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. 2022 Nov 29;46(1):68–79. doi: 10.1002/nur.22278

Linking chronic stress to insomnia symptoms in older adults: The role of stress co‐occurrence during the pandemic

Wan‐Chin Kuo 1,✉, Anne L Ersig 1, Fatih Kunkul 1, Roger L Brown 1, Linda D Oakley 1
PMCID: PMC9839487  PMID: 36445114

Abstract

Studies examining the associations of chronic stressors with sleep health in older adults have shown conflicting results. While the COVID‐19 pandemic increased perceived stress at the population level, less is known about chronic stressors experienced by older adults in the context of the COVID‐19 pandemic and its impact on sleep health in an aging population. This study aims to examine the association of older adults' chronic stress with insomnia symptoms during the first year of the COVID‐19 pandemic. A cross‐sectional analysis was performed using early‐release COVID‐19 data from the Health and Retirement Study. Data on chronic stressors and insomnia symptoms in older adults (N = 2021; mean age = 68.8) were examined. Co‐occurrence network analysis, latent class analysis, Rao–Scott χ 2 tests, and multivariable logistic regression were used to characterize the co‐occurrence of chronic stressors and associations with insomnia symptoms. The most common co‐occurring chronic stressors during the first year of the COVID‐19 pandemic were self‐health issues, family‐health issues, and financial stress. Older adults experiencing frequent stress co‐occurrence had 91% higher odds of difficulty initiating sleep (p < 0.001), 40% higher odds of frequent nocturnal awakening (p = 0.028), and 83% higher odds of nonrestorative sleep (p < 0.001). However, adjustment for health risk factors and COVID‐19 concerns attenuated the effects, leaving strongest association for difficulty initiating sleep (odds ratio = 1.51, p = 0.010). Frequent stress co‐occurrence plays an important role linking chronic stress to insomnia symptoms in an aging population. Ongoing research is needed to examine the lingering effects of frequent stress co‐occurrence on older adults' sleep health in the post COVID‐19 era.

Keywords: chronic stress, COVID‐19, insomnia symptoms

Patient and Public Contributions

The Health and Retirement Study (HRS) COVID‐19 project is accomplished by the active response of research participants and the HRS team members at different stages, including the shift to remote data collection, new measures of COVID‐19‐related impacts, and the rapid release of the high‐quality dataset that researchers can use to address important questions about the challenges and opportunities of aging.

1. INTRODUCTION

Insomnia affects nearly 50% of older adults in the US, with prevalence rates from 8.2% to 74.8% depending on sampling methods and measures of insomnia symptoms (Dopheide, 2020; Nguyen et al., 2019). Insomnia is defined as difficulty initiating or maintaining sleep in the context of adequate opportunities to sleep, and evidence of daytime consequences (American Academy of Sleep Medicine, 2008; Sateia, 2014). Insomnia symptoms typically include difficulty initiating sleep or difficulty falling back to sleep after nocturnal awakening, early morning awakening, and nonrestorative sleep. As the most common sleep disorder, insomnia has a substantial economic impact on the healthcare system, costing an estimated $100 billion USD per year in direct treatment expenditures and indirect costs in the US, including healthcare utilization, medications, and risk of accidents (Kaufmann et al., 2013; Wickwire et al., 2016).

Changes in circadian rhythm and decreases in slow wave sleep occur with normal aging, resulting in a nearly 10‐min reduction of total sleep time per decade of age (Moraes et al., 2014; Suzuki et al., 2017). Yet, insomnia is not part of the normal aging process (Kamel & Gammack, 2006; Suzuki et al., 2017). Indeed, insomnia involves a set of underlying causes, including predisposing factors, precipitating factors, and perpetuating factors, known as the 3Ps model (Bastien et al., 2004; Spielman et al., 1987). Although the 3Ps model sheds light on the etiology of insomnia, it does not explain why certain individuals are more susceptible to these predisposing, precipitating, and perpetuating factors. Furthermore, the 3Ps model does not explain the role of chronic stress in the continuum of insomnia development. Drake and colleagues further proposed the stress‐diathesis model of insomnia, which posits that exposure to major life stressors increases individuals' sleep reactivity by sensitizing individuals' sleep systems and leading to greater vulnerability to insomnia (Drake et al., 2014; Kalmbach, Cuamatzi‐Castelan, et al., 2018). Observational studies further demonstrate that changes in sleep reactivity tend not to return to baseline after the remission of life stressors, highlighting the long‐term effects of stress exposure on vulnerability to future insomnia episodes (Fernández‐Mendoza et al., 2010; Kalmbach, Anderson, et al., 2018).

