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. Author manuscript; available in PMC: 2016 Nov 1.
Published in final edited form as: Psychosom Med. 2015 Nov-Dec;77(9):1050–1057. doi: 10.1097/PSY.0000000000000237

Avoidant coping and poor sleep efficiency in dementia caregivers

Briana J Taylor 1, Leah A Irish 2, Lynn M Martire 3, Greg J Siegle 4, Robert T Krafty 5, Richard Schulz 1, Martica H Hall 4
PMCID: PMC4643353  NIHMSID: NIHMS711952  PMID: 26458234

Abstract

Objective

Caring for a spouse with dementia is a source of chronic stress and is associated with a heightened prevalence of self-reported sleep problems. Styles and strategies for coping with stress have been associated with objective measures of sleep in non-caregiver populations. The current study evaluated relationships between caregiver coping style and sleep disturbance using in-home polysomnography.

Methods

Sixty spousal caregivers (mean age 73.31±7.05; 81.7% female), completed the Brief Cope (COPEB), the Hamilton Rating Scale for Depression (HRSD) and three nights of in-home polysomnography. Participants were categorized into two groups based on the presence or absence of clinically significant low sleep efficiency (less than 80%). A factor analysis of the COPEB yielded higher order factors that included approach coping and avoidant coping (explained variance, 27.2% and 16.9%, respectively). Coping factors were entered into a binary logistic regression predicting sleep efficiency group while controlling for sleep apnea, medication use and depression, as measured by the HRSD.

Results

In fully adjusted models, for each unit increase on the avoidant coping factor participants were 4.9 times more likely to be classified in the low sleep efficiency group (B=1.601, χ2(1)=3.943, p=.047, exp(B)=4.956, 95% CI:1.021–24.057). Approach coping was unrelated to sleep efficiency in both adjusted and unadjusted models.

Conclusions

These findings highlight the importance of coping among caregivers and indicate that avoidant coping may be a modifiable predictor of sleep disturbance in conditions of chronic stress.

Keywords: sleep, coping, stress, caregivers, dementia, Alzheimer’s

BACKGROUND

Caring for a spouse with an advanced form of dementia is a stressful life circumstance that confers risk for disease morbidity and poor quality of life (1–7). Caregivers of dementia patients frequently report poor sleep; however, not all caregivers experience sleep disturbances based on objective sleep measurements and clinically significant criteria (8–11). The majority of studies of sleep in caregivers have examined the role of dementia patient characteristics such as disease severity (5) or disruptive nocturnal behavior (12, 13). Few studies have considered the influence of caregiver characteristics on caregiver sleep outcomes. For example, coping strategies vary among caregivers and are known to correlate with self-reported sleep. However, we are not aware of any studies that have examined coping in dementia caregivers in relation to objective measures of sleep; this is an important oversight given the possible influence of negative affective bias on self-report measures. Here, we examine associations between coping strategies and objectively measured sleep efficiency in elderly caregivers, in order to better explain observed individual differences in caregiver sleep. Findings from the current study contribute to the broader literature on stress and health by elucidating the relationship between coping and sleep disturbance within a chronic stress paradigm.

Poor sleep efficiency, characterized by prolonged sleep latency and/or fragmented sleep, is one of the most common sleep complaints among familial caregivers (14, 15) and is not without consequences. Difficulties initiating and maintaining sleep are associated with downstream risks for adverse mental and physical health outcomes (16). In caregivers, fragmented sleep has been attributed to patient awakenings in studies using self-reported sleep measures (17). However, objective measures of both patient and caregiver sleep suggest that dementia patient awakenings are insufficient to explain sleep efficiency in caregivers (17), suggesting that other aspects of caregiving, such as stress, may also impinge caregivers’ ability to initiate and maintain sleep. Indeed, a review of the stress and sleep literature by Kim and Dimsdale (2007) has shown that stressful daily events and traumatic experiences are most consistently associated with increased wakefulness after sleep onset and decreased overall sleep efficiency (18).

Avoidant coping strategies such as frequent use of distraction and denial are considered maladaptive and have been associated with increased self-reported insomnia symptoms (19, 20) and subjective sleep complaints (21). Individual differences in coping with stress may help to explain sleep efficiency within this chronically stressed population. Coping styles and strategies can be separated into two broad categories: approach and avoidant (22). Approach coping is characterized by acceptance of threat-relevant information or active problem-solving (23). Conversely, avoidant coping is characterized by unsuccessful attempts to suppress unwanted or intrusive thoughts (24, 25). Several studies have shown that avoidant coping and intrusive thoughts may disturb sleep (26–29), by eliciting physiological arousal (30, 31), thereby delaying the onset of sleep and/or prolonging periods of wakefulness after sleep onset. Acceptance coping has been associated with higher sleep efficiency and better self-reported sleep quality (29, 32). The association between coping and objectively measured sleep in caregivers, however, remains unknown.

The current study assessed whether approach and avoidant coping styles systematically differed between dementia caregivers with and without clinically low sleep efficiency. Sleep was measured in participants’ own homes via three nights of ambulatory polysomnography (PSG). A clinically relevant PSG-derived sleep efficiency cutoff of ≤ 80% was used to create two groups: a fragmented sleep group and a non-fragmented sleep group. Less than or equal to 80% sleep efficiency was chosen as a clinically relevant threshold to distinguish between caregivers with and without sleep disturbances due to its association with increased risk for sub-optimal health outcomes, including all-cause mortality (16, 33). This clinically relevant threshold was also chosen in order to identify caregivers who would be most likely to benefit from a stress management intervention, based on previous studies that have found strong correlations between poor sleep efficiency and psychosocial stress (18). Analyses conducted using continuous measures of sleep efficiency are presented in the supplemental material. Coping styles were self-reported by participants using a standardized questionnaire on the first night of in-home PSG. On the basis of the coping literature, we hypothesized that avoidant coping styles would predict a greater likelihood of clinically-significant poor sleep efficiency while endorsing an approach coping style would predict lower likelihood of clinically-significant poor sleep efficiency. We hypothesized that these associations would be robust to adjustment for known factors related to sleep efficiency including apnea hypopnea index (AHI), medications that affect sleep and symptoms of depression.

