Depressive symptoms are an independent risk factor of cardiovascular disease (CVD)1 and remain underdiagnosed as well as undertreated by healthcare providers.2,3 Depressive symptoms describe the presence of symptoms that do not reach the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, criteria for any of the included depressive disorders. The presentation of at least 2 symptoms of major depression excluding depressed mood and anhedonia (lack of interest or pleasure) present for at least 2 weeks is the criterion for this subset of depression and has been suggested to be labeled as subsyndromal symptomatic depression or subthreshold depression.4 The most frequently reported symptoms for subsyndromal symptomatic depression are insomnia, feeling tired all the time, recurrent thoughts of death, trouble concentrating, significant weight gain, slowed thinking, and hypersomnia.4 In clinical research, depressive symptoms are indicated by the presence of symptoms lower than the instrument cutoff score suggesting clinical depression. More than 15% of persons with CVD have depressive symptoms, and women are twice as likely to experience these symptoms when compared with men.5,6 Furthermore, an individual’s overall quality of life (QOL) can be significantly affected by depressive symptoms.7,8 Most significantly, however, depressive symptoms are associated with greater cardiovascular morbidity and mortality9,10 particularly for women.
Depressive symptoms have also been found to decrease one’s ability to comply with recommendations to alter unhealthy behaviors and other aspects of a medical treatment plan.11–14 Furthermore, a meta-analysis reported that there was a dose-response relationship between depressive symptoms and adherence to their medical regimen. This dose-response relationship indicated that persons with depressive symptoms were 3 times more likely to exhibit decreased adherence.12 Thus, depressive symptoms adversely affect risk for CVD.
The American Heart Association (AHA) guidelines for CVD prevention in women classify their risk as optimal risk, at risk, and high risk for CVD.15 Although there has been an improvement in the awareness of women regarding heart disease risk, nearly half (44%) remain unaware that heart disease is their number one killer.16 Women who have depressive symptoms are particularly at risk for CVD because of the underdiagnosis of depressive symptoms. In addition to being underdiagnosed, depressive symptoms have not been recognized as one of the traditionally described modifiable CVD risk factors (ie, diabetes, hypertension, hyperlipidemia, obesity, smoking, and sedentary lifestyle), as evidenced by their absence in the most recent AHA report of heart disease statistics.17
The literature indicates that there is significant evidence to support a relationship between depressive symptoms and incident CVD, as noted by 5 systematic reviews18–22 and 27 prospective and observational studies. Of these studies, a dose-response relationship was identified in 2 of the systematic reviews18,20 and 22 (81.5%) of the prospective and observational studies. Thus, only 5 of the prospective and observational studies did not report a dose-response relationship between depressive symptoms and incident CVD.23–27
A dose-response relationship has been reported between depressive symptoms and health promotion28,29 as well as 2 indicators of subclinical atherosclerosis, coronary calcium score, and carotid intima-media thickness.30,31 Dose-response relationships have also been reported between depressive symptoms and cardiac risk factors such as physical activity,32–35 metabolic syndrome,36 and unhealthy behaviors such as smoking.34 In these studies, as depressive symptoms increased (ie, the dose of depressive symptoms), there was a corresponding worsening of risk factors (ie, the response noted with lower levels of physical activity). A dose-response relationship has also been reported between depressive symptoms and nonfatal ischemic heart disease5,11,37,38,39,40,41,42,43,44 as well as fatal ischemic heart disease and all-cause mortality so that increased depressive symptoms were found to contribute to an increase in morbidity and mortality.5,11,37,38,44–47 To date, there have been no studies examining the relationship between depressive symptoms and QOL in women at risk for CVD.
Most clinicians associate the concept of dose-response relationship to the nonlinear relationship between escalating drug dosage and the drug’s increased effect. In epidemiological studies, a dose-response relationship describes changes in incidence and prevalence for a stated effect’s relationship with changes in the level of a possible cause.48 The epidemiological definition for a dose-response relationship applies to this study.
The purpose of this study, therefore, was to investigate the relationship between depressive symptoms, health-promoting lifestyle behaviors, heart disease risk awareness, cardiac risk, and QOL in healthy women being screened for risk for CVD. The specific aims were (1) to determine the relationship between depressive symptoms, awareness of heart disease risk, cardiac risk level, health-promoting lifestyle behaviors, and perceived QOL; (2) to determine whether the relationship might be a dose-response relationship, that is, to determine whether depressive symptoms affect the response in health-promoting lifestyle behaviors, cardiac risk, and QOL; and (3) to determine whether the effect of depressive symptoms on QOL was mediated by cardiac risk and/or health-promoting lifestyle behaviors.
Conceptual Model and Study Design
For decades, QOL has been considered essential to CVD investigations.49 The revised Wilson-Cleary Health-Related Quality of Life Model (Figure 1)50 was used to guide this descriptive study of depressive symptoms in healthy women being screened for CVD risk. In this model, there are 5 outcome measures that impact the QOL of an individual. These include biological function, symptoms, functional status, general health perceptions, and overall QOL. In addition, characteristics of the individual including sociodemographic variables and characteristics of the environment, such as social support, can also impact QOL. The measurements for the study are organized according to these variables in the revised Wilson-Cleary Health-Related Quality of Life Model.
A convenience sample of women who attended either individual or large-group cardiovascular screening events held at 1 Michigan healthcare organization was recruited. Using methods suggested by Green51 and Cohen,52 a sample size of 123 was needed based on 7 predictor variables, 80% power, and a large effect size (R2 ≥ 0.26). Inclusion criteria for participation included women aged 30 to 75 years who were able to read, write, and speak English and were able to participate in the informed consent process. Exclusion criteria were previous participation in the health screening or presence of any of the following self-reported conditions: preexisting heart disease, stroke, peripheral vascular disease, dementia, drug dependency, alcoholism, and diagnosed mental illness other than depression.
