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Psychosomatic Medicine 66:190-197 (2004)
© 2004 American Psychosomatic Society

ORIGINAL ARTICLES

Gender Differences in Quality of Life Among Cardiac Patients

Charles F. Emery, PhD, David J. Frid, MD, Tilmer O. Engebretson, PhD, Angelo A. Alonzo, PhD, Anne Fish, PhD, Amy K. Ferketich, PhD, Nancy R. Reynolds, PhD, Jean-Pierre L. Dujardin, PhD, JoAnn E. Homan, MSN and Stephen L. Stern, MD

From Ohio State University, Columbus, Ohio.

Address correspondence and reprint requests to Charles F. Emery, Ph.D., 213 Townshend Hall, 1885 Neil Avenue, Ohio State University, Columbus, OH, 43210. E-mail: emery.33{at}osu.edu


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
 NOTES
 ACKNOWLEDGMENTS
 REFERENCES
 
OBJECTIVE: Prior studies of quality of life among cardiac patients have examined mostly men. This study evaluated gender differences in quality of life and examined the degree to which social support was associated with quality of life.

METHODS: A sample of 536 patients (35% women) was recruited during a 14-month period from the inpatient cardiology service of a University-based hospital. Participants completed assessments at baseline and at 3-month intervals over the subsequent 12 months, for a total of 5 assessments. Measures at each assessment included quality of life [Mental Component Score (MCS) and Physical Component Score (PCS) from the Medical Outcomes Study—Short Form 36] and social support [Interpersonal Support Evaluation List—Short Form].

RESULTS: A total of 410 patients completed the baseline assessment and at least one follow-up, and were included in the data analyses. Linear mixed effects modeling of the MCS score revealed a significant effect of gender (p = .028) and time (p < .001), as well as a significant interaction of gender by social support (p = .009). Modeling of the PCS revealed a significant effect of gender (p = .010) and time (p < .001).

CONCLUSIONS: Women with cardiac disease indicated significantly lower quality of life than men with cardiac disease over the course of a 12-month longitudinal follow-up. Social support, especially a sense of belonging or companionship, was significantly associated with emotional quality of life (MCS) among women. Strategies to increase social support may be important for health and well-being of women with cardiac disease.

Key Words: quality of life, • cardiovascular disease, • gender differences, • social support.

Abbreviations: MI = myocardial infarction;; CHD = coronary heart disease;; PsyFAHD = psychosocial functioning and heart disease;; SF-36 = Medical Outcomes Study—Short Form 36;; PCS = physical component score;; MCS = mental component score;; ISEL-SF = Interpersonal Support Evaluation List—Short Form;; BDI = Beck Depression Inventory;; LOT = Life Orientation Test;; PSS = Perceived Stress Scale.


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
METHODS
 RESULTS
 DISCUSSION
 NOTES
 ACKNOWLEDGMENTS
 REFERENCES
 
Prior research has documented profound negative effects of cardiac disease on quality of life as exemplified by recent studies of post-bypass patients (1), post-myocardial infarction (MI) patients (2), and patients undergoing cardiac catheterization (3,4). Impaired quality of life, in turn, has been associated with increased morbidity and mortality among cardiac patients (5). Most prior studies have included samples that consisted of primarily male cardiac patients (4–6), despite the fact that cardiac disease is the leading cause of death among women as well as men. The limited available data pertaining to quality of life among women indicates that women with cardiac disease experience significant reductions in quality of life (3) and that poor quality of life is associated with other negative prognostic indicators for women with cardiac disease (7).

There are 3 major limitations of prior research in this area. First, most studies have not addressed quality of life among women with cardiac disease, and the extant studies addressing quality of life among women are primarily cross-sectional studies. Two recent longitudinal studies evaluating psychological outcomes of MI among women have indicated that women may experience greater distress and more negative consequences of cardiac disease (8,9), but these studies did not utilize standard indicators of quality of life. Another recent longitudinal study of men and women post-MI found that women had lower quality of life than men at 12-month follow-up (10), but the study was not designed to evaluate variables associated with the gender difference. Instead, the study identified 4 baseline variables that predicted overall quality of life at 12 months including depression at baseline, living alone, severity of cardiac disease, and anxiety. Thus, the latter study identified variables relevant to long-term quality of life among men and women with cardiac disease. However, a second limitation of research in this area is that studies have not identified factors that may contribute to reduced quality of life among women with cardiac disease. A third concern is that most prior research has addressed quality of life among patients after MI or bypass surgery, excluding other cardiac diagnostic groups. Thus, generalizability of the results may be limited to those patient groups evaluated.