The stress–diathesis model of Insomnia has been applied to explain the underlying mechanisms that link stress reactivity to insomnia, yet, these studies are primarily based on the working‐age population (Drake et al., 2014; Garefelt et al., 2020; M. H. Hall et al., 2015; Meaklim et al., 2021). Among the few studies examining the stress–insomnia relationship in older adults, results have been inconsistent. For instance, in a convenience sample of community‐dwelling older adults, Friedman et al. (1995) found that life event stress, assessed by elders' life stress inventory, did not differ between good and poor sleepers. Similarly, Fichten et al. (2000) concluded that the number of chronic stressors did not differ between insomnia versus noninsomnia groups in another convenience sample of community‐dwelling older adults. In contrast, cohort studies showed that certain stressors, including financial stress and caregiving stress, are associated with a higher prevalence of insomnia symptoms in older adults (Gao et al., 2019; M. Hall et al., 2008).

Social distancing during the COVID‐19 pandemic restructured social interactions and stress experiences in older adults (Simonelli et al., 2021). The devastating effects of the pandemic might have amplified the pre‐existing chronic stressors and generated additional stressors in aging populations (Egger et al., 2021). According to the stress process model, supportive factors may buffer the negative impact of a single stressor on health outcomes; however, the co‐occurrence of multiple chronic stressors and stress proliferation often compromise an individual's coping mechanisms, leading to poor physical and mental health outcomes (Avison et al., 2009; Pearlin, 2010). Increased uncertainty about infection rates, social isolation, and mortality during the initial outbreak led to high levels of perceived stress and insomnia at the population level (Limcaoco et al., 2020; Zitting et al., 2021). However, less is known about stressors experienced by older adults in the context of the COVID‐19 pandemic and whether the co‐occurrence of chronic stressors is associated with an increased risk of insomnia symptoms in an aging population. Therefore, this study has three specific aims designed to address these gaps: (1) examine the co‐occurrence of chronic stressors in older adults during the first year of the COVID‐19 pandemic, (2) identify hidden patterns of chronic stressors based on the occurrence and severity of stressors experienced by older adults during the first year of the COVID‐19 pandemic, and (3) examine the degree to which co‐occurrence of chronic stressors is associated with insomnia symptoms in older adults during the first year of the COVID‐19 pandemic.

2. METHODS

2.1. Design, setting, and sample

A cross‐sectional analysis was performed using data from the Health and Retirement Study (HRS) COVID‐19 data, an ancillary project of the HRS. The HRS is a nationally representative longitudinal survey of more than 37,000 individuals over age 50 in 23,000 U.S. households (Sonnega et al., 2014). Beginning in 1992, socioeconomic data, psychological data, and health data are assessed in this core sample approximately every 2 years. In 2006, half of the core sample was randomly assigned to enhanced face‐to‐face (EFTF) interviews with physical and biological measures and a mail‐back psychosocial questionnaire. Data from the EFTF interviews are available for every wave on half of the core sample and on the full sample every 4 years (Sonnega et al., 2014). To represent the U.S. older population, the HRS applies sampling weights to account for nonresponse and differential selection probabilities by race/ethnicity and birth cohort (Lee et al., 2021). Details about the design of the HRS are described elsewhere (Sonnega et al., 2014).

In 2020, the HRS International Network began its COVID‐19 Research Initiatives, which included a random subsample of 50% of HRS households that completed EFTF interviews in previous waves. The current data analysis was based on the HRS COVID‐19 early release data on 3266 respondents, which were updated in February 2021 with a response rate of 62% (Health and Retirement Study, 2021). The preliminary COVID‐19 sampling weights were used to adjust for the selection and nonresponse. Definitions of what ages define “senior citizens” are based on different legal, social, and health contexts (e.g., social security benefits, senior housing, retirement saving plan, or Medicare). To cover a broad range of the aging population, respondents who were 55 or older in 2020 were included in the current data analysis (Liebzeit et al., 2022). In total, 2021 respondents were included in the current data analysis, after deleting respondents who were under the age of 55 or had missing data in the primary exposure or outcome variables (i.e., chronic stressors and insomnia symptoms). Figure 1 illustrates the flowchart of final sample size included in the current analysis.

Figure 1.

Figure 1

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Flowchart of the final sample included in the current study. EFTF, enhanced face‐to‐face; HRS, health and retirement study.

The HRS was reviewed and approved by the University of Michigan's Health Sciences Institutional Review Board (IRB). All participants read a confidentiality statement and gave oral or implied consent by agreeing to participate in the interviews. This secondary data analysis was approved by the Health Sciences IRB at the University of Wisconsin–Madison.