METHODS

Participants

Participants were drawn from the Caregiver study of the “Aging Well, Sleeping Efficiently: Intervention Studies” (AgeWise) Program Project, designed to promote sleep and health in older adults. All data were collected between November, 2003, and June, 2008. Spousal caregivers of individuals with Alzheimer’s, Huntington’s or advanced forms of dementia who reported disruptive sleep problems were recruited for participation in a longitudinal study that examined objective measures of sleep before and after a stress management intervention. The present report is a cross-sectional analysis of data collected at participants’ baseline assessment; findings from analysis of intervention effects are not presented here. Participants were recruited through media advertisements, flyers and clinical referrals. Primary eligibility criteria dictated that prospective participants were ≥ 60 years of age, lived at home with their spouse, endorsed caregiver strain (“It is a significant physical and emotional strain for me to care for my spouse”) and reported disturbed sleep. Exclusion criteria were established through self-report questionnaires, via medical and psychiatric interviews, as well as an initial assessment of sleep disordered breathing. Specifically, in order to obtain a sample of caregivers with self-reported sleep problems, participants were only admitted to the study if they scored greater than or equal to 5 on the Pittsburgh Sleep Quality Index (PSQI). Because we wanted to avoid the confounding effect of sleep disturbances due to sleep disordered breathing, participants with severe sleep apnea, defined as an apnea-hypopnea index of greater than 30 (American Academy of Sleep Medicine, 2008) were excluded from the study. Participants were also excluded if they suffered from a psychotic or substance abuse disorder as assessed by the Structured Clinical Interview for DSM-IV or if they scored lower than 24 on the Mini-Mental State Exam. Participants with health conditions that posed no major limitations to activities of daily living were admitted to the study. A total of 97 caregivers were screened and 60 were enrolled. Financial compensation was provided to study participants.

Procedure

The study was approved by the University of Pittsburgh Institutional Review Board and written informed consent was obtained from individual participants prior to collection of data. Exclusion criteria were established via medical and psychiatric interviews performed by a study clinician. Participants completed self-report questionnaires during the evening hours on the first of three nights of in-home PSG.

Demographics

Participant characteristics were obtained either through self-report questionnaires or by certified mental health clinicians. Age, sex, race and socio-economic information were self-reported at the time of the in-home PSG sleep assessment. Also at this time, participants completed the Perceived Stress Scale as a measure of mental health burden (34). Information about mental and physical health was collected by study staff during participants’ initial assessment. At this time, a study clinician obtained a history of current and past medications as well as participants’ height and weight in order to calculate body mass index (BMI; kg/m2). Chronic and acute health problems were measured using the Health Review, a clinician assisted questionnaire (35). The study clinician also administered the Hamilton Rating Scale for Depression (HRSD) (36) in order to quantify depressive symptoms as another indicator of mental health burden. In addition, caregivers completed the revised Memory Related Behavior Problem (r-MRBP) (37) checklist to measure the frequency and severity of patients’ disruptive behaviors.

Sleep

Overnight PSG sleep studies were conducted in participants’ homes using ambulatory monitors (Compumedics Siesta) for three consecutive nights. Participants were prepared for sleep studies by trained sleep technicians who performed quality assurance routines prior to leaving participants’ homes. Sleeping and waking occurred at participants’ usual sleep-wake times, as determined by sleep diaries (38) completed for 2-weeks prior to PSG sleep studies. As recommended by the American Academy of Sleep Medicine the international 10–20 PSG montage was used. The PSG montage included bilateral central and occipital electroencephalogram (EEG) channels, electro-oculogram (EOG), submentalis electromyogram (EMG) and a V2 electrocardiogram (EKG) lead. Additional signals were collected on the first study night to assess participants’ apnea hypopnea index (AHI), as previously described (39). Sleep records were scored in 20-second epochs by registered PSG technicians.

Sleep efficiency was selected as the primary variable of interest due to its association with psychosocial stress (40, 41). Sleep efficiency was calculated by dividing total minutes spent asleep by total minutes spent in bed and multiplying by 100, or, the percent of time in bed that was spent asleep. We used average sleep efficiency values derived from nights 2 and 3 of sleep studies due to the disruptive effects of sleep apnea monitoring on indices of sleep continuity. In the event that both nights of sleep data were unavailable (n=7, 11.67%), one night was used. Although sleep efficiency can be evaluated as a continuous variable, it was dichotomized in the present study to identify vulnerable caregivers as defined by sleep efficiency values less than 80% (16) in primary analyses. Analyses based on continuous measures of sleep efficiency are presented in the supplemental material. In addition, correlations between variables of interest and other indices of sleep are also presented (see Table S1, Supplemental Digital Content 1). Other indices of sleep include sleep latency, wakefulness after sleep onset (WASO) and sleep duration.

Coping

All participants completed the Brief Cope (COPEB; (42), a self-report questionnaire assessing 12 coping styles and strategies including acceptance, approach coping, behavioral disengagement, denial, seeking of emotional support, alcohol/drug use, humor, mental disengagement, planning, positive reinterpretation and growth, religion and the focus on and venting of emotions. Participants endorsed coping mechanisms used since their partner was diagnosed with dementia using a 4-point scale ranging from “never” to “a lot.” Factor analysis was conducted on the 24 items of the COPEB to extract higher order factors. A principal component extraction method with orthogonal varimax rotation was used on a Pearson correlation matrix. Scree plots and parallel analysis yielded three factors with eigenvalues above one. The first factor (eigenvalue = 5.437, with factor loadings between .501 and .799) explained 20.11% of the variance among the items and consisted of items from five COPEB subscales: active coping, planning, seeking of emotional support, religion and positive reinterpretation and growth. This factor was labeled “approach coping.” The second factor (eigenvalue = 2.989, with factor loadings between .448 and .673) explained an additional 13.02% of the variance among the items and consisted of four COPEB subscales: denial, mental disengagement, behavioral disengagement and focus on and venting of emotions. This factor was labeled “avoidant coping.” The third factor (eigenvalue = 2.06, with factor loadings between .466 and −.689) explained an additional 10.56% of the variance among the items and consisted of items from the acceptance coping subscale as well as one item from the positive reinterpretation and growth subscale. Items from the alcohol and substance use subscale were negatively loaded onto the third factor as well. After reverse scoring items from the alcohol and substance use subscale, this factor was labeled “acceptance coping.” The items of the humor subscale failed to load with approach, avoidance or acceptance coping.

Results from the factor analysis were fairly consistent with other factor analyses of the COPEB (43–45) and with other conceptual definitions of approach, avoidance and acceptance coping (46–50). Consistent with recommendations published in Practical Assessment, Research & Evaluation, composite variables were constructed by averaging the unstandardized subscale values within each factor (52). The factor loadings for alcohol and substance use were negative and, therefore, humor was reverse scored before averaging values within the acceptance coping factor (51). Approach coping (mean=−1.166±0.72) demonstrated high internal consistency, Cronbach’s α=.862. Internal consistency for avoidant coping (mean=0.704±0.52) was also within the acceptable range, Cronbach’s α=.707. The internal consistency of the acceptance coping factor was substantially lower, Cronbach’s α=.531, likely due to our limited sample size. Due to the low internal consistency of the acceptance coping factor and the moderate correlation between acceptance coping and approach coping (Pearson Correlation Coefficient = .447), acceptance coping was not included in the final analyses.

