respiratory distress, gastrointestinal discomfort, and genitourinary pain to sleep quality in 89 persons with and without dementia was examined in the current exploratory, cross-sectional, observational quantitative design. Sources of pain were recorded, with 37.1% of the sample reporting urinary retention, 30.3% reporting genitourinary discomfort, and 53.9% reporting gastrointestinal discomfort. Mild scores of musculoskeletal pain and respiratory discomfort were elicited in >75% of the sample. An actigraph was used to measure sleep variables. Findings from this research illustrated dementia, gender, pillow use, respiratory distress, and urinary retention were statistically significantly associated with nighttime sleep quality. Results suggest comprehensive assessments of pain and sleep for older adults with and without dementia are needed. Developing and testing interventions that address factors contributing to sleep quality may improve outcomes. [Research in Gerontological Nursing, 13(6), 297–308.]

Abstract

The association of musculoskeletal pain, respiratory distress, gastrointestinal discomfort, and genitourinary pain to sleep quality in 89 persons with and without dementia was examined in the current exploratory, cross-sectional, observational quantitative design. Sources of pain were recorded, with 37.1% of the sample reporting urinary retention, 30.3% reporting genitourinary discomfort, and 53.9% reporting gastrointestinal discomfort. Mild scores of musculoskeletal pain and respiratory discomfort were elicited in >75% of the sample. An actigraph was used to measure sleep variables. Findings from this research illustrated dementia, gender, pillow use, respiratory distress, and urinary retention were statistically significantly associated with nighttime sleep quality. Results suggest comprehensive assessments of pain and sleep for older adults with and without dementia are needed. Developing and testing interventions that address factors contributing to sleep quality may improve outcomes. [Research in Gerontological Nursing, 13(6), 297–308.]

In older adults, self-reported sleep difficulties range from 50% to 60%, yet the literature reports a larger prevalence of poor sleep quality in people with dementia (PWD) (Fung et al., 2012). Poor sleep quality affects 25% to 80% of PWD and remains a problem even after moving to long-term care settings (Neikrug & Ancoli-Israel, 2010; Rongve et al., 2010; Roth, 2012). A cascade of negative health outcomes can occur as a result of poor sleep quality, which include falls, injury, physical and emotional distress, decreased performance with activities of daily living (ADLs), worsening quality of life, increased placement in long-term care settings, and mortality (Greenblum & Rowe, 2012; Martin & Ancoli-Israel, 2008; Zdanys & Steffens, 2015).

Pain is another common problem that occurs in old age (Chen et al., 2011). The pathophysiology of dementia results in neurodegeneration to areas of the brain that are responsible for perception of pain and communication (Zdanys & Steffens, 2015; Zuurbier et al., 2015). Neurodegeneration of the brain further affects communication of needs, which complicates the task of recognizing and reporting pain (Flo et al., 2014; Monroe et al., 2014). Limited research has been conducted on the neural networks that connect pain to sleep. Although no theoretical framework currently exists that describes the association between pain and sleep, it is evident that pain and sleep have some relationship that needs to be explored.

Approximately 40% of older adults report that pain interferes with ADLs (Dzierzewski et al., 2010; Monroe et al., 2014). Pain is recognized in 45% to 83% of long-term care residents, including PWD (Dzierzewski et al., 2010; Lukas et al., 2013; Monroe et al., 2014). Pain is underre-ported, underrecognized, and consequently undertreated in PWD (Flo et al., 2014; Husebo et al., 2014; Lukas et al., 2013; Passmore & Cunningham, 2014). Consequences of untreated pain can lead to negative outcomes, such as poor sleep quality, impaired ambulation, depression, refusal of care, worsening cognitive impairment, and agitation (Paulson et al., 2014).

Although the concept of sleep quality remains poorly defined, empirical evidence supports that good sleep quality is associated with positive health outcomes, less daytime sleepiness, and greater well-being and psychological functioning (Ohayon et al., 2017). Understanding factors related to sleep are important to management of overall quality of life in older adults with and without dementia. Currently, the literature supports many causes of poor sleep, such as physiological, cognitive, behavioral, environmental, and pharmacological factors (Martin & Ancoli-Israel, 2008; Zdanys & Steffens, 2015). Pain is the physiological factor most commonly associated with PWD experiencing poor sleep quality (Chen et al., 2011; Neikrug & Ancoli-Israel, 2010; Roth, 2012). Unfortunately, specific types of physical pain that are associated with poor sleep quality in PWD have not been elucidated in the literature.

Accordingly, the purpose of the current study was to determine the relationship between sources of physical pain and sleep quality in adults with and without dementia. The specific aims were to (1) describe the frequency and severity of musculoskeletal, respiratory, gastrointestinal, and genitourinary pain and sleep quality in a sample of cognitively intact older adults and older adults with dementia; (2) evaluate the differences in severity of sources of pain between those with and without dementia; and (3) examine the association of sources of pain to sleep quality.

Method

Study Design, Sample, and Setting

This exploratory, cross-sectional, observational, quantitative study was conducted using convenience sampling at one continuing care retirement community (CCRC) that included independent apartments, assisted living, and skilled nursing level of care. Participants were excluded if they had movement disorders that could interfere with actigraphy, current acute illness, or were undergoing rehabilitation. Persons with an indwelling catheter were excluded only from bladder scanning procedures. Inclusion criteria entailed persons age ≥55 and residing within any setting of the CCRC. Participants did not have to report pain or sleep disturbances to participate in the study. Participants were recruited using convenience sampling. Researchers set up a table in a public area of the CCRC with information on the research project as well as held informational town hall meetings. Researchers also worked with the social worker in each area of the CCRC to contact family members and mail information about the study.

The study was approved by an academic Institutional Review Board through the University of Wisconsin–Milwaukee. Informed consent was obtained from the designated signatory. Participants were deemed to have capacity to give consent through staff report of cognitive screening tests conducted through the CCRC or medical records, and participants did not have an activated health care power of attorney (POA) or guardianship. Likewise, participants unable to consent on their own still needed to provide assent even if the POA or guardian provided consent. The study occurred over 9 months, from April to December 2018.

After adjusting for multiple relationships, sample size was calculated based on an effect size of 0.2 (small-medium), power level of 0.8, and probability (alpha) level of 0.05. The minimum sample size to be adequately powered was 83 participants. Figure 1 illustrates participant recruitment. A total of 244 residents were solicited and 103 residents were consented. Four residents were not eligible due to movement disorder, two were resistant to keeping the actigraphy watch on, seven withdrew due to scheduling conflicts, and one resident was dropped due to comorbid illness interfering with actigraphy measurement. A total of 89 participants were included for data analysis.