2.2. Variables of interest

2.2.1. Insomnia symptoms

Insomnia is defined as difficulty initiating or maintaining sleep in the context of adequate opportunities to sleep, and evidence of daytime consequences (American Academy of Sleep Medicine, 2008; Sateia, 2014). The Jenkins Sleep Questionnaire (JSQ) is a brief screening tool used to describe insomnia‐related symptoms, including four items: (1) “How often do you have trouble falling asleep?” (2) “How often do you have trouble with waking up during the night?” (3) “How often do you have trouble with waking up too early and not being able to fall asleep again?” and (4) “How often do you feel really rested when you wake up in the morning?” (Jenkins et al., 1988; Kaufmann et al., 2016). Beginning in 2002, the HRS added the JSQ to its longitudinal data collection and adapted the original questionnaire into face‐to‐face interviews, phone interviews, and web‐interview. Symptom frequency was rated using a 3‐point Likert‐type scale (0 = rarely or never, 1 = sometimes, and 2 = most of the time), with the fourth question on nonrestorative sleep reverse‐coded to ensure consistency with other symptoms. Each symptom answered with “sometimes” or “most of the time” was defined as a currently experienced insomnia symptom (Kaufmann et al., 2013). JSQ has been shown to predict 2‐year health services utilization in older adults (adjusted odds ratio [OR] = 1.28, p < 0.001) with good predictive validity and internal consistency (Juhola et al., 2021; Kaufmann et al., 2013).

2.2.2. Chronic stressors

The eight‐item chronic stressors scale (CSS‐8) captured the subjective experience of chronic stressors in eight areas of life: (1) self‐health problems, (2) family health problems, (3) alcohol or drug use in a family member, (4) difficulties at work, (5) financial strain, (6) housing problems, (7) problems in a close relationship, and (8) caregiving stress. The CSS‐8 was originally developed by Troxel et al. (2003) based on Bromberger and Matthews's (1996) life stressor inventory. Informed by the stress process model (Avison et al., 2009; Pearlin et al., 1981), the CSS‐8 was added to the HRS longitudinal data collection in 2010. Respondents evaluated the occurrence of each chronic stressor and subjective upset associated with each stressor during the past 12 months using a Likert‐type scale (0 = did not occur; 1 = not upsetting; 2 = somewhat upsetting; and 3 = very upsetting). For occupational stress (i.e., difficulties at work), respondents were coded as 0 (did not occur) if they were not currently working for pay and reported missing values in occupational stress. Because the eight stressors represent eight different areas of life, they are not intended to have a high degree of internal consistency (Health and Retirement Study, 2017). The scale demonstrated acceptable reliability with Cronbach's α of 0.66 in the current sample, which is consistent with prior literature using the CSS‐8 (Yang et al., 2019).

2.2.3. Covariates

Based on previous literature, age, sex, years of education, race, Hispanicity (Hispanic or non‐Hispanic), current work status, history of depression, history of sleep disorders, and number of comorbidities (hypertension, diabetes, cancer, lung diseases, cardiovascular diseases, stroke, psychiatric disorders, and arthritis) were identified as possible covariates. Current work status was a binary variable indicating whether the respondents were doing any work for pay (including a part‐time and full‐time job) at the present time. Respondents who self‐identified as retirees or homemakers were categorized as not currently working for pay. Furthermore, individual concern about the COVID‐19 pandemic was included as a covariate to account for direct worry associated with the COVID‐19 pandemic. Data were obtained using a single question with response options ranging from 1 (not at all worried) to 10 (very worried). Missing values in covariates were checked for missing patterns and handled with multiple imputations.

2.3. Statistical analysis

Sample characteristics were summarized using descriptive statistics. Statistical analyses were performed using SAS software version 9.4 (SAS Institute Inc., 2020) and Mplus software version 8 (Muthén & Muthén, 2017). All reported p values were two‐tailed with p values less than 0.05 considered as significant. Missing patterns in covariates were diagnosed using Little's test for missing completely at random (MCAR). Multiple imputations were performed using Monte Carlo Markov Chain (MCMC) method based on 100 MCMC iterations with five imputed datasets (Asparouhov & Muthén, 2010). The preliminary COVID‐19 sampling weights were applied in multiple imputations.

2.3.1. Aim 1: Examine the co‐occurrence of chronic stressors in older adults during the first year of the COVID‐19 pandemic

The co‐occurrence network analysis was used to examine the co‐occurrence of chronic stressors that were somewhat or very upsetting. The network was built based on the Fruchterman–Reingold algorithm using Gephi software (Bastian et al., 2009; Strayer et al., 2022). The goals of this algorithm are to (1) distribute the nodes evenly in the frame, (2) make the lengths of edges uniform, (3) minimize the number of edge crossings, and (4) reflect inherent symmetry (Fruchterman & Reingold, 1991; Gajdoš et al., 2016). The co‐occurrence matrix was created based on the eight chronic stressors collected through CSS‐8, including (1) self‐health problems, (2) family health problems, (3) alcohol or drug use in family members, (4) difficulties at work, (5) financial strain, (6) housing problems, (7) problems in a close relationship, and (8) caregiving stress. We further created a matrix with eight entities in rows (ER) and eight entities in columns (EC), leaving 56 pairs of combinations. Next, to compute the frequency of co‐occurrence, we calculated the number of times each ER appears in the same participants as each EC.

