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. Author manuscript; available in PMC: 2023 Sep 1.
Published in final edited form as: J Women Aging. 2021 Aug 25;34(5):637–648. doi: 10.1080/08952841.2021.1967654

Changes in Life Circumstances and Mental Health Symptoms during the COVID-19 Pandemic among Midlife Women with Elevated Risk for Cardiovascular Disease

Megan M Brown 1, Danielle Arigo 1
PMCID: PMC8873233  NIHMSID: NIHMS1736429  PMID: 34432597

Abstract

Cardiovascular disease (CVD) remains the leading cause of death among women. During midlife (ages 40–60), universal aging processes, sex-specific factors such as menopause, psychological distress, and conditions such as hypertension substantially increase women’s risk for CVD. The onset of the COVID-19 pandemic has impacted employment, social interactions, caregiving responsibilities, and overall well-being worldwide; however, little research has investigated how COVID-19 has affected women in midlife. The present study was designed to determine how COVID-19 has affected midlife women with elevated risk for CVD, by examining changes in their mental health symptoms and life domains across three time points: prior to COVID-19 (2019), during stay-at-home orders (April-June 2020), and during initial reopening (August 2020). Midlife women with one or more CVD risk conditions (e.g., hypertension; n = 35) responded to questions related to COVID-19, changes in life circumstances, and mental health symptoms at each time point. Findings showed meaningful changes in caregiving, medical visits, and employment status, as well as significant changes in depression and sleep quality scores across time. However, findings also showed that women were distressed prior to COVID-19 and did not exhibit changes in perceived stress, body dissatisfaction, or anxiety symptoms over time. Findings from this study highlight the impact of the COVID-19 pandemic on an at-risk group of women, which may be used to help guide future health promotion efforts specifically tailored to this population.

Keywords: women’s health, midlife, cardiovascular risk, COVID-19, mental health

Risk factors such as aging, obesity, sedentary behavior, and conditions such as hypertension, type 2 diabetes and metabolic syndrome are known to contribute to the development of cardiovascular disease (CVD), which remains the leading cause of death among adults in the United States (Benjamin et al., 2017). However, there are gender disparities in the development of CVD, such that women during midlife (ages 40–60) experience sharply elevated risk due to aging processes and the menopausal transition (Safdar & Mangi, 2020). Further, mental health concerns such as anxiety and depression have also been shown to increase risk for CVD (Cohen et al., 2015; Daumit et al., 2020), and may be tied to experiences such as the loss of employment and increased caregiving responsibilities (Greene et al., 2017; Killgore et al., 2021; Olesen et al., 2013). Symptoms of anxiety and depression are tied to CVD risk, particularly as women reach midlife (ages 40–60), a period during which the transition into menopause also presents independent risk for CVD (El Khoudary et al., 2020; Matthews et al., 2009). For instance, during midlife (compared to early adulthood and adolescence), women report an increase in symptoms of serious psychological distress, such as depression, anxiety, and sleep disturbances (Reeves et al., 2011; Sassarini, 2016; Min et al., 2021), stress (Schreiber & Dautovich, 2017), and body dissatisfaction (McGuinness et al., 2016).

According to the Transactional Stress and Coping Model (Lazarus & Folkman, 1984), adaptive coping techniques such as problem-solving and seeking social support can be protective against CVD risk. However, failure to cope effectively can lead to physiological and psychological distress, further increasing the likelihood of CVD. The emergence of novel Coronavirus 19 (COVID-19) and the associated public health crisis has presented a new set of stressors that have disproportionately affected women (Conway, 2020; Power, 2020). For example, the onset of stay-at-home orders in Spring 2020 and gradual reopenings required dramatic adjustments to daily routines, which affected employment, access to social support resources, caregiving responsibilities, and overall well-being (Balanzá-Martínez et al., 2021; Mattioli et al., 2020). Available data suggest that increases in unhealthy lifestyle behaviors (e.g., unhealthy diet, decreases in physical activity) and worsening of mental health due to the pandemic pose increased risk for the development of CVD (Martinez-Ferran et al., 2020; Mattioli et al., 2020). Attention has also been directed to the postponing of medical visits and otherwise seeking medical attention, due to fear of worsening pre-existing illnesses or contracting new ones (Anderson et al., 2021; Hipolito et al., 2020).

