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. Author manuscript; available in PMC: 2025 Mar 1.
Published in final edited form as: J Psychosom Res. 2024 Jan 17;178:111594. doi: 10.1016/j.jpsychores.2024.111594

Negative Work-to-Family Spillover Stress and Heightened Cardiovascular Risk Biomarkers in Midlife and Older Adults

Andree Hartanto 1, K T A Sandeeshwara Kasturiratna 1, Meilan Hu 1, Shu Fen Diong 1, Verity Y Q Lua 2
PMCID: PMC10947879  NIHMSID: NIHMS1961159  PMID: 38262325

Abstract

Objectives:

The current study aimed to investigate the health implications of negative work-to-family spillover on cardiovascular risk biomarkers.

Methods:

In a large-scale cross-sectional dataset of working or self-employed midlife and older adults in the United States (N = 1,179), we examined five biomarkers linked to cardiovascular risk, including high-density lipoprotein (HDL) cholesterol, low-density lipoprotein cholesterol, triglyceride, interleukin-6, and C-reactive protein. Negative work-to-family spillover, measured using a four-item self-reported questionnaire, was included into our model to study its association with these cardiovascular risk biomarkers.

Results:

Our findings indicate a significant association between negative work-to-family spillover and cardiovascular risk biomarkers – higher triglycerides (β = .108, p < .001), interleukin-6 (β = .065, p = .026), and C-reactive protein (β = .067, p = .022), and lower HDL cholesterol (β = −.104, p < .001). The associations on triglycerides (β = .094, p = .001) and HDL cholesterol (β = −.098, p < .001) remained significant even after controlling numerous control variables of demographics, medication, health-status, and health-related behaviors. The findings were also consistent against slight variations in the analytic method and adjustment for multiple comparisons.

Conclusions:

The current study supports the premise that spillover of work-related tensions into family life is associated with objective physiological changes that contribute to cardiovascular risk.

Keywords: Negative work-to-family spillover, work-life balance, cardiovascular risk, interleukin-6, C-reactive protein, cholesterol

In today’s fast-paced society, the concept of work-life balance has become increasingly prominent due to the tension between professional duties and individual personal commitment [1–4]. With increasing job demands, extended working hours, and blurring of boundaries between work and personal domains due to technology [5–8], there is an increasing propensity for challenges and stressors from the workplace to permeate personal and family life, a phenomenon often referred to as negative work-to-family spillover [9]. The rising prevalence of negative work-to-family spillover is of significant concern, especially given the consistent findings from past research that outline its mental health, familial, and organizational implications [10–13]. Specifically, negative work-to-family spillover has been linked to an increase in depressive symptoms, burnout, substance abuse, and strained family relationships [9,14,15]. Concurrently, there is evidence linking negative work-to-family spillover to a decrease in work productivity and job satisfaction [10,15–18].

Beyond the mental health, familial, and organizational implications, there are growing concerns on the implication of negative work-to-family spillover on physical health conditions [9, 19]. Research has consistently shown that stress exposure, similar with the kind experienced in negative work-to-family, can have a direct influence on biomarkers such as interleukin-6 and C-reactive protein [20–24]. Additionally, studies in animal models have demonstrated that prolonged stress exposure can lead to dysregulation of lipid metabolism [25–27]. It is also posited that the persistent rumination on work-related stressors triggers a prolonged activation of various biological systems that accumulate toll on the body and precipitate downstream physical health issues [28,29]. This notion aligns with the perseverative cognition hypothesis, which posits that repetitive thoughts or ruminations about past or anticipated stressors can detrimentally impact physiological processes and, subsequently, long-term health outcomes [30,31]. In this context, the concept of allostatic load – the cumulative wear and tear on the body’s systems owing to chronic stress exposure [32,33] – becomes particularly relevant. The framework helps to understand how continuous activation of stress response systems can lead to dysregulation and, ultimately, various health impairments, including cardiovascular conditions [34–36].

In line with this, studies have shown that negative work-to-family spillover is associated with a spectrum of health outcomes, such as psychosomatic health complaints, fatigue, poor subjective health, self-reported chronic illnesses, and perceived deviations in sleep quality [19,37–39]. However, the focus has largely been on subjective health outcomes, while invaluable for capturing an individual’s perception of their health [40,41], has been shown to be influenced by various factors such as personal bias, mood at the time of assessment, and cultural and sociodemographic factors [42,43]. More importantly, subjective health may not be sensitive to some physiological changes within individuals that are in the context of silent or asymptomatic conditions [44,45]. This is especially relevant for cardiovascular health, the leading causes of mortality globally [46], which often manifest silently, with physiological changes developing unnoticed for years before discernible symptoms emerge [47].

Considering the global prominence of cardiovascular diseases [48] and their potential linkage with chronic stressors [49,50], the current study aimed to investigate the health implications of negative work-to-family spillover on cardiovascular risk biomarkers. In a large-scale dataset of working or self-employed adults (N = 1,179), we examined five biomarkers linked to cardiovascular risk, including high-density lipoprotein (HDL) cholesterol, low-density lipoprotein (LDL) cholesterol, triglyceride, interleukin-6, and C-reactive protein [51–53]. Given that higher levels of HDL cholesterol are indicative of lower cardiovascular risk while higher levels of LDL cholesterol, triglycerides, interleukin-6, and C-reactive protein are indicative of higher cardiovascular risk, we hypothesized that the increase in negative work-to-family spillover would be positively associated with HDL cholesterol and negatively associated with LDL cholesterol, triglycerides, interleukin-6, and C-reactive protein.