Measurement

Dependent Variable: Nighttime Sleep Quality. Measurement of sleep quality was performed using the Micro Mini-Motionlogger® actigraph by Ambulatory Monitoring Inc. This device is particularly feasible for use in older adults with dementia residing in long-term care settings due to its ease of use and low invasiveness (Shambroom et al., 2012). The actigraph is a biophysical tool typically worn on the non-dominant wrist for a minimum of 72 hours. Participants were expected to wear the actigraphy watch for 72 hours. Self-reported sleep logs were used as an adjunct to actigraphy for an accurate mark of the nighttime sleep period. In PWD, self-reported sleep logs were completed by personal caregiver, family member or spouse, and/or nursing staff. Sleep logs have been used in research to code actigraphy data and generally have good agreement in capturing changes in sleep patterns (Johansson et al., 2014). Agreements between actigraphy and polysomnography for sleep and wake were 86.3% and 85.7%, respectively, in healthy community-dwelling adults (Shambroom et al., 2012).

Sleep quality was characterized objectively by total sleep time, sleep efficiency, sleep latency, wake after sleep onset (WASO), and sleep fragmentation captured through actigraphy. Total sleep time is the length in minutes of nighttime sleep (Moore et al., 2015). The percentage of time spent asleep is characterized as sleep efficiency (Enderlin et al., 2011). Sleep latency is defined as the duration in minutes to fall asleep or the first period of persistent inactivity (Moore et al., 2015). WASO is the number of minutes awake during nighttime from sleep onset to final awakening (Moore et al., 2015). Sleep fragmentation is an index of restlessness during the nighttime and is expressed as a percentage by dividing the total number of awakenings per night by the total sleep time. Higher scores indicate more disrupted sleep (Ancoli-Israel et al., 2003).

Predictor Variables. Observational measures, clinical assessment data, and biophysiological measures were used in data collection. All data collection procedures were conducted by a trained geriatric RN researcher. Pain has been described as an unpleasant sensory or emotional occurrence associated with actual or potential tissue damage (Ferrell & Coyle, 2010). Pain is a highly subjective experience. The current study identified pain or discomfort in the musculoskeletal, respiratory, gastrointestinal, and genitourinary systems. Acute and chronic pain were not differentiated.

Musculoskeletal Pain. The Pain Assessment in Advanced Dementia (PAINAD) observational scale was used because it primarily captures assessment of musculoskeletal pain during activities (Warden et al., 2003). Total scores range from 0 to 10, and higher scores indicate more severe pain. Construct validity and high internal consistency reliability were established in this study (α = 0.782).

Respiratory Discomfort. Respiratory discomfort was captured using the Respiratory Distress Observation Scale (RDOS) (Campbell, 2008; Campbell et al., 2010). RDOS scores range from 0 to 16, with higher scores indicating more severe respiratory distress. Internal consistency and convergent validity with dyspnea self-report and discriminant validity with pain and no dyspnea has been shown (Cronbach's alpha = 0.64) (Campbell et al., 2010). In the current study, Cronbach's alpha was 0.735, indicating strong internal consistency reliability. For descriptive purposes, oxygen saturation was also measured using a pulse oximeter, and the number of pillows used and elevation of head of the bed were also assessed.

Gastrointestinal Discomfort. Because there is no known tool that specifically targets gastrointestinal pain, chart review and staff report were used to collect symptom information. If participants were cognitively intact, self-report was also used. One point was recorded for each symptom that day: diarrhea, constipation, nausea, vomiting, complaint of gastrointestinal distress, decreased appetite (≤50% of diet eaten compared to the previous day or past 1 or 3 days), and current or history of hiatal hernia. Although no reliability or validity information was found for simple questions of gastrointestinal discomfort, patients are commonly questioned about these sources of discomfort as a part of routine physical assessments (Haws, 2015). Standard clinical questions evaluating gastrointestinal discomfort in the current study were found to have poor internal consistency reliability (α = 0.352).

Genitourinary Discomfort. Genitourinary discomfort included assessment of urinary retention using a bladder scanner (Cubescan™ BioCon-700) at bedtime with a proxy value of ≥100 mL as an indication of discomfort (American Urological Association, 2016). The bladder scanner is a clinical device that uses ultrasound waves to determine the volume of urine inside the bladder. This brand of bladder scanner was equivalent to computed tomography scan of bladder volume (Claxton & Appleyard, 2017). Symptoms of urinary discomfort, such as burning, irritation, pressure, frequency, urgency, and/or active urinary infections, at the time of data collection were collected using self-report (dependent on level of cognition), proxy, or staff. Chart review of genitourinary symptoms was conducted for participants unable to verbalize discomfort who were receiving skilled nursing level of care.

Covariates. Gender. Gender was classified as male or female determined through chart review.

Level of Dementia. Level of dementia was measured with the 30-point Mini Mental State Examination (MMSE; Folstein et al., 1975). A score ≥24 indicates normal cognition, 19 to 23 indicates mild cognitive impairment, 10 to 18 indicates moderate impairment, and ≤9 indicates severe impairment. The MMSE has an average sensitivity of 75% and specificity of 62% to 100% (Tombaugh & McIntyre, 1992).

Comorbid Illness. The Cumulative Illness Rating Scale for Geriatrics (CIRS-G) was used to quantify medical burden along a 14-item Likert-type scale (Miller et al., 1992). Intraclass correlations of 0.78 and 0.88 have been reported, and Cronbach's alpha was 0.67 in the current study.

Medications that Promote and Inhibit Sleep. Medications that promote and inhibit sleep were obtained using the medical record. Sleep-inhibiting medications were classified as acetylcholinesterase-inhibitors, beta blockers, pseudoephedrine, corticosteroids, and some antidepressants (e.g., tricyclic, selective serotonin reuptake inhibitor, serotonin-norepinephrine reuptake inhibitor). Melatonin, trazadone, benzodiazepines, nonbenzodiazepine hypnotics, and sedating antidepressants were classified as sleep-promoting medications (Zdanys & Steffens, 2015).

Procedures

Data collection occurred approximately once per week over 9 months by a trained geriatric RN researcher who worked for the research department at the site where data collection occurred. Up to six new participants started the study on a Monday and completed participation on a Thursday of each week that did not include religious work-restricted holidays. Based on resident availability and unit convenience, approximately one half of residents were scheduled for pain assessments on a Tuesday and one half received pain assessments on a Wednesday. Assessments of pain were conducted on Tuesdays or Wednesdays to eliminate the potential “first night effect” using wrist actigraphy. Interrater reliability checks were performed by two nurse researchers periodically to reduce type I error.