As a result of these new stressors, coping resources may be overwhelmed, and midlife women with elevated risk for CVD are placed at risk for future onset of both CVD and clinically significant mental health conditions. Additionally, CVD risk factors such as hypertension and type 2 diabetes not only place midlife women at greater risk for CVD, but also increase their chances of developing worse COVID-19 outcomes (Bansal, 2020). However, given the novelty of this virus and the associated changes in life circumstances, little research has observed how the COVID-19 pandemic has affected midlife women who are already at elevated risk for CVD, and how their experiences have changed over the course of this public health crisis.

The purpose of the present study was to better understand how the COVID-19 pandemic has affected midlife women with elevated risk for CVD, by examining changes in their professional, caregiving, and health experiences across three time points: pre-pandemic (2019), during widespread stay-at-home orders (April-June 2020) and during initial reopening (August 2020). Initial reopenings varied depending on jurisdiction; the present study chose the initial reopening period (August 2020) that aligned with the local area where the study was conducted (i.e., southern New Jersey and southeast Pennsylvania, USA). Given the significant negative toll of the pandemic based on available data, we expected to observe (1) high proportions of women in midlife reporting negative changes to their daily routines, specifically, working at home with children who need care, weight gain, postponing medical care, and changes in employment status and social interactions and (2) significant worsening of mental health outcomes: depression, anxiety, perceived stress, body dissatisfaction, and sleep quality over time. With respect to mental health symptoms, we expected to observe only small improvements from COVID-19 lockdown to initial reopening, suggesting limited recovery by that point.

Materials and Methods

Participants and Procedures

Participants were enrolled in a larger investigation of experiences among women in midlife with elevated CVD risk (Arigo et al., 2020). Study procedures were approved by the Institutional Review Board at the supporting institutions. Women were eligible if they were between the ages of 40 and 60 (inclusive) and reported a diagnosis of at least one of the following CVD risk factors: prehypertension or hypertension, hyperlipidemia or hypercholesterolemia, prediabetes or type 2 diabetes, metabolic syndrome, or smoking (current or quit within the past 3 months). Additionally, women had to be fluent in English, could not be pregnant at the time of participation, and had no reported medical or psychiatric contraindications to engaging in physical activity (i.e., a focus of the larger study). Women were recruited via web and print advertisements and through physician referral in family medicine offices. Women who expressed interest in the larger study were screened for eligibility via phone calls; they provided written informed consent and had their height and weight measured at a setup visit and completed global-self-report measures electronically.

Women who participated in the larger study prior to the onset of COVID-19 (n = 62) were invited to participate in follow-up surveys at the start of the widespread stay-at-home orders (April 2020) and during initial reopening in the study’s geographic area (August 2020). These surveys were completed electronically and all participants provided electronic informed consent. To reduce the potential for COVID-19 exposure or illness (yes vs. no) to confound observations about change in mental health symptoms, the present study enrolled only women who were not currently experiencing COVID-19 illness or symptoms at either time point. None of the participants who expressed interest at either follow-up point reported exposure or active illness; those who did experience COVID-19 more directly likely declined to participate to focus on their health, though they did not disclose this information to the research team. Participants who elected to return for follow-up did not differ from those who declined or did not respond, with respect to baseline mental health symptoms (ps > 0.30). The final sample included 35 women with one or more risk factors for CVD. The average participant was 51 years old at baseline (SD = 5.28) and had a starting BMI of 32.2 kg/m2 (SD = 5.42). The majority of participants identified as Caucasian (80.0%) and the largest subset reported a previous diagnosis of high cholesterol (45.7%; see Table 1).

Table 1.

Participant demographics (n = 35).