After institutional review board approval at the research site, 469 women were screened for potential participation. Of these, just more than half (n = 240) had previously participated in the screening and thus were ineligible. On the basis of the exclusion criteria, women were excluded because of a cardiac condition (n = 16); being older than 75 years (n = 7); or having a history of dementia, drug dependency, and alcoholism or having been diagnosed with a mental illness other than depression (n = 6). Thus, 200 women were eligible to participate, but 49 did not come for their scheduled screening appointments, which yielded 151 eligible women who were asked to participate in the study. From the final number of eligible women, 125 (83% participation rate) completed the study (8 declined to participate and 18 signed consent forms but did not complete any of the data collection).
Characteristics of the Individual
Sociodemographic variables included ethnicity, race, and household income; highest level of education; marital status; and employment status. These variables were measured as self-report using categories generated from the census survey and national guidelines.53 Age was self-reported and was part of the heart disease screening form.
Cardiac risk was measured by 2 methods in this study. The first method was the calculated Framingham Estimate of the 10-Year Risk.54 This value was obtained from the screening center’s data of self-reported age and smoking history and from the lipid profile and blood pressure obtained during the screening. The second method was the risk classification categories according to the AHA CVD prevention guidelines for women.15 Only 2 “high-risk” criteria were relevant in this sample, diabetes and a 10-year Framingham global risk of greater than 20%, because the other high-risk criteria were exclusions for study participation. The “at-risk” criteria assessed include the risk factors of smoking, poor diet, physical inactivity, obesity and especially central adiposity, a family history of premature CVD, hypertension, dyslipidemia, and presence of metabolic syndrome. Optimal risk is determined by a Framingham global risk of less than 10% and leading a healthy lifestyle with no risk factors.
Parts of the cardiac risk assessment were the measurement of obesity, central adiposity, percentage of body fat, and lipid profile. These assessments were all obtained as part of the screening center’s cardiac risk assessment procedures. All staff members who participated in the screenings were regular hospital staff who also received special training before each screening event. All blood samples were obtained by trained professionals, and the blood testing device also met the routine quality checks set by the laboratory. Furthermore, before each screening event, the principal investigator reviewed the study protocol with the event staff. During the private screening sessions, the same clinical nurse specialist obtained all the screening center data.
Obesity was defined by the body mass index (BMI) classifications of the Centers for Disease Control and Prevention.55 All BMI values (reported as kilograms per square meter) were obtained by measuring weight and height during the screening event. A BMI of less than 18.5 kg/m2 is considered underweight. A healthy weight is described by a BMI in the range of 18.5 to 24.9. A BMI between 25.0 and 29.9 defines overweight, and a BMI of 30 or more is considered obese. A BMI of greater than or equal to 40 identifies extreme obesity. Central adiposity was measured by waist circumference using a standard tape measure. The desired waist circumference for women is less than 35 in according to the AHA metabolic syndrome criteria.56 Percentage of body fat was obtained by the Omron Model HBF-306 Fat Loss Monitor.57 The lipid profile was obtained from a nonfasting finger-stick sample processed with the Cholestech LDX.58 The finger-stick method correlates with venous plasma values (r ≥ 0.95) that meet the National Cholesterol Education Program guidelines.58,59 The screening center’s questionnaire provided additional CVD risk assessment information such as family history of premature heart disease and the self-reported presence of major CVD risk factors.
To assess for chronic illnesses, a general health history was obtained using the Functional Comorbidity Index (FCI).60 The FCI is a self-report survey of 18 coexisting diagnosed medical problems, such as arthritis, asthma, diabetes, osteoporosis, respiratory disorder, depression, anxiety, and upper gastrointestinal disease. The FCI score is a simple sum of the number of checked items plus the presence of a BMI of greater than or equal to 30. The FCI is well established for assessing comorbidity and has been used in studies in which QOL was considered the outcome of interest.61
Depressive symptoms were measured by the Center for Epidemiologic Studies Depression Scale (CES-D).62 It is a 20-item self-report scale to measure depressive symptoms within the past week on a Likert scale (0, rarely or none of the time/less than 1 day; 1, some or little of the time/1–2 days; 2, occasionally or a moderate amount of time/3–4 days; and 3, most or all of the time/5–7 days). The CES-D has been well established for reliability and validity in the general population.62 An advantage of the CES-D is its emphasis on mood and affect rather than on the physical manifestations of depression, which is desirable when measuring depressive symptoms in the presence of comorbid medical conditions.63 The CES-D is a frequently used depression instrument in studies examining depressive symptoms and CVD. The Cronbach’s α was 0.87 for this study. On a separate form to provide additional information about depression, the women were also asked if there was a family history of depression, if they had ever been diagnosed as depressed, if they had ever been treated for depression, and if they were currently taking any antidepressants.
The health-promoting lifestyle behaviors were measured by the Health-Promoting Lifestyle Profile–II (HPLP-II).64 The HPLP-II is a 52-item instrument that measures the multidimensional pattern of self-initiated health-promoting behaviors. These behaviors are intended to maintain or enhance the individual’s health potential and wellness level.65 The HPLP-II 4-point response scale measures the frequency of the self-reported health-promoting behaviors as never, 1; sometimes, 2; often, 3; and routinely, 4. The HPLP-II is scored by calculating a mean of the individual’s responses to all 52 items. The total score indicates the overall health-promoting lifestyle score. There are also 6 subscales: (1) health responsibility, (2) physical activity, (3) nutrition, (4) spiritual growth, (5) interpersonal relations, and (6) stress management. No studies using the HPLP-II in a similar population have been identified. For this study, the Cronbach’s α was 0.92 for the total mean score, with subscales ranging from 0.76 to 0.86 (0.76 for health responsibility, 0.85 for physical activity, 0.79 for nutrition, 0.86 for spiritual growth, 0.81 for interpersonal relations, and 0.77 for stress management), which is consistent with those reported by others.64,65
General Health Perceptions
Heart disease risk awareness was measured by asking 4 questions: (1) Did the participant know her chance of getting heart disease before the screening, (2) Did the participant know if she should be evaluated for potential risk for heart disease, (3) Did the participant learn about heart disease risk from her primary care provider, and (4) Did the participant learn about heart disease risk from the media or from participation in the Women’s Heart Advantage (the center’s screening program)?
