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. Author manuscript; available in PMC: 2026 Jan 13.
Published in final edited form as: J Aging Phys Act. 2025 Aug 5;34(1):72–81. doi: 10.1123/japa.2024-0245

Social Isolation and Daily Steps Are Associated With Fatigue in Older Women With Cardiovascular Disease: A Secondary Analysis of the MindMoves Trial

Shamatree Shakya 1, Lisa Sharp 2, Nathan Tintle 3, Anne M Fink 2, Rosalba Hernandez 3, Shannon Halloway 2
PMCID: PMC12795641  NIHMSID: NIHMS2125755  PMID: 40763916

Abstract

Background/Objective:

Fatigue is a common cardiovascular disease (CVD) symptom in older women; however, contributing factors are unclear. This study examined the association of background characteristics, social isolation, movement behaviors, and serum biomarkers with fatigue in older women with CVD.

Methods:

This cross-sectional study used baseline data from older women (≥65 years, N = 246) with CVD participating in the MindMoves trial. We examined background characteristics (age, race/ethnicity, education, marital status, body mass index, pain, and comorbidities), social isolation, movement behaviors (sedentary behavior, light physical activity, moderate–vigorous physical activity, daily step count, and cardiorespiratory fitness test), and serum biomarkers (brain-derived neurotrophic factor, vascular endothelial growth factor-A, and insulin-like growth factor-1). Fatigue was assessed using two items (“could not get going” or “felt everything was an effort”) from the Center for Epidemiologic Studies-Depression scale. Two-sample t tests examined differences in background characteristics across subgroups with fatigue versus without, and logistic regression examined whether social isolation, movement behaviors, and serum biomarkers were associated with fatigue.

Results:

Fatigue was present in 17% of participants. A unit increase in social isolation score was associated with greater odds of fatigue (adjusted odds ratio = 2.38; 95% confidence interval [1.41, 3.99]), while an increase in walking steps by 1,000 per day was associated with lower odds of fatigue (adjusted odds ratio = 0.74; 95% confidence interval [0.59, 0.93]) in the fully adjusted models. Other factors were not associated with fatigue.

Conclusion:

Prospective studies are needed to investigate fatigue-related factors in diverse patients with CVD.

Significance/Implication:

Interventions involving walking and group exercise may mitigate fatigue in older women with CVD.

Keywords: fatigue syndrome, exercise, biomarkers, older adults

Cardiovascular disease (CVD) is the leading cause of disability in the United States in older adults, irrespective of their sex (Cui et al., 2020). At older ages, CVD impacts women more than age-comparable men (78.2% vs. 77.2% among adults 60–79 years and 91.8% vs. 89.3% among adults ≥80 years; Rodgers et al., 2019), and with more severe symptoms (Eckhardt et al., 2014). The added risk of CVD in women has been attributed to hormonal shifts in menopause (Knowlton & Korzick, 2014), resulting in the loss of the cardiovascular protective effect of estrogen (Knowlton & Lee, 2012). In CVD, fatigue or exhaustion (hereafter fatigue) is a common symptom, and it is prevalent in 15%–29% of older adults (Ekmann et al., 2013; Gecaite-Stonciene et al., 2021). Women with CVD are particularly more likely to experience fatigue (18.6%) than men with CVD (9.8%; Ekmann et al., 2013), which may make women more susceptible to functional decline, disability, and low quality of life related to fatigue (Knoop et al., 2021). Thus, it is important to understand the contributing factors to fatigue in women with CVD.

Fatigue is a symptom that involves chronic extreme tiredness, exhaustion, weakness, and a lack of energy not relieved by rest or sleep (Krupp & Pollina, 1996; Machado et al., 2021). Chronic and severe fatigue can be debilitating (Krupp & Pollina, 1996; Machado et al., 2021). Fatigue is an early cardinal feature of frailty involving a depletion in physiological homeostasis in multiple systems (Fried et al., 2001). Fatigue severely limits activities of self-care, daily living, and social roles, with increased risk of functional decline, disability, and hospitalization (Knoop et al., 2021). Despite the increased prevalence of fatigue in women versus men (Ekmann et al., 2013), there is still a lack of comprehensive understanding of contributors to fatigue in this population. Multiple factors, such as sociodemographic, psychosocial, behavioral, and biological factors may influence fatigue. Identifying such factors may help better understand fatigue in older women with CVD and, thus, can inform targeted intervention categories.