Statistical Analysis

Data analysis was performed using the PASW 18.0 statistical software package (SPSS Inc., Chicago, IL). Statistical significance was set at the level of p≤0.05 (2-tailed). Sleep efficiency was dichotomized into two levels poor efficiency (≤80%) and good efficiency (>80%). Based on multivariate power estimates, the primary analysis was limited to a maximum of four covariates (52). Variables were included as covariates if they could conceptually explain the relationship between coping and sleep efficiency and if they differed by sleep efficiency group on T-tests and Chi-square tests at a significance level of p<.20 (53). Variables that significantly differed by sleep efficiency group included AHI (p=.014), use of medications that affect sleep (p=.095), depressive symptoms (p=.068) and perceived stress (p=.064). Because symptoms of depression and perceived stress were highly correlated (p=.001), we selected symptoms of depression to represent mental health burden in analyses. Age, sex, chronic pain, frequency and severity of disruptive behaviors were all unrelated to sleep efficiency in this sample (p=.48, p=.14, p=.13, p=.62 and p=.39 respectively). Bivariate correlations between predictor variables and covariates were assessed to confirm that AHI, use of medications that affect sleep and depressive symptoms met published recommendations for inclusion as covariates (54, 55).

A binary logistic regression analysis was performed to determine whether coping style predicted membership to the clinically-significant poor sleep efficiency group, those with a sleep efficiency of less than 80% sleep. Both coping styles were included in the model, adjusting for AHI, use of medications that affect sleep (yes/no) and depressive symptoms. An extensive literature indicates a relationship between depressive symptoms and sleep disturbances (56). Depressive symptoms were therefore entered in a separate step following AHI and medication use to demonstrate the extent to which coping style, a modifiable characteristic, was associated with fragmented sleep above and beyond depressive symptoms.

RESULTS

Sample Characteristics

Demographic characteristics, mental and physical health characteristics and indices of sleep are summarized in Table 1. The sample was primarily female (81.7%) and white (96.7%) with a mean age of 73.31 (sd=7.05). All participants were married and living with their spouse, consistent with study eligibility criteria. On average, the sample was overweight (BMI=27.69±4.0 kg/m2) and demonstrated moderate sleep apnea (AHI=10.10±8.39). Average scores on the HRSD (6.10±4.9) were above the clinically-significant cutoff of 5 (57). On average, the sample demonstrated lower than average sleep efficiencies (i.e. less than 80%; mean = 78.48±8.1) according to normative sleep efficiency values reported in a 2004 meta-analysis (58). The sample also reported clinically-significant sleep complaints, indicated by PSQI scores ≥5, (6.25±3.2). Average sleep duration (368.6 minutes±51.9) and sleep latency (24.2 minutes±25.3) fell within clinically healthy parameters for older adults (i.e. ≥360 minutes and ≤30 minutes for sleep duration and sleep latency, respectively) (58).

Table 1.

Sample characteristics according to absence or presence of low sleep efficiency

Sleep Efficiency Groups
Total N = 60 High N = 27 (45%) Low N = 33 (55%) Test Statistic1
Age (years) – mean (sd) 73.31 (7.05) 7.3 (73.14) 7.2 (73.44) −.161
Sex – N (%), Female 49 (81.7) 23 (85.2) 26 (78.8) .406
Race – N (%), white 58 (96.7) 27 (100) 31 (93.9) 1.693
Income – N (%), <$50,000/year 37 (61.7) 18 (66.7) 19 (57.6) 1.358
Body Mass Index – mean (sd) 27.69 (4.0) 27.8 (3.8) 27.61 (4.2) .182
Medications that affect sleep – N (%) 48 (80.0) 22 (81.5) 26 (78.8) .067
Apnea Hypopnea Index – mean (sd) 10.10 (8.39) 8.16 (7.7) 11.74 (8.7) −1.657
Hamilton Rating Scale for Depression – mean (sd) 6.10 (4.95) 4.74 (4.6) 7.21 (5.0) −1.969
Perceived Stress Scale – mean (sd) 4.76 (2.83) 4.08 (2.8) 5.30 (2.8) −1.678
Frequency of Disruptive Behaviors – mean (sd) 2.76 (2.08) 2.63 (2.0) 2.81 (2.2) −.378
Severity of Disruptive Behaviors – mean (sd) 0.80 (0.87) 0.64 (0.7) 0.90 (1.0) −1.132
Sleep Duration (mins) – mean (sd) 368.61 (51.9) 379.18 (53.9) 359.95 (49.4) 1.440
Sleep Efficiency (%) – mean (sd) 78.48 (8.09) 91.48 (3.69) 73.15 (6.68) 8.237 ***
Sleep Latency (mins) – mean (sd) 24.18 (25.3) 19.56 (13.2) 27.96 (31.7) −1.288
Sleep Quality (PSQI) – mean (sd) 6.25 (3.22) 5.54 (2.9) 6.82 (3.4) −1.531
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1

Test statistic = t-statistic for all but sex, race, ethnicity and income (Chi square statistics); for tests of significance,

***

P<.001.

Bivariate correlations (see Table S1) indicated that those who were older or who had higher BMIs had shorter sleep durations on average (β=−.295, p=.022 and β=−.283, p=.030, respectively). Higher AHI was associated with decreased sleep efficiency (β=−.318, p=.014) and greater depression as measured by the HRSD was associated with increased WASO (β=−.298, p=.021). Bivariate correlations between approach and avoidant coping and continuous measures of sleep, including sleep efficiency, sleep latency, WASO and sleep duration, are also presented in Table S1. Significant bivariate correlations were found between avoidant coping and both sleep efficiency and sleep latency (β=−.268, p=.040 and β=.378, p=.003, respectively). Approach coping was unrelated to sleep efficiency and sleep latency in non-parametric analyses. Neither coping variable was correlated with either WASO or sleep duration.