Overall Quality of Life
Life satisfaction was used to assess QOL with the Ferrans-Powers Quality of Life Index Generic Version–III (QLI).66 This version is recommended for assessment of QOL in healthy individuals. The instrument consists of 66 items, in which half of the items ask questions about satisfaction with aspects of life on a Likert scale (1, very dissatisfied, to 6, very satisfied). The other half of the items address the importance of these aspects of life to the individual on a Likert scale (1, very unimportant, to 6, very important). The QLI generates an overall QOL score and subscale scores in the following 4 domains: health and functioning, family, social and economic, and psychological/spiritual. A QLI total score and the 4 subscale scores can range from 0 to 30. A score of less than 19 indicates a poorer QOL (Carol Ferrans, PhD, RN, e-mail communication, August 2007). The QLI has well established psychometrics, and those from this study were consistent with previous reports. For this study, the internal consistency of the QLI was 0.87 for overall QOL, with subscales ranging from 0.73 to 0.88 (0.85 for health and functioning, 0.73 for social and economic, 0.88 for psychological/spiritual, and 0.73 for family), using Cronbach’s α.
The Statistical Package for the Social Sciences version 15 (Statistical Package for the Social Sciences Inc, Chicago, Illinois) was used to perform the statistical analyses. To describe and examine relationships among the variables, descriptive statistics, χ2 analyses, and correlations were used, and logistic regression, specifically binary logistic regression, was used to examine for a dose-response relationship. For the logistic regression analysis, depressive symptoms were entered as a continuous variable, and the following variables were entered simultaneously in the models: BMI was entered as a continuous variable, whereas family history and socioeconomic variables (education, employment, income, and marital status) were entered as dichotomized categorical variables. For the mediational analysis, simple linear regressions were conducted using the methods of Baron and Kenny67 and Holmbeck.68 The covariates for the regression analyses were entered in the first model and included the CVD risk factors of BMI and family history of CVD, along with the socioeconomic variables educational level, employment, income, and marital status.
To investigate the impact of depressive symptoms on health-promoting lifestyle behaviors and QOL, the sample was divided into 2 groups using the CES-D split score of 16 (<16 is not depressed and ≥16 is depressed).62 The outcome of health-promoting lifestyle behaviors was dichotomized at a score of 2.5. This split was based on the 1 (“never”) to 4 (“routinely”) scale for the mean HPLP-II score, with 2.5 as the median score. The QOL outcome was dichotomized at the recommended QLI score of 19.
Characteristics of the Individual
The women who participated had a mean (SD) age of 57.7 (9.6) years; were predominantly non-Hispanic (95%), white (86%), and married (60%); had some college education (40%); were employed full-time (39%); had a household income between $25,000 and $49,999 (40%); and resided in an urban community (86%). When compared by depressive symptom status, the only statistically significant different sociodemographic variable was education (P < 0.04), indicating that women who were better educated were not depressed.
Using the Framingham risk scoring method, the mean (SD) Framingham score was 3% (3.9%). This indicated that 94% of the women were in the AHA “optimum risk” category, which the Framingham system would label as low risk. Despite the low Framingham risk score, there were significantly high numbers of cardiovascular risk factors identified. Most of the women (83%) had a total cholesterol of greater than 160 mg/dL, whereas 58% had a high-density lipoprotein cholesterol of less than 50 mg/dL. Seventy percent had a systolic blood pressure higher than the recommended normal of 120 mm Hg. Although only 4% of the women reported smoking and 10% had diabetes, more than half (56%) of the women had a BMI of greater than or equal to 30. Almost all women (94%) had greater than 30% body fat, and 57% had a waist circumference of greater than or equal to 35 in. Ten percent were estimated to have metabolic syndrome.
Given the high percentage of women with major risk factors, cardiac risk was also determined using the AHA’s risk classification system for women.14 Using this method, there was a considerable shift in the numbers of women considered “at risk.” With the Framingham scoring system, only 6% (n = 7) of the women were classified as “at risk” and 94% (n = 117) were classified as “optimum risk.” With the AHA risk classification, once women with diabetes and physical inactivity were identified, the number of women classified at “high risk” was increased to 11% (n = 14), the number now considered as having “optimum risk” was reduced to 25% (n = 31), and there was a substantial decrease in the number of women classified as “at risk” (n = 80, 64%).
In terms of chronic illnesses, on the basis of the scores of the FCI, these women were healthy with a mean (SD) score of 2.2 (1.6). Obesity (58%) was the most frequently observed chronic condition followed by arthritis (34%) and upper gastrointestinal disease (24%).
The mean (SD) CES-D score for the sample was 14 (SD, 9). The median score was 12, with a range of 1 to 41. Using the CES-D’s cutoff score of 16, more than one-third (34%, n = 42) of the women reported significant depressive symptoms. Given the number of women who were found to have significant depressive symptoms, the CVD risk profile was examined according to depression status using the dichotomized CES-D score (Table 1). Only 3 CVD risk factors were statistically different between the depressed and nondepressed women. On average, the depressed women were 4 years younger, with a 3-in greater waist circumference and 20 mg/dL lower total cholesterol.
With a mean (SD) HPLP-II total score of 2.63 (0.38), these women on average performed health-promoting lifestyle behaviors in the range of sometimes to often (Table 1). Both for the entire sample and when compared by depressive symptom status, scores on the interpersonal relations subscale (mean scores for the total sample and depressed and nondepressed participants ranging from 2.8 to 3.2 for activities such as maintaining meaningful and fulfilling relationships and spending time with close friends) and spiritual growth subscale (mean scores for the total sample and depressed and nondepressed participants ranging from 2.7 to 3.2 for activities such as believing that her life has purpose and feeling content and at peace with herself) were the highest scores. Physical activity (with activities such as following a planned exercise program and getting exercise during usual daily activities) followed by stress management (with activities such as getting enough sleep and taking time for relaxation each day) had the lowest subscale scores, with scores of the total sample and depressed versus nondepressed participants ranging from 2.1 to 2.6. Furthermore, with the exception of the physical activity subscale, the mean and subscale scores were statistically significantly different when compared by depressive symptom status. The scores indicated that, on average, the depressed women were less frequently performing these health-promoting lifestyle behaviors, and the low levels of physical activity were not different for the depressed compared with nondepressed women.