Background characteristics, such as sociodemographic and health-related factors, may provide contextual understanding for identifying patients with CVD who may have a greater likelihood of experiencing fatigue. Fatigue can vary by background characteristics in those with chronic conditions (Goërtz et al., 2021). For example, greater fatigue is documented in participants who are older (Zengarini et al., 2015), unmarried, and have lower educational status (Torossian & Jacelon, 2021; Zengarini et al., 2015). Although findings are mixed, most studies show that health-related factors, such as increased pain (Conrad et al., 2018; Fishbain et al., 2003), higher body mass index (BMI; Inglis et al., 2020), and a greater number of comorbidities (Horne et al., 2019) are associated with fatigue in patients with chronic conditions. Understanding more about background characteristics associated with fatigue in older women with CVD is essential for managing fatigue and related disability in this at-risk population.

Social isolation is a measure of having fewer meaningful social relationships and ties. Social isolation is prevalent in more than 50% of older adults due to loss of social ties, loss of jobs, and limitations posed by comorbidities (Fakoya et al., 2020). Social isolation has been shown to predict CVD (Golaszewski et al., 2022). Greater social isolation is associated with increased fatigue in older adults and patients with rheumatic arthritis (Cho et al., 2019; Jaremka et al., 2014; Riemsma et al., 1998). To our knowledge, associations between social isolation and fatigue in older women with CVD have not yet been examined, and investigation of this relation will help to inform strategies targeting older women with CVD.

Movement behavior is engagement in physical activity of varying intensities and sedentary behavior measured daily (Tremblay et al., 2017). Physical activity involves bodily movement requiring energy expenditure and is related to positive cardiorespiratory fitness and cardiovascular health (Cunningham et al., 2020). Physical activity improves fatigue in patients with chronic conditions (Wender et al., 2022), including multiple sclerosis (Razazian et al., 2020), cancer (Oberoi et al., 2018), and diabetes (Wender et al., 2022). Unlike physical activity, sedentary behavior includes sitting, lying, and reclining positions, expending less than 1.5 metabolic equivalent tasks during waking hours (Tremblay et al., 2017). Increased sedentary behavior is associated with worsening fatigue (Ellingson et al., 2014) and worse cardiovascular outcomes (Young et al., 2016). Particularly, there is a lack of research examining objectively measured movement behaviors (i.e., sedentary behavior, light physical activity, moderate–vigorous physical activity, step count, and cardiorespiratory fitness test) associated with fatigue in older women with CVD.

Biomarkers are biological factors (Van Bogart et al., 2022) that may elucidate underlying mechanistic pathways of fatigue in patients with chronic conditions, including CVD. Certain serum biomarkers have been shown to be associated with fatigue in patients with chronic conditions like cancer (Saligan et al., 2015; Van Bogart et al., 2022), and elevated levels may also be early indicators of frailty (Sepúlveda et al., 2022). Some of these serum biomarkers include (a) brain-derived neurotrophic factor (BDNF)—a protein that promotes nerve and blood vessel growth (Sleiman et al., 2016), (b) vascular endothelial growth factor-A (VEGF-A)—a protein that activates blood vessel growth (Vital et al., 2014), and (c) insulin-like growth factor (IGF-1)—a hormone that manages the development of bones and tissues (Jiang et al., 2020). However, no prior studies have examined the association between these serum biomarkers and fatigue in older women with CVD (Klimas et al., 2012).

The purpose of this study was to (a) compare background characteristics (age, race/ethnicity, education, marital status, pain, BMI, and number of comorbidities) in older women (≥65 years) with CVD experiencing fatigue versus older women with CVD without fatigue and (b) determine whether social isolation, objectively measured movement behaviors, and serum biomarkers are associated with fatigue in older women with CVD who participated in the MindMoves trial. We hypothesized that background characteristics would vary among subgroups with fatigue versus without; as such, greater social isolation, increased daily step count, greater time spent in light or moderate-vigorous physical activity, and greater cardiorespiratory fitness test will be associated with reduced fatigue, whereas increased time spent in sedentary behavior will be associated with greater fatigue in women with CVD. Similarly, we hypothesized that lower BDNF, VEGF-A, and IGF-1 will be associated with greater fatigue in women with CVD.