Following consent, the MMSE was administered and the medical record was reviewed for exclusion criteria, demographics, and medications that may promote or inhibit sleep. On Monday morning, an actigraph was placed on the non-dominant wrist and a sleep log was distributed to participants or staff depending on participants' level of cognition. On the pain assessment day (Tuesday or Wednesday), measurement of musculoskeletal pain, respiratory distress, gastrointestinal discomfort, and genitourinary discomfort were assessed in the evening up to 1 hour prior to nighttime sleep during and after bedtime care. Measurements and observations were conducted as close to nighttime sleep as possible to capture data that may have an effect on nighttime sleep, as the researcher was not observing for pain during times of sleep. On Thursday, the actigraph was removed and the sleep log was retrieved.

Measurement of musculoskeletal pain using the PAINAD occurred over 5 minutes. Measures of respiratory distress using the RDOS occurred over 5 minutes and pulse oximetry over 1 minute at rest. Gastrointestinal discomfort was evaluated using self-report and/or staff report with chart review of constipation, diarrhea, nausea, vomiting, complaints of gastrointestinal discomfort, loss of appetite, and current or history of hiatal hernia. Bowel sounds were auscultated, and the abdomen was palpated with observation for signs and symptoms of discomfort. Evaluation with the bladder scanner for urinary retention was completed with participants lying flat after emptying their bladder. Assessment of signs and symptoms of discomfort, such as frequency, urgency, burning, irritation, pressure, and/or active urinary infections, was conducted through self-report and/or staff report with chart review.

Analyses

SPSS version 24 was used for all analyses. To ensure accuracy, data were cleaned and examined for errors and missing data using a two-person cross-checking technique. No data or cases needed to be excluded for analyses. Bladder volume was the only variable with missing data related to exclusion criteria of catheter use. The frequency of distributions of all variables were checked for skew before proceeding with analyses. Data with skew were managed by reporting medians, ranges, and percentages. Age, length of stay, musculoskeletal pain, respiratory discomfort, total sleep time, sleep efficiency, sleep latency, WASO, and sleep fragmentation were variables that had skew. Non-parametric testing was used for variables with skew. For inferential analyses, the alpha coefficient was set at 0.05.

To address specific Aim 1, descriptive statistics were used to describe the frequency and severity of musculoskeletal, respiratory, gastrointestinal, and genitourinary pain and sleep quality in a sample of older adults with and without dementia. Frequencies, percentages, medians, ranges, means, and standard deviations were used. Chi-square analyses and independent sample t tests were used to evaluate the differences in the severity and sources of pain between participants with and without dementia.

To address specific Aim 2, independent t test and Mann-Whitney U analyses were conducted to evaluate the differences in severity of sources of pain and sleep between participants with and without dementia. Covariate and predictor variables were initially tested for their relationship to sleep quality variables using chi-square analyses. The only covariate that was statistically significantly related to sleep quality was dementia. Therefore, all independent variables and the statistically significant covariate of dementia were included in the logistic regression model.

To address specific Aim 3, comorbid illness, dementia, and medications that promote and inhibit sleep were measured for possible inclusion as covariates. Binary logistic regression analyses were performed to identify significant predictors of sleep quality. Sleep quality variables, dementia, and pillow use were recoded into dichotomous variables to indicate normal or abnormal values. Normal values commonly used in the literature were used as guidelines to dichotomize variables into normal or abnormal values. A total sleep time ≥420 minutes, sleep efficiency ≥80%, sleep latency values ≤20 minutes, WASO values ≤10% of total sleep minutes, and sleep fragmentation values ≤20 indicated normal values (Ancoli-Israel et al., 2003). There was no evidence of multicollinearity in the model.

Results

Demographic and clinical characteristics of participants with and without dementia are presented in Table 1. Overall, the sample did not display statistically significant differences among characteristics except for comorbid illness severity scores. Participants were primarily female, White, and had an average age of 85.5 years for participants without dementia and 88.8 years for PWD. The sample was highly educated, with 21.3% having completed high school, 50.6% having attended college, and 28.1% with graduate level degrees. Participants resided at the CCRC for a median length of stay of 38.5 months for participants without dementia and 44.6 months for PWD. Forty-four participants lived in the nursing home, 22 participants lived in assisted living, and 23 participants lived independently.

Demographic and Clinical Characteristics of Participants With and Without Dementia (N = 89)

Table 1:

Demographic and Clinical Characteristics of Participants With and Without Dementia (N = 89)

Forty-five persons had no dementia, whereas 44 persons had some level of cognitive impairment. Table 1 illustrates the average medical burden of comorbid illness using the CIRS-G. Scores of 1 to 2 indicate mild to moderate medical burden and scores of 3 to 4 demonstrate severe medical burden (Miller et al., 1992). Of 14 body systems affected by chronic illness, the sample had on average 10 for participants without dementia and 12 for PWD. In the total sample, 37 (41.6%) participants were taking medications that promoted sleep versus 78 (87.6%) participants who were prescribed medications that potentially had sleep-inhibiting properties.

Frequency and Severity of Pain Symptoms

Median musculoskeletal pain score was 3 (range = 0 to 9), indicating the sample had mild musculoskeletal pain prior to nighttime sleep (Table 2). The median respiratory discomfort score was 3, suggesting the sample had mild respiratory discomfort prior to nighttime sleep. A large portion of the sample had a retained bladder volume (n = 62, 69.7%), with 37.1% having urinary retention >100 mL. Twenty-seven (30.3%) participants had some report of genitourinary discomfort. Forty-eight (53.9%) participants had reports of gastrointestinal discomfort the day prior to nighttime sleep. Symptoms of decreased appetite were reported in 27%; complaints of gastrointestinal discomfort were reported in 22.5%; and 53.9% had symptoms of constipation, nausea, vomiting, and/or diarrhea on the day of testing.

Descriptive Characteristics of Pain and Sleep Quality

Table 2:

Descriptive Characteristics of Pain and Sleep Quality

Sleep Quality Variables

The majority of the sample (n = 59, 66.3%) slept <7 hours at night (Table 2). Although the average total sleep time was normal (476.5 minutes), the majority of the sample (51.7%) had multiple arousals during the night, as indicated by a median of 4.1. Fifty-six (62.9%) participants had high WASO values, with a median of 74 minutes. Thirty (33.7%) participants had poor sleep efficiency, with a median score of 87. Only 14.6% of the sample (n = 13) had poor sleep latency. The median time for this sample to fall asleep was 8 minutes.