General Health Perceptions
Measurement of risk awareness identified 4 key findings. Many women (67%, n = 84) were aware of their risk for heart disease before attending the screening, and most (77%, n = 96) indicated that they were aware of the importance of women receiving screening for heart disease risk. Most women (85%, n = 106) said they had learned about heart disease risk in women either from their membership in the hospital’s Heart Advantage program or from the media. Finally, less than a half (43%, n = 54) reported that they had learned about their heart disease risk from their primary care provider.
Overall Quality of Life
Overall, the women were satisfied with their QOL (QLI mean [SD], 22.4 ; median, 22.7; range, 9–30) (Table 1). With a total QLI score of less than 19 indicating a poor QOL, 13% of the women fell within this category. When the women were compared by depressive symptom status, the overall QOL and its domains were significantly different. The depressed women reported lower overall QOL and lower QOL in the following domains: psychological/spiritual, family, social/economic, and health and functioning when compared with the women who were not depressed.
Relationships Among the Study Variables
The first study aim was to examine whether there was a relationship between depressive symptoms, risk awareness, Framingham risk score, AHA group risk, health-promoting behaviors, and overall QOL. Having depressive symptoms was inversely related to health-promoting behaviors (r = −0.37, P < 0.01) and overall QOL (r = −0.51, P < 0.01). Health-promoting behaviors were positively associated with overall QOL (r = 0.46, P < 0.01). Finally, there was an inverse association between AHA risk groups and health-promoting behaviors (r = −0.26, P < 0.01). These correlations remained significant after adjustment with the Bonferroni correction factor. Thus, the women who were depressed were less frequently performing health-promoting behaviors and reported a lower QOL. No correlation was identified between depressive symptoms and either method of measuring cardiac risk.
The second study aim was to determine whether there was a dose-response relationship between depressive symptoms and health-promoting lifestyle behaviors and QOL. A dose-response relationship with health-promoting lifestyle behaviors (odds ratio, 0.92; 95% confidence interval, 0.88–0.97; P < 0.001) and QOL (odds ratio, 0.85; 95% confidence interval, 0.79–0.92; P < 0.001) was identified with depressive symptoms. These data indicate that, as depressive symptoms worsened (ie, the dose of depressive symptoms), the odds for more frequently performing health-promoting behaviors and perceiving a satisfactory QOL were reduced (ie, response).
While controlling for BMI, education, employment, family history, income, and marital status, health-promoting lifestyle behaviors were found to mediate the relationship between depressive symptoms and perceived QOL (Table 2). The effect of depressive symptoms on QOL as being mediated by the health-promoting lifestyle behaviors was found to be significant using the Sobel test statistic.67,69 These results can be described as a partial mediation because the coefficient of depressive symptoms remained significant when the health-promoting lifestyle behaviors were being controlled. Perfect mediation would exist if the independent variable, depressive symptoms, did not affect QOL when health-promoting lifestyle behaviors were being controlled. In the final regression analysis model with both the mediator and the predictor entered, depressive symptoms were the stronger predictor (β = −0.52, P < 0.001) contrasted to health-promoting lifestyle behaviors (β = 0.21, P < 0.01). The revised study model depicts the path values (Figure 2).
This study identified that, in a group of women being screened for their CVD risk, there was an inverse relationship between depressive symptoms and both health-promoting lifestyle behaviors and QOL. Furthermore, this was a dose-response relationship so that, as depressive symptoms (ie, the dose) worsened, the odds for performing more frequent health-promoting behaviors and the perception of a satisfactory QOL were reduced (ie, response). It was also identified that health-promoting lifestyle behaviors mediated the relationship between depressive symptoms and QOL. Regardless of method used, calculated cardiac risk was not related to depressive symptoms in this CVD risk–aware group of women. No relationship was identified between risk awareness and depressive symptoms.
The median age of the sample is the same as the median age for the studies of depressive symptoms in incident CVD cited in the literature review. The women were predominantly white and non-Hispanic, which reflects most of these studies and corresponds to the racial composition of the 3 counties serviced by the screening center. Despite the fact that half of the sample lived in the study center’s city, the black and Hispanic populations were underrepresented.
In terms of cardiovascular risk, although the mean (SD) Framingham risk score was 3% (3.9%) with a median score of 1% (range, <1%–30%) and matches the 1999-2004 NHANES Framingham score for women for the 1988-1994 NHANES data,70 the number of women with abnormal results for traditional risk factors portrays a picture much more consistent with the number of women who would be classified as “at risk” using the AHA classification for women method.15 The CVD risk profile revealed that most of the women (83%) had a total cholesterol at or greater than the Framingham risk score target of 160 mg/dL, and 40% had a high-density lipoprotein cholesterol of less than the target of 50 mg/dL. It should be noted that the lipid testing was nonfasting, which may have influenced the total cholesterol score. Seventy percent of the women had a systolic blood pressure of greater than 120 mm Hg, and half of these women were being treated for hypertension. Measures of adiposity indicate that most women were obese; more than half had a waist circumference of greater than or equal to 35 in and more than half had a BMI of greater than or equal to 30, with an additional 12% having a BMI of greater than or equal to 40. An alarming 94% of the women had a body fat measurement of greater than 30%. This sample of women matches the trend exposed by a recent analysis of NHANES data with a mean BMI of 34.1 (SE, 1.4).70
Given the low mean Framingham score and the CVD risk profile of the women, the CVD risk status was reexamined using the AHA cardiac risk profile for women,15 yielding a rather dramatic shift in how these women would be classified. Two CVD risk factors were responsible for the shift in their CVD risk status. Twelve optimum-risk women with diabetes were now considered to be in the high-risk group. The women who were physically inactive increased the number of at-risk women from 6% to 64%, leaving only 25% of the women in the optimum-risk group, a reduction of 69% in this classification. These findings add support to the discussion of the inadequacies of the traditional Framingham risk score to depict risk status in women particularly because it is directed narrowly at coronary heart disease and not the more global CVD risk.71,72 The percentages of women in this study classified as AHA high risk and at risk are very close to the AHA risk classification of the Women’s Health Initiative,73 in which 11% were classified as high risk and 72% were classified as at risk. The study of Hsia et al73 reported that the AHA classification accurately identified cardiovascular risk, lending support to the use of this simple risk guideline.