Methods

Study Design and Participants

This study is a secondary analysis of cross-sectional baseline data from the MindMoves trial, a 24-week multimodal lifestyle intervention trial examining the independent and combined efficacy of physical activity and cognitive training on memory performance and serum biomarkers in older women (≥65 years) with CVD (NCT04556305). Thus, the current study is limited to older women with CVD in the parent MindMoves trial. Inclusion criteria and study protocols for the parent MindMoves trial have been described in detail previously (Halloway et al., 2021, 2023). Briefly, the MindMoves trial included 253 older women (≥65 years) with CVD (e.g., hypertension or coronary artery disease) recruited from outpatient cardiology clinics in the urban Midwest who received CVD treatment per standard clinical guidelines (Halloway et al., 2021). The eligibility criteria included participants without physical disabilities, cardiorespiratory symptoms, or uncontrolled CVD (e.g., stroke in the past 3 months or blood pressure > 160/100 mmHg) that would prevent participation in moderate physical activity. Additionally, they should not have been engaged in regular moderate–vigorous physical activity (less than 90 min of weekly moderate–vigorous physical activity in the past month) and without a history or symptoms of cognitive impairment (Blind Montreal Cognitive Assessment score > 19 and no history of self-reported dementia diagnosis). The Institutional Review Board of the University of Illinois Chicago approved the original study protocol.

Sample Size Calculation With Sample Size Calculation for the Parent MindMoves study

The parent MindMoves trial was adequately powered to detect the effect of the interventions on the primary outcomes of cognitive function, as detailed in the published protocol (Halloway et al., 2021).

We used G-power to conclude that our two-sided, multivariable logistic regression method was well-powered (>80%) to detect adjusted odds ratio (aORs) >1.50 or aORs < 0.67, generally accepted as clinically significant. The sample size of 246 in the current secondary analysis study (after eliminating those missing) is sufficient to attain at least 80% statistical power to detect clinically significant effect size in a multivariable logistic regression model.

Measurements

Outcome

Fatigue.

Fatigue was an outcome of interest in this study. We used two items from the Center for Epidemiological Studies Depression (CES-D) scale to measure fatigue in the current study because it was screened and assessed as part of frailty-related fatigue in the parent MindMoves trail using the Frail Non-Disabled instrument. The Frail Non-Disabled instrument is a valid self-reported questionnaire tool to assess frailty among people without disabilities (Cesari et al., 2014). In the Frail Non-Disabled instrument, fatigue is assessed using the two items in the CES-D questionnaires (“could not get going” or “everything was an effort”; Lewinsohn et al., 1997) because they have the highest factor loadings in the validated Fatigue Assessment Scale (Michielsen et al., 2003). These two items have been validated to screen and assess the manifestation of frailty-related fatigue in older adults with chronic conditions (Knoop et al., 2019). The responses for these two items in the CES-D were captured on a Likert scale (0 = rarely or most of the time; 1 = some or a little of the time; 2 = occasionally or moderate amount of time; and 3 = most or all of the time). Based on affirmative responses (most or moderate amount of time) to either one of these two items, the older adults with CVD were divided into two subgroups: (a) with fatigue and (b) without fatigue (Fried et al., 2001).

Predictors

Background Characteristics.

Background characteristics included self-reported age (continuous), race and ethnicity (non-Hispanic White vs. others), education (assessed as highest grade completed), marital status (married/committed relationships, separated/divorced/widowed, and never married), and social isolation (Halloway et al., 2021). Health factors included BMI (in kilogram per square meter; Halloway et al., 2021), pain was measured as part of the Global Health Questionnaire (Riggs et al., 2021) per self-report on a Likert scale ranging from 0 (none) to 5 (very severe), and chronic health problems (CVD, stroke, osteoarthritis, rheumatoid arthritis, asthma, cancer, multiple sclerosis, and renal disease) per self-report and electronic health record review (Halloway et al., 2021).