As seen in Table 1, over half of the sample had clinically low sleep efficiency (55%). The low and high sleep efficiency groups did not differ on any demographic or physical health variable. Symptoms of depression and perceived stress were higher among those with low sleep efficiency though these differences were not statistically significant (t(58)=−1.97, p=.054; t(57)=−1.678, p=.099). Sleep duration, sleep quality, and daytime sleepiness did not differ between sleep efficiency groups (t(57)=−1.531, p=.13; t(57)=−1.497, p=.14; t(58)=1.440, p=.15). Results from the logistic regression yielded no significant difference between observed and predicted group membership, Hosmer-Lemeshow χ2(8)=7.303, p=.50, indicating good model fit. The overall classification rate was also good based on a receiver operating curve (ROC) area of .772, SE=.065, and the cutoff value of 0.5 was maintained. Seventy-three percent of participants were classified correctly (poor sleep efficiency, 76.7%; good sleep efficiency, 70.4%).

We hypothesized that approach and avoidant coping styles would predict membership in the sleep fragmented group after adjusting for AHI, medication use and depressive symptoms. Unadjusted and adjusted results are presented in Table 2. As hypothesized, coping was a significant predictor of sleep efficiency group, X2(5, N=60)=11.967, p=.035, Negelkerke R2=.253. Prior to adjusting for covariates, each unit increase in the avoidant coping composite variable was associated with more than a six-fold increase in the likelihood of classification in the poor sleep efficiency group, B=1.857, χ2(1)=5.886, p=.011, exp(B)=6.405, CI: 1.425–28.787. This association persisted after adjusting for AHI and medication use (Model 1); B=1.877, χ2(1)=5.524, p=.019, exp(B)=6.531, CI:1.366–31.235. After further adjustment for symptoms of depression (Model 2), predictions of group membership by avoidant coping remained significant. For each unit increase on the avoidant coping composite factor, participants were about five times more likely to be classified in the poor sleep efficiency group, B=1.601, χ2(1)=3.943, p=.047, exp(B)=4.956, CI:1.021–24.057. Contrary to our expectations, approach coping was not associated with sleep efficiency group in any model (See Table 2). However, when analyzed as a continuous variable, sleep efficiency was associated with both approach and avoidant coping in fully adjusted models (β=.302, p=.013 and β=−.320, p=.013, respectively; see Table S2, Supplemental Digital Content 2). Moreover, avoidant coping was also associated with significantly longer sleep latency (β=.421, p=.002), while neither approach nor avoidant coping were associated with WASO or sleep duration.

Table 2.

Summary of results from binary logistic regression predicting sleep efficiency group by approach and avoidant coping styles

Coping Unadjusted Model 11 Model 22
OR (95% CI) sig OR (95% CI) sig OR (95% CI) sig
Approach .590(.26 – 1.35) .21 .528(.22 – 1.25) .15 .535(.22 – 1.29) .17
Avoidant 6.405(1.41–28.79) .015 6.531(1.37–31.24) .019 4.956(1.02–24.06) .047
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OR = odds ratio;

1

Model 1 is adjusted for apnea hypopnea index and use of medications that affect sleep;

2

Model 2 is adjusted for covariates in Model 1 and symptoms of depression.

DISCUSSION

The current study tested whether coping styles were systematically different between spousal caregivers with and without clinically-significant sleep disturbance, measured objectively in caregivers’ home environments. Consistent with our hypothesis, avoidant coping was associated with a greater likelihood of having clinically-significant poor sleep efficiency. This association was independent of apnea hypopnea index (AHI), medication use and symptoms of depression. Contrary to hypothesis, approach coping was unrelated to clinically-significant poor sleep efficiency. Our findings extend the caregiver literature by identifying avoidant coping style as a significant correlate of disturbed sleep in older caregivers. To our knowledge, this is the first study to show that avoidant coping is a significant correlate of clinically-significant poor sleep efficiency in spousal caregivers of patients with Alzheimer’s or other advanced forms of dementia.

These findings are generally consistent with those reported in the coping and sleep literature. While numerous studies have linked avoidant coping with psychological distress and poor mental health (59–61), fewer studies have examined coping in relation to sleep (62). Several studies have reported strong associations between coping styles and sleep in the context of physical illness. For instance, in a sample of 55 men newly diagnosed with cancer, higher endorsement of avoidant coping was associated with increased self-reported sleep problems while approach coping was unrelated (20). Avoidant coping has also been linked to poor sleep in both breast and prostate cancer patients (29). To our knowledge, only one study has yielded findings contrary to our own (63). In a sample of 36 young adults, “disengagement coping” was unrelated to sleep, measured with actigraphy, and did not moderate the effect of academic stress on sleep efficiency (63). This discrepancy may be due to qualitative differences in stressors, differences in study measures, and/or marked differences in sleep efficiency in young compared to older adults. From a theoretical perspective, avoidant coping may be an ineffective strategy for reducing distress in spousal caregivers of dementia patients given the progressive, uncontrollable and often protracted disease course of Alzheimer’s, Huntington’s and other advanced dementia’s (64) (65). It is, thus, not surprising that avoidant coping was associated with clinically-significant poor sleep efficiency in the present sample.

Several limitations should be considered when interpreting the present findings. First, results cannot be generalized to younger or minority populations given known associations between age and race with coping and sleep (66–74). Second, causal inferences are not warranted by the cross-sectional nature of the data. While it is plausible that avoidant coping disrupts sleep through stress pathways (75), it is also plausible that disrupted sleep interferes with adaptive coping strategies via poor emotion regulation and impaired executive functioning (76, 77). Third, we did not examine other pathways that may contribute to disturbed sleep in dementia caregivers such as disruptive and potentially dangerous “sundowning” behavior patterns in the dementia patient or psychophysiological vigilance responses in the caregivers (27, 78–80). Lastly, data derived from in-home PSG may differ from laboratory-based studies. For instance, study technicians are not present during home recordings and are, thus, unable to manage technical problems that may arise during the night including failure of the recording equipment, sweat artifact, loose electrodes, and signal loss. The home environment is also less controlled with respect to exogenous factors that may disrupt sleep such as noise and other environmental factors. The limitations of ambulatory sleep studies are offset by the increased feasibility and ecological validity of studying caregivers in their home environments. Moreover, in-home PSG studies may result in a more generalizable sample given that activity is often restricted for caregivers without financial resources for respite care or other forms of instrumental social support (81, 82).

Conclusions

These cross-sectional findings suggest that coping may be an important correlate of sleep in spousal caregivers of dementia patients. In particular, avoidant coping systematically differed between caregivers with and without clinically significant poor sleep efficiency. Moreover, our results indicate that coping strategies among caregivers may be particularly indicative of risk for caregivers, independent of care recipient behaviors, given that neither the frequency nor severity of disruptive patient behaviors were associated with caregivers’ sleep efficiency in this sample. More research is needed to disentangle cause from effect, which will be critical for risk stratification and the development and implementation of effective interventions to enhance coping, sleep, and their downstream effects on health and functioning in this growing section of the adult population.