In terms of risk awareness, although there is room for much improvement, when compared with the national data,16 a larger percentage of these women were aware of the risk for heart disease in women. A lingering concern, however, is that more than half of the women said that they did not learn about their heart disease risk from their primary care provider but from the media or from being a member of the screening center’s Heart Advantage program. This would suggest that many primary care providers in the area of the screening center may not be discussing heart disease risk with their female patients. In a recent AHA survey of women’s awareness, only 23% of women reported their provider discussing actions to improve their cardiovascular health.16 Although the study participants had increased awareness of the risk for heart disease in women, this awareness had not contributed to an improved cardiovascular health profile. The inability to translate awareness into health-promoting behaviors might reflect physicians reporting time as a barrier to discussing cardiovascular health guidelines with their patients or might be related to the high percentage of women in the sample who were depressed and who had depressive symptoms.15
The measurement of the health-promoting lifestyle behaviors indicates that there is much room for improvement in the frequency of self-initiated health promotion behaviors, and, therefore, the CVD risk modification recommendations and support provided to the women attending a CVD risk screening program are encouraging. Overall, these women were physically inactive and performed physical activity lower than the recommended levels when defining cardiovascular health.74 Recent investigations have added to the evidence for the relationship between physical activity and CVD risk. Physical inactivity has been identified to independently predict CVD risk75 and has been recommended to be added to the traditional CVD risk assessment.71 Thus, the finding that low physical activity increased the CVD risk assessment classification in this group of women coming for CVD risk assessment supports the data on physical activity and CVD risk particularly in women.
In addition to being generally physically inactive, these women had room to improve the frequency of performing healthy nutrition behaviors because only a small fraction routinely performed these behaviors. The number of women who generally did not follow a heart-healthy diet and were physically inactive is consistent with the large number of women who were obese, reflected by their BMI, percentage of body fat, and waist circumference and the abnormal results of their lipid profiles.
Identifying that one-third of these apparently healthy women had significant depressive symptoms is alarming. This prevalence of depression is more than 6 times the rate of major depression identified in a national study6 and is twice the incidence reported in studies investigating the relationship of depression to incident CVD in women31 and is greater than 26% of respondents to a national survey of heart disease awareness.16 Of concern was the significant impact that these symptoms had in influencing health-promoting behaviors.
The large percentage of women with low physical activity in this sample who had significant depressive symptoms supports the finding of an inverse relationship between physical activity and depressive symptoms. Thus, women who were less active had more depressive symptoms. This has previously been reported in 4 studies.32–35 Farmer et al33 found this relationship to be more pronounced in women, whereas Knox et al34 did not find a gender difference. The other 2 studies enrolled only women. The unique aspect of this study was the measurement of a comprehensive span of health-promoting lifestyle behaviors that revealed that these risk-aware women were at a higher AHA cardiac risk status than revealed by their Framingham risk score. The other contribution of this study was the investigation of the relationship of depressive symptoms and health-promoting lifestyle behaviors to perceived QOL.
These women who were essentially healthy, albeit with significant cardiac risk factors, generally perceived their QOL as acceptable. The mean (SD) overall QOL score was in the acceptable range (22.4 ), and the subscale scores ranged from 21.5 (4.6) for health and functioning to 23.2 (4.9) for the psychological/spiritual subscale. Consistent with the scores of health-promoting lifestyle behaviors, the health and functioning QLI subscale was the lowest-scoring subscale.
The depressed women tended to have a poorer QOL overall and particularly on the health and functioning subscale compared with the women who were not depressed (19.9 vs 23.8, P < 0.01). There were significant differences on the subscales as well, with the depressed women consistently scoring lower than the women who were not depressed. No studies were identified that investigated QOL and depressive symptoms in women before a cardiac event. Depression has been found to be associated with a poorer QOL in women who have had a myocardial infarction (MI),76 which may extend as long 1 year after MI.77
While controlling for BMI, education, employment, family history, income, and marital status, health-promoting lifestyle behaviors were found to mediate the relationship between depressive symptoms and perceived QOL (Table 2). Poor physical activity and nutrition were the leading unhealthy lifestyle behaviors for this sample of women. More than 70% of the women in this study reported levels less than the AHA recommended physical activity Impact Goals for 2010.74 There was a statistically significant difference for nutrition between the depressed and nondepressed women (Table 1). These findings are consistent with the most recent heart disease statistics17 that reported that poor diet and insufficient physical activity are among the leading factors of CVD mortality, with one-third of adults reporting that they did not participate in any leisure-time physical activity. These same data identified that physical inactivity was more common among women than men and worsened with increased age. Furthermore, an investigation of whether behavioral mechanisms might explain the relationship between depressive symptoms and MI or death78 reported that physical inactivity explained approximately one-fifth of the relationship between depressive symptoms and cardiac risk.
The Heart and Soul Study investigated how depressive symptoms and cardiac function each contributed to health status measures, including QOL.79 An inverse dose-response relationship was found between depressive symptoms and QOL. After adjusting for measures of cardiac function and CVD risk factors, depressive symptoms were found to be independently associated with all measures of health status. Thus, the results of this study provide additional evidence that screening for depressive symptoms should not be delayed until women present with coronary artery disease. At the very least, we need to investigate whether the management of depressive symptoms might increase the likelihood of adhering to recommendations to improve one’s heart health factors.
As with any study, there are limitations. Participant selection bias occurred because this was a convenience sample of women who voluntarily presented to a free heart disease screening program. Although half of the sample came from within 5 miles of the screening center in a community in which race was fairly evenly divided between white and black, the sample was disproportionately white. Increasing the racial and ethnic participation rate in clinical research remains a challenge. This is problematic in light of the results of the Well-Integrated Screening and Evaluation for Women Across the Nation study,80 which concluded that racial/ethnic risk factor disparities were statistically significant and that the greatest risk for CVD was found in black women; these findings were only partially explained by community characteristics. Also of concern is whether the percentage of women with depressive symptoms may not include those who are so significantly depressed that they would not consider participation in such a health-oriented program.