Psychosocial Factor.

Social isolation was measured using the adapted version of the De Jong Gierveld loneliness scale (De Jong Gierveld & Van Tilburg, 2010) to assess perceived social isolation using five items: (a) “feeling of a general sense of emptiness,” (b) “miss having people around,” (c) “not having enough friends,” (d) “feeling of a sense of abandonment,” (e) “feeling of a lack of a good friend.” A 5-point Likert scale (1 = representing strong disagreement and 5 = representing strong agreement) was used to score each item, and the scores across the items were averaged to obtain a cumulative score ranging from 1 to 5; a higher score indicated a greater social isolation (Halloway et al., 2021).

Movement Behavior.

In the MindMoves trial, the ActiGraph GT3XE-Plus Triaxial Accelerometer was used to objectively measure movement behaviors, including minutes per day of sedentary behavior, light physical activity, and moderate–vigorous physical activity (Halloway et al., 2021). An accelerometer was placed on the hip of the participants for seven consecutive days (Halloway et al., 2021). A wear time of at least 10 hr per day for 3 days or more was considered a valid cut-off for assessing movement behavior (Halloway et al., 2021). The accelerometer provides an objective measure of walking, running, and daily activity by recording vertical accelerations or “activity counts,” assessed as a weighted sum of the number of accelerations over 1 min (Bassett et al., 2012; John et al., 2010). Daily step count captures all forms of physical activity, including light physical activity that participants engage in daily (Halloway et al., 2021). Based on the Miller count cut-offs for physical activity intensity in older adults, sedentary behavior (<100), light (100–1,565), moderate (1,566–6,139), and vigorous physical activity (>6,140) were determined (Miller et al., 2010).

Cardiorespiratory Fitness.

A 2-min step test was used to measure cardiorespiratory fitness (Berlanga et al., 2023; Halloway et al., 2021). It assesses the ability of participants to make as many steps as possible such that their knees reach the height at the midway between the participants’ midpoint of their patella and the top of the iliac crest while marching in the place (Wood, 2005). A 2-min step test of ≤72 steps in older women reflects the lower functional ability and physiological response (Wood, 2005).

Serum Biomarkers.

Three serum biomarkers (BDNF, VEGF-A, IGF-1) were analyzed using the Luminex FlexMAP 3D (Daly et al., 2014). Utilizing a five-parametric fit algorithm, serum blood concentration was centered on 7-point standard curves. The blood samples for the serum biomarkers were collected in the morning between 8 and 10 a.m. post 8-hr fast, following validated blood collection procedures for the serum biomarkers by a trained phlebotomist in the research lab or participant’s home. The collected blood samples were taken to the lab for processing within 2 hr and stored in a −80° freezer (Halloway et al., 2021).

Statistical Analysis

We conducted descriptive analyses to report key variables and background characteristics. We summarized continuous variables as means (M) and SD and nominal variables as numbers and percentages (%). We then compared background characteristics (age, race/ethnicity, education, marital status, social isolation, pain, BMI, and comorbidities) across two subgroups of older women with and without fatigue using chi-square (χ2) tests for the categorical variables and two-sample t tests for the continuous variables. Prior to analysis, we confirmed the normality assumption by examining histograms with normal distribution curves, skewness, and kurtosis values.

We examined bivariate, unadjusted logistic regression analyses of movement behavior (time spent sedentary, in light physical activity, moderate–vigorous physical activity, daily step count, and cardiorespiratory fitness test), serum biomarkers (BDNF, VEGF-A, and IGF-1), and social isolation (a statistically significant background characteristic in a bivariate t test) in association with fatigue. Statistically significant predictors in bivariate logistic regression analyses were included in the partially and fully adjusted logistic regression models, controlling for age, race/ethnicity, marital status, and education, to maintain the model’s parsimony and simplicity. As missing data was less (<5%), we used the listwise deletion for the analyses.

For all analyses, two-sided tests were conducted using a significance level of α = .05 for each test (Aguinis et al., 2021). We used the odds ratio in the logistic regression to denote the effect size in multivariable logistic regression models. We conducted statistical analyses in SAS (version 9.4.2).