M (SD)
Age 51.0 (5.28)
BMI 32.2 (5.42)
Number of CVD Risk Factors 1.34 (1.00)
Racial Identification n (%) Marital Status n (%)
 Caucasian/White 28 (80.0%)  Never married 5 (14.3%)
 African American/Black 5 (14.3%)  Widowed 1 (2.9%)
 Asian or Pacific Islander 1 (2.9%)  Divorced 6 (17.1%)
 Hispanic/Latina 1 (2.9%)  Separated 1 (2.9%)
 Mixed/Other 0 (0.0%)  Married 22 (62.9%)
Highest Educational Level Menopause Status
 High School or GED 5 (14.3%)  Pre-menopause 9 (25.7%)
 Associate’s degree, technical degree, or partial college 4 (11.4%)  Perimenopause 4 (11.4%)
 Bachelor’s degree 10 (28.6%)  Post-menopause 15 (42.9%)
 Graduate/professional degree 16 (45.7%)  Other (e.g., surgical intervention) 5 (14.3%)
 Unspecified 2 (5.7%)
CVD Risk Condition(s) Household Income
 Hypercholesterolemia or hyperlipidemia 16 (45.7%)  < $25,000 1 (2.9%)
 Hypertension or prehyptertension 14 (40.0%)  $25,000–$50,000 7 (20.0%)
 Type 2 diabetes 12 (34.3%)  $50,000–$75,000 3 (8.6%)
 Metabolic syndrome 2 (5.7%)  > $75,000 22 (62.9%)
 Smoker (or quit in last 3 months) 3 (8.6%)  Unspecified 2 (5.7%)
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Measures

Demographics.

At baseline, participants self-reported on demographics such as age, CVD risk status, income, education level, race, ethnicity, menopause status, marital status, number of children (if any), and caregiving responsibilities (not exclusive to children).

Changes due to COVID 19.

At Times 2 and 3 (i.e., during the widespread stay-at-home orders [April-June 2020] and initial reopening [August 2020], respectively), participants were asked to describe changes in weight gain/loss, caregiving, medical care, employment status, and social interactions. For changes in weight gain/loss, participants were asked the following, “Has your weight been stable over the last month?” and they could select responses ranging from, My weight has stayed about the same in the last month to I’ve gained more than five pounds in the last month. Changes in caregiving were also assessed with questions such as, “Are you working from home with children in the same house?” (Yes/No) and “Over the past year, have you provided regular care for someone other than your children or partner? (Such as a parent, other relative, or close friend)” (Yes/No).

Medical care changes were assessed using the following question, “Since the pandemic began, have you put off any of your treatment for this/these condition(s), such as delaying medical appointments or prescription refills?” (Yes/No) as well as, “Which of the following describes what’s happened with your medical appointments, etc.?” where participants were able to indicate whether or not they put off their medical visits due to the pandemic. Changes in employment status were assessed by asking questions such as, “Have COVID-19 (coronavirus) precautions changed your work schedule or location?” (Yes/No), “Which describes your work situation since the onset of COVID-19 precautions?” (options ranged from No change to Laid off or furloughed), and “Which describes your time spent working since the onset of COVID-19 precautions?” (responses ranged from Working fewer hours to Working more hours). Finally, participants were invited to report on changes in their social interactions, by responding to the question, “Since the onset of COVID-19 precautions, how often are you interacting with others, either in person, over the phone, or over video?” and responses ranged from Much less than I was before to Much more than I was before.

Participants also provided open-ended responses to the following survey item: “Please share anything else you’d like to share about how COVID-19 precautions have affected your physical activity, work, or social interactions.” To summarize these responses, categories were created for common themes such as mental health and well-being, and then responses were coded as either 1 (Yes) or 0 (No). For example, a response such as, “Has caused me to experience more depression and anxiety episodes” was coded as a 1 (Yes) for the categories “Anxiety Increase” and “Depression Increase.”

Center for Epidemiologic Studies-Depression Scale (CES-D; Radloff, 1977).

This 20-item measure describes symptoms associated with depression in a general population. Participants are asked to rate the frequency of experiences over the past week (e.g., “I felt lonely,” “I had crying spells”) on a 4-point scale ranging from 0 (Rarely or none of the time/Less than 1 day) to 3 (Most or all of the time/5–7 days). Summed scores range from 0–60, with higher scores suggesting more severe depressive symptoms and a score of 16 or higher indicating risk for clinically significant depression. Internal consistency has been reported as Cronbach’s α = 0.85–0.90. In the present study, α = 0.88.