The purpose of this study was to evaluate quality of life among men and women with coronary heart disease (CHD) in a naturalistic, prospective study with 12-month follow-up. The study recruited both male and female cardiac patients to address gender differences in quality of life. In addition, all available patients with CHD were recruited, regardless of specific cardiac diagnosis, with the goal of enhancing generalizability of the results. Prior data suggest that women with cardiac disease are more likely than men to be confronted with continuing demands in the home environment (eg, housework), and may be more likely to neglect health care needs (11). Thus, it was hypothesized that quality of life among women would be more impaired than quality of life among men, regardless of diagnosis, across all times of measurement.

It was further hypothesized that quality of life would be more strongly associated with social support among women than among men. Numerous studies have now documented the importance of social support as an independent risk factor for morbidity and mortality among patients with CHD (12–15). To the degree that social support also may provide a buffer against stress and impaired quality of life (10,16), it was expected that social support would be particularly relevant for women with cardiac disease.


   METHODS
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
RESULTS
 DISCUSSION
 NOTES
 ACKNOWLEDGMENTS
 REFERENCES
 
Participants
Data for this investigation came from a naturalistic study of psychosocial functioning and heart disease (PsyFAHD). The procedures for this study were reviewed and approved by the Ohio State University Institutional Review Board. Adult patients treated through the inpatient cardiology service at Ohio State University Hospital were approached for recruitment during a 14-month period. Exclusionary criteria were: age less than 18 years; presence of dementia or delirium; or being too ill or impaired to complete self-report questionnaires. A total of 536 patients (35% women) were recruited for the study. Mean age of the sample was 59.5 (±11.9) years and most participants were white (88%). The most common diagnoses were unstable angina (36%) and myocardial infarction (33%). Other diagnoses included congestive heart failure (14%), dysrhythmia (12%), valvular heart disease (3%), and transplant surgery (2%). Additional medical and demographic data are included in Table 1.


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  		TABLE 1. Characteristics of Study Participants
 
Procedure
After approval by each patient’s attending cardiologist, a staff nurse described the study to the patient. Patients also were instructed that they would be recontacted at 3-month intervals over the course of 1 year to complete additional questionnaires. Patients who agreed to participate then signed the approved informed consent and completed a 45-minute battery of questionnaires. At each of the 4 follow-up assessments (3 months, 6 months, 9 months, and 12 months), questionnaires were sent to the participant’s home with a stamped, addressed envelope for returning the questionnaire. If the return envelope was not received within 2 weeks, participants were contacted by telephone up to 3 times to confirm that they had received the packet and to encourage them to return it as soon as possible.

The demographic and health variables listed in Table 1 and utilized in the statistical modeling were extracted from each patient’s medical chart, including a single-item (yes/no) global rating of sedentary lifestyle made at the time of admission.

Assessment
Each assessment included measures of quality of life, social support, and psychological functioning. For purposes of this study, quality of life and social support were the 2 primary outcome measures of interest.

Quality of Life
Quality of life was assessed with the Medical Outcomes Study—Short Form 36 (SF-36; (17)), a 36-item self-report measure providing 4 dimensions of physical health-related quality of life (physical functioning, role limitations due to physical functioning, bodily pain, and general health perception) summarized in a Physical Component Score (PCS), as well as 4 dimensions of mental health–related quality of life (role limitations due to emotional functioning, social functioning, mental health, and vitality) summarized in a Mental Component Score (MCS). The primary outcomes of interest for this study were the two component scores (PCS and MCS), which have been shown to be valid quality-of-life indicators (18), with excellent internal reliability among post-MI patients (PCS = 0.89; MCS = 0.84).

Social Support
The Interpersonal Support Evaluation List—Short Form (ISEL-SF) is a 16-item measure of functional support, with each item rated either "mostly true" or "mostly false" by the respondent (19). In addition to a total social support score, the scale provides 4 subscale scores: appraisal (eg, availability of a confidant), belonging (eg, sense of companionship and support from a peer group), tangible (eg, instrumental support for completing errands, housework), and self-esteem (eg, rating of self in comparison to peers). Internal reliability estimates for the total scale and 4 subscales are generally good (total = 0.73; appraisal = 0.68; belonging = 0.72; tangible = 0.76; self-esteem = 0.59).

Depression, Optimism, and Perceived Stress
In addition, each assessment included the following three psychological measures that were utilized in the statistical modeling as control variables.