Results

Participant Characteristics

The study initially had 253 participants, and we included 246 participants with complete data. The baseline sample included 246 women with an average age of 72.7 years (SD = ±5.9; range = 65–90 years). At baseline, 57.6% identified as non-Hispanic White, 59.6% reported having a college degree or higher, and 45.5% were married, partnered, or in a committed relationship. The average social isolation score was 2 (SD = ±0.7, range = 1–4.8). Participants had an average of three or more chronic conditions, their average BMI was 30.9 kg/m2 (SD = ±7.4, range = 18.2–60.6), and the average pain score was 2 (SD = ±0.9, range = 1–5).

Participants were engaged in an average of 10.7 hr of sedentary behavior per day (SD = ±2.7 hr, range = 5.3–22.8), 3.5 hr of light physical activity per day (SD = ±1.2, range = 0.9–7.7), and 0.2 daily hours of moderate–vigorous physical activity (SD = ±0.3, range = 0.4–130.7), indicating lower variability in physical activity. Their average daily step count was 3,452.8 (SD = ±2,181.8, range = 356.7–13,126.3), indicating higher variability in daily step count. The average cardiorespiratory fitness was 70.8 (SD = ±20.9). Regarding serum biomarkers, BDNF levels ranged from 0.02 (limit of detection) to 3.9 ng/ml (M = 0.4 ng/ml, SD = 0.4); VEGF-A ranged from 0.2 (limit of detection) to 113.4 ng/ml (M = 27.6 ng/ml, SD = 19.7); and IGF-1 ranged from 0.03 (limit of detection) to 23.3 ng/ml (M = 0.8 ng/ml, SD = 2.1). Among 246 older women, 17.1% had fatigue (see Table 1). The comparison of baseline characteristics between subgroups with and without fatigue using χ2 and t tests (for categorical and continuous variables, respectively) showed no significant differences (see Table 2).

Table 1.

Descriptive Statistics of Older Women With Cardiovascular Disease in a MindMoves Trial (N = 246)

Characteristic n % M SD
Age (years) 72.7 5.9
Racial and ethnic background
 Non-Hispanic White 140 57.6
 Others 103 42.4
Education (highest grade completed)
 High school degree or less 24 9.8
 Some college 75 30.6
 Graduated college or higher 146 59.6
Marital status
 Married/committed relationship 112 45.5
 Separated/divorced/widowed 96 39.0
 Never married 38 15.5
Average social isolation score 2.0 0.7
Health-related factors
 BMI (kg/m2) 30.9 7.4
 Pain 2.0 0.9
 Number of comorbidities 3.8 1.9
Physical activity variables
 Daily hours of sedentary behavior 10.7 2.7
 Daily hours of light physical activity 3.5 1.2
 Daily hours of moderate-vigorous physical activity 0.2 0.3
 Daily step count 3,452.8 2,181.8
 Cardiorespiratory fitness (number of steps) 70.8 20.9
Serum biomarkers
 BDNF (ng/ml) 0.4 0.5
 VEGF-A (ng/ml) 27.6 19.7
 IGF-1 (ng/ml) 0.8 2.1
Fatigue (yes) 42 17.1
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Note. The social isolation score was assessed in a cumulative score across the items on the 5-point Likert scale, with a higher score reflecting greater social isolation. Pain was reported on the 5-point Likert scale on the Global Health Questionnaire, with a higher score reflecting greater severity of pain. BDNF = brain-derived neurotrophic factor; IGF-1 = insulin-like growth factor-1; VEGF-A = vascular endothelial growth factor-A; BMI = body mass index.

Table 2.

Comparison of the Baseline Characteristics Between Subgroups With Fatigue and Without Fatigue

Fatigue (N = 42)
 Without fatigue (N = 204)
Variables N (%) or M (SD) N (%) or M (SD) p
Age 72.7 (5.8) 72.7 (5.8) .99
Race and ethnicity .44
 Non-Hispanic White 25 (59.5) 115 (56.7)
 Others 17 (40.5) 88 (43.6)
Education .46
 High school 2 (4.9) 22 (10.8)
 Some college 12 (29.3) 63 (30.9)
 College degree or higher 27 (65.9) 119 (58.3)
Marital status .54
 Never married 8 (19.1) 30 (14.7)
 Separated/divorced/widowed 18 (42.9) 78 (38.2)
 Married/partnered/committed 16 (38.0) 96 (47.1)
Health-related factors
 Body mass index 31.4 (7.3) 30.8 (7.5) .66
 Pain 2.2 (0.9) 1.9 (0.8) .08
 Number of comorbidities 4.1 (2.1) 3.7 (1.8) .24
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Note. Differences in continuous variables across the two subgroups were tested using two-sample t test. Differences in categorical variables across the two subgroups were tested using the chi-square test.