Differences in Pain and Sleep Between Participants

Mann-Whitney U test for independent samples analyses revealed that people with cognitive impairment had statistically significant worse sleep efficiency (p = 0.018), time awake after the onset of sleep (p = 0.005), and more sleep fragmentation (p = 0.043) than those with normal cognition (Table 3). The median percentage of time spent asleep (sleep efficiency) for those with cognitive impairment was 78.88 compared to 90.22 for those with normal cognition. The median time awake after the onset of sleep was 114 minutes for those with cognitive impairment compared to 48.5 minutes for those with normal cognition. The median sleep fragmentation index was 4.81 for those with cognitive impairment compared to 3.22 for those with normal cognition.

Differences in Pain and Sleep Between Participants With and Without Dementia (N = 89)

Table 3:

Differences in Pain and Sleep Between Participants With and Without Dementia (N = 89)

The Mann-Whitney U test revealed statistically significant differences in gastrointestinal discomfort between those with and without dementia (p < 0.001, median in PWD = 0.00, median without dementia = 1.00). PWD were less likely to report gastrointestinal discomfort (61% [n = 43] with dementia versus 39% [n = 28] without dementia). Even though the median bladder volume for people with dementia was more than double the median volume of those without dementia, the differences were not statistically significant.

There were no statistically significant differences between those with and without dementia in the time it took to fall asleep (p = 0.990) or the total minutes of sleep at night (p = 0.089). The median time it took to fall asleep (sleep latency) for those with cognitive impairment was 8 minutes compared to 8.5 minutes for those with normal cognition. The median total time of sleep at night was 503 minutes for those with cognitive impairment compared to 452 minutes for those with normal cognition.

Factors Predicting Sleep Quality

Dementia, female gender, urinary retention, pillow use, and respiratory distress were statistically significant predictors of sleep quality (Table 4). Participants without dementia were 4.78 times more likely to have normal sleep efficiency than those with dementia. Participants using two or more pillows were 6.86 times more likely to have normal sleep efficiency than those using zero to one pillow. Females were 7.61 times more likely to have normal sleep efficiency than males. The model was classified correctly 78.6% of the time.

Summary of Binary Logistic Regression Analysis Predicting Sleep Quality

Table 4:

Summary of Binary Logistic Regression Analysis Predicting Sleep Quality

Participants without dementia were 3.75 times more likely to have normal WASO than those with dementia. Females were 4.93 times more likely to have normal WASO than males. Participants without urinary retention were 0.79 times more likely to have normal WASO than those with urinary retention. Cases were classified correctly 70.2% of the time.

Participants without dementia were 0.83 times more likely to have normal sleep fragmentation than those with dementia. Participants using two or more pillows were 0.82 times more likely to have normal sleep fragmentation than those using zero to one pillow. Females were 0.85 times more likely to have normal sleep fragmentation than males. For a unit change in respiratory distress, the odds of sleep fragmentation increased by 1.23 times. Cases were classified correctly 79.8% of the time.

Discussion

The association of dementia, comorbidity, and physiological sources of pain with sleep quality in older adults with and without dementia was examined in this study. Dementia, female gender, pillow use, urinary retention, and respiratory distress significantly predicted level of sleep quality. This study adds to the body of literature supporting poor sleep quality in PWD (Chen et al., 2016; Fung et al., 2012; Kim et al., 2014). In particular, the percent of time actually spent asleep and arousals during the nighttime were two characteristics of sleep quality that were below normal for this group.

According to the Centers for Medicare & Medicaid Services (2018), the CCRC sites of the current study provide high-quality care with a better than average rating. The small to moderate prevalence of musculoskeletal, respiratory, and gastrointestinal discomfort may be explained by the quality of care at these sites. This is the first study to report a high incidence of urinary retention at bedtime. Bliwise et al. (2015) reported urinary retention prevalence of approximately 33% in men age ≥80. Urinary retention has been associated with comorbid health conditions, increase in age, anticholinergic medication use, diabetes, and constipation (Griebling, 2013).

Gordon et al. (2009) found the type of pillow used, rather than the number of pillows, was related to sleep quality and cervical pain outcomes. Their research suggests that the use of rubber pillows instead of standard or feather pillows promotes sleep quality by preventing cervical pain. Other literature promotes good sleep hygiene and physical comfort that involves the use of a few pillows or placing essential oils on the pillow prior to nighttime sleep (Chen et al., 2010). Chronic health conditions involving the lungs or heart are associated with difficulty breathing and pillow use. Prevalence of severe heart conditions was 24.7% and lung conditions was 32.6% in the current study. Congestive obstructive pulmonary disease and heart failure are two conditions prevalent in older adults that require management through physical comfort measures, such as positioning of the head of the bed, increased pillow use, and medication management (Lainscak & Anker, 2015; Neikrug et al., 2014).

Strengths

This cross-sectional observational design supported the study hypothesis that pain is associated with nighttime sleep. This design was not costly and did not require a lot of time or burden for participants to complete data collection. This study occurred within a long-term care site known for high-quality care and adequate staffing. Multiple variables of pain were observed while sleep was continually measured until data collection was complete. Findings from this study support multiple sources of pain can be targeted in pain assessments. Consistent data collection methods were used and interrater reliability checks were performed periodically through data collection between two nurse researchers to reduce type I error. One nurse trained geriatric RN researcher conducted testing of all participants.

Limitations

Although the use of convenience sampling in the current study allowed for quick and inexpensive recruitment, it lends to selection bias and generalizability limitations. Inferences about this sample are limited to this population. The convenience sampling method does not give participants the same chance of being selected for participation. Rather recruitment was dependent upon residents attending the town hall meetings and family members responding to mailed consents.

Participants residing in independent living, assisted living, and skilled nursing units were not differentiated. Causal claims cannot be inferred through this exploratory observational study. Only prevalence can be evaluated, not incidence. The actigraph has its limitations in that it cannot distinguish between awake and lying still or sleeping. Yet, the actigraph remains the best way to objectively measure sleep outcomes due to feasibility, ease of use, low participant burden, and cost effectiveness.

Accuracy of pain and sleep scores may not be robust due to only 1 day of measurement for pain and 3 days of sleep measures. Respiratory distress may be related to pillow use, but this study did not examine this association. Although various participants may experience other types of pain, the current study aimed to specifically examine physical sources of pain rather than other variations of pain, such as psychological discomfort, due to feasibility. It is possible that low levels of pain observed during data collection may be related to prior medication administration. Medications administered for on-going genitourinary and gastrointestinal discomfort were not captured in this study.