Beck Anxiety Inventory (BAI; Beck et al., 1988).

A 21-item measure that assesses the severity of common anxiety symptoms. Participants are asked to rate the intensity of their symptoms over the past week (e.g., “Feelings of choking,” “Nervous,” “Sweating not due to heat”) on a 4-point scale, ranging from 0 (Not at all) to 3 (Severely; I could barely stand it). Summed scores range from 0–63. Scores between 0–7 suggest minimal anxiety; scores between 8–15 are considered mild anxiety, scores between 16–25 indicate moderate anxiety, and scores between 26–63 reflect severe anxiety. Internal consistency has been reported as α = 0.92; in the present study, α = 0.92.

Body Image Quality of Life Inventory (BIQLI; Cash & Fleming, 2002).

This 19-item measure assesses the respondent’s perception of the influence that their body image has on their life. Items such as “My ability to control what and how much I eat” and “My ability to control my weight” are rated on a 7-point Likert scale ranging from −3 (Very negative influence on quality of life) to +3 (Very positive influence on quality of life). Higher scores suggest a more positive perception of the effect body image has on quality of life. This measure has shown high internal consistency (α = 0.95); in the present study, α = 0.96.

Perceived Stress Scale (PSS; Cohen et al., 1983).

A 14-item measure that assesses an individual’s perception of their stress level over the past month. Participants are asked to respond to questions such as “In the last month, how often have you been upset because of something that happened unexpectedly?” on a 5-point scale, ranging from 0 (Never) to 4 (Very often). Scores range from 0–56, with higher scores suggesting greater perceived stress. Previous estimates of internal consistency have been α = 0.84–0.86; in the present study, α = 0.89.

Pittsburgh Sleep Quality Index (PSQI Global Score; Buysse et al., 1989).

This 19-item measure evaluates an individual’s perceived sleep quality by assessing the duration, frequency, and severity of sleep problems. Participants rate items such as “During the past month, how often have you had trouble sleeping because you: Cannot get to sleep within 30 minutes” on a 4-point scale, ranging from 0 (Not during the past month) to 3 (Three or more times a week). Global scores are summed and range between 0–21, with higher scores reflecting poorer sleep quality and a score above a 5 placing an individual in the “poor sleep” range. In previous studies, internal consistency has been reported as α = 0.83; in the present study, α = 0.75.

Data Analysis

Reports of weight gain/loss, caregiving, medical care, employment, and social interactions were evaluated with descriptive statistics (frequencies) and to determine the relative proportions of women experiencing change in these domains over the course of the study. Categories of open-ended responses describing salient changes since the start of COVID-19 also were evaluated with descriptive statistics (frequencies) to capture common global, subjective perceptions. Intraclass correlation coefficients (ICCs) were used to estimate variability in mental health symptom scores at the between- versus within-person levels. Finally, changes in scores on mental health symptom questionnaires were examined with separate two-level multilevel models. This approach is appropriate for the nested structure of the dataset (i.e., time points nested within individuals) and produces unbiased estimates for level 2 sample sizes of 30 or larger (present level 2 n = 35; McNeish & Stapleton, 2016). All models employed restricted maximum likelihood estimation to address missing data (which was <1% of expected observations) and controlled for baseline age and BMI using SAS PROC MIXED (SAS Institute, Cary, NC). Time point was treated categorically in each model: Time 1 = 2019 (pre-pandemic), Time 2 = during stay-at-home orders (April-June 2020), and Time 3 = during initial reopening (August 2020). The threshold for statistical significance was set at p < 0.05 and effect sizes are expressed as semipartial correlation coefficients (sr).