There were small to moderate, statistically significant correlations across the movement behavior variables, ranging from r = −.11 to .42 (all p < .05; see Supplementary Table S1 [available online]). Correlations across the variables representing movement behavior were not strong, except the correlations of the daily steps (r = .86) and light physical activity (r = .69) with moderate–vigorous physical activity. Therefore, these movement behaviors were included in the logistic regression model that examined the association between statistically significant movement behavior and fatigue. Table 2 shows no statistically significant differences in sociodemographic characteristics and health-related factors across the subgroups with and without fatigue.

Logistic Regression Analyses Findings

As shown in Table 3, in bivariate unadjusted logistic regression analyses (see Model 1), each point increase in social isolation score was associated with increased odds of fatigue by 2.31 times (OR = 2.31, 95% confidence interval [CI] [1.42, 3.76], p = .0008), whereas an increase in step count by 1,000 per day was associated with lower odds of fatigue by 0.81 times (OR = 0.81, 95% CI [0.67, 0.99], p = .03). In multivariable logistic regression models adjusting for age, race/ethnicity, marital status, and education (see Model 2), a point increase in social isolation score was associated with greater odds of fatigue by 2.38 times (aOR = 2.38; 95% CI [1.41, 3.99], p = .001), and an increase in step count by 1,000 per day was associated with decreased odds of fatigue in older women by 0.74 times (aOR = 0.74, 95% CI [0.59, 0.93], p =.009). An increase in 1,000 steps per day was associated with a 21% decrease in odds of fatigue in older women with CVD (aOR = 0.79, 95% CI [0.63, 0.98], p = .04) in the fully adjusted model adjusted for age, race/ethnicity, marital status, education, and social isolation. The CI signifies a small effect size. Each point increase in a social isolation score was associated with 2.38 times increased odds of fatigue in older women with CVD (aOR = 2.38, 95% CI [1.41, 3.99], p =.004) in the fully adjusted model adjusted for age, race/ethnicity, marital status, education, and daily step count. The CI signifies medium effect size. These effect sizes suggest that social isolation and daily steps are potentially clinically significant and are meaningful to target for a future intervention for fatigue in older women with CVD.

Table 3.

Logistic Regression Analysis Examining the Association Between Physical Activity Variables and Fatigue

Model 1
 Model 2
 Model 3
Variables OR (95% CI) aOR (95%CI) aOR (95% CI)
Social isolation 2.31 [1.42, 3.76]*** 2.38 [1.41, 3.99]*** 2.20 [1.29, 3.73]***
Physical activity variables
 Daily hours of sedentary behavior 1.08 [0.97, 1.21]
 Daily hours of light physical activity 0.76 [0.57, 1.01]
 Daily hours of moderate–vigorous physical activity 0.26 [0.04, 1.59]
 Daily step counts (in 1,000) 0.81 [0.67, 0.99]* 0.74 [0.59, 0.93]* 0.79 [0.63, 0.98]*
 Cardiorespiratory fitness 0.99 [0.97, 1.0]
Serum biomarkers
 BDNF (ng/ml) 0.99 [0.50, 1.99]
 VEGF-A (ng/ml) 0.89 [0.63, 1.24]
 IGF-1 (ng/ml) 0.99 [0.98, 1.01]
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Note. Model 1 shows bivariate unadjusted logistic regression analysis examining the independent association between social isolation, physical activity measures, and serum biomarkers with fatigue. Model 2 shows partially adjusted logistic regression analysis examining the association between social isolation and daily step counts with fatigue, each adjusted for age, race/ethnicity, education, and marital status. Model 3 is a fully adjusted logistic regression analysis examining the association of social isolation, daily step count with fatigue in a fully adjusted model for age, race/ethnicity, education, and marital status. BDNF = brain-derived neurotropic factor; IGF-1 = insulin-like growth factor-1; VEGF-A = vascular endothelium growth factor-A; OR = odds ratio; aOR = adjusted odds ratio; CI = confidence interval.