Future Research

Findings suggest there is a need to further investigate the effects of gender, dementia, and physiological sources of pain in older adults on sleep quality. Varying levels of discomfort were identified in this study, with 77.5% of the sample with musculoskeletal pain, 85.2% with respiratory distress, 37.1% with urinary retention, and 53.9% with signs and symptoms of gastrointestinal discomfort. Therefore, assessment of pain and sleep should be included as vital signs (Chasens et al., 2017; Purser et al., 2014).

Future research should examine the relationship of timing of pain medication administration and timing of pain assessments prior to nighttime sleep. Data collection may include more nights of testing and more time points of measurement during day and evening hours. Nighttime observations of sleep quality and behaviors in addition to actigraphy may add to the strength of evidence. Repeated measures would improve accuracy of pain and sleep data. It is not feasible to wake people up in the middle of the night for assessments. Instead, measurement of pain can occur prior to nighttime sleep to prevent poor sleep quality. Future research may examine sleep outcomes between persons who do and do not use scheduled or as-needed pain medications.

Replication studies with larger sample sizes are needed to further strengthen research results addressing pain and sleep in PWD. Future studies could expand across multiple CCRC sites to capture multiple populations not limited to one setting. Research is needed to explain variability in prevalence and intensity of pain between multiple CCRC sites with varying levels of quality rating. Future research may investigate concurrent associations not limited to physiological pain; psychological distress; and social, environmental, and biological factors.

Validity of assessment strategies surrounding gastrointestinal discomfort need to be explored as current assessment strategies are lacking. There is difficulty obtaining accurate and reliable subjective measures from PWD; therefore, measurement needs to involve objective measures. In addition to the actigraph, future research may include subjective tools for sleep assessment reported by staff or caregivers of PWD. Interventional research could examine groups using two pillows versus one pillow (control group) on pain and sleep outcomes. This research would be feasible and cost-effective, while potentially improving sleep outcomes without using pharmacological methods.

Research Implications

Implications of the effects of unrecognized and therefore unmanaged pain on nighttime sleep in PWD should be acknowledged. Although a small to moderate prevalence of pain was reported herein, this study illustrates musculoskeletal, respiratory, and gastrointestinal discomfort exists. In particular, nurses have the ability to conduct thorough pain assessments and provide means to reduce the effects of pain through nonpharmacological and pharmacological methods. Pain is one area that can be easily addressed by assessment strategies to improve sleep outcomes in PWD.

High incidence of urinary retention was found in this study. Benefits of examining all residents for urinary retention prior to nighttime sleep may reduce negative health and sleep outcomes in this population. Examination of urinary retention through use of bladder scanning could occur upon admission to a CCRC and periodically to preemptively address this source of discomfort.

Sleep hygiene practices should be routine for long-term care settings, particularly for residents with dementia. Assessments of pain during mobility, elimination prior to sleep, and administration of routine medications may reduce the effects of pain on nighttime sleep. The electronic charting system could incorporate and require assessments for varying sources of pain, thus prompting nurses to evaluate multiple sources of pain as part of routine practice. Evaluation of sleep using the actigraph could be conducted on admission to a CCRC to obtain baseline measurements. If a resident with dementia is exhibiting increased daytime behaviors of agitation, restlessness, or changes to mentation, evaluation of sleep could support observed changes thus prompting further investigation of symptoms.

Pain and sleep need to be considered as vital signs in nursing practice. Although pain has already been suggested as the fifth vital sign, a more robust pain assessment needs to occur. Pain assessment needs to target varying body systems, not simply generalized pain. In addition, sleep assessments are not routinely evaluated in long-term care settings or as standard nursing practice. The actigraph is a simple and cost-effective tool to obtain objective measurements of sleep considering its' limitations on certain populations. It may not be realistic for all nurses to be trained in use of the actigraph, but it is possible there could be a few specialty trained nurses or nurse practitioners who follow patients within these settings that could be responsible for sleep evaluation.

Conclusion

Sleep problems, pain, dementia, and comorbid health conditions in long-term care settings are prevalent and require further investigation to optimize quality of life, comfort outcomes, and management of associated health care costs. Sleep patterns and quality of life may be improved through understanding factors associated with poor sleep and developing and testing interventions that address contributing factors. Comprehensive assessment strategies need to be targeted and implemented to evaluate effectiveness of identifying antecedents of poor sleep quality. Methods for understanding and measuring sleep quality in PWD are inadequate, which limits the ability to identify and effectively manage poor sleep and its associated negative outcomes (Gitlin et al., 2014; Simpson & Carter, 2013).

The current study aimed to comprehensively assess specific physiological sources of pain and nighttime sleep quality in older adults with and without dementia. Dementia, female gender, pillow use, respiratory distress, and genitourinary discomfort were found to significantly contribute to nighttime sleep quality in this population. Knowledge gained from this research should stress the importance of further developing and conducting a more comprehensive pain assessment for older adults with and without dementia. Promoting routine sleep assessments of PWD and evaluating the long-term effects of poor sleep quality may be vital to achieving positive health outcomes for patients within health care settings.