2.3.2. Aim 2: Identify the hidden patterns of chronic stressors based on the occurrence and severity of the stressors experienced by older adults during the first year of the COVID‐19 pandemic

Latent class analysis (LCA) with ordinal indicators was used to examine hidden patterns of chronic stressors. The purpose of LCA is to create a model that categorizes participants into different subtypes based on similarity of responses to a set of measured variables while detecting latent sample heterogeneity (Weller et al., 2020). The initial freely‐estimated one‐class model was examined followed by models that gradually increased number of classes (e.g., two‐class, three‐class, and four‐class). Next, a partial‐conditional independence model was used to relax the local independence assumption for LCA. Finally, demographic covariates (i.e., age, sex, years of education, current work status, race, and Hispanicity) were adjusted in the LCA to account for the covariates that differentiate membership across classes. Model fit indices among freely‐estimated models, partial‐conditional independence models, and covariates‐adjusted models were compared to select the optimal model solution. Optimal model selection was based on theoretical interpretability and recommended indices, including (1) significant Lo–Mendell–Rubin likelihood ratio test (LMR‐LRT), (2) higher entropy, (3) lower adjusted Bayesian information criterion (BIC), (4) average latent class posterior probability >0.80, and (5) parsimony (Bratzke et al., 2018; Nylund et al., 2007; Weller et al., 2020). The preliminary COVID‐19 sampling weights were applied in LCA.

2.3.3. Aim 3: Examine the degree to which co‐occurrence of chronic stressors is associated with insomnia symptoms

Since each insomnia symptom was dichotomized into whether the symptom occurred or not, we used Rao–Scott corrected χ 2 tests to examine whether the co‐occurrence of chronic stressors is associated with each insomnia symptom. Rao–Scott corrected χ 2 test was chosen because the HRS sample is based on a multistage, stratified, and area‐clustered survey design, which could substantially alter the significance levels of χ 2 values. The Rao–Scott correction with sampling weights provides a more accurate χ 2 than traditional raw‐weight χ 2 test (Rao & Scott, 1987). Next, to account for the confounding effects and sampling weights, we used multivariable logistic regression models to examine the degree to which co‐occurrence of chronic stressors is associated with insomnia symptoms while holding the covariates constant.

3. RESULTS

3.1. Sample characteristics

As shown in Supporting Information: 1, missing values were identified in five covariates, including education, race, Hispanicity, history of sleep disorders, and COVID‐19 concerns. The diagnosis of missing patterns with Little's test for MCAR did not reject the null hypothesis (p = 0.085), which supported the approach of multiple imputations. Demographic characteristics and health characteristics are presented in Table 1. The sample was, on average, 69 years old, 47% male, and 84% White. Approximately, 67% had at least two chronic conditions. As shown in Supporting Information: 2, this study included a wide range of late adulthood (55–99) from the youngest–old to the old–old.

Table 1.

Weighted mean, standard error, and weighted percentage for the sample characteristics

Demographics Total samplea
Age, mean (SE) 68.8 (0.25)
Male (%) 46.8
Race (%)
White 83.7
Black 8.7
Otherb 7.6
Hispanic (%) 7.4
Years of education, mean (SE) 13.7 (0.07)
Currently working (%) 28.7
Health characteristics
History of depression (%) 19.9
History of sleep disorders (%) 16.0
Number of comorbiditiesc (%)
None 11.3
One 21.5
Two 27.0
Three or more 40.2
COVID‐19 concerns, mean (SD)d 7.6 (0.07)
Insomnia symptoms
Difficulty initiating sleep (%) 47.8
Frequent nocturnal awakening (%) 62.2
Early morning awakening (%) 46.4
Nonrestorative sleep (%) 44.2
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Abbreviation: SE, standard error.

a

COVID‐19 sampling weights were applied in all descriptive analyses.

b

Includes American–Indian, Alaska Native, Asian, or Pacific Islander.

c

Comorbidities include hypertension, diabetes, cancer, lung diseases, cardiovascular diseases, stroke, psychiatric disorders, and arthritis.

d

Higher values indicate higher levels of concern about COVID‐19 (range: 1–10).

3.2. Co‐occurrence of chronic stressors

Co‐occurrence network analysis was used to examine the co‐occurring patterns of chronic stressors during the first year of the COVID‐19 pandemic. As shown in Figure 2, each node represents a chronic stressor rated as somewhat upsetting or very upsetting. Node size indicates the frequency of each upsetting chronic stressor, with larger nodes indicating higher frequency. The thickness of the lines connecting two nodes indicates co‐occurrence frequency, with thicker lines reflecting more frequent co‐occurrence. The most frequently reported upsetting chronic stressors were self‐health issues (36%), family health issues (27%), financial stress (14%), and relationship stress (11%). Self‐health issues commonly co‐occurred with family health issues and financial stress with prevalence rates of 16% and 9%, respectively.

Figure 2.

Figure 2

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Co‐occurrence matrix of chronic stressors in older adults during the first year of the COVID‐19 pandemic

3.3. Hidden patterns of chronic stressors

LCA was used to examine the hidden patterns of chronic stressors. As shown in Table 2, LMR‐LRT values supported the two‐class solution. A careful comparison of the fit indices led to the selection of the covariate‐adjusted two‐class model, due to better entropy, sample‐adjusted BIC, parsimony, and theoretical justification. Figure 3 illustrates the conditional item‐response probabilities of stress co‐occurrence in two classes based on estimated posterior probabilities.