The inability to establish causal relationships is another limitation of this study. However, this study did illuminate the relationship of depressive symptoms and health-promoting lifestyle behaviors to perceived QOL and, in doing so, added to the knowledge of the health status of women being screened for CVD risk.
As we shift to the current AHA goals for cardiovascular health promotion,74 the nurses who most often manage cardiovascular health promotion programs will find it important to understand the impact that depressive symptoms have on health-promoting lifestyle behaviors and QOL. These data indicate that depression may be present in as many as one-third of healthy women, and most women have at least some depressive symptoms. Therefore, assessment for depression and depressive symptoms should be a routine component of cardiovascular health assessment. What has yet to be determined is the “dose” of depressive symptoms that is truly toxic to living a health-promoting and good QOL for women.
The strong, inverse, and dose-response relationship between depressive symptoms and health-promoting behaviors indicates that as the “dose” of depressive symptoms increases, the “response” of performing health-promoting behaviors decreases. The same pattern exists for QOL. The more frequent the number of depressive symptoms, the lower will be the perceived QOL. Fortunately, a strong positive relationship was identified between health-promoting behaviors and QOL; women who more frequently perform health-promoting behaviors more frequently report an overall increased perception of QOL.
The literature supporting depressive symptoms as an independent risk factor of CVD supports the necessity of continued investigations of depressive symptoms along the entire continuum, from precardiac to postcardiac event.81 What we do not yet know is how much more effective CVD risk modification programs might be if depressive symptoms were identified and successful management of depressive symptoms was implemented. This line of investigation would be a great opportunity for cardiovascular clinical nurse specialists and nurse practitioners to investigate. Therefore, it is important for the nurse to assess for depression or depressive symptoms. Current evidence-based guidelines for the prevention of CVD state that depression is a risk factor for women.15 This study suggests that the assessment of depressive symptoms occur along with CV risk factor assessment and not be delayed until a cardiac event has occurred. The findings that the use of the AHA classification for CV risk factors for women included risk factors not captured by the current Framingham method adds to the discussion of the implications of the gender limitations of the Framingham method.
Current screening guidelines82 recommend that a 2-item screening for depression begin the assessment for depression; these 2 items are a less burdensome process than the instrument used in this study. Evidence indicates that early treatment with cognitive behavioral therapy and increased physical activity can reduce depressive symptoms. In addition, it has been reported that these same interventions can produce increased adherence to CVD risk modification programs.9 Depressed patients with heart disease should have their depression treated, selecting from the options of antidepressant drugs, cognitive behavioral therapy, and physical activity.82 A recent study reported that the combination of exercise and pharmacological treatment of depression in patients with coronary heart disease was beneficial.83
Because depressive symptoms are an independent risk factor of CVD, it is suggested that the presence of depressive symptoms more completely informs the CVD risk status of women. This study found that depressive symptoms are significantly inversely associated with health-promoting lifestyle behaviors and QOL for women. Furthermore, there was a dose-response relationship between depressive symptoms and both health-promoting lifestyle behaviors and QOL. Thus, one does not need to be diagnosed with clinical depression before the presence of depressive symptoms may exert an adverse effect on these 2 variables. Early detection and treatment of depressive symptoms would be important for participation in healthy lifestyle behaviors, which could result in improved QOL.
What’s New and Important
- Depressive symptoms have a strong and inverse dose-response relationship to health-promoting lifestyle behaviors and QOL, and the association between depressive symptoms and QOL was mediated by health-promoting lifestyle behaviors.
- The Framingham score could be misleading when screening women for CVD risk status. Physical inactivity increased the number of women classified as “at risk” based upon the AHA guidelines for CVD prevention in women compared with just the use of the Framingham global risk score.
- Recommendations for nursing practice include adding screening for depressive symptoms when screening for CVD risk to help inform the CVD risk status.
1. Van der Kooy K, van Hout H, Marwijk H, et al. Depression
and the risk for cardiovascular diseases: systematic review and meta-analysis. Int J Geriatr Psychiatry. 2007; 22 (7): 613–626.
2. Huffman JC, Smith FA, Blais MA, Beiser ME, Januzzi JL, Fricchione GL. Recognition and treatment of depression
and anxiety in patients with acute myocardial infarction. Am J Cardiol. 2006; 98 (3): 319–324.
3. Ziegelstein RC, Kim SY, Kao D, et al. Can doctors and nurses recognize depression
in patients hospitalized with an acute myocardial infarction in the absence of formal screening? Psychosom Med. 2005; 67 (3): 393–397.
4. Sadek N, Bona J. Subsyndromal symptomatic depression
: a new concept. Depress Anxiety. 2000; 12 (1): 30–39.
5. Ferketich AK, Schwartzbaum JA, Frid DJ, Moeschberger ML. Depression
as an antecedent to heart disease among women
and men in the NHANES I study. Arch Intern Med. 2000; 160 (9): 1261–1268.
6. Hasin DS, Goodwin RD, Stinson FS, Grant BF. Epidemiology of major depressive disorder. Results from the National Epidemiologic Survey on Alcoholism and Related Conditions. Arch Gen Psychiatry. 2005; 62 (10): 1097–1106.
7. Ford ES, Mokdad AH, Li C, et al. Gender differences in coronary heart disease and health-related quality of life
: findings from 10 states from the 2004 Behavioral Risk Factor Surveillance System. J Womens Health (Larchmt). 2008; 17 (5): 757–768.
8. Kobau R, Safran MA, Zack MM, Moriarty DG, Chapman D. Sad, blue, or depressed days, health behaviors and health-related quality of life
: Behavioral Risk Factor Surveillance System, 1995–2000. Health Qual Life Outcomes. 2004; 2: 40. http://www.hqlo.com/content/2/1/40
. Accessed December 20, 2009.
9. Franklin BA, Cushman M. Recent advances in preventive cardiology and lifestyle medicine: a themed series. Circulation. 2011; 123 (20): 2274–2283.
10. Whang W, Kubzansky LD, Kawachi I, et al. Depression
and risk of sudden cardiac death and coronary heart disease in women
: results from the nurse’s health study. J Am Coll Cardiol. 2009; 53 (11): 950–958.