Method

Participants

The current study involved a cross-sectional sample of 1,179 working or self-employed adults from the National Survey of Midlife Development in the United States (MIDUS) II: Biomarker Project [54] and MIDUS Refresher: Biomarker Project [55]. MIDUS II Biomarker Project, which took place between 2004–2009, is a subset of a large-scale longitudinal project from the original MIDUS I survey that initiated in 1995, with 7,108 noninstitutionalized adults recruited through random digit sampling across 48 contiguous states of the United States. MIDUS Refresher Biomarker Project, conducted from 2012 to 2016, is derived from the MIDUS Refresher baseline cohort that commenced in 2011. Similar to MIDUS I, participants in the MIDUS Refresher survey were also selected through random digit dialing from the 48 contiguous states, evenly distributed by age and gender. The MIDUS Refresher was specifically designed to recruit new participants to replenish the original MIDUS I cohort. The average number of work hours per week for the current participants was 41.14 (SD = 15.88) for MIDUS II and 40.85 (SD = 16.60) for MIDUS Refresher.

Similar data collection methodology and identical measures were employed in both MIDUS II Biomarker Project and MIDUS Refresher: Biomarker Project. In both projects, participants attended an overnight stay at one of the three general clinical research centers in the United States, which includes University of California, Los Angeles, Georgetown University, and University of Wisconsin-Madison. During their stay, participants underwent a physical exam that included the collection of a fasting blood sample before breakfast on the second day of the participant’s hospital stay [56]. The data collection received approval from the Health Sciences Institutional Review Boards at the University of Wisconsin-Madison (H-2008–0060) and all data collection procedures adhered according to the approved guidelines and regulations. Before taking part in the study, all participants have signed a written informed consent. We followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline [57]. Table 1 presents the descriptive statistics for demographics and key variables of the sample.

Table 1.

Participants’ Characteristics

n M SD Range
Demographics
 Age 1,179 52.64 10.98 26–83
 Sex (% male) 1,179 50.30%
 Race (% white) 1,179 88.96%
 Education Attainment 1,176 8.32 2.34 1–12
 Household Income (in thousands) 1,155 93.86 65.29 0–300
Health Status and Behaviors
 Diabetes (%) 1,173 6.14%
 Hypertension (%) 1,173 20.46%
 Stroke (%) 1,173 0.34%
 Smoking (% current smoker) 1,177 9.18%
 Exercise (% exercise regularly) 1,179 79.30%
 Alcohol consumption (drink per month) 1,179 2.66 1.52 1–6
 Antihyperlipidemic Medication 1,178 24.19%
Main Variable
 Negative work-to-family spillover 1,106 10.47 2.76 4–20
Cardiovascular Risk Biomarkers
 Triglycerides (mg/dL) 1,171 126.75 122.73 25–3299
 HDL cholesterol (mg/dL) 1,169 56.18 18.50 20–137
 LDL cholesterol (mg/dL) 1,169 104.83 33.88 16–283
 Interleukin-6 (pg/mL) 1,170 2.35 2.09 0.12–21.82
 C- reactive protein (ug/mL) 1,169 2.63 4.87 0.03–79.30
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Note. Values are shown before imputation and winsorization. Education attainment was rated on a scale of 1 (No school) to 12 (PhD, EdD, MD, LLB, LLD, JD, or other professional degree). HDL = high-density lipoprotein, LDL = low-density lipoprotein.

Measures

Negative Work-to-Family Spillover.

Negative work-to-family spillover was measured using a four-items “Stress at work makes you irritable at home” (M = 3.53, SD = 0.82, range = 1–5), “Job worries or problems distract you when you are at home” (M = 3.62, SD = 0.87, range = 1–5), “Your job reduces the effort you can give to activities at home” (M = 3.15, SD = 0.91, range = 1–5), and “Your job makes you feel too tired to do the things that need attention at home” (M = 3.24, SD = 0.86, range = 1–5) in MIDUS II and MIDUS Refresher. Participants rated themselves on a 5-point scale (1 = All of the time, 5 = Never). The measure was constructed by calculating the sum of the values of the 4 items. The measure was developed for the MIDUS study (9) and has been widely used and psychometrically validated by existing studies using MIDUS dataset [16,58–60]. Higher scores were coded to indicate higher negative work-to-family spillover (Cronbach’s αMIDUS II = .805; Cronbach’s αMIDUS Refresher = .806).

Serum Lipid.

In both MIDUS II: Biomarker Project and MIDUS Refresher: Biomarker Project, HDL cholesterol, triglyceride, and total cholesterol levels were determined using enzymatic colorimetric assays. For HDL cholesterol, the inter-assay and intra-assay coefficients of variability were 6.52% and 1.1–1.4% respectively in MIDUS II, and 3.56% and 1.1–1.4% respectively in MIDUS Refresher. For triglycerides, the inter-assay and intra-assay coefficients of variability were 1.01% and 1.6% respectively in MIDUS II and 2.51% and 1.6% respectively in MIDUS Refresher. For total cholesterol, the inter-assay and intra-assay coefficients of variability were 2.65% and 0.51–0.81% respectively in MIDUS II and 4.13% and 0.51–0.81% respectively in MIDUS Refresher. LDL cholesterol was estimated using the Friedewald formula [61] from direct measurements of total cholesterol, triglycerides, and HDL cholesterol, with a formula as follows: LDL = total cholesterol – HDL – triglycerides/5. Given that the Fridewald formula begins to be unreliable when triglycerides are elevated [62], when calculating LDL cholesterol, 400 mg/dl was used as the upper limit for triglycerides if the Subject’s triglyceride level is more than 400 mg/dl. The inter-assay coefficients of variability for LDL cholesterol in MIDUS II and MIDUS Refresher were 10.11% and 4.7%, respectively. Higher levels of HDL cholesterol are indicative of lower cardiovascular risk while higher levels of LDL cholesterol and triglycerides are indicative of higher cardiovascular risk.