Table 2.

Fit statistics of latent class analysis (LCA) models

Number of classes LMR‐LRT Entropy Average latent class posterior probability Sample adjusted BIC
Freely estimated LCA 1 – – – 27,537.304
2 p  < 0.001 0.68 0.87, 0.92 26,562.651
3 p = 0.2253 0.65 0.87, 0.86, 0.79 26,255.818
4 p = 0.7794 0.73 0.78, 0.85, 0.87, 0.88 26,162.157
LCA with partial conditional independence 2 p = 0.5217 0.55 0.88, 0.80 26,175.455
3 p = 0.5008 0.55 0.83, 0.77, 0.80 26,108.511
4 p = 0.7610 0.62 0.83, 0.85, 0.78, 0.77 26,124.249
Covariate‐adjusted LCA 2* p  <0.001 0.73 0.88, 0.93 26,236.036
3 p = 0.3236 0.67 0.88, 0.81, 0.86 25,918.212
4 p = 0.7603 0.69 0.85, 0.81, 0.91, 0.76 25,794.813
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Note: Best fitting model indicated in bold. LMR‐LRT is Lo–Mendell–Rubin likelihood ratio test for (K‐1) classes, where a significant p value indicates that the (K‐1) class model should be rejected in favor of a model with at least K classes.

Abbreviation: BIC, Bayesian information criterion.

Figure 3.

Figure 3

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A profile plot illustrating the conditional item‐response probabilities of chronic stressors experienced by older adults during the first year of the COVID‐19 pandemic in two subgroups: Frequent stress co‐occurrence (red curve) and sporadic stress co‐occurrence (blue curve).

3.3.1. Class 1: Frequent stress co‐occurrence group

As shown in Figure 3 (red curve), the most frequently reported stressors in this group were self‐health issues followed by financial stress and family health issues (conditional item‐response probabilities >0.7). All respondents (100%) in this group reported at least two co‐occurring chronic stressors during the COVID‐19 pandemic. Further examination of the data found that 59% of respondents in this group reported at least three chronic stressors that were somewhat or very upsetting. Overall, this group experienced more frequent co‐occurrence of chronic stressors, and a higher degree of upset from chronic stressors, compared to the sporadic stress co‐occurrence group.

3.3.2. Class 2: Sporadic stress co‐occurrence group

Participants in this group reported sporadic chronic stressors, with the lowest chance of reporting housing problems (conditional item‐response probabilities <0.1). Further examination of the data revealed 80% of respondents in this group reported zero or one chronic stressor that was somewhat or very upsetting.

3.4. The association between stress co‐occurrence and insomnia symptoms

On the basis of two subgroups identified from LCA, we further performed Rao–Scott corrections to χ 2 tests to examine the association between stress co‐occurrence and insomnia symptoms. As shown in Table 3 (Model 1), participants in the frequent stress co‐occurrence group were more likely to report difficulty initiating sleep (OR = 1.91, 95% confidence interval, CI = [1.44, 2.53], p < 0.001), frequent nocturnal awakening (OR = 1.40; 95% CI = [1.04, 1.89], p = 0.028), and nonrestorative sleep (OR = 1.83; 95% CI = [1.38, 2.43], p < 0.001), compared to those with sporadic stress co‐occurrence. The association between co‐occurrence of chronic stressors and early morning awakening was not significant (p = 0.268).

Table 3.

The association between stress co‐occurrence classes and insomnia outcomes in unadjusted and covariate‐adjusted models

Difficulty initiating sleep Frequent nocturnal awakening Early morning awakening Nonrestorative sleep
Latent classes of stress co‐occurrence OR (95% CI) p Value OR (95% CI) p Value OR (95% CI) p Value OR (95% CI) p Value
Model 1: Unadjusted model
Class 1: Sporadic Reference Reference Reference Reference
Class 2: Frequent 1.91 (1.44, 2.53) <0.001 1.40 (1.04, 1.89) 0.028 1.17 (0.89, 1.55) 0.268 1.83 (1.38, 2.43) <0.001
Model 2: Demographic adjusted model
Class 1: Sporadic Reference Reference Reference Reference
Class 2: Frequent 1.72 (1.27, 2.33) <0.001 1.45 (1.06, 1.97) 0.020 1.10 (0.82, 1.47) 0.531 1.67 (1.24, 2.24) <0.001
Model 3: Demographic and health‐adjusted model
Class 1: Sporadic Reference Reference Reference Reference
Class 2: Frequent 1.51 (1.10, 2.06) 0.010 1.38 (1.00, 1.90) 0.048 1.02 (0.76, 1.38) 0.888 1.38 (1.01, 1.87) 0.042
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Note: COVID‐19 sampling weights were applied in all three models.