11. Barefoot JC, Schroll M. Symptoms of depression
, acute myocardial infarction, and total mortality in a community sample. Circulation. 1996; 93 (11): 1976–1980.
12. DiMatteo MR, Lepper HS, Croghan TW. Depression
is a risk factor for noncompliance with medical treatment: meta-analysis of the effects of anxiety and depression
on patient adherence. Arch Intern Med. 2000; 160 (14): 2101–2107.
13. Katon WJ. Clinical and health services relationships between major depression
, depressive symptoms, and general medical illness. Biol Psychiatry. 2003; 54 (3): 216–226.
14. Vinkers DJ, Gussekloo J, Stek ML, van der Mast RC, Westendorp RGJ. Does depression
specifically increase cardiovascular mortality? [Letter to the Editor]. Arch Intern Med. 2005; 165 (1): 119.
15. Mosca L, Benjamin EJ, Berra K, et al. Evidence-based guidelines for cardiovascular disease prevention in women
: 2011 update: a guideline from the American Heart Association. Circulation. 2011; 123 (11): 1243–1262.
16. Mosca L, Hammond G, Mochari-Greenberger H, et al. Fifteen-year trends in awareness of heart disease in women
: results of a 2012 American Heart Association National Survey. Circulation. 2013; 127 (11): 1254–1263.
17. Go AS, Mozaffarian D, Roger VL, et al.on behalf of the American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Heart disease and stroke statistics—2013 update: a report from the American Heart Association. Circulation. 2013; 127 (1): e6–e245.
18. Kubzansky LD, Kawachi I. Going to the heart of the matter: do negative emotions cause coronary heart disease? J Psychiatr Res. 2000; 48 (4-5): 323–337.
19. Kuper H, Marmot M, Hemingway H. Systematic review of prospective cohort studies of psychosocial factors in the etiology and prognosis of coronary heart disease. Semin Vasc Med. 2002; 2 (3): 267–314.
20. Rugulies R. Depression
as a predictor for coronary heart disease: a review and meta-analysis. Am J Prev Med. 2002; 23 (1): 51–61.
21. Strike PC, Steptoe A. Psychosocial factors in the development of coronary artery disease. Prog Cardiovas Dis. 2004; 46 (4): 337–347.
22. Wulsin LR, Singal BM. Do depressive symptoms increase the risk for the onset of coronary disease? A systematic quantitative review. Psychol Med. 2003; 65 (2): 201–210.
23. Astin F, Jones K, Thompson DR. Prevalence and patterns of anxiety and depression
in patients undergoing elective percutaneous transluminal coronary angioplasty. Heart Lung. 2005; 34 (6): 393–401.
24. Ford DE, Mead LS, Chang PP, et al. Depression
is a risk factor for coronary artery disease in men: the precursors study. Arch Intern Med. 1998; 158 (13): 1422–1426.
25. Rasul F, Stansfield SA, Hart CL, et al. Psychological distress, physical illness and risk of coronary heart disease. J Epidemiol Community Health. 2005; 59 (2): 140–145.
26. Rosengren A, Hawken S, Ounpuu S, et al.for the INTERHEART Investigators. Association of psychosocial risk factors
with risk of acute myocardial infarction in 11 119 cases and 13 648 controls from 52 countries (the INTERHEART): case-control study. Lancet. 2004; 364 (9438): 953–962.
27. Yusuf S, Hawken S, Ounpuu S, et al.on behalf of the INTERHEART study investigators. Effect of potentially modifiable risk factors
associated with myocardial infarction in 52 countries (the INTERHEART study): case-control study. Lancet. 2004; 364 (9438): 937–952.
28. Bonnet F, Irving K, Terra JL, Nony P, Berthezene F, Moulin P. Anxiety and depression
are associated with unhealthy lifestyle in patients at risk of cardiovascular disease. Atherosclerosis. 2005; 178 (2): 339–344.
29. Rosal MC, Ockene JK, Hebert JR, et al. Behavioral risk factors
among members of a health maintenance organization. Prev Med. 2001; 33 (6): 586–594.
30. Agatisa PK, Matthews KA, Bromberger JT, Edmundowicz D, Chang Y-F, Sutton-Tyrrell K. Coronary and aortic calcification in women
with a history of major depression
. Arch Intern Med. 2005; 165 (11): 1229–1236.
31. Elovainio M, Keltikangas-Jarvinen L, Kivimaki M, et al. Depressive symptoms and carotid artery intima-media thickness in young adults: the cardiovascular risk in young Finns study. Psychosom Med. 2005; 67 (4): 561–567.
32. Brown WJ, Ford JH, Burton NW, Marshall AL, Dobson AJ. Prospective study of physical activity and depressive symptoms in middle-aged women
. Am J Prev Med. 2005; 29 (4): 265–272.
33. Farmer ME, Locke BZ, Moscicki EK, Dannenberg AL, Larson DB, Radloff LS. Physical activity and depressive symptoms: the NHANES-I epidemiologic follow-up study. Am J Epidemiol. 1988; 128 (9): 1340–1351.
34. Knox S, Barnes A, Kiefe C, et al. History of depression
, race, and cardiovascular risk in CARDIA. Int J Behav Med. 2006; 13 (1): 44–50.
35. Wassertheil-Smoller S, Shumaker S, Ockene J, et al. Depression
and cardiovascular sequelae in postmenopausal women
: the Women
’s Health Initiative (WHI). Arch Intern Med. 2004; 164 (3): 289–298.
36. Raikkonen K, Matthews KA, Kuller LH. The relationship between psychological risk attributes and the metabolic syndrome in healthy women
: antecedent or consequence? Metabolism. 2002; 51 (12): 1573–1577.
37. Whang W, Kubzansky LD, Kawachi I, et al. Depression
and risk of sudden death and coronary heart disease in women
: results from the Nurses’ Health Study. J Am Coll Cardiol. 2009; 53 (11): 950–958.
38. Ariyo AA, Haan M, Tangen CM, et al.for the Cardiovascular Health Study Collaborative Research Group. Depressive symptoms and risks of coronary heart disease and mortality in elderly Americans. Circulation. 2000; 102 (15): 1773–1779.