C-reactive protein.

C-reactive protein was measured using the BNII nephelometer (N Antiserum to Human Fibrinogen; Dade Behring, Inc., Deerfield, IL) with a particle-enhanced immunonepholometric assay range of 0.175–1100 μg/mL (reference range < 3 μg/ml) for MIDUS II and 0.164–800 μg/mL (reference range < 3 μg/ml) for MIDUS Refresher. The inter-assay and intra-assay coefficients of variability were 2.1%–5.7% and 2.3%–4.4% respectively in MIDUS II and 1.08%–4.3% and 2.3%–4.4% respectively in MIDUS Refresher. If participants’ measure using BNII nephelometer were below the assay range, samples were re-assayed by immunoelectrochemiluminescence using the Meso Scale Diagnostics #K151STG high-sensitivity kit. Higher levels of C-reactive protein are indicative of higher cardiovascular risk.

Interleukin-6.

Interleukin-6 was measured using the Quantikine® High-sensitivity enzyme-linked immunosorbent assay (ELISA) kit #HS600B (R&D Systems, Minneapolis, MN), with an assay range of 0.156–10 pg/mL and reference range of 0.45 to 9.96 pg/mL in both MIDUS II and MIDUS Refresher. All samples were tested in duplicate. The laboratory inter-assay and intra-assay coefficients of variance for interleukin-6 were 12.31% and 3.25%, respectively in MIDUS II, and 15.66% and 3.73% respectively in MIDUS Refresher. Higher levels of interleukin-6 are indicative of higher cardiovascular risk.

Education Attainment.

Education attainment was measured by asking the participants “what is the highest grade of school or year of college you completed?” through a phone interview in MIDUS II and MIDUS Refresher. It was rated on a 12-points scale (1 = No school/some grade school (1st - 6th grade); 2 = Eight grade/junior high school (7th – 8th grade); 3 = Some high school (9th – 12th grade; no diploma/no GED); 4 = GED; 5 = Graduated from high school; 6 = 1 to 2 years of college, no degree yet; 7 = 3 or more years of college, no degree yet; 8 = Graduated from a two-year college or vocational school, or associate’s degree; 9 = Graduated from a four- or five-year college, or bachelor’s degree; 10 = Some graduate school; 11 = Master’s degree; 12 = Ph.D., ED.D., MD, DDS, LLB, LLD, JD, or other professional degree).

Use of Antihyperlipidemic Agent Medication.

The use of cholesterol medication was recorded by requiring participants to bring all their medication in their original containers during the study to ensure accuracy in MIDUS II and MIDUS Refresher. Each medication was matched through the Lexicomp® Lexi-Data database to their generic names and drug IDs, and ultimately to their therapeutic and pharmacologic class codes. The use of any form of antihyperlipidemic agent medication (e.g., HMG-CoA reductase inhibitor, fibric acid derivatives, bile acid sequestrants, cholesterol absorption inhibitors) was dummy coded (1 = yes, 0 = no).

Data Analysis

The current study aimed to examine the association between negative work-to-family spillover and cardiovascular risk biomarkers indexed by HDL cholesterol, LDL cholesterol, triglyceride, interleukin-6, and C-reactive protein. For each cardiovascular risk biomarker, ordinary least squares regression was performed with negative work-to-family spillover as the independent variable. Two separate models were estimated for each criterion. In the first model, we controlled for demographic variables, such as age, gender, education attainment, household income and race, that may be associated with cardiovascular risk [63–67]. In the second model, in order to ensure the robustness of our estimate, we controlled for comorbidities of cardiovascular disease including history of hypertension, diabetes, and stroke for the past 12 months [68–70]. We also controlled for health-related behaviors variables that are associated with cardiovascular health [71,72], including current smoking status, regular exercise, and the use of antihyperlipidemic agent medication. We winsorized all our outcome indices to 3 SDs to reduce the influence of extreme outliers. MIDUS II: Biomarker Project and MIDUS Refresher: Biomarker Project do not incorporate sampling weights. Data analysis was performed using SPSS Version 25. Missing values were imputed using the expectation-maximisation (EM) algorithm (see Table 1 for the exact sample size for each variable).

Results

For lipid profile, as shown in Table 2, after controlling for demographics in Model 1, we found that negative work-to-family spillover significantly associated with HDL cholesterol (β = −.104, b = −0.704, SE = 0.183, 95% CI = [−1.062, −0.345], p < .001) and triglycerides (β = .108, b = 2.897, SE = 0.775, 95% CI = [1.376, 4.418], p < .001), but not LDL cholesterol (β = .036, b = 0.436, SE = 0.368, 95% CI = [−0.286, 1.158], p = .236). In our Model 2, after controlling for health status and health-related behaviors, the association between negative work-to-family spillover and HDL cholesterol remained significant (β = −.098, b = −0.665, SE = 0.173, 95% CI = [−1.003, −0.326], p < .001) and triglycerides (β = .094, b = 2.532, SE = 0.775, 95% CI = [1.011, 4.054], p = .001). However, in Model 2, the association between negative work-to-family spillover and LDL cholesterol was no longer significant (β = .039, b = 0.480, SE = 0.353, 95% CI = [−0.213, 1.174], p = .174).

Table 2.