Model 1: Unadjusted model.

Model 2: Adding demographics (age, gender, education, race, ethnicity, and work status) into Model 1.

Model 3: Adding health risk factors (comorbidities, sedentary lifestyle, history of depression, and history of sleep disorders) and COVID‐19 concerns into Model 2.

Abbreviations: CI, confidence interval; OR, odds ratio.

As shown in Table 3 (Model 2), after the adjustment for demographic covariates, frequent stress co‐occurrence group had higher likelihood of difficulty initiating sleep (OR = 1.72, p < 0.001), frequent nocturnal awakening (OR = 1.45, p = 0.020), and nonrestorative sleep (OR = 1.67; p < 0.001). However, further adjustment for health risk factors and COVID‐19 concerns attenuated the effects of frequent stress co‐occurrence on all three symptoms (Table 3, Model 3), with the strongest effect remaining for difficulty initiating sleep (OR = 1.51, p = 0.010), followed by nonrestorative sleep (OR = 1.38, p = 0.042), while frequent nocturnal awakening became marginally significant (OR = 1.38; p = 0.048). Table 4 describes the fully adjusted logistic regression models of the four insomnia symptoms.

Table 4.

Factors that are associated with the four insomnia symptoms

Difficulty initiating sleep Frequent nocturnal awakening Early morning awakening Nonrestorative sleep
OR (95% CI) p Value OR (95% CI) p Value OR (95% CI) p Value OR (95% CI) p Value
Frequent stress co‐occurrence (Ref. = sporadic) 1.51 (1.10, 2.06) 0.010 1.38 (1.00, 1.90) 0.048 1.02 (0.76, 1.38) 0.888 1.38 (1.01, 1.87) 0.042
Demographics
Age 0.99 (0.98, 1.01) 0.192 1.02 (1.00, 1.03) 0.037 1.00 (0.99, 1.02) 0.997 0.98 (0.97, 1.00) 0.021
Sex (Ref. = male) 1.91 (1.48, 2.46) <0.001 0.98 (0.76, 1.26) 0.866 1.02 (0.79, 1.31) 0.879 1.00 (0.77, 1.29) 0.997
Education 0.94 (0.90, 0.99) 0.013 0.98 (0.93, 1.02) 0.298 0.94 (0.90, 0.98) 0.008 0.95 (0.91, 0.99) 0.024
Race (Ref. = white)
Black 0.95 (0.68, 1.32) 0.593 1.08 (0.78, 1.50) 0.387 1.15 (0.83, 1.59) 0.512 0.71 (0.51, 1.00) 0.049
Other 0.73 (0.47, 1.16) 0.229 0.84 (0.51, 1.36) 0.380 1.03 (0.64, 1.67) 0.868 1.10 (0.68, 1.78) 0.282
Ethnicity (Ref. = Hispanic) 0.81 (0.53, 1.23) 0.325 1.06 (0.68, 1.63) 0.806 0.94 (0.63, 1.39) 0.737 0.85 (0.56, 1.29) 0.436
Work status (Ref. = working for pay) 1.22 (0.88, 1.70) 0.237 1.19 (0.86, 1.65) 0.284 0.96 (0.70, 1.32) 0.812 1.08 (0.78, 1.51) 0.641
Health risk factors
History of depression (Ref. = no) 1.53 (1.08, 2.16) 0.017 1.10 (0.77, 1.58) 0.594 1.15 (0.82, 1.61) 0.429 1.74 (1.23, 2.45) 0.002
History of sleep disorders (Ref. = no) 1.33 (0.93, 1.91) 0.120 1.69 (1.16, 2.47) 0.006 1.66 (1.18, 2.33) 0.004 1.50 (1.06, 2.12) 0.022
Number of comorbidities 1.07 (0.97, 1.19) 0.190 0.99 (0.89, 1.09) 0.831 1.01 (0.92, 1.11) 0.879 1.12 (1.02, 1.24) 0.020
COVID‐19 concerns 1.03 (0.98, 1.08) 0.319 1.05 (1.00, 1.11) 0.035 1.06 (1.01, 1.11) 0.026 1.02 (0.97, 1.07) 0.401
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Note: All reported p values were two‐tailed with p values less than 0.05 considered as significant. Adjusted for demographics (age, gender, education, race, ethnicity, and work status), health risk factors (comorbidities, sedentary lifestyle, history of depression, and history of sleep disorders), and COVID‐19 concerns.

Abbreviations: CI, confidence interval; OR, odds ratio.

4. DISCUSSION

Although chronic stress has been linked to an increased risk of insomnia, findings have been primarily focused on the working‐age population. Our findings add to the current literature by investigating older adults' stress experiences during the first year of the COVID‐19 pandemic and their association with insomnia symptoms. We found that older adults experiencing frequent stress co‐occurrence during the first year of the COVID‐19 pandemic were more likely to report difficulty initiating sleep, frequent nocturnal awakening, and nonrestorative sleep, compared to those who experienced sporadic stress co‐occurrence during the first year of the COVID‐19 pandemic.