*

p ≤ .05.

***

p ≤ .001.

Discussion

In a cross-sectional sample of older women with CVD involved in an ongoing trial, we compared background characteristics in those with fatigue versus those without fatigue. There were no statistically significant differences in age, race/ethnicity, education, marital status, BMI, pain, and comorbidities among subgroups of women with fatigue versus those without fatigue. Women experiencing fatigue reported higher social isolation, fewer daily step counts, and less time spent in moderate–vigorous physical activity. Logistic regression findings showed that social isolation and daily step counts were statistically significantly associated with fatigue in women with CVD. Specifically, greater social isolation was associated with increased odds of fatigue independently and after adjusting for age, race/ethnicity, education, and marital status. An increase in step count of 1,000 per day was statistically significantly associated with lower odds of fatigue in older women with CVD independently and after adjusting for age, race/ethnicity, education, and marital status.

Consistent with prior studies, higher social isolation was observed in women experiencing fatigue (Asbring, 2001), and greater social isolation scores increased the odds of fatigue in older women with CVD (Cho et al., 2019; Choi et al., 2015; Jaremka et al., 2014). Although the exact mechanism linking greater social isolation and fatigue is not entirely clear, it is possible that prolonged social isolation may trigger underlying biological responses, including activating the hypothalamus-pituitary axis that can lead to an inflammatory stress cascade (Smith et al., 2020; Yang et al., 2016). These responses increase inflammatory markers (Van Bogart et al., 2022) and induce low-grade chronic inflammation, which may increase the risk of fatigue (Lacourt et al., 2018). There was no variation across the subgroups with fatigue versus those without in other background characteristics, such as age, race/ethnicity, education, marital status, pain, BMI, and number of comorbidities. The statistically insignificant differences in background characteristics across the two subgroups with and without fatigue may be attributed to less sample variability across participants in the parent trial.

In line with our hypothesis and earlier research involving older adults and patients with cancer (Singh et al., 2022) and rheumatic arthritis (Katz et al., 2018), increasing daily step count was related to lower levels of fatigue in this cohort of older women with CVD. Daily step count captures all forms of physical activity of all intensities that are accumulated over walking hours. Increasing walking steps is shown to have positive physiological and psychological benefits that can improve circulation, reduce cortisol, and enhance mood (Ungvari et al., 2023). Thus, this finding strengthens the evidence that increasing daily steps might be beneficial in reducing fatigue in women with CVD.

Contrary to prior longitudinal studies (Oberoi et al., 2018; Razazian et al., 2020), other physical activity-related measures, such as cardiorespiratory fitness and time spent in light and moderate-vigorous physical activity were not associated with fatigue in this study. It may be due to the limited variability of physical activity levels among the participants in the parent MindMoves trial. The parent trial aimed to improve the lifestyle-based physical activity of the participants; thus, it specifically targeted older women with lower levels of moderate–vigorous physical activity at baseline. The findings may differ from other studies that examined the associations between moderate-vigorous physical activity or cardiorespiratory fitness and fatigue in an observational study (Barakou et al., 2023). Fatigue is important to consider in patients with CVD as higher levels of fatigue may signify the underlying loss of muscle strength, muscle mass, and function (Evans & Lambert, 2007; Larsson et al., 2019), common issues that impact up to 10%–69% of patients with CVD (Zuo et al., 2023). However, muscle strength, muscle mass, and function were not assessed as part of the assessment in the parent MindMoves trial, which limited our current evaluation of movement behavior and fatigue.