Standard Coefficient Estimates of the Negative Work-to-Family Spillover on the Lipid Profile

Triglycerides HDL Cholesterol LDL Cholesterol
Model 1 Model 2 Model 1 Model 2 Model 1 Model 2
β b (SE) β b (SE) β b (SE) β b (SE) β b (SE) β b (SE)
Negative Work-to-Family Spillover .108 2.90 (0.78)** .094 2.53 (0.78)* −.104 −.704 (.183)** −.098 −.665 (.173)** .036 .436 (.368) .039 .480 (.353)
Age .016 0.11 (0.19) −.020 −0.13 (0.20)* .056 .-092 (.045)** .077 .127 (.045)* −0.19 −0.56 (.090) .088 .265 (.093)*
Sex (% male) .263 37.96 (4.06)** .257 37.03 (4.10)** −.425 −15.4 (.956) −.449 −16.3 (.912) .040 2.62 (1.93) .074 4.87 (1.87)*
Race (% white) .064 14.75 (6.51)* .073 16.80 (6.48)* −.020 −1.18 (1.53) −.037 −2.14 (1.44) .032 3.40 (3.09) .035 3.69 (2.95)
Education Attainment −.042 −1.30 (0.90) −.023 −0.71 (0.91) .095 .737 (.213)* .053 .410 (.203) −.068 −.959 (.428) −.060 −.841 (.416)
Household Income −.064 −0.08 (0.03) −.057 −0.06 (0.03) .009 .003 (.008) −.025 −.007 (.007) −.050 −.025 (.016) −.010 −.005 (.015)
Diabetes (%) .024 7.32 (8.68) −.017 −1.32 (1.93) −.081 −11.1 (3.96)
Hypertension (%) .094 16.81 (5.29)* −.069 −3.13 (1.18)* .020 1.65 (2.41)
Stroke (%) −.031 −38.74 (34.43) .014 4.34 (7.66) −.017 −9.78 (15.7)
Smoking (% current smoker) .035 8.75 (7.06) −.059 −3.70 (1.57) .084 9.55 (3.22)*
Exercise (% exercise regularly) −.082 −14.56 (5.03)* .091 4.07 (1.12)** .006 .484 (2.29)
Alcohol consumption −.018 −0.87 (1.37) .285 3.41 (.304)** −.018 −3.98 (.622)
Antihyperlipidemic Medication .033 5.62 (5.17) −.083 −3.50 (1.15)* −.296 −22.7 (2.36)**
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β = standardized regression coefficient.

*

p < .05,

**

p < .001

For inflammation biomarkers, after controlling for demographics in Model 1, negative work-to-family spillover was significantly associated with interleukin-6 (β = .065, b = 0.042, SE = 0.019, 95% CI = [0.005, 0.078], p = .026) and C-reactive protein (β = .067, b = 0.080, SE = 0.035, 95% CI = [0.011, 0.148], p = .022). However, after controlling for health-status and health-related behaviors in Model 2, the association between negative work-to-family spillover and interleukin-6 was no longer significant (β = .045, b = 0.029, SE = 0.018, 95% CI = [−0.007, 0.065], p = .115) and C-reactive protein (β = .047, b = 0.056, SE = 0.034, 95% CI = [−0.012, 0.123], p = .107; see Table 3)

Table 3.

Standard Coefficient Estimates of the Negative Work-to-Family Spillover on Systemic Inflammation

Interleukin-6 C-reactive Protein
Model 1 Model 2 Model 1 Model 2
β b (SE) β b (SE) β b (SE) β b (SE)
Negative Work-to-Family Spillover .065 .042 (.019) .045 .029 (.018) .067 .080 (.035) .047 .056 (.034)
Age .228 .036 (.005)** .173 .027 (.005)** .007 .002 (.009) −.023 .007 (.009)
Sex (% male) −.020 −.067 (0.97) −.022 −.077 (.097) −.0160 −1.02 (.183) −.154 −.980 (.182)**
Race (% white) −.074 −.406 (.156) −.059 −.322 (.153) −.011 −.111(.293) .003 .035 (.287)
Education Attainment −.076 −.055 (−.022) −.047 −.034 (.−022) −.140 −.189 (.041) −.118 −.161 (.041)**
Household Income −.042 −.001 (.001) −.029 −.001 (.001) .017 .001 (.001) .031 .002 (.001)
Diabetes (%) .073 .522 (.205) .101 1.34 (.285)*
Hypertension (%) .153 .647 (.125)** .120 .946 (.234)**
Stroke (%) .003 .091 (.812) .064 3.51 (1.53)
Smoking (% current smoker) .033 .195 (.167) −.011 −.122 (.313)
Exercise (% exercise regularly) −.104 −.438 (.119)** −.099 −.773 (.223)*
Alcohol consumption −.074 −.083 (.032) −.072 −.150 (.061)
Antihyperlipidemic Medication .039 .155 (.122) −.037 −.275 (.229)
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β = standardized regression coefficient.

*

p < .05,

**

p < .001

In addition, we also conducted sensitivity analyses to further ensure the robustness of our results in triglycerides and HDL cholesterol to slight variations in the analytic method and adjustment for multiple comparisons, such as using multiple imputation, using listwise deletion, using pairwise deletion, correcting for multiple comparisons using Bonferroni procedure, winsorizing triglycerides and HDL cholesterol to 4 SD, log-transforming triglycerides and HDL cholesterol levels after winsorization, analyzing only participants not taking antihyperlipidemic medication, using unadjusted model, and treating the items in the negative work-to-family spillover measure separately in the model. Overall, slight variations of the analysis method did not influence the positive associations between negative work-to-family spillover and triglycerides and the negative associations between negative work-to-family spillover and HDL cholesterol, with an exception for the correlation between the item “Your job reduces the effort you can give to activities at home” and triglycerides in Model 2 (see Table S1 in the Supplemental Materials).