Supplementary Material

FINAL PRODUCTION FILE_ SDC1
FINAL PRODUCTION FILE_ SDC2

Acknowledgments

Source of Funding: The AgeWise program project was supported by the National Institutes of Health (NIH), DHHS, through the National Institute on Aging (NIA) (P01 AG20677). Additional support for BJT, LAI and LMM were provided by T32 HL07560, NIH MH019986 and K02 AG039412. Sleep data were processed with the support of UL1RR024153.

We greatly appreciate and acknowledge the study staff and research participants for their time, hard work and dedication.

Acronyms

AHI

apnea hypopnea index

BMI

body mass index

COPEB

brief cope

EEG

electroencephalogram

EKG

electrocardiogram

EMG

electromyogram

EOG

electro-oculogram

HRSD

Hamilton rating scale for depressive symptoms

PSG

polysomnography

ROC

receiver operating curve

Footnotes

Conflicts of Interest: The authors report no conflicts of interest.

The content of this paper is solely the responsibility of the authors.

Reference List

  • 1.von KR, Mills PJ, Mausbach BT, Dimsdale JE, Patterson TL, Ziegler MG, Ancoli-Israel S, Allison M, Chattillion EA, Grant I. Effect of Alzheimer caregiving on circulating levels of C-reactive protein and other biomarkers relevant to cardiovascular disease risk: a longitudinal study. Gerontology. 2012;58(4):354–65. doi: 10.1159/000334219. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.von KR, Mausbach BT, Dimsdale JE, Mills PJ, Patterson TL, Ancoli-Israel S, Ziegler MG, Roepke SK, Chattillion EA, Allison M, Grant I. Cardiometabolic effects in caregivers of nursing home placement and death of their spouse with Alzheimer’s disease. J Am Geriatr Soc. 2011;59(11):2037–44. doi: 10.1111/j.1532-5415.2011.03634.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Mausbach BT, von KR, Roepke SK, Moore R, Patterson TL, Mills PJ, Dimsdale JE, Ziegler MG, Ancoli-Israel S, Allison M, Grant I. Self-efficacy buffers the relationship between dementia caregiving stress and circulating concentrations of the proinflammatory cytokine interleukin-6. Am J Geriatr Psychiatry. 2011;19(1):64–71. doi: 10.1097/JGP.0b013e3181df4498. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Mausbach BT, Roepke SK, Ziegler MG, Milic M, von KR, Dimsdale JE, Mills PJ, Patterson TL, Allison MA, ncoli-Israel S, Grant I. Association between chronic caregiving stress and impaired endothelial function in the elderly. J Am Coll Cardiol. 2010;55(23):2599–606. doi: 10.1016/j.jacc.2009.11.093. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Mills PJ, Ancoli-Israel S, von KR, Mausbach BT, Aschbacher K, Patterson TL, Ziegler MG, Dimsdale JE, Grant I. Effects of gender and dementia severity on Alzheimer’s disease caregivers’ sleep and biomarkers of coagulation and inflammation. Brain Behav Immun. 2009;23(5):605–10. doi: 10.1016/j.bbi.2008.09.014. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Zhang S, Guo Q, Edwards H, Yates P, Li C. Self-efficacy moderation and mediation roles on BPSD and social support influences on subjective caregiver burden in Chinese spouse caregivers of dementia patients. Int Psychogeriatr. 2014:1–9. doi: 10.1017/S1041610214000994. [DOI] [PubMed] [Google Scholar]
  • 7.Takai M, Takahashi M, Iwamitsu Y, Oishi S, Miyaoka H. Subjective experiences of family caregivers of patients with dementia as predictive factors of quality of life. Psychogeriatrics. 2011;11(2):98–104. doi: 10.1111/j.1479-8301.2011.00354.x. [DOI] [PubMed] [Google Scholar]
  • 8.Pollak CP, Stokes PE. Circadian rest-activity rhythms in demented and nondemented older community residents and their caregivers. Journal of the American Geriatrics Society. 1997;45(4):446–52. doi: 10.1111/j.1532-5415.1997.tb05169.x. [DOI] [PubMed] [Google Scholar]
  • 9.McCurry SM, Vitiello MV, Gibbons LE, Logsdon RG, Teri L. Factors associated with caregiver reports of sleep disturbances in persons with dementia. Am J Geriatr Psychiatry. 2006;14(2):112–20. doi: 10.1097/01.JGP.0000192499.25940.da. [DOI] [PubMed] [Google Scholar]
  • 10.Beaudreau SA, O’Hara R. Late-life anxiety and cognitive impairment: a review. Am J Geriatr Psychiatry. 2008;16(10):790–803. doi: 10.1097/JGP.0b013e31817945c3. [DOI] [PubMed] [Google Scholar]
  • 11.Castro CM, Lee KA, Bliwise DL, Urizar GG, Woodward SH, King AC. Sleep patterns and sleep-related factors between caregiving and non-caregiving women. Behav Sleep Med. 2009;7(3):164–79. doi: 10.1080/15402000902976713. [DOI] [PubMed] [Google Scholar]
  • 12.Creese J, Bedard M, Brazil K, Chambers L. Sleep disturbances in spousal caregivers of individuals with Alzheimer’s disease. Int Psychogeriatr. 2008;20(1):149–61. doi: 10.1017/S1041610207005339. [DOI] [PubMed] [Google Scholar]
  • 13.Pietrantonio AM, Wright E, Gibson KN, Alldred T, Jacobson D, Niec A. Mandatory reporting of child abuse and neglect: crafting a positive process for health professionals and caregivers. Child Abuse Negl. 2013;37(2–3):102–9. doi: 10.1016/j.chiabu.2012.12.007. [DOI] [PubMed] [Google Scholar]
  • 14.Wilcox S, King AC. Sleep complaints in older women who are family caregivers. Journal of Gerontology: Psychological Sciences. 1999;54B:P189–P198. doi: 10.1093/geronb/54b.3.p189. [DOI] [PubMed] [Google Scholar]
  • 15.McKibbin CL, Ancoli-Israel S, Dimsdale J, Archuleta C, von Kanel R, Mills P, Patterson TL, Grant I. Sleep in spousal caregivers of people with Alzheimer’s disease. Sleep. 2005;28(10):1245–50. doi: 10.1093/sleep/28.10.1245. [DOI] [PubMed] [Google Scholar]