Results

Frequencies for reports of weight change, caregiving, healthcare engagement, employment situation, and social interactions at each time point are presented in Table 2. Quantitative data showed that the largest subsets of participants reported no change in weight at each time point. However, this proportion decreased over time (51.4% at Time 1, 48.6% at Time 2, and 33.3% at Time 3), whereas the proportion that indicated losing weight decreased at Time 2 and then increased at Time 3. A smaller subset reported gaining weight at Time 2 (5.7% gained >5lb), though this group increased at Time 3 (19.4% gained >5lb, 11.1% gained <5lb). At Time 2, 54.3% endorsed working from home with children in the house, and there was a small drop to 51.4% reporting this at Time 3. At Time 1, almost one third of women reported providing regular care for someone other than their children or partner (31%); this proportion remained the same at Time 2 and dropped to 23% at Time 3. At Time 3, nearly one third of participants (31.4%) reported delaying their doctor’s visits or other medical care since the beginning of the COVID-19 pandemic.

Table 2.

Descriptive statistics for self-report baseline questions and mental health symptoms.

Time 1
n (%) 		Time 2
n (%) 		Time 3
n (%)
Weight Change
 Lost <5lb 1 (2.9%) 5 (14.3%) 3 (8.3%)
 Lost >5lb 9 (25.7%) 4 (11.4%) 10 (27.8%)
 No Change 18 (51.4%) 17 (48.6%) 12 (33.3%)
 Gained <5lb 4 (11.4%) 7 (20.0%) 4 (11.1%)
 Gained >5lb 3 (8.6%) 2 (5.7%) 7 (19.4%)
Self-Care and Caregiving
 Working from Home While Caring for Children --- 19 (54.3%) 18 (51.4%)
 Caregiving for Those Other than Children 11 (31%) 11 (31%) 8 (23%)
 Delayed Doctor Visits/Other Medical Care --- --- 11 (31.4%)
Employment and Work Hours
 Change in Employment (Hours, Format) --- 26 (74.3%) 23 (65.7%)
 No Change- Working --- 6 (17.1%) 5 (14.3%)
 Working from Home Instead of at my Worksite --- 22 (62.9%) 15 (42.9%)
 Laid Off or Furloughed --- 2 (5.7%) 2 (5.7%)
 Retired --- --- 4 (11.4%)
 Working Fewer Hours Per Week --- 12 (36.4%) 4 (11.8%)
 Working Same Number of Hours Per Week --- 14 (42.4%) 13 (38.2%)
 Working More Hours Per Week --- 7 (21.2%) 12 (35.3%)
Social Interactions
 Much less than before --- 18 (51.4%) 4 (11.4%)
 A Little Less than Before --- 6 (17.1%) 5 (14.3%)
 No Change --- 3 (8.6%) 8 (22.9%)
 A Little More than Before --- 5 (14.3%) 14 (40.0%)
 Much More than Before --- 3 (8.6%) 4 (11.4%)
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The majority of participants quantitatively reported a change in employment at Time 2 (74.3%) and at Time 3 (65.7%). The largest subsets reported “Working from home instead of at my worksite” (62.9% at Time 2, 42.9% at Time 3), though they reported no change in the number of hours worked per week at both follow-up time points (42.4% at Time 2, 38.2% at Time 3). Reports of “no change - working” decreased from Time 2 to Time 3 (17.1% to 14.3%), whereas reports of being laid off or furloughed did not change (5.7% at both time points). A proportion of participants reported working more hours per week since the start of the COVID-19 pandemic (21.2% at Time 2) and this increased at Time 3 (35.3%). There was a decrease in reports of fewer hours worked from Time 2 (36.4%) to Time 3 (11.8%). At Time 3, 11.4% of participants reported being retired. At Time 2, the majority of participants also reported having social interactions “much less than before” the pandemic (51.4%), whereas at Time 3, a large subset reported having social interactions “a little more” than during lockdown (40.0%).

Qualitative Feedback on Changes since the Start of COVID-19

Qualitative reports from Time 2 to Time 3 showed that there was a 2.9% increase in the number of times participants cited depression as a key feature of their experience since the onset of COVID-19 (2.9% to 5.7% of responses). The frequency of referencing anxiety stayed the same at both time points (2.9%). Somewhat more frequent were participants’ reports of stress, including such as stress in general or specific to work, COVID-19, finances, and family. Although the frequency of citing general stress was the same at Time 2 and Time 3 (5.7%), work-related stress increased from Time 2 (14.3%) to Time 3 (22.9%). We also saw a meaningful increase in reports of COVID-related stress from Time 2 (8.6%) to Time 3 (25.7%), as well as an increase in reports of family-related stress (14.3% at Time 2 and 20.0% at Time 3). Finally, responses indicated that the frequency of those reporting financial stress was consistent across Time 2 and Time 3 (2.9%).