Discussion

In the present study, we examined the relationship between negative work-to-family spillover and serum biomarkers in midlife and older adults to elucidate the cardiovascular risk implications of work-life imbalance. Our findings indicate a significant association between negative work-to-family spillover and two biomarkers: higher triglycerides and lower HDL cholesterol. More importantly, the positive association between negative work-to-family spillover and triglyceride as well as the negative association between negative work-to-family spillover and HDL cholesterol remained significant after controlling for numerous control variables of demographics, medication, health-status, and health-related behaviors. The findings were also consistent against slight variations in the analytic method and adjustment for multiple comparisons. The current study supports the premise that spillover of work-related tensions into family life may manifest in objective physiological changes that contribute to cardiovascular risk.

Perseverative cognition hypothesis offers a valuable framework for understanding the heightened triglycerides and lower HDL cholesterol in individuals with high negative work-to-family spillover [30,31]. The hypothesis suggests that prolonged mental engagement with stressors, like those originating from work, can ignite a cascade of physiological responses. Indeed. chronic activation of the stress system, primarily the hypothalamic-pituitary-adrenal (HPA) axis and the sympathetic-adrenal-medullary (SAM) system has been shown to lead to dysregulation in lipid metabolism [73]. Over time, this dysregulation can manifest as elevated triglycerides and reduced HDL cholesterol, which has been widely recognized as crucial indicators of cardiovascular health [51].

Interestingly, the current study also observed positive associations between negative work-to-family spillover with inflammation biomarkers such as interleukin-6 and C-reactive protein. However, the results were weakened after health-status and health-related behaviors were controlled in the analyses. The findings may imply distinct pathways between negative work-to-family spillover and unhealthy lipid profile and between negative work-to-family spillover and systemic inflammation. While the association between negative work-to-family spillover and unhealthy lipid profile is likely to be driven by dysregulation in lipid metabolism as predicted by perseverative cognition hypothesis [30,31], it is plausible that the relationship between negative work-to-family spillover is explained by the higher likelihood of those experience negative work-to-family spillover to engage in poor health behaviors such as sedentary lifestyle and smoking [9,74], resulting in chronic disease such as hypertension and diabetes.

The current study does present some limitations. First, the current findings were mainly grounded in a cross-sectional design, restricting any causal inferences in the current study. Although the current study systematically controlled for a wide array of confounding variables, the risk for potential third variables cannot be entirely ruled out. Second, due to the cross-sectional design, reverse causation is still plausible where individuals with healthier health status and lower cardiovascular risk may be better equipped to manage work-to-family spillover effectively. This underscores the need for future studies to employ longitudinal study to ascertain the directionality of the observed relationship. Lastly, our study exclusively focused on midlife and older adults in the United States. For a comprehensive understanding, it is necessary for future studies to examine the relationship between negative work-to-family spillover and cardiovascular risk biomarkers across different age groups and in various cultures to ensure generalizability.

In conclusion, the current study reveals a significant association between negative work-to-family spillover and heightened cardiovascular risk biomarkers – specifically elevated triglycerides and reduced HDL cholesterols – in midlife and older adults. The findings may suggest a tangible physiological implication of work-life imbalances among midlife and older adults. Findings from the current study underscore the urgency to address work-life imbalances, not only for psychological well-being but also for cardiovascular health, which holds broader implications for workplace policies and health interventions.

Supplementary Material

1

Highlights.

  • NWFS is associated with heightened cardiovascular risk in midlife and older adults

  • NWFS is associated with higher triglycerides and lower HDL cholesterol

  • The findings were robust against variations in the analytic method

  • The findings suggest cardiovascular risk implications of work-life imbalances

Funding

The data of this research was supported by grants from the NIH National Institute on Aging (P01-AG020166) to conduct the MIDUS II and MIDUS Refresher baseline surveys. The biomarker projects were further supported by the NIH National Center for Advancing Translational Sciences (NCATS) Clinical and Translational Science Award (CTSA) program (UL1TR001409, UL1TR001881, and 1UL1RR025011) as well as the NIH National Institute on Aging (5P01AG020166). Andree Hartanto was supported by a grant from the Ministry of Education Academy Research Fund Tier 1 conferred (22-SOSS-SMU-041).

Footnotes

Declaration of Interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

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Data Availability Statement

All MIDUS datasets and documentation are archived and publicly available at the ICPSR repository (http://www.icpsr.umich.edu/) at the University of Michigan.