References

  • American Urological Association. (2016).Non-neurogenic chronic urinary retention: Consensus definition, management strategies, and future opportunities. http://www.auanet.org/guidelines/chronic-urinary-retention
  • Ancoli-Israel, S., Cole, R., Alessi, C., Chambers, M., Moorcroft, W. & Pollak, C. P. (2003). The role of actigraphy in the study of sleep and circadian rhythms. Sleep, 26(3), 342–392 doi:10.1093/sleep/26.3.342 [CrossRef] PMID:12749557
  • Bliwise, D. L., Holm-Larsen, T., Goble, S., Juul, K. V., van der Meulen, E. & Nørgaard, J. P. (2015). Delay of first voiding episode is associated with longer reported sleep duration. Sleep Health, 1(3), 211–213 doi:10.1016/j.sleh.2015.05.001 [CrossRef] PMID:29073442
  • Campbell, M. L. (2008). Psychometric testing of a respiratory distress observation scale. Journal of Palliative Medicine, 11(1), 44–50 doi:10.1089/jpm.2007.0090 [CrossRef]
  • Campbell, M. L., Templin, T. & Walch, J. (2010). A respiratory distress observation scale for patients unable to self-report dyspnea. Journal of Palliative Medicine, 13(3), 285–290 doi:10.1089/jpm.2009.0229 [CrossRef] PMID:20078243
  • Centers for Medicare & Medicaid Services. (2018). Medicare nursing home compare results. https://www.medicare.gov/nursinghomecompare/search.html
  • Chasens, E. R., Yang, K., Baniak, L. M., Choi, J. & Imes, C. C. (2018). Sleep and other correlates of high-level health in older adults. Geriatric Nursing, 39(4), 344–349 doi:10.1016/j.gerinurse.2017.11.002 [CrossRef] PMID:29248354
  • Chen, J. C., Espeland, M. A., Brunner, R. L., Lovato, L. C., Wallace, R. B., Leng, X., Phillips, L. S., Robinson, J. G., Kotchen, J. M., Johnson, K. C., Manson, J. E., Stefanick, M. L., Sarto, G. E. & Mysiw, W. J. (2016). Sleep duration, cognitive decline, and dementia risk in older women. Alzheimer's & Dementia, 12(1), 21–33 doi:10.1016/j.jalz.2015.03.004 [CrossRef] PMID:26086180
  • Chen, P. H., Kuo, H. Y. & Chueh, K. H. (2010). Sleep hygiene education: Efficacy on sleep quality in working women. The Journal of Nursing Research, 18(4), 283–289 doi:10.1097/JNR.0b013e3181fbe3fd [CrossRef] PMID:21139448
  • Chen, Q., Hayman, L. L., Shmerling, R. H., Bean, J. F. & Leveille, S. G. (2011). Characteristics of chronic pain associated with sleep difficulty in older adults: The Maintenance of Balance, Independent Living, Intellect, and Zest in the Elderly (MOBILIZE) Boston study. Journal of the American Geriatrics Society, 59(8), 1385–1392 doi:10.1111/j.1532-5415.2011.03544.x [CrossRef] PMID:21806564
  • Claxton, K. & Appleyard, R. (2017). Implementation of ultrasound bladder volume scanning for patients receiving intensity-modulated radiotherapy to the cervix or endometrium: Clinical experiences from a United Kingdom radiotherapy department. Journal of Radiotherapy in Practice, 16(3), 232–244 doi:10.1017/S1460396917000231 [CrossRef]
  • Dzierzewski, J. M., Williams, J. M., Roditi, D., Marsiske, M., McCoy, K., McNamara, J., Dautovich, N., Robinson, M. E. & McCrae, C. S. (2010). Daily variations in objective nighttime sleep and subjective morning pain in older adults with insomnia: Evidence of covariation over time. Journal of the American Geriatrics Society, 58(5), 925–930 doi:10.1111/j.1532-5415.2010.02803.x [CrossRef] PMID:20406316
  • Enderlin, C. A., Coleman, E. A., Cole, C., Richards, K. C., Kennedy, R. L., Goodwin, J. A., Hutchins, L. F. & Mack, K. (2011). Subjective sleep quality, objective sleep characteristics, insomnia symptom severity, and daytime sleepiness in women aged 50 and older with nonmetastatic breast cancer. Oncology Nursing Forum, 38(4), E314–E325 doi:10.1188/11.ONF.E314-E325 [CrossRef]
  • Ferrell, B. R. & Coyle, N (Eds.). (2010). Oxford textbook of palliative nursing. Oxford University Press. doi:10.1093/med/9780195391343.001.0001 [CrossRef]
  • Flo, E., Gulla, C. & Husebo, B. S. (2014). Effective pain management in patients with dementia: Benefits beyond pain?Drugs & Aging, 31(12), 863–871 doi:10.1007/s40266-014-0222-0 [CrossRef] PMID:25373921
  • Folstein, M. F., Folstein, S. E. & McHugh, P. R. (1975). “Mini-mental state.” A practical method for grading the cognitive state of patients for the clinician. Journal of Psychiatric Research, 12, 189–198.
  • Fung, C. H., Martin, J. L., Chung, C., Fiorentino, L., Mitchell, M., Josephson, K. R., Jouldjian, S. & Alessi, C. (2012). Sleep disturbance among older adults in assisted living facilities. The American Journal of Geriatric Psychiatry, 20(6), 485–493 doi:10.1097/JGP.0b013e318252e3e0 [CrossRef] PMID:22531104
  • Gitlin, L. N., Hodgson, N., Piersol, C. V., Hess, E. & Hauck, W. W. (2014). Correlates of quality of life for individuals with dementia living at home: The role of home environment, caregiver, and patient-related characteristics. American Journal of Geriatric Psychiatry, 22(6), 587–597 doi:10.1016/j.jagp.2012.11.005 [CrossRef] PMID:23890928
  • Gordon, S. J., Grimmer-Somers, K. & Trott, P. (2009). Pillow use: The behaviour of cervical pain, sleep quality and pillow comfort in side sleepers. Manual Therapy, 14(6), 671–678 doi:10.1016/j.math.2009.02.006 [CrossRef] PMID:19427257
  • Greenblum, C. A. & Rowe, M. A. (2012). Nighttime activity in individuals with dementia: Understanding the problem and identifying solutions. Journal of Gerontological Nursing, 38(5), 8–11 doi:10.3928/00989134-20120410-02 [CrossRef] PMID:22533348
  • Griebling, T. (2013). Detrusor underactivity and urinary retention in geriatric patients: Evaluation, management and recent research. Current Bladder Dysfunction Reports, 8(2), 92–100 doi:10.1007/s11884-013-0183-x [CrossRef]
  • Haws, J. (2015). Nursing assessment: Head-to-toe assessment in pictures. Health Assessment in Nursing.
  • Husebo, B. S., Ballard, C., Fritze, F., Sandvik, R. K. & Aarsland, D. (2014). Efficacy of pain treatment on mood syndrome in patients with dementia: A randomized clinical trial. International Journal of Geriatric Psychiatry, 29(8), 828–836 doi:10.1002/gps.4063 [CrossRef] PMID:24806873