  1. Beck Depression Inventory (BDI; 20). The BDI is a 21-item questionnaire of depressive symptoms that is highly correlated with clinical assessments of depression. It is perhaps the most widely used self-report measure of depression and has been shown to be a valid and reliable indicator of depressive symptoms. Test–retest reliability estimates have been found to range from 0.74 to 0.93 for a variety of groups (21).
  2. The Life Orientation Test (LOT) is a 13-item questionnaire that has been utilized in prior studies among cardiac patients (22,23) as an indicator of dispositional optimism, reflecting the respondent’s outcome expectancies for future events. Good internal reliability (0.76) and test–retest reliability (0.79) have been demonstrated (22).
  3. The Perceived Stress Scale (PSS) is a self-report measure of the degree to which the respondent views life as stressful (eg, unpredictable and uncontrollable). Prior studies have demonstrated excellent internal reliability (0.75–0.86) for this measure (24,25).

Data Analysis
The objective of the data analysis was to determine whether MCS and PCS scores changed over time, from baseline to 1 year, and if the trajectory over time differed between men and women. A linear mixed effects model was fit to each outcome (MCS and PCS), with random subject effects for the intercept, slope for time, and time2. The quadratic effect of time was included to account for nonlinear change over time. It was anticipated that change would likely not be linear; thus time2 was included to avoid systematic overestimation or underestimation of data points. The mixed effects model included terms for age, race, left ventricular ejection fraction (LVEF), cardiac diagnosis, hypertension, diabetes, obesity, personal history of heart disease, family history of heart disease, smoking, physical activity, marital status, education, baseline LOT, and baseline PSS. The rationale for including cardiac and risk factor variables, in addition to optimism and perceived stress, was to control for variables that have been found to influence quality of life among patients with cardiac disease (10) and variables that might influence quality of life differentially between men and women. The PCS model included the same control variables in addition to baseline BDI. BDI scores were not included in the MCS model because MCS scores, in part, reflect depressive symptoms. The primary variables of interest in the analyses were time and time2, gender, and baseline level of social support measured with the ISEL, as well as significant interactions of gender with time or with social support. When a significant interaction of gender by social support was observed, the ISEL subscale scores (tangible, self-esteem, belonging, and appraisal) also were examined to determine aspects of social support that would be most relevant. Each model was fit using the procedures for linear mixed effects models in S-Plus (26). For the linear mixed effects model, the assumptions include equal variances and normality of the residuals. After the models were fit to the data, it was found that these 2 assumptions were met.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
DISCUSSION
 NOTES
 ACKNOWLEDGMENTS
 REFERENCES
 
There were 410 participants (265 men, mean age = 59.4 ± 11.9 years; and 145 women, mean age = 60.5 ± 12.5 years) who had completed a baseline SF-36 questionnaire and at least 1 follow-up questionnaire during the 1-year period. A number of participants dropped out, and by the end of the study there were 322 participants remaining (201 males and 121 females). Because of the biased results that can arise from complete case analysis, the data were treated as unbalanced and the analyses included data for the 410 participants with a baseline assessment and at least 1 additional assessment. When unbalanced repeated measures data are modeled, the parameters must be estimated using maximum likelihood methods. The assumption for this analysis is that data are missing at random, which is less restrictive than complete case analysis where data for each complete case are assumed to be missing at random.

Representativeness of the Sample
Before initiating the statistical modeling, demographic and health variables were analyzed to determine whether or not there were baseline differences between the study sample (N = 410) and the study dropouts (N = 126). Categorical variables were evaluated with chi-square tests and continuous variables were evaluated with t tests. The distribution of cardiac diagnoses at admission differed between participants and dropouts [{chi}2(5) = 20.13, p = .001]. However, analysis of individual diagnostic groups revealed no significant differences between participants and dropouts. The participant group had a higher proportion of patients with MI (38% vs. 31% in the dropout group) and a lower proportion of patients with congestive heart failure (10% vs. 25% in the dropout group), but neither of these differences was statistically significant. Consistent with diagnostic differences, the dropouts had significantly lower LVEF [F (1, 491) = 16.40, p < .001]. In addition, dropouts were less likely to be white [{chi}2(2) = 6.79, p = .03], had less education [{chi}2(3) = 9.75, p = .02], and were more likely to have a diagnosis of diabetes [{chi}2(1) = 12.01, p < .001]. There were no other differences between the study participants and the dropouts on the remaining demographic, health, and cardiac risk factor variables, as shown in Table 1. Logistic regression predicting dropout (vs. participant) status indicated that there were no statistically significant interactions of gender with any of the variables for which differences were observed between participants and dropouts.