References

  • [1].Le H, Newman A, Menzies J, Zheng C, Fermelis J. Work–life balance in Asia: A systematic review. Hum Resour Manag Rev. 2020. Dec 1;30(4):100766. [Google Scholar]
  • [2].Lua VYQ, Majeed NM, Leung AK y., Hartanto A. A daily within-person investigation on the link between social expectancies to be busy and emotional wellbeing: the moderating role of emotional complexity acceptance. Cogn Emot. 2022. May 19;36(4):773–80. [DOI] [PubMed] [Google Scholar]
  • [3].Rashmi K, Kataria A. Work–life balance: a systematic literature review and bibliometric analysis. Int J Sociol Soc Policy. 2021. Jan 1;42(11/12):1028–65. [Google Scholar]
  • [4].Sirgy MJ, Lee DJ. Work-Life Balance: an Integrative Review. Appl Res Qual Life. 2018. Mar 1;13(1):229–54. [Google Scholar]
  • [5].Adisa TA, Gbadamosi G, Osabutey ELC. What happened to the border? The role of mobile information technology devices on employees’ work-life balance. Pers Rev. 2017. Jan 1;46(8):1651–71. [Google Scholar]
  • [6].Donthu N, Gustafsson A. Effects of COVID-19 on business and research. J Bus Res. 2020. Sep 1;117:284–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [7].Park Y, Fritz C, Jex SM. Relationships between work-home segmentation and psychological detachment from work: The role of communication technology use at home. J Occup Health Psychol. 2011;16(4):457–67. [DOI] [PubMed] [Google Scholar]
  • [8].Van Fossen JA, Baker NM, Mack EA, Chang CH, Cotten SR, Catalano I. The Moderating Effect of Scheduling Autonomy on Smartphone Use and Stress Among Older Workers. Work Aging Retire. 2022. Jul 19;waac017. [Google Scholar]
  • [9].Grzywacz JG, Marks NF. Reconceptualizing the work–family interface: An ecological perspective on the correlates of positive and negative spillover between work and family. J Occup Health Psychol. 2000;5(1):111–26. [DOI] [PubMed] [Google Scholar]
  • [10].Lee S, Davis KD, Neuendorf C, Grandey A, Lam CB, Almeida DM. Individual- and Organization-Level Work-to-Family Spillover Are Uniquely Associated with Hotel Managers’ Work Exhaustion and Satisfaction. Front Psychol. 2016;7:1180. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [11].Marchiondo LA, Fisher GG, Cortina LM, Matthews RA. Disrespect at Work, Distress at Home: A Longitudinal Investigation of Incivility Spillover and Crossover Among Older Workers. Work Aging Retire. 2020. Jul 10;6(3):153–64. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [12].Repetti R, Wang S wen. Effects of job stress on family relationships. Curr Opin Psychol. 2017. Feb 1;13:15–8. [DOI] [PubMed] [Google Scholar]
  • [13].Sirgy MJ, Lee DJ, Park S, Joshanloo M, Kim M. Work–Family Spillover and Subjective Well-Being: The Moderating Role of Coping Strategies. J Happiness Stud. 2020. Dec 1;21(8):2909–29. [Google Scholar]
  • [14].Arefin MdS, Alam MdS, Li SL, Long L. Spillover effects of organizational politics on family satisfaction: the role of work-to-family conflict and family support. Pers Rev. 2020. Jan 1;50(5):1426–44. [Google Scholar]
  • [15].Goodman WB, Crouter AC, Investigators TFLPK. Longitudinal Associations Between Maternal Work Stress, Negative Work-Family Spillover, and Depressive Symptoms. Fam Relat. 2009;58(3):245–58. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [16].Grzywacz JG, Almeida DM, McDonald DA. Work–Family Spillover and Daily Reports of Work and Family Stress in the Adult Labor Force*. Fam Relat. 2002;51(1):28–36. [Google Scholar]
  • [17].Okechukwu CA, El Ayadi AM, Tamers SL, Sabbath EL, Berkman L. Household Food Insufficiency, Financial Strain, Work–Family Spillover, and Depressive Symptoms in the Working Class: The Work, Family, and Health Network Study. Am J Public Health. 2012. Jan;102(1):126–33. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [18].Grzywacz J Work-Family Spillover and Health During Midlife: Is Managing Conflict Everything? Am J Health Promot AJHP. 2000. Mar 1;14:236–43. [DOI] [PubMed] [Google Scholar]
  • [19].Tsukerman D, Leger KA, Charles ST. Work-family spillover stress predicts health outcomes across two decades. Soc Sci Med. 2020. Nov 1;265:113516. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [20].Chiang JJ, Park H, Almeida DM, Bower JE, Cole SW, Irwin MR, et al. Psychosocial stress and C-reactive protein from mid-adolescence to young adulthood. Health Psychol. 2019. Mar;38(3):259–67. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [21].Johnson TV, Abbasi A, Master VA. Systematic Review of the Evidence of a Relationship Between Chronic Psychosocial Stress and C-Reactive Protein. Mol Diagn Ther. 2013. Jun;17(3):147–64. [DOI] [PubMed] [Google Scholar]
  • [22].Marsland AL, Walsh C, Lockwood K, John-Henderson NA. The effects of acute psychological stress on circulating and stimulated inflammatory markers: A systematic review and meta-analysis. Brain Behav Immun. 2017. Aug;64:208–19. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [23].Wirtz PH, Von Känel R. Psychological Stress, Inflammation, and Coronary Heart Disease. Curr Cardiol Rep. 2017. Nov;19(11):111. [DOI] [PubMed] [Google Scholar]
  • [24].Hartanto A, Yee-Man Lau I, Yong JC. Culture moderates the link between perceived obligation and biological health risk: Evidence of culturally distinct pathways for positive health outcomes. Soc Sci Med. 2020. Jan;244:112644. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [25].Berger DF, Starzec JJ, Mason EB, Devito W. The Effects of Differential Psychological Stress on Plasma Cholesterol Levels in Rats: Psychosom Med. 1980. Sep;42(5):481–92. [DOI] [PubMed] [Google Scholar]