  • 16.Dew MA, Hoch CC, Buysse DJ, Monk TH, Begley AE, Houck PR, Hall M, Kupfer DJ, Reynolds CF., III Healthy older adults’ sleep predicts all-cause mortality at 4 to 19 years of follow-up. Psychosom Med. 2003;65(1):63–73. doi: 10.1097/01.psy.0000039756.23250.7c. [DOI] [PubMed] [Google Scholar]
  • 17.McCurry SM, Pike KC, Vitiello MV, Logsdon RG, Teri L. Factors associated with concordance and variability of sleep quality in persons with Alzheimer’s disease and their caregivers. Sleep. 2008;31(5):741–8. doi: 10.1093/sleep/31.5.741. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Kim EJ, Dimsdale JE. The effect of psychosocial stress on sleep: a review of polysomnographic evidence. Behav Sleep Med. 2007;5(4):256–78. doi: 10.1080/15402000701557383. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Bastien CH, Vallieres A, Morin CM. Validation of the Insomnia Severity Index as an outcome measure for insomnia research. Sleep Med. 2001;2(4):297–307. doi: 10.1016/s1389-9457(00)00065-4. [DOI] [PubMed] [Google Scholar]
  • 20.Hoyt MA, Thomas KS, Epstein DR, Dirksen SR. Coping style and sleep quality in men with cancer. Ann Behav Med. 2009;37(1):88–93. doi: 10.1007/s12160-009-9079-6. [DOI] [PubMed] [Google Scholar]
  • 21.Carter PA, Acton GJ. Personality and coping: predictors of depression and sleep problems among caregivers of individuals who have cancer. J Gerontol Nurs. 2006;32(2):45–53. doi: 10.3928/0098-9134-20060201-11. [DOI] [PubMed] [Google Scholar]
  • 22.Roth S, Cohen LJ. Approach, avoidance, and coping with stress. Am Psychol. 1986;41(7):813–9. doi: 10.1037//0003-066x.41.7.813. [DOI] [PubMed] [Google Scholar]
  • 23.Marcks BA, Woods DW. Role of thought-related beliefs and coping strategies in the escalation of intrusive thoughts: an analog to obsessive-compulsive disorder. Behav Res Ther. 2007;45(11):2640–51. doi: 10.1016/j.brat.2007.06.012. [DOI] [PubMed] [Google Scholar]
  • 24.Wenzlaff RM, Wegner DM, Roper DW. Depression and mental control: the resurgence of unwanted negative thoughts. J Pers Soc Psychol. 1988;55(6):882–92. doi: 10.1037//0022-3514.55.6.882. [DOI] [PubMed] [Google Scholar]
  • 25.Fisher S, Kent TA, Bryant SG. Postmarketing surveillance by patient self-monitoring: preliminary data for sertraline versus fluoxetine. J Clin Psychiatry. 1995;56(7):288–96. [PubMed] [Google Scholar]
  • 26.Hall M, Buysse DJ, Dew MA, Prigerson HG, Kupfer DJ, Reynolds CF. Intrusive thoughts and avoidance behaviors are associated with sleep disturbances in bereavement-related depression. Depression and Anxiety. 1997;6(3):106–12. [PubMed] [Google Scholar]
  • 27.Hall M, Buysse DJ, Frank E, Sherrill JT, Kupfer DJ. Stressful life events interfere with sleep in healthy men and women. Sleep Res. 1997;26:293. [Google Scholar]
  • 28.Ironson G, Wynings C, Schneiderman N, Baum A, Rodriguez M, Greenwood D, Benight C, Antoni M, LaPerriere A, Huang HS, Klimas N, Fletcher MA. Posttraumatic stress symptoms, intrusive thoughts, loss, and immune function after Hurricane Andrew. Psychosom Med. 1997;59(2):128–41. doi: 10.1097/00006842-199703000-00003. [DOI] [PubMed] [Google Scholar]
  • 29.Thomas KS, Bower J, Hoyt MA, Sepah S. Disrupted sleep in breast and prostate cancer patients undergoing radiation therapy: the role of coping processes. Psychooncology. 2010;19(7):767–76. doi: 10.1002/pon.1639. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.McGaugh JL, McIntyre CK, Power AE. Amygdala modulation of memory consolidation: interaction with other brain systems. Neurobiol Learn Mem. 2002;78(3):539–52. doi: 10.1006/nlme.2002.4082. [DOI] [PubMed] [Google Scholar]
  • 31.Hogh A, Hansen AM, Mikkelsen EG, Persson R. Exposure to negative acts at work, psychological stress reactions and physiological stress response. J Psychosom Res. 2012;73(1):47–52. doi: 10.1016/j.jpsychores.2012.04.004. [DOI] [PubMed] [Google Scholar]
  • 32.Vandekerckhove M, Kestemont J, Weiss R, Schotte C, Exadaktylos V, Haex B, Verbraecken J, Gross JJ. Experiential versus analytical emotion regulation and sleep: breaking the link between negative events and sleep disturbance. Emotion. 2012;12(6):1415–21. doi: 10.1037/a0028501. [DOI] [PubMed] [Google Scholar]
  • 33.Cohen S, Doyle WJ, Alper CM, Janicki-Deverts D, Turner RB. Sleep habits and susceptibility to the common cold. Arch Intern Med. 2009;169(1):62–7. doi: 10.1001/archinternmed.2008.505. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Cohen S, Kamarck T, Mermelstein R. A global measure of perceived stress. J Health Soc Behav. 1983;24:385–96. [PubMed] [Google Scholar]
  • 35.Jenkins CD, Kreger BE, Rose RM, Hurst M. Use of a monthly health review to ascertain illness and injuries. Am J Public Health. 1980;70(1):82–4. doi: 10.2105/ajph.70.1.82. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Hamilton M. A rating scale for depression. Journal of Neurology, Neurosurgery, and Psychiatry. 1960;23:56–62. doi: 10.1136/jnnp.23.1.56. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Teri L, Truax P, Logsdon RG, Uomoto J, Zarit S, Vitaliano PP. Assessment of behavioral problems in dementia: The revised memory and behavior problems checklist. Psychol Aging. 1992;7:622–31. doi: 10.1037//0882-7974.7.4.622. [DOI] [PubMed] [Google Scholar]
  • 38.Monk TH, Reynolds CF, Kupfer DJ, Buysse DJ, Coble PA, Hayes AJ, Machen MA, Petrie SR, Ritenour AM. The Pittsburgh Sleep Diary. Journal of Sleep Research. 1994;3(2):111–20. [PubMed] [Google Scholar]