Baseline Gender Differences
Evaluation of gender differences in the study sample at baseline indicated no difference in age, cardiac diagnosis, diabetes, history of CHD, sedentary lifestyle, optimism, or social support. As shown in Table 2, women were more likely to have hypertension, obesity, and family history of CHD. In addition, women were more likely to be single and have less education. However, men were more likely to be current smokers and had lower LVEF. There were trends indicating that women were somewhat more sedentary than men and less likely to have a personal history of CHD. At baseline, women had lower scores on both quality of life indicators (MCS and PCS), and women indicated greater levels of depression and perceived stress. Linear mixed effects models were used to evaluate interactions of gender with each of the variables for which there was a significant gender difference or trend (ie, LVEF, hypertension, obesity, personal history of CHD, family history of CHD, smoking status, sedentary lifestyle, marital status, education, BDI, and PSS) for each outcome (MCS and PCS). For MCS, there were significant interactions of gender by marital status and gender by personal history of CHD. To control for the influence of the gender interactions, these 2 terms were included in the final mixed effects model for MCS. None of the interactions was significant for PCS.


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  		TABLE 2. Mean and Proportional Gender Differences at Baseline (n = 410)
 
Emotional Quality of Life
As shown in Table 3, the linear mixed effects model for MCS revealed significant effects of time (p < .001), time2 (p < .001), and gender (p = .028), as well as a gender by ISEL interaction (p = .009). Thus, emotional quality of life increased significantly over time in a curvilinear pattern for both men and women. Figure 1 depicts the significant interaction of gender and ISEL, using the upper and lower quartiles of ISEL for illustrative purposes. ISEL scores were associated with MCS scores among women but not among men. To further explicate the interaction of gender and social support, models were evaluated with each of the 4 ISEL subscales (appraisal, tangible, belonging, and esteem) in place of the ISEL total score. There was a statistically significant interaction of gender only with the belonging subscale (p = .004). As shown in Figure 2, belonging scores among women were strongly associated with MCS scores, whereas among men belonging scores were not associated with MCS.


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  		TABLE 3. Linear Mixed Effects Model for Mental Component Score (SF-36)
 


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  		Figure 1. Mental Composite Score (SF-36) plotted over time, by gender and social support (low support = 25th percentile; high support = 75th percentile). Plots are calculated with coefficients from the model fit to the entire data set. Because of the interaction of Interpersonal Support Evaluation List (ISEL) and gender, plots are created using upper and lower quartile values of ISEL as reference points. SF-36 = Medical Outcomes Study—Short Form 36.

 


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  		Figure 2. Mental Composite Score (SF-36) plotted over time, by gender and Belonging (ISEL) subscale (low belonging = 25th percentile; high belonging = 75th percentile). Plots are calculated with coefficients from the model fit to the entire data set. Because of the interaction of Belonging and gender, plots are created using upper and lower quartile values of Belonging as reference points. SF-36 = Medical Outcomes Study—Short Form 36.

 
Because MCS items may also overlap with PSS and LOT items, post hoc analyses were conducted, evaluating the above model a second time without baseline PSS and LOT scores. Results indicated that the interaction of gender and social support remained significant (p = .013), reflecting the same pattern of results.

Physical Qualify of Life
As shown in Table 4, the linear mixed effects model for PCS scores indicated a significant effect of time (p < .001), time2 (p = .003), and gender (p = .010), but no interaction of gender with ISEL. A plot of the longitudinal data for men and women is presented in Figure 3. Quality of life increased significantly over time for both men and women, but women had significantly lower PCS compared with men across all assessments.


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  		TABLE 4. Linear Mixed Effects Model for Physical Component Score (SF-36)
 


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  		Figure 3. Physical Composite Score (SF-36) plotted over time, by gender. SF-36 = Medical Outcomes Study—Short Form 36.

 

   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
NOTES
 ACKNOWLEDGMENTS
 REFERENCES
 
Results of this study supported the primary hypothesis, indicating that physical quality of life among women with cardiac disease was significantly lower than for men, regardless of cardiac diagnosis, illness severity, age, race, education, cardiac risk factor profile, depression, perceived stress, or optimism. Quality of life remained significantly worse for women over the course of the 12-month follow-up, although quality of life for both men and women increased during that time. These data thus provide additional evidence that women with cardiac disease may experience greater impairments in physical life quality than men (3,7–9). The methodological strengths of this study (ie, use of a standardized measure of quality of life, inclusion of all patients admitted to a cardiac unit, and statistical control of demographic, health, and psychological variables that might contribute to the observed gender differences) support the validity of the findings. Although normative data for the SF-36 indicate that women generally tend to report lower quality of life than men (18), the magnitude of the gender difference in the normative sample of men and women in this age group (55–64 years) is 2% to 4% (or approximately 1/10 of a SD), whereas the magnitude of the gender difference in this sample of cardiac patients was 7% to 10% (approximately 1/3 of a SD). The consistency of the difference and the magnitude of the difference relative to normative groups underscores the relevance of gender for quality of life among cardiac patients.