  • [26].Kaplan JR, Adams MR, Clarkson TB, Manuck SB, Shively CA, Williams JK. Psychosocial Factors, Sex Differences, and Atherosclerosis: Lessons from Animal Models. Psychosom Med. 1996;58(6):598–611. [DOI] [PubMed] [Google Scholar]
  • [27].Ricart-Jan[eacute] D, Rodr[iacute]guez-Sureda V, Benavides A, Peinado-Onsurbe J, L[oacute]pez-Tejero MD, Llobera M. Immobilization stress alters intermediate metabolism and circulating lipoproteins in the rat. Metabolism. 2002. Jul;51(7):925–31. [DOI] [PubMed] [Google Scholar]
  • [28].Juster RP, McEwen BS, Lupien SJ. Allostatic load biomarkers of chronic stress and impact on health and cognition. Neurosci Biobehav Rev. 2010. Sep 1;35(1):2–16. [DOI] [PubMed] [Google Scholar]
  • [29].Lundberg U Stress hormones in health and illness: The roles of work and gender. Psychoneuroendocrinology. 2005. Nov 1;30(10):1017–21. [DOI] [PubMed] [Google Scholar]
  • [30].Brosschot JF, Gerin W, Thayer JF. The perseverative cognition hypothesis: A review of worry, prolonged stress-related physiological activation, and health. J Psychosom Res. 2006. Feb 1;60(2):113–24. [DOI] [PubMed] [Google Scholar]
  • [31].Clancy F, Prestwich A, Caperon L, O’Connor DB. Perseverative Cognition and Health Behaviors: A Systematic Review and Meta-Analysis. Front Hum Neurosci [Internet]. 2016. [cited 2023 Sep 3];10. Available from: https://www.frontiersin.org/articles/10.3389/fnhum.2016.00534 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [32].McEwen BS. Stress, Adaptation, and Disease: Allostasis and Allostatic Load. Ann N Y Acad Sci. 1998. May;840(1):33–44. [DOI] [PubMed] [Google Scholar]
  • [33].McEwen BS, Stellar E. Stress and the Individual: Mechanisms Leading to Disease. Arch Intern Med. 1993. Sep 27;153(18):2093. [PubMed] [Google Scholar]
  • [34].Logan JG, Barksdale DJ. Allostasis and allostatic load: expanding the discourse on stress and cardiovascular disease. J Clin Nurs. 2008. Jul;17(7b):201–8. [DOI] [PubMed] [Google Scholar]
  • [35].McEwen BS. The Brain on Stress: Toward an Integrative Approach to Brain, Body, and Behavior. Perspect Psychol Sci. 2013. Nov;8(6):673–5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [36].Schnorpfeil P, Noll A, Schulze R, Ehlert U, Frey K, Fischer JE. Allostatic load and work conditions. Soc Sci Med. 2003. Aug;57(4):647–56. [DOI] [PubMed] [Google Scholar]
  • [37].Golparvar M, Sadeghi E. Relationship of work-family conflict and spillover with psychosomatic complaints considering the mediating role of work anxiety. Psychol Res. 2017;19(2):28–46. [Google Scholar]
  • [38].Grzywacz. Work-Family Spillover and Health during Midlife: Is Managing Conflict Everything? Am J Health Promot. 2000. Mar 1;14(4):236–43. [DOI] [PubMed] [Google Scholar]
  • [39].Lee S, Mogle JA, Jackson CL, Buxton OM. What’s not fair about work keeps me up: Perceived unfairness about work impairs sleep through negative work-to-family spillover. Soc Sci Res. 2019. Jul 1;81:23–31. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [40].Albrecht GL. Subjective health assessment. In: Jenkinson C, editor. Measuring health and medical outcomes. London: Routledge; 2013. p. 7–27. [Google Scholar]
  • [41].Pinquart M Correlates of subjective health in older adults: A meta-analysis. Psychol Aging. 2001;16(3):414–26. [DOI] [PubMed] [Google Scholar]
  • [42].Vaillant N, Wolff FC. On the reliability of self-reported health: Evidence from Albanian data☆. J Epidemiol Glob Health. 2012;2(2):83. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [43].Zajacova A, Dowd JB. Reliability of Self-rated Health in US Adults. Am J Epidemiol. 2011. Oct 15;174(8):977–83. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [44].Molenaar EA, Ameijden EJCV, Grobbee DE, Numans ME. Comparison of routine care self-reported and biometrical data on hypertension and diabetes: results of the Utrecht Health Project. Eur J Public Health. 2007. Apr 1;17(2):199–205. [DOI] [PubMed] [Google Scholar]
  • [45].Okura Y, Urban LH, Mahoney DW, Jacobsen SJ, Rodeheffer RJ. Agreement between self-report questionnaires and medical record data was substantial for diabetes, hypertension, myocardial infarction and stroke but not for heart failure. J Clin Epidemiol. 2004. Oct 1;57(10):1096–103. [DOI] [PubMed] [Google Scholar]
  • [46].Roth GA, Mensah GA, Johnson CO, Addolorato G, Ammirati E, Baddour LM, et al. Global Burden of Cardiovascular Diseases and Risk Factors, 1990–2019: Update From the GBD 2019 Study. J Am Coll Cardiol. 2020. Dec 22;76(25):2982–3021. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [47].Soliman EZ. Silent myocardial infarction and risk of heart failure: Current evidence and gaps in knowledge. Trends Cardiovasc Med. 2019. May 1;29(4):239–44. [DOI] [PubMed] [Google Scholar]
  • [48].Lindstrom M, DeCleene N, Dorsey H, Fuster V, Johnson CO, LeGrand KE, et al. Global Burden of Cardiovascular Diseases and Risks Collaboration, 1990–2021. J Am Coll Cardiol. 2022. Dec 20;80(25):2372–425. [DOI] [PubMed] [Google Scholar]
  • [49].Osborne MT, Shin LM, Mehta NN, Pitman RK, Fayad ZA, Tawakol A. Disentangling the Links Between Psychosocial Stress and Cardiovascular Disease. Circ Cardiovasc Imaging. 2020. Aug 1;13(8):e010931. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [50].Steptoe A, Kivimäki M. Stress and cardiovascular disease. Nat Rev Cardiol. 2012. Jun;9(6):360–70. [DOI] [PubMed] [Google Scholar]