We acknowledge some limitations of this study. First, this study is cross-sectional; thus, we cannot determine a cause-and-effect relationship between studied variables and fatigue. The parent trial involved older women with an education level higher than the United States average, and participants primarily identified as non-Hispanic White and Black racial and ethnic background. Similarly, the participants had lower levels of physical activity without disability or substantial cognitive impairment due to the inclusion criteria for the parent trial. In addition, study participants were being seen by cardiology providers, and they were receiving guideline therapies for their CVD, such as medication and health education, which may also have influenced their fatigue. Due to the reasons described above, the generalizability of the findings to a larger population of older women or older adults with CVD may be limited. Second, we only used two items from the CES-D scale to assess fatigue. Even though these two items have been previously validated as measures of fatigue, with a high factor loading in the fatigue assessment scale, these two items alone may not fully capture the severity and chronicity of fatigue nor differentiate between different domains of fatigue.

To address the limitations of the current study, there is a need for prospective or pre–post studies to explain the directionality of the association of social isolation, movement behaviors, and serum biomarkers with fatigue in a diverse group of older women with CVD. Specifically, future studies should use a comprehensive fatigue assessment tool to inform the formulation of the targeted intervention for alleviating fatigue in older women with CVD. There is a need to examine indicators, such as muscle strength, muscle mass, and function to elucidate the physiological mechanism contributing to lower physical activity in older women with CVD who are experiencing fatigue. Future studies are encouraged to examine the potential inflammatory and neuroendocrinal stress biomarkers that may be associated with fatigue in older women with CVD. These markers may also help to elucidate the underlying biological mechanisms between higher social isolation and fatigue. Finally, fatigue in CVD may be attributed to increased physiological demands on cardiopulmonary (Ramalho & Shah, 2021), sleep disturbances (Mentzelou et al., 2023), muscle strength (Jäkel et al., 2021), and cardiovascular drug-related side effects (Huffman & Stern, 2007); thus, these factors need to be adjusted in future studies.

To our knowledge, this is the first study to investigate the association between background characteristics, social isolation, movement behaviors, and serum biomarkers associated with fatigue in older women with CVD engaged in health care and who can benefit from timely interventions to lessen debilitating consequences related to fatigue. Our findings show that increasing daily steps is associated with reduced fatigue in older women with CVD, whereas greater social isolation is associated with increased fatigue. These findings may have some practical implications. For example, home-based walking exercises are a cost-effective way to promote walking among older adults and are shown to be successful (Thangada et al., 2023). Furthermore, digital devices, such as pedometers, smartphone pedometer applications, and pedometer watches, can be used to track daily step counts in older adults (Fong et al., 2016). Daily step count data can be used to deliver personalized motivational messages to encourage older women to engage in walking and increase daily step counts (Fong et al., 2016). Previous trials have demonstrated that low-impact stepping exercises improve fatigue in older adults with multimorbidity (Chan & Yu, 2022), and group-based walking can alleviate fatigue in older adults with cancer (Wang et al., 2022). Social support from friends, family, and exercise groups is important in improving walking in older adults (Sheinhoff & Reiner, 2024). Based on these past studies, home-based walking and a group-based low-impact exercise program targeted toward improving steps and walking can be designed for older women with CVD to alleviate fatigue.

Conclusions

Our study examined the unidirectional association between multi-dimensional factors (background characteristics, social isolation, movement behaviors, and serum biomarkers) and fatigue. Specifically, study findings underscored that greater social isolation was associated with increased odds of fatigue, whereas increasing daily steps was associated with lower odds of fatigue. These findings indicate a need to develop/test interventions promoting walking and increasing daily step counts in a socially supported environment to alleviate fatigue in older women with CVD.

Supplementary Material

Supplementary table

Key Points.

  • An increase in daily step count was associated with reduced fatigue in older women with cardiovascular disease.

  • Greater social isolation was associated with increased fatigue in older women with cardiovascular disease.

Acknowledgments

The research team acknowledges Sachin Vispute (EPI research coordinator, Rush University Medical Center) and Kaitlin H. Wilhelm (Data Analyst, Rush University Medical Center) for their assistance in data curation and management (Rush University Medical Center). The authors thank Kevin Grandfield, Publication Manager for the UIC Department of Biobehavioral Nursing Science, for editorial assistance.

Funding:

The National Institute of Health funded the original parent trial. The current study is the secondary analysis of the parent study data and was not funded. This study is registered at www.clinicaltrials.gov (NCT04556305).

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