  • Johansson, A., Adamson, A., Ejdebäck, J. & Edéll-Gustafsson, U. (2014). Evaluation of an individualised programme to promote self-care in sleep-activity in patients with coronary artery disease—A randomised intervention study. Journal of Clinical Nursing, 23(19–20), 2822–2834 doi:10.1111/jocn.12546 [CrossRef] PMID:24479893
  • Kim, S. S., Oh, K. M. & Richards, K. (2014). Sleep disturbance, nocturnal agitation behaviors, and medical comorbidity in older adults with dementia: Relationship to reported caregiver burden. Research in Gerontological Nursing, 7(5), 206–214 doi:10.3928/19404921-20140512-01 [CrossRef] PMID:24877599
  • Lainscak, M. & Anker, S. D. (2015). Heart failure, chronic obstructive pulmonary disease, and asthma: Numbers, facts, and challenges. ESC Heart Failure, 2(3), 103–107 doi:10.1002/ehf2.12055 [CrossRef] PMID:27708851
  • Lukas, A., Mayer, B., Fialová, D., Topinkova, E., Gindin, J., Onder, G., Bernabei, R., Nikolaus, T. & Denkinger, M. D. (2013). Pain characteristics and pain control in European nursing homes: Cross-sectional and longitudinal results from the Services and Health for Elderly in Long TERm care (SHELTER) study. Journal of the American Medical Directors Association, 14(6), 421–428 doi:10.1016/j.jamda.2012.12.010 [CrossRef] PMID:23375521
  • Martin, J. L. & Ancoli-Israel, S. (2008). Sleep disturbances in long-term care. Clinics in Geriatric Medicine, 24(1), 39–50.
  • Miller, M. D., Paradis, C. F., Houck, P. R., Mazumdar, S., Stack, J. A., Rifai, A. H., Mulsant, B. & Reynolds, C. F. III. . (1992). Rating chronic medical illness burden in geropsychiatric practice and research: Application of the Cumulative Illness Rating Scale. Psychiatry Research, 41(3), 237–248 doi:10.1016.0165-1781(92)90005-n [CrossRef] PMID:1594710
  • Monroe, T. B., Misra, S. K., Habermann, R. C., Dietrich, M. S., Cowan, R. L. & Simmons, S. F. (2014). Pain reports and pain medication treatment in nursing home residents with and without dementia. Geriatrics & Gerontology International, 14(3), 541–548 doi:10.1111/ggi.12130 [CrossRef] PMID:24020433
  • Moore, C. M., Schmiege, S. J. & Matthews, E. E. (2015). Actigraphy and sleep diary measurements in breast cancer survivors: Discrepancy in selected sleep parameters. Behavioral Sleep Medicine, 13(6), 472–490 doi:10.1080/15402002.2014.940108 [CrossRef] PMID:25117292
  • Neikrug, A. B. & Ancoli-Israel, S. (2010). Sleep disturbances in nursing homes. The Journal of Nutrition, Health & Aging, 14(3), 207–211 doi:10.1007/s12603-010-0051-8 [CrossRef] PMID:20191255
  • Neikrug, A. B., Liu, L., Avanzino, J. A., Maglione, J. E., Natarajan, L., Bradley, L., Maugeri, A., Corey-Bloom, J., Palmer, B. W., Loredo, J. S. & Ancoli-Israel, S. (2014). Continuous positive airway pressure improves sleep and daytime sleepiness in patients with Parkinson disease and sleep apnea. Sleep (Basel), 37(1), 177–185 doi:10.5665/sleep.3332 [CrossRef] PMID:24470706
  • Ohayon, M., Wickwire, E. M., Hirshkowitz, M., Albert, S. M., Avidan, A., Daly, F. J., Dauvilliers, Y., Ferri, R., Fung, C., Gozal, D., Hazen, N., Krystal, A., Lichstein, K., Mallampalli, M., Plazzi, G., Rawding, R., Scheer, F. A., Somers, V. & Vitiello, M. V. (2017). National Sleep Foundation's sleep quality recommendations: First report. Sleep Health, 3(1), 6–19 doi:10.1016/j.sleh.2016.11.006 [CrossRef] PMID:28346153
  • Passmore, P. & Cunningham, E. (2014). Pain assessment in cognitive impairment. Journal of Pain & Palliative Care Pharmacotherapy, 28(3), 305–307 doi:10.3109/15360288.2014.941136 [CrossRef] PMID:25166774
  • Paulson, C. M., Monroe, T. & Mion, L. C. (2014). Pain assessment in hospitalized older adults with dementia and delirium. Journal of Gerontological Nursing, 40(6), 10–15 doi:10.3928/00989134-20140428-02 [CrossRef] PMID:24800815
  • Purser, L., Warfield, K. & Richardson, C. (2014). Making pain visible: An audit and review of documentation to improve the use of pain assessment by implementing pain as the fifth vital sign. Pain Management Nursing, 15(1), 137–142 doi:10.1016/j.pmn.2012.07.007 [CrossRef] PMID:24602432
  • Rongve, A., Boeve, B. F. & Aarsland, D. (2010). Frequency and correlates of caregiver-reported sleep disturbances in a sample of persons with early dementia. Journal of the American Geriatrics Society, 58(3), 480–486 doi:10.1111/j.1532-5415.2010.02733.x [CrossRef] PMID:20398116
  • Roth, H. L. (2012). Dementia and sleep. Neurologic Clinics, 30(4), 1213–1248 doi:10.1016/j.ncl.2012.08.013 [CrossRef] PMID:23099135
  • Shambroom, J. R., Fábregas, S. E. & Johnstone, J. (2012). Validation of an automated wireless system to monitor sleep in healthy adults. Journal of Sleep Research, 21(2), 221–230 doi:10.1111/j.1365-2869.2011.00944.x [CrossRef] PMID:21859438
  • Simpson, C. & Carter, P. (2013). Dementia caregivers' lived experience of sleep. Clinical Nurse Specialist, 27(6), 298–306 doi:10.1097/NUR.0b013e3182a87287 [CrossRef] PMID:24107753
  • Tombaugh, T. N. & McIntyre, N. J. (1992). The Mini-Mental State Examination: A comprehensive review. Journal of the American Geriatrics Society, 40(9), 922–935 doi:10.1111/j.1532-5415.1992.tb01992.x [CrossRef]. PMID: 1512391
  • Warden, V., Hurley, A. C. & Volicer, L. (2003). Development and psychometric evaluation of the Pain Assessment in Advanced Dementia (PAINAD) scale. Journal of the American Medical Directors Association, 4(1), 9–15 doi:10.1097/01.JAM.0000043422.31640.F7 [CrossRef] PMID:12807591
  • Zdanys, K. F. & Steffens, D. C. (2015). Sleep disturbances in the elderly. The Psychiatric Clinics of North America, 38(4), 723–741 doi:10.1016/j.psc.2015.07.010 [CrossRef] PMID:26600105
  • Zuurbier, L. A., Ikram, M. A., Luik, A. I., Hofman, A., Van Someren, E. J., Vernooij, M. W. & Tiemeier, H. (2015). Cerebral small vessel disease is related to disturbed 24-h activity rhythms: A population-based study. European Journal of Neurology, 22(11), 1482–1487 doi:10.1111/ene.12775 [CrossRef] PMID:26206535