Conversely, more participants qualitatively reported an increase in social interactions at Time 2 (14.3%) compared to Time 3 (5.7%). An unexpected observation was that an even larger subset of participants reported decreases in social interactions at Time 3 (22.9%), indicating that they were interacting less during reopening than during stay-at-home orders. There was no change in reports of time spent working from Time 2 to Time 3 (both 8.6%); however, more participants reported a decrease in work at Time 2 (2.9%) compared to Time 3 (0%). Lastly, qualitative reports of weight increasing stayed the same at both time points (2.9%), and there were no indications of weight decreasing at either time point (0%).

Additional Information about Mental Health Symptoms

Baseline scores on validated measures for all mental health domains indicated that participants began with moderate to severe symptoms of anxiety, depression, stress, body dissatisfaction, and sleep problems (see Table 3). In fact, average scores at baseline were above the thresholds for clinically meaningful depressive symptoms (16; Radloff, 1977) and sleep problems (5; Buysse et al., 1989). ICCs for mental health symptom scores over the three time points indicated that although the majority of variability in these scores reflected stable differences between people, within-person variability across time was significant (ps < 0.001). As detailed in Table 3, participants’ scores for depressive symptoms showed significant change over time (F[2,67] = 3.47, p = 0.04, sr = 0.31), with symptoms worsening from Time 1 to Time 2 and showing minimal change from Time 2 to Time 3. Scores for sleep quality showed a similar pattern (F[2,68] = 4.47, p = 0.02, sr = 0.36). In contrast, scores for anxiety symptoms, perceived stress, and body image quality of life showed only small and nonsignificant changes across time points (ps > 0.25).

Table 3.

Descriptive statistics and multilevel model estimates for change in mental health symptoms from pre-COVID-19 (Time 1), during stay-at-home orders (Time 2), and during initial reopening (Time 3) among women in midlife with elevated risk for cardiovascular disease (n = 35).

Descriptives Time 1
M (SE) 		Time 2
M (SE) 		Time 3
M (SE)
 Depression 35.54 (2.09) 40.05 (2.09) 39.54 (2.10)
 Anxiety 35.28 (1.80) 35.54 (1.80) 34.63 (1.82)
 Body Image −3.67 (3.80) 0.33 (3.80) −1.61 (3.82)
 Perceived Stress 19.45 (1.25) 20.00 (1.26) 19.61 (1.26)
 Sleep Quality 7.34 (0.75) 8.91 (0.75) 8.29 (0.75)
Multilevel Model Estimates Depression
B (SE) 		Anxiety
B (SE) 		Body Image
B (SE)
 Intercept 39.55 (2.10)** 34.64 (1.82)** −1.64 (3.82)**
 BMI 0.25 (0.34) 0.38 (0.28) −1.21 (0.67)
 Age −0.03 (0.35) −0.19 (0.29) 0.35 (0.68)
 Time 1 −4.00 (1.89)* 0.64 (1.77) −2.07 (2.64)
 Time 2 0.51 (1.89) 0.90 (1.77) 1.93 (2.64)
 Time 3 -- -- --
 Multilevel Model Estimates Perceived Stress
B (SE) 		Sleep Quality
B (SE)
 Intercept 19.61 (1.26)** 8.29 (0.75)**
 BMI 0.11 (0.21) 0.13 (0.13)
 Age −0.17 (0.21) −0.04 (0.14)
 Time 1 −0.15 (1.11) −0.94 (0.53)*
 Time 2 0.40 (1.22) 0.63 (0.53)
 Time 3 -- --
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Note: Time 3 is the reference point for each multilevel model;

*

p < 0.05;

**

p < 0.01.