  • 39.Hall M, Thayer JF, Germain A, Moul D, Vasko R, Puhl M, Miewald J, Buysse DJ. Psychological stress is associated with heightened physiological arousal during NREM sleep in primary insomnia. Behav Sleep Med. 2007;5(3):178–93. doi: 10.1080/15402000701263221. [DOI] [PubMed] [Google Scholar]
  • 40.Hall M, Buysse DJ, Nofzinger EA, Reynolds CF, Thompson W, Mazumdar S, Monk TH. Financial strain is a significant correlate of sleep continuity disturbances in late-life. Biological Psychology. 2008;77(2):217–22. doi: 10.1016/j.biopsycho.2007.10.012. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Pereira D, Elfering A. Social stressors at work, sleep quality and psychosomatic health complaints--a longitudinal ambulatory field study. Stress Health. 2014;30(1):43–52. doi: 10.1002/smi.2494. [DOI] [PubMed] [Google Scholar]
  • 42.Carver CS. You want to measure coping but your protocol’s too long: Consider the Brief COPE. Int J Behav Med. 1997;4(1):92–100. doi: 10.1207/s15327558ijbm0401_6. [DOI] [PubMed] [Google Scholar]
  • 43.Webb HM, Baker EA, McNutt MD. Associations between coping, affect, and social support among low-income African American smokers. Addict Behav. 2013;38(11):2736–40. doi: 10.1016/j.addbeh.2013.07.005. [DOI] [PubMed] [Google Scholar]
  • 44.Ruiz RJ, Gennaro S, O’Connor C, Marti CN, Lulloff A, Keshinover T, Gibeau A, Melnyk B. Measuring Coping in Pregnant Minority Women. West J Nurs Res. 2014 doi: 10.1177/0193945914527176. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Zatzick DF, Marmar CR, Weiss DS, Browner WS, Metzler TJ, Golding JM, Stewart A, Schlenger WE, Wells KB. Posttraumatic stress disorder and functioning and quality of life outcomes in a nationally representative sample of male Vietnam veterans. Am J Psychiatry. 1997;154(12):1690–5. doi: 10.1176/ajp.154.12.1690. [DOI] [PubMed] [Google Scholar]
  • 46.Litman JA. The COPE inventory: Dimensionality and relationships with approach- and avoidance-motives and positive and negative traits. Personality and Individual Differences. 2006;41(2):273–84. [Google Scholar]
  • 47.Austenfeld JL, Stanton AL. Coping through emotional approach: a new look at emotion, coping, and health-related outcomes. J Pers. 2004;72(6):1335–63. doi: 10.1111/j.1467-6494.2004.00299.x. [DOI] [PubMed] [Google Scholar]
  • 48.Gerson R, Wong C, Davidson L, Malaspina D, McGlashan T, Corcoran C. Self-reported coping strategies in families of patients in early stages of psychotic disorder: an exploratory study. Early Interv Psychiatry. 2011;5(1):76–80. doi: 10.1111/j.1751-7893.2010.00251.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Hoyt MA, Marin-Chollom AM, Bower JE, Thomas KS, Irwin MR, Stanton AL. Approach and avoidance coping: Diurnal cortisol rhythm in prostate cancer survivors. Psychoneuroendocrinology. 2014;49C:182–6. doi: 10.1016/j.psyneuen.2014.07.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.Jim HS, Richardson SA, Golden-Kreutz DM, Andersen BL. Strategies used in coping with a cancer diagnosis predict meaning in life for survivors. Health Psychol. 2006;25(6):753–61. doi: 10.1037/0278-6133.25.6.753. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51.DiStefano C, Zhu M, Mindrila D. Understanding and using factor scores: Considerations for the applied researcher. Practical Assessment, Research & Evaluation. 14(20):1–12. 10-1-0004. [Google Scholar]
  • 52.Tabachnick BG, Fidell LS. Using Multivariate Statistics. 3. New York: Harper Collins; 1996. [Google Scholar]
  • 53.Loftin LB, Madison SQ. Advances in educational research: Substantive findings, methodological developments. Vol. 1. Greenwhich, CT: JAI Press; 1991. The extreme dangers of covariate corrections; pp. 133–147. [Google Scholar]
  • 54.Miller GA, Chapman JP. Misunderstanding analysis of covariance. Journal of Abnormal Psychology. 2001;110(1):40–8. doi: 10.1037//0021-843x.110.1.40. [DOI] [PubMed] [Google Scholar]
  • 55.Zinbarg RE, Suzuki S, Uliaszek AA, Lewis AR. Biased parameter estimates and inflated Type I error rates in analysis of covariance (and analysis of partial variance) arising from unreliability: alternatives and remedial strategies. J Abnorm Psychol. 2010;119(2):307–19. doi: 10.1037/a0017552. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 56.Tsuno N, Besset A, Ritchie K. Sleep and depression. J Clin Psychiatry. 2005;66(10):1254–69. doi: 10.4088/jcp.v66n1008. [DOI] [PubMed] [Google Scholar]
  • 57.Romera I, Perez V, Menchon JM, Polavieja P, Gilaberte I. Optimal cutoff point of the Hamilton Rating Scale for Depression according to normal levels of social and occupational functioning. Psychiatry Res. 2011;186(1):133–7. doi: 10.1016/j.psychres.2010.06.023. [DOI] [PubMed] [Google Scholar]
  • 58.Ohayon MM, Carskadon MA, Guilleminault C, Vitiello MV. Meta-analysis of quantitative sleep parameters from childhood to old age in healthy individuals: developing normative sleep values across the human lifespan. Sleep. 2004;27(7):1255–73. doi: 10.1093/sleep/27.7.1255. [DOI] [PubMed] [Google Scholar]
  • 59.Donovan-Kicken E, Caughlin JP. Breast cancer patients’ topic avoidance and psychological distress: the mediating role of coping. J Health Psychol. 2011;16(4):596–606. doi: 10.1177/1359105310383605. [DOI] [PubMed] [Google Scholar]
  • 60.Kashdan TB, Barrios V, Forsyth JP, Steger MF. Experiential avoidance as a generalized psychological vulnerability: comparisons with coping and emotion regulation strategies. Behav Res Ther. 2006;44(9):1301–20. doi: 10.1016/j.brat.2005.10.003. [DOI] [PubMed] [Google Scholar]