Although the 3Ps model of Insomnia characterizes the etiology of insomnia into three domains (i.e. predisposing, precipitating, and perpetuating factors), it does not explain the role of stress in the development of insomnia symptoms. The stress–diathesis model of insomnia emphasizes that the development of chronic insomnia often involves stress reactivity in conjunction with neurophysiologic and cognitive‐emotional hyperarousal, manifested by decreased inhibitory neurotransmitters (e.g., γ‐aminobutyric acid) and increased excitatory neurotransmitters (e.g., glutamate) (Buysse, 2013; Drake et al., 2014; Plante et al., 2012). Our findings add to the stress–diathesis model of insomnia, highlighting the role of co‐occurring chronic stressors in the continuum of insomnia development (Fernández‐Mendoza et al., 2010; Kalmbach, Cuamatzi‐Castelan, et al., 2018). Co‐occurring chronic stressors may predispose, precipitate, and perpetuate insomnia in older adults, particularly when chronic stress interacts with trait psychological factors, frequent rumination, and cognitive intrusion (Kalmbach, Anderson, et al., 2018). Furthermore, when too many pandemic experiences disrupt normal routines, stress responses may make some insomnia symptoms self‐perpetuating (Cox & Olatunji, 2021). Prospective longitudinal studies with comprehensive assessments of chronic stressors, sleep reactivity, and objective measures of sleep are needed to understand the long‐term impacts of the COVID‐19 pandemic on older adults' stress experiences and sleep health.

Consistent with previous literature (Peng et al., 2021), we found that advanced age was not a salient predictor of difficulty initiating sleep in this sample of older adults 55–99 years old (Table 4). Indeed, previous literature has shown that sleep‐onset insomnia in older adults is often underdiagnosed or untreated due to older patients' belief in the myth that difficulty initiating night‐time sleep is part of their aging process (Kamel & Gammack, 2006; Suzuki et al., 2017). As shown in Table 4, we did find that higher ages were associated with a higher likelihood of frequent nocturnal awakening (OR = 1.02; 95% CI = [1.00, 1.03]; p = 0.037) and a lower likelihood of non‐restorative sleep (OR = 0.98; 95% CI = [0.97, 1.00]; p = 0.021), yet the effects were weak with marginal significance. As shown in Table 4, the four insomnia symptoms represent distinct demographic factors and health risk factors, highlighting the complexity of each insomnia symptom and the importance of comprehensive assessment when addressing older adults' insomnia conditions.

As the COVID‐19 pandemic is transitioning to the endemic, countries around the world are facing postpandemic inflation and slowing economic growth, whereby the consumer price index has surges at the fastest pace in 40 years (U.S. Bureau of Labor Statistics, 2022). Historical evidence from the great recession has demonstrated that such economic instability tends to worsen health disparities because it hits savings and income harder for poorer or middle‐class households than for wealthy households (Bambra et al., 2020; Cingano, 2014; Margerison‐Zilko et al., 2016). What this means to an aging population is the potential threat to older adults with limited savings, pre‐existing chronic conditions, and family members with health concerns (Hadjistavropoulos & Asmundson, 2022). As shown in Figure 3, financial stress tends to co‐occur with self‐health stress, family health stress, and housing stress in the frequent stress co‐occurrence group (red curve). Older adults who live with social security benefits are more likely to struggle with out‐of‐pocket medical expenses along with everyday commodities (e.g., gasoline, natural gas, and food) during an inflation surge. Our findings highlight the importance of health policies to prevent the worsening health disparities arising from inflation.

According to the U.S. Census Bureau, all the baby boomers in the US will be older than age 65 by 2030; at that time, one in every five Americans will reach retirement age (U.S. Census Bureau, 2020). On top of this retirement wave, a recent survey has indicated that 38% of baby boomers hold less than $100,000 of retirement savings, meaning that nearly half of retirees will be living off of their social security benefits (Petrini, 2018). Age‐related changes in life circumstances also post financial stress in this age group. For instance, the increasing medical costs of declining health, income lost with retirement, and life events associated with aging can jointly complicate the impacts of financial stress on health behaviors and health outcomes (Block et al., 2009). In our study, self‐health stress, family‐health stress, and financial stress are the top three interrelated stressors reported by older adults during the COVID‐19 pandemic (Figure 2). Furthermore, in this sample, 67% had at least two chronic conditions (Table 1). What this means is that the majority of older Americans, to date, are not dealing with just one chronic condition or one chronic stressor. Instead, one chronic condition is likely to increase the risk of other comorbidities, compromise the health in family members, and deepen the financial burden from medical expenses or sick leave (Wickrama et al., 2021). Strikingly, emerging evidence has shown that stress proliferation and interrelated stress experience tend to deteriorate self‐management behaviors, due to behavioral maladaptation to chronic stress, enhanced habitual‐responding system, and diminished goal‐directed processing (Avison et al., 2009; Meier et al., 2022). Learning from the hard lesson of the COVID‐19 pandemic, the nursing profession has realized how various sources of chronic stress play an important role in dictating older patients' health outcomes and self‐management. Yet, our current understanding of what interventions are needed for our aging population with multiple chronic conditions and stress proliferation is limited (Jin et al., 2022). This study highlights an important opportunity for future nursing research, where more intervention studies are needed to identify the effective strategies and ingredients that block stress proliferation in older adults and foster their resilience when managing multiple chronic conditions.