Discussion

The goal of the present study was to better understand how changes in daily life circumstances during the COVID-19 pandemic has affected midlife women with elevated risk for CVD, by examining reported change in their professional, caregiving, and mental health experiences across three time points (pre-pandemic, beginning of widespread stay-at-home orders, and during initial reopening). Findings showed reports of meaningful changes in caregiving, medical visits, and employment status across these three time points. The majority of participants reported working from home with children in the house during stay-at-home orders, without much change during reopening, and a meaningful subset also provided care to those other than their children or partners. Interestingly, with respect to medical visits, we observed only some reported delay due to the pandemic. These findings contrast with existing research, which has shown a significant decrease in medical visits since the beginning of the pandemic among peri- and postmenopausal women (Hipolito et al., 2020), as well as the broader U.S. primary care patient population (Alexander et al., 2020).

It is possible that the present sample, which was selected for existing chronic conditions such as hypertension and type 2 diabetes, continued to engage in treatment for these conditions to prevent additional health problems, and that women who were not already engaged in treatment (who were not included in the present study) may have put off primary preventive care. Given the present sample’s elevated risk for CVD, it is encouraging that more women did not report delaying medical care. However, the subset who did was substantial (31.4%), and it is critical for future research to determine how to promote continued engagement in medical care for these women during public health crises.

The majority of women in this study reported a change in employment from the beginning of stay-at-home orders to initial reopening, indicating that they were now working from home, and the most common response indicated no change in the number of hours worked per week. Again, these findings contrast with recent literature revealing that there has been a 10.0% decline in hours worked per week due to the pandemic, specifically related to careers in Leisure & Hospitality and Retail Trade (Kurmann et al., 2020). However, the present sample of women likely had careers outside of these sectors that allowed them to transition to working from home without any disruption in hours worked per week. As research has documented, there has been additional pressure placed on women (compared to men) to transition to working from home in order to take on the increased responsibilities associated with childcare (Sevilla & Smith, 2020). This transition may have negative long-term effects on women’s mental health and career advancement (Yamamura & Tsustsui, 2021), which suggests that the context of work hours may matter more than the overall number of hours worked.

Changes in Mental Health Symptoms over the Course of COVID-19

We expected to observe an increase in mental health symptoms from before to during the most restrictive phase of the pandemic, and then a decrease in scores once lockdown restrictions began to lift. Interestingly, although some symptoms worsened during stay-at-home orders, not all did, and those that worsened showed little evidence of recovery at reopening. Available literature shows similar patterns for suicide mortality among the Japanese population, demonstrating an unexpected, rapid decline in suicide rates during the first five months of the pandemic (Tanaka & Okamoto, 2021). In the present study, depression and sleep quality scores were the only two mental health domains to significantly change across time, and both showed a similar pattern of increasing from pre-pandemic to the beginning of stay-at-home orders, and then sustaining from stay-at-home orders to initial reopening. Previous studies that have shown an association between self-reported depressive symptoms and sleep quality scores (Brown et al., 2009; Thase, 2006), and sleep difficulties represent a symptom of depression as a broader syndrome (Nutt et al., 2008). As such, the similarities in change over time observed herein suggest consistency in experiences and self-reports of mental health symptoms in this population. The current findings also are somewhat consistent with observations of an increase in depressive symptoms from April to September 2020 among adults worldwide (Veldhuis et al., 2021), though that study did not have a pre-pandemic assessment point for comparison.

The present findings indicate that symptoms of depression and sleep problems increased as social interactions decreased, from pre-pandemic to stay-at-home orders. From stay-at-home orders to initial reopening, there was an increase in self-reported social interactions and a plateau in depressive symptoms and sleep problems. Social isolation from quarantine is known to have a negative impact on mental health (Hawryluck et al., 2004; Jeong et al., 2016), and the ability to interact with others again may have buffered the present sample against a continued rise in mental health symptoms observed in other studies (e.g., Veldhuis et al., 2021).