  • 61.Spira AP, Zvolensky MJ, Eifert GH, Feldner MT. Avoidance-oriented coping as a predictor of panic-related distress: a test using biological challenge. J Anxiety Disord. 2004;18(3):309–23. doi: 10.1016/S0887-6185(02)00249-9. [DOI] [PubMed] [Google Scholar]
  • 62.Germain A, Buysse DJ, Ombao H, Kupfer DJ, Hall M. Psychophysiological reactivity and coping styles influence the effects of acute stress exposure on rapid eye movement sleep. Psychosom Med. 2003;65(5):857–64. doi: 10.1097/01.psy.0000079376.87711.b0. [DOI] [PubMed] [Google Scholar]
  • 63.Sadeh A, Keinan G, Daon K. Effects of stress on sleep: the moderating role of coping style. Health Psychol. 2004;23(5):542–5. doi: 10.1037/0278-6133.23.5.542. [DOI] [PubMed] [Google Scholar]
  • 64.Lazarus RS, DeLongis A. Psychological stress and coping in aging. Am Psychol. 1983;38(3):245–54. doi: 10.1037//0003-066x.38.3.245. [DOI] [PubMed] [Google Scholar]
  • 65.Chattillion EA, Ceglowski J, Roepke SK, von KR, Losada A, Mills PJ, Romero-Moreno R, Grant I, Patterson TL, Mausbach BT. Pleasant events, activity restriction, and blood pressure in dementia caregivers. Health Psychol. 2013;32(7):793–801. doi: 10.1037/a0029412. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 66.Aldwin CM, Sutton KJ, Chiara G, Spiro A., III Age differences in stress, coping, and appraisal: findings from the Normative Aging Study. J Gerontol B Psychol Sci Soc Sci. 1996;51(4):179–88. doi: 10.1093/geronb/51b.4.p179. [DOI] [PubMed] [Google Scholar]
  • 67.Trouillet R, Doan-Van-Hay LM, Launay M, Martin S. Impact of age, and cognitive and coping resources on coping. Can J Aging. 2011;30(4):541–50. doi: 10.1017/S0714980811000456. [DOI] [PubMed] [Google Scholar]
  • 68.Diehl M, Coyle N, Labouvie-Vief G. Age and sex differences in strategies of coping and defense across the life span. Psychol Aging. 1996;11(1):127–39. doi: 10.1037//0882-7974.11.1.127. [DOI] [PubMed] [Google Scholar]
  • 69.Bautista RE. Racial differences in coping strategies among individuals with epilepsy. Epilepsy Behav. 2013;29(1):67–71. doi: 10.1016/j.yebeh.2013.06.035. [DOI] [PubMed] [Google Scholar]
  • 70.Jones AC, Kwoh CK, Groeneveld PW, Mor M, Geng M, Ibrahim SA. Investigating racial differences in coping with chronic osteoarthritis pain. J Cross Cult Gerontol. 2008;23(4):339–47. doi: 10.1007/s10823-008-9071-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 71.Lunsford SL, Simpson KS, Chavin KD, Hildebrand LG, Miles LG, Shilling LM, Smalls GR, Baliga PK. Racial differences in coping with the need for kidney transplantation and willingness to ask for live organ donation. Am J Kidney Dis. 2006;47(2):324–31. doi: 10.1053/j.ajkd.2005.10.018. [DOI] [PubMed] [Google Scholar]
  • 72.Mezick EJ, Matthews KA, Hall M, Strollo PJ, Buysse DJ, Kamarck TW, Owens JF, Reis SE. Influence of race and socioeconomic status on sleep: Pittsburgh SleepSCORE project. Psychosom Med. 2008;70:410–6. doi: 10.1097/PSY.0b013e31816fdf21. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 73.Hall MH, Matthews KA, Kravitz HM, Gold EB, Buysse DJ, Bromberger JT, Owens JF, Sowers M. Race and financial strain are independent correlates of sleep in midlife women: the SWAN sleep study. Sleep. 2009;32(1):73–82. [PMC free article] [PubMed] [Google Scholar]
  • 74.Huang Y, Mai W, Hu Y, Wu Y, Song Y, Qiu R, Dong Y, Kuang J. Poor sleep quality, stress status, and sympathetic nervous system activation in nondipping hypertension. Blood Press Monit. 2011;16(3):117–23. doi: 10.1097/MBP.0b013e328346a8b4. [DOI] [PubMed] [Google Scholar]
  • 75.Hall M, Thayer JF, Vasko R, Puhl MD, Kupfer DJ, Buysse DJ. Coping is associated with heart period variability in patients with insomnia. Sleep. 2004;27:A279. Abstract Supplement. [Google Scholar]
  • 76.Minkel JD, McNealy K, Gianaros PJ, Drabant EM, Gross JJ, Manuck SB, Hariri AR. Sleep quality and neural circuit function supporting emotion regulation. Biol Mood Anxiety Disord. 2012;2(1):22. doi: 10.1186/2045-5380-2-22. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 77.Mauss IB, Troy AS, LeBourgeois MK. Poorer sleep quality is associated with lower emotion-regulation ability in a laboratory paradigm. Cogn Emot. 2013;27(3):567–76. doi: 10.1080/02699931.2012.727783. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 78.Hall M, Buysse DJ, Nowell PD, Nofzinger EA, Houck P, Reynolds CF, III, Kupfer DJ. Symptoms of stress and depression as correlates of sleep in primary insomnia. Psychosom Med. 2000;62(2):227–30. doi: 10.1097/00006842-200003000-00014. [DOI] [PubMed] [Google Scholar]
  • 79.Morin CM, Rodrigue S, Ivers H. Role of stress, arousal, and coping skills in primary insomnia. Psychosom Med. 2003;65(2):259–67. doi: 10.1097/01.psy.0000030391.09558.a3. [DOI] [PubMed] [Google Scholar]
  • 80.Drake C, Richardson G, Roehrs T, Scofield H, Roth T. Vulnerability to stress-related sleep disturbance and hyperarousal. Sleep. 2004;27(2):285–91. doi: 10.1093/sleep/27.2.285. [DOI] [PubMed] [Google Scholar]
  • 81.Williamson GM, Schulz R. Activity restriction and prior relationship history as contributors to mental health outcomes among middle-aged and older spousal caregivers. Health Psychol. 1998;17(2):152–62. doi: 10.1037//0278-6133.17.2.152. [DOI] [PubMed] [Google Scholar]
  • 82.Williamson GM, Schulz R. Pain, activity restriction, and symptoms of depression among community-residing elderly adults. J Gerontol. 1992;47(6):P367–P372. doi: 10.1093/geronj/47.6.p367. [DOI] [PubMed] [Google Scholar]

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