Demographic and Clinical Characteristics of Participants With and Without Dementia (N = 89)

CharacteristicParticipants With Dementia (n = 44)Participants Without Dementia (n = 45)Test Statisticp Value
n (%)Mean (SD)Median (Range)n (%)Mean (SD)Median (Range)
Female39 (60)26 (40)1.29a0.255
Race (White)49 (56)39 (44)0.74a0.388
Education
  High school12 (24)7 (18)3.03a0.805
  Attended college27 (53)18 (47)
  Graduate degree12 (24)13 (34)
Age (years)88.8 (9.1)85.5 (10)1.66b0.101
Length of stay (months)44.6 (35.5)38.5 (36.2)0.8b0.426
Medications
  Sleep-promoting0.64 (0.857)0.59 (0.818)0.29b0.738
  Sleep-inhibiting1.53 (1.085)1.97 (1.063)1.97b0.176
Comorbid illness (severe)
  Musculoskeletal3 (2 to 4)3 (0 to 4)0.224
  Psychiatric illness4 (0 to 4)2 (0 to 4)<0.001
  Genitourinary3 (0 to 4)3 (0 to 4)0.15
  Neurological3 (0 to 4)2 (0 to 4)0.071
  Endocrine2 (0 to 4)2 (0 to 4)0.548
  Vascular2 (0 to 4)3 (0 to 4)0.068
  Eyes/ears/nose/throat2 (0 to 4)2 (0 to 3)0.544
  Respiratory0 (0 to 4)2 (0 to 4)0.065
  Heart2 (0 to 4)2 (0 to 4)0.507
  Lower gastrointestinal2 (0 to 3)2 (0 to 3)0.013
  Hematopoietic0 (0 to 3)1 (0 to 4)0.035
  Upper gastrointestinal2 (0 to 3)2 (0 to 3)0.845
  Renal0 (0 to 4)0 (0 to 3)0.078
  Liver0 (0 to 1)0 (0 to 1)0.748

Descriptive Characteristics of Pain and Sleep Quality

Characteristic (Measure)n (%)Mean (SD)Median (Range)Possible ValuesNormal Values
Musculoskeletal (PAINAD)69 (77.5)3.33 (2.73)3 (0 to 9)0 to 10
Respiratory (RDOS)75 (85.2)3.61 (3.21)3 (0 to 12)0 to 16
Genitourinary
Retained volume (mL)62 (73.8)99.07 (123.36)60.5 (0 to 724)
Urinary retention (mL)33 (37.1)0.39 (0.49)0 (0 to 1)>100
Reported discomfort (yes/no)27 (30.3)0.3 (0.46)0 (0 to 1)0 to 1
Gastrointestinal
Total score (symptom count)48 (53.9)0.84 (1)1 (0 to 4)0 to 7
Decreased appetite (yes/no)24 (27)0.27 (0.45)0 (0 to 1)0 to 1
Discomfort (symptom count)27 (22.5)0.25 (0.48)0 (0 to 2)0 to 4
Total sleep time (TST, minutes)59 (66.3)476.5 (144)495 (1 to 825)420 to 540
Sleep efficiency (%)30 (33.7)82.5 (14.7)87 (35.7 to 99.3)80 to 85
Sleep latency (minutes)13 (14.6)15.4 (27.2)8 (0 to 212)<20
Wake after sleep onset (minutes)56 (62.9)106.3 (107)74 (0 to 679)10% TST
Sleep fragmentation (minutes)46 (51.7)6 (10.8)4.1 (0.59 to 100)<20

Differences in Pain and Sleep Between Participants With and Without Dementia (N = 89)

CharacteristicParticipants With Dementia (n = 44)Participants Without Dementia (n = 45)p Value
Mean (SD)Median (Range)Mean (SD)Median (Range)
Musculoskeletal3.77 (2.47)2.42 (1.99)0.006a
Respiratory3 (0 to 11)2 (0 to 12)0.539b
Gastrointestinal0 (0 to 2)1 (0 to 4)<0.001b
Genitourinary99.5 (0 to 724)43 (0 to 400)0.25b
Total sleep time (minutes)503 (1 to 825)452 (138 to 688)0.089b
Sleep efficiency (%)78.88 (35.7 to 99.27)90.22 (55.01 to 97.41)0.0018b
Sleep latency (minutes)8 (0 to 212)8.5 (0 to 50)0.99b
Wake after sleep onset (minutes)114 (0 to 679)48.5 (10 to 195)0.005b
Sleep fragmentation4.81 (0.59 to 100)3.22 (0.96 to 12.85)0.043b

Summary of Binary Logistic Regression Analysis Predicting Sleep Quality

VariableBSEOR95% CIWald Statisticp Value
Sleep efficiency
  Dementia1.560.574.78[1.56, 14.62]7.520.006
  Pillow use1.930.706.86[1.73, 27.23]7.490.006
  Female2.030.697.61[1.97, 29.36]8.670.003
Wake after sleep onset
  Dementia1.320.543.75[1.31, 10.74]6.030.014
  Female1.600.714.93[1.22, 19.89]5.020.025
  Urinary retention−1.350.550.26[0.09, 0.76]6.050.014
Sleep fragmentation
  Dementia−1.570.530.21[0.07, 0.59]8.840.003
  Pillow use−1.520.590.22[0.07, 0.7]6.610.01
  Female−1.720.690.18[0.05, 0.7]6.180.013
  Respiratory distress0.200.0091.23[1.04, 1.45]5.720.017
Authors

Dr. Evans is Assistant Nursing Professor, Ruth S. Coleman College of Nursing and Health Sciences, Cardinal Stritch University, and Dr. Kovach is Director of Research, Ovation Communities, Distinguished Professor Emeritus, University of Wisconsin-Milwaukee, Milwaukee, Wisconsin.

The authors have disclosed no potential conflicts of interest, financial or otherwise. This study received support from Bader Philanthropies and the Jewish Home Foundation of Milwaukee.

Dr. Kovach was not involved in the peer review or decision-making process of this manuscript.

Address correspondence to Crystal-Rae D. Evans, PhD, RN, Assistant Nursing Professor, Ruth S. Coleman College of Nursing and Health Sciences, Cardinal Stritch University, 149 N. 73rd Street, Milwaukee, WI 53213; email: crystalraeevans@gmail.com.

Received: January 09, 2019
Accepted: April 21, 2020
Posted Online: October 09, 2020

10.3928/19404921-20201002-02

Sign up to receive

Journal E-contents