  • [51].Gilstrap LG, Wang TJ. Biomarkers and cardiovascular risk assessment for primary prevention: An update. Clin Chem. 2012. Jan;58(1):72–82. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [52].Hartanto A, Majeed NM, Lua VYQ, Wong J, Chen NRY. Dispositional gratitude, health-related factors, and lipid profiles in midlife: a biomarker study. Sci Rep. 2022. Apr 11;12(1):6034. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [53].Ridker PM, Rifai N, Stampfer MJ, Hennekens CH. Plasma concentration of interleukin-6 and the risk of future myocardial infarction among apparently healthy men. Circulation. 2000. Apr 18;101(15):1767–72. [DOI] [PubMed] [Google Scholar]
  • [54].Ryff CD, Seeman T, Weinstein M. Midlife in the United States (MIDUS 2): Biomarker Project, 2004–2009: Version 4 [Internet]. Inter-University Consortium for Political and Social Research; 2010. [cited 2023 Sep 3]. Available from: https://www.icpsr.umich.edu/icpsrweb/NACDA/studies/29282/versions/V4 [Google Scholar]
  • [55].Weinstein M, Ryff CD, Seeman TE. Midlife in the United States (MIDUS Refresher 1): Biomarker Project, 2012–2016: Version 6 [Internet]. ICPSR - Interuniversity Consortium for Political and Social Research; 2017. [cited 2023 Sep 3]. Available from: https://www.icpsr.umich.edu/web/NACDA/studies/36901/versions/V6 [Google Scholar]
  • [56].Dienberg Love G, Seeman TE, Weinstein M, Ryff CD. Bioindicators in the MIDUS National Study: Protocol, Measures, Sample, and Comparative Context. J Aging Health. 2010. Dec 1;22(8):1059–80. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [57].Von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. The Lancet. 2007. Oct;370(9596):1453–7. [DOI] [PubMed] [Google Scholar]
  • [58].Allen TD, Regina J, Wiernik BM, Waiwood AM. Toward a better understanding of the causal effects of role demands on work–family conflict: A genetic modeling approach. J Appl Psychol. 2023. Mar;108(3):520–39. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [59].Cho E, Tay L, Allen TD, Stark S. Identification of a dispositional tendency to experience work–family spillover. J Vocat Behav. 2013. Jun;82(3):188–98. [Google Scholar]
  • [60].Li A, Shaffer JA, Wang Z, Huang JL. Work-family conflict, perceived control, and health, family, and wealth: A 20-year study. J Vocat Behav. 2021;127:103562. [Google Scholar]
  • [61].Friedewald WT, Levy RI, Fredrickson DS. Estimation of the Concentration of Low-Density Lipoprotein Cholesterol in Plasma, Without Use of the Preparative Ultracentrifuge. Clin Chem. 1972. Jun 1;18(6):499–502. [PubMed] [Google Scholar]
  • [62].Martin SS, Blaha MJ, Elshazly MB, Brinton EA, Toth PP, McEvoy JW, et al. Friedewald-Estimated Versus Directly Measured Low-Density Lipoprotein Cholesterol and Treatment Implications. J Am Coll Cardiol. 2013. Aug;62(8):732–9. [DOI] [PubMed] [Google Scholar]
  • [63].Abdalla SM, Yu S, Galea S. Trends in Cardiovascular Disease Prevalence by Income Level in the United States. JAMA Netw Open. 2020. Sep 25;3(9):e2018150. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [64].Hartanto A, Majeed NM, Ng WQ, Chai CKN, Lua VYQ. Subjective age and inflammation risk in midlife adults: Findings from the Midlife in the United States (MIDUS) studies. Compr Psychoneuroendocrinology. 2021. Aug 1;7:100072. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [65].Lakatta EG. Age-associated Cardiovascular Changes in Health: Impact on Cardiovascular Disease in Older Persons. Heart Fail Rev. 2002. Jan 1;7(1):29–49. [DOI] [PubMed] [Google Scholar]
  • [66].Roth GA, Forouzanfar MH, Moran AE, Barber R, Nguyen G, Feigin VL, et al. Demographic and Epidemiologic Drivers of Global Cardiovascular Mortality. N Engl J Med. 2015. Apr 2;372(14):1333–41. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [67].Winkleby MA, Jatulis DE, Frank E, Fortmann SP. Socioeconomic status and health: how education, income, and occupation contribute to risk factors for cardiovascular disease. Am J Public Health. 1992. Jun;82(6):816–20. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [68].Petrie JR, Guzik TJ, Touyz RM. Diabetes, Hypertension, and Cardiovascular Disease: Clinical Insights and Vascular Mechanisms. Can J Cardiol. 2018. May 1;34(5):575–84. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [69].Sowers JR, Epstein M, Frohlich ED. Diabetes, Hypertension, and Cardiovascular Disease. Hypertension. 2001. Apr;37(4):1053–9. [DOI] [PubMed] [Google Scholar]
  • [70].Watkins PJ. Cardiovascular disease, hypertension, and lipids. BMJ. 2003. Apr 19;326(7394):874–6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [71].Loprinzi PD, Branscum A, Hanks J, Smit E. Healthy Lifestyle Characteristics and Their Joint Association With Cardiovascular Disease Biomarkers in US Adults. Mayo Clin Proc. 2016. Apr 1;91(4):432–42. [DOI] [PubMed] [Google Scholar]
  • [72].Mozaffarian D, Wilson PWF, Kannel WB. Beyond Established and Novel Risk Factors. Circulation. 2008. Jun 10;117(23):3031–8. [DOI] [PubMed] [Google Scholar]
  • [73].Kyrou I, Tsigos C. Stress hormones: physiological stress and regulation of metabolism. Curr Opin Pharmacol. 2009. Dec 1;9(6):787–93. [DOI] [PubMed] [Google Scholar]
  • [74].Moen P, Lam J, Ammons S, Kelly EL. Time Work by Overworked Professionals: Strategies in Response to the Stress of Higher Status. Work Occup. 2013. May 1;40(2):79–114. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

1
NIHMS1961159-supplement-1.docx (83.4KB, docx)

Data Availability Statement

All MIDUS datasets and documentation are archived and publicly available at the ICPSR repository (http://www.icpsr.umich.edu/) at the University of Michigan.

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