The strengths of this study include the analysis of data from a large population‐based survey and a novel approach to assess the co‐occurrence of chronic stressors among older adults during the first year of the COVID‐19 pandemic. However, limitations should also be considered when interpreting and applying the findings. First, due to the cross‐sectional and observational nature of this study, causal inference and recursive relationships between stress exposures and insomnia cannot be determined. Future longitudinal studies are encouraged to uncover the causal relationships and underlying mechanisms linking the co‐occurrence of chronic stressors and insomnia symptoms. Second, the Jenkins sleep scale was not implemented with a specific length of recall period in the HRS. Although respondents were asked to recall their recent status, the variations in the recall period of insomnia symptoms assumed by each participant might pose a threat to the internal validity. Third, the CSS‐8 in the current study did not assess some important stressors (e.g., structural racism and environmental injustice) or other prominent stressors emerging from the COVID‐19 pandemic (e.g., social isolation). Due to the transition to retirement, information about occupational stress could not authentically reflect the challenging occupational environment the participants experienced throughout their lifetime. Furthermore, due to the wide range of late adulthood from 55 to 99 years old in current study (Supporting Information: 2), health and socioeconomic issues vary greatly within this range. Although sociodemographic and health risk factors were adjusted for as covariates, undoubtedly, the heterogeneity of stress patterns among different stages of late adulthood—and how it affects other domains of health outcomes—will remain worthy of continuous study. Finally, although the present study used LCA and co‐occurrence analysis to assess co‐occurrence of chronic stressors, the design does not support quantification of stress proliferation, because the exact timing and duration of each stressor experienced by the participants could not be determined. As additional data are collected following the COVID‐19 pandemic, scientists will be able to model stress proliferation processes and related health outcomes in older adults in the post COVID‐19 era.

5. CONCLUSION

Frequent stress co‐occurrence plays an important role in linking chronic stress to insomnia symptoms, especially sleep‐onset insomnia. Prospective longitudinal studies with comprehensive assessments of chronic stressors and stress responses are needed to investigate the lingering effects of frequent stress co‐occurrence on older adults' sleep health in the post‐COVID‐19 era.

AUTHOR CONTRIBUTIONS

Each person listed as an author participated in this work in a substantive manner. Wan‐Chin Kuo and Anne L. Ersig: Conceptualization. Wan‐Chin Kuo and Roger L. Brown: Methodology. Wan‐Chin Kuo and Fatih Kunkul: Validation. Wan‐Chin Kuo, Roger L. Brown, and Fatih Kunkul: Analysis. Wan‐Chin Kuo: Writing – original draft preparation. Wan‐Chin Kuo, Anne L. Ersig, Fatih Kunkul, Roger L. Brown, and Linda D. Oakley: Writing – review and editing. All authors have read and agreed to the published version of the manuscript.

CONFLICTS OF INTEREST

The authors declare no conflicts of interest.

PEER REVIEW

The peer review history for this article is available at https://publons.com/publon/10.1002/nur.22278.

Supporting information

Supporting information.

Click here for additional data file. (219.1KB, docx)

ACKNOWLEDGMENTS

The Health and Retirement Study (HRS) data is sponsored by the National Institute on Aging (Grant Number: U01AG009740) and is conducted by the University of Michigan. This secondary data analysis project was supported by the Midwest Nursing Research Society/Sigma Theta Tau International Honor Society Research Award and the University of Wisconsin–Madison Graduate School Research Fund. The authors would like to thank the participants who participated in the HRS.

Kuo, W.‐C. , Ersig, A. L. , Kunkul, F. , Brown, R. L. , & Oakley, L. D. (2023). Linking chronic stress to insomnia symptoms in older adults: The role of stress co‐occurrence during the pandemic. Research in Nursing & Health, 46, 68–79. 10.1002/nur.22278

DATA AVAILABILITY STATEMENT

Program codes supporting the findings of this study are available from the corresponding author on request. All users who analyze Health and Retirement Study (HRS) data should follow the HRS Data Access User Agreement. The data that support the findings of this study are openly available in HRS at https://hrs.isr.umich.edu/about

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Associated Data

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Supplementary Materials

Supporting information.

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Data Availability Statement

Program codes supporting the findings of this study are available from the corresponding author on request. All users who analyze Health and Retirement Study (HRS) data should follow the HRS Data Access User Agreement. The data that support the findings of this study are openly available in HRS at https://hrs.isr.umich.edu/about

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