These data also confirmed the secondary study hypothesis that social support is associated with gender differences in quality of life. Perceived social support was more relevant for emotional quality of life among women with cardiac disease than among men with cardiac disease. In particular, the component of social support that appeared to be responsible for this effect was the sense of belonging or companionship. Women were more likely to report a poor quality of life if they indicated that they did not feel included in social events with friends or did not meet and talk regularly with friends and family. The influence of social support was evident for the emotional component of quality of life (MCS) but not for the physical component of quality of life (PCS). Thus, the data do not suggest an effect of social support on physical aspects of quality of life, and the data do not provide additional insight into gender differences that were observed for physical aspects of life quality. It is noteworthy that there were not baseline gender differences in social support. Rather, social support appears to be more important for emotional quality of life among women than among men.

From a practical perspective, these data indicate that women reported significantly greater difficulty than men in performing physical activities of daily living (eg, self-care, walking, moderate and vigorous activities), that perceptions of health were generally poorer among women than men, and that perceptions of support and belonging among women were strongly associated with emotional aspects of life quality (eg, depression, anxiety, energy level, feelings of accomplishment, social activities). One limitation of this study is that quality of life and social support assessments were self-report and therefore did not incorporate objective reports. Because quality of life was not assessed before the hospital admission, it is not possible to determine the extent to which gender differences might have existed in this sample before the hospitalization. However, comparison of these data with the normative data suggest that quality of life is significantly impaired for both men and women in this sample, and that the magnitude of the gender difference in quality of life is greater than would be expected in healthy adults. In addition, past studies have indicated objective health outcomes associated with self-report indicators of life quality and social support (5,14,15). Thus, although use of objective markers of functioning and social relationships would further enhance this area of research, the self-report data reflect potentially important gender-related differences among cardiac patients. Unfortunately, a disproportionate number of minority participants with more severe disease were not retained in the follow-up assessments. This problem was addressed by statistically controlling the influence of those variables on which the dropouts differed from participants, but the generalizability of results may be restricted by the participant attrition.

Overall, the results of this study suggest that women with cardiac disease have a more negative subjective experience than do men with cardiac disease. The magnitude of the gender difference in quality of life is greater than that found among healthy men and women, and these data indicate that the difference persists over the course of at least a year. Furthermore, it appears that social support, especially a feeling of companionship, interacts with gender to influence quality of life. Thus, the results provide evidence of perceived social support influencing quality of life among women. Because the study relied on self-report of social support, these data highlight the importance of self-perceptions for quality of life. Interventions aimed at increasing social support may have limited impact if self-perceptions are not altered. One strategy for increasing feelings of companionship among women with cardiac disease would be to provide support groups or other mechanisms that facilitate a sense of camaraderie and belonging. Somewhat surprisingly, prior data have documented that individualized psychosocial interventions may have negative effects among women with cardiac disease (27). Although such data would appear to negate the value of social support interventions for women, in fact it may be that individualized interventions do not provide a sense of belonging within a larger social support network. Thus, efforts to provide individualized stress management interventions and support may be less efficacious. Group support and sense of belonging may be important components to include in theoretical models of quality of life among women with cardiac disease. In practice, it is possible that interventions designed to facilitate group support would be of particular benefit to women with heart disease. Despite the efficacy of support groups for women with breast cancer (28), no prior studies have evaluated the influence of support groups among women with cardiac disease. This would be a fruitful area for further work, given the observed relationship in this study between perceived support and quality of life among women with cardiac disease.


   ACKNOWLEDGMENTS
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
 NOTES
 ACKNOWLEDGMENTS
REFERENCES
 
This work was supported, in part, by a grant from the National Heart, Lung, and Blood Institute (HL45290).

The authors thank the OSU Center for Wellness and Prevention for providing space and equipment for data entry and storage, and Molly Ray for help with preparation of the manuscript.


   NOTES
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
 NOTES
ACKNOWLEDGMENTS
 REFERENCES
 
D.J.F. is now at Pfizer, Inc.; A.F. is now at University of Missouri, St. Louis, Missouri; S.L.S. is now at University of Texas Health Sciences Center, San Antonio, Texas.

Received for publication May 14, 2003.


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
 NOTES
 ACKNOWLEDGMENTS
 REFERENCES
 

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