Importantly, however, average scores for the mental health measures used in this study show that our sample of women in midlife was already highly distressed prior to the pandemic (i.e., with scores in the severe ranges). Although qualitative responses showed that less than 6% of women cited anxiety or depressive symptoms as particularly problematic during stay-at-home orders or initial reopening, quantitative findings showed depression and anxiety scores in the moderate to severe ranges on validated symptom measures at these time points. It is possible that this discrepancy is due to the wording of our qualitative item, which did not specifically ask about mental health symptoms (“Please share anything else you’d like to share about how COVID-19 precautions have affected your physical activity, work, or social interactions). Additionally, the qualitative item was created for this study and was presented after validated measures of mental health symptoms, therefore, participants may not have thought to report again on these symptoms, after having just responded to the quantitative measures. However, these inconsistent findings also raise the possibility that women in midlife with CVD risk conditions adapt to the severity of their mental health symptoms over time (Husser & Roberto, 2009), and do not fully acknowledge these difficulties (e.g., in response to broad open-ended questions) until they are directly asked to report on their experiences (e.g., in response to numeric scales that promote awareness of specific symptoms). This potential interpretation is consistent with previous observations that women in midlife often put others’ needs before their own, which can result in neglecting their own health concerns (Smith-DiJulio et al., 2008; Thomas et al., 2018).

Our findings also align with previous literature which has shown that women in midlife often report higher levels of psychological distress compared to men (Reeves et al., 2011; Sassarini, 2016), and are a particularly vulnerable group due to their increased risk of developing poor health outcomes such as co-occurring obesity and depression (Schreiber & Dautovich, 2017). The present findings indicate that women in midlife who are distressed prior to a public health crisis do not show global increases in distress, though the symptoms most closely tied to increased risk of CVD (depressive symptoms and sleep problems; Bucciarelli et al., 2020; Matsuda et al., 2017) are those that increase. The present study adds to existing literature by emphasizing the importance of attention to this population of women to better understand the consequences of their mental health symptoms for their risk of CVD morbidity and mortality.

Strength, Limitations, and Future Directions

Strengths of the present study were a focus on an at-risk population, repeated assessments for each participant at key points in the first year of the COVID-19 pandemic (before the start, during stay-at-home orders, and during initial reopening), the use of validated measures to assess mental health symptoms, and use of a multilevel statistical approach that addresses the nested structure of repeated assessments (Singer, Willett, & Willett 2003). Limitations of the study were a modest sample size and limited diversity with respect to racial/ethnic identification and socioeconomic status. Further, although inclusion of women without active COVID-19 illness or symptoms was intentional, this decision precludes examination of global or time-sensitive differences in mental health symptoms based on relevant illness experiences. Given that adults with preexisting CVD risk are likely to experience cardiovascular events after contracting COVID-19 (Sabatino et al., 2020), women in midlife with elevated risk for CVD who experienced (or those who will or continue to experience the direct effects of) COVID-19 warrant further attention.

These limitations notwithstanding, this study is one of the first to describe changes in life circumstances and mental health across multiple time points during the COVID-19 pandemic, specifically in a population of midlife women with elevated CVD risk. Findings from this study contribute critical information on the influence of the COVID-19 pandemic (i.e., social, familial, and professional changes due to the public health crisis) on an at-risk group of women, which may be used to help guide future health promotion efforts specifically tailored to this population.

Conclusions

To our knowledge, this study is one of the first to observe self-reported changes in life circumstances and mental health symptoms among women in midlife at-risk for CVD before and during the COVID-19 pandemic. These women may not show global increases in distress (relative to other populations), due to their already increased rates of distress related to their health circumstances and the various roles they may take on during this stage of life, though symptoms that may exacerbate their CVD risk do increase. Information obtained from targeted health screenings during medical visits may help to implement tailored interventions aimed at reducing mental health symptoms, and ultimately, reducing the risk of developing CVD in at-risk groups during crises such as the COVID-19 pandemic.

Acknowledgments:

The authors would like to thank Kristen Pasko, Laura Travers, M. Cole Ainsworth, and Emily Vendetta for their contributions to data collection and management.

Funding:

This work was supported by the National Institutes of Health under Grant K23 HL136657 (PI: D. Arigo).

Footnotes

Declaration of Interest Statement: No interests to disclose.

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