39. Gilmore H. Depression
and risk of heart disease: statistics Canada, catalogue n. 82-003-XPE. Health Rep. 2008; 19 (3): 7–17.
40. Mittag O, Meyer T. The association of depressive symptoms and ischemic heart disease in older adults is not moderated by gender, marital status or education. Int J Public Health. 2012; 57 (1): 79–85.
41. Pratt LA, Ford DE, Crum BM, Armenian HK, Gallo JJ, Eaton WW. Depression
, psychotropic medication, and risk of myocardial infarction: prospective data from the Baltimore ECA follow-up. Circulation. 1996; 94 (12): 3123–3129.
42. Sesso HD, Kawachi I, Vokonas PS, Sparrow D. Depression
and the risk of coronary heart disease in the Normative Aging Study. Am J Cardiol. 1998; 82 (7): 851–856.
43. Thurston RC, Kubzansky LD. Multiple sources of psychological disadvantage and risk of coronary heart disease. Psychosom Med. 2007; 69 (8): 748–754.
44. Brown JM, Stewart JC, Stump TE, Callahan CM. Risk of coronary heart disease events over 15 years among older adults with depressive symptoms. Am J Geriatr Psychiatry. 2011; 19 (8): 721–729.
45. Gump BB, Matthews KA, Eberly LE, Chang Yfor the MRFIT Research Group. Depressive symptoms and mortality in men: results from the Multiple Risk Factor Intervention Trial. Stroke. 2005; 36 (1): 98–102.
46. Pennix B, Beekman A, Honig A, et al. Depression
and cardiac mortality: results from a community-based longitudinal study. Arch Gen Psychiatry. 2001; 58 (3): 221–227.
47. Wulsin LR, Evans JC, Vasan RS, Murabito JM, Kelly-Hayes M, Benjamin EJ. Depressive symptoms, coronary heart disease, and overall mortality in the Framingham Heart Study. Psychosom Med. 2005; 67 (5): 697–702.
49. Spertus J, Green-Conaway D. Quality-of-life issues for women
with coronary disease. In Shaw LJ, Redberg RF, eds. Contemporary Cardiology: Coronary Disease in Women
: Evidence-based Diagnosis and Treatment. Totowa, NJ: Humana Press; 2004.
50. Ferrans CE, Zerwic JJ, Wilbur JE, Larson JL. Conceptual model of health-related quality of life
. J Nurs Scholarsh. 2005; 37 (4): 336–342.
51. Green SB. How many subjects does it take to do a regression analysis? Multivariate Behav Res. 1991; 26 (3): 499–510.
52. Cohen J. Statistical Power Analysis for the Behavioral Sciences. 2nd ed. New York, NY: Lawrence Erlbaum; 1988.
54. Wilson PWF, D’Agostino RB, Levy D, Belanger AM, Silbershatz H, Kannel WB. Prediction of coronary heart disease using risk factor categories. Circulation. 1998; 97 (18): 1837–1847.
56. Grundy SM, Cleeman JI, Daniels SR, et al. Diagnosis and management of the metabolic syndrome: an American Heart Association/National Heart, Lung, and Blood Institute scientific statement: executive summary. Circulation. 2005; 112 (17): e285–e290.
58. Cholestech Corp. Accuracy and reproducibility of point-of-care lipid methods are certified by the cholesterol reference method laboratory network. Technical brief. 2006. http://choleastech.com
. Accessed August 6, 2007.
59. Working Group on Lipoprotein Measurement. National Cholesterol Education Program: recommendations on lipoprotein measurement. National Institutes of Health (NIH). National Heart, Lung and Blood Institute. 1995; NIH Publication No. 95-3044, 1-186.
60. Groll DL, To T, Bombardier C, Wright JG. The development of a comorbidity index with physical function as the outcome. J Clin Epidemiol. 2005; 58 (6): 595–602.
61. Fortin M, Hudon C, Dubois M-F, Almirall J, Lapointe L, Soubhi H. Comparative assessment of three different indices of multimorbidity for studies on health-related quality of life
. Health Qual Life Outcomes. 2005; 3 (74). http://www.hqlo.com/content/3/1/74
. Accessed August 3, 2007.
62. Radloff LS. The CES-D scale: a self-report depression
scale for research in the general population. App Psychol Meas. 1977; 1 (3): 385–401.
63. Hann D, Winter K, Jacobsen P. Measurement of depressive symptoms in cancer patients. Evaluation of the Center for Epidemiologic Studies Depression
Scale (CES-D). J Psychosom Res. 1999; 46 (5): 437–443.
64. Walker SN, Sechrist KR, Pender NJ. The Health-promoting Lifestyle Profile-II. Omaha, NE: University of Nebraska Medical Center, College of Nursing; 1995.
65. Acton GJ, Malathum P. Basic need status and health-promoting self-care behaviors in adults. Western J Nurs Res. 2000; 22 (7): 796–811.
67. Baron RM, Kenny DA. The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. J Pers Soc Psychol. 1986; 51 (6): 1173–1182.
68. Holmbeck GN. Toward terminological, conceptual, and statistical clarity in the study of mediators and moderators: examples from the child-clinical and pediatric psychology literatures. J Consult Clin Psychol. 1997; 65 (4): 599–610.
69. Sobel ME. Asymptotic confidence intervals for indirect effects in structural equation models. In Leinhart S, ed. Sociological Methodology. San Francisco, CA: Jossey-Bass; 1982.
70. Towfighi A, Zheng L, Ovbiagele B. Sex-specific trends in midlife coronary heart disease risk and prevalence. Arch Intern Med. 2009; 169 (19): 1762–1766.
71. D’Agostino RB, Vasan RS, Pencina MJ, et al. General cardiovascular risk profile for use in primary care: the Framingham Heart Study. Circulation. 2008; 117 (6): 743–753.
72. Sacco RL, Khatri M, Rundek T, et al. Improving global vascular risk prediction with behavioral and antropometic factors. The multiethnic NOMAS (Northern Manhattan Cohort Study). J Am Coll Cardiol. 2009; 54 (24): 2303–2311.
73. Hsia J, Rodabough RJ, Manson JE, et al. Evaluation of the American Heart association cardiovascular disease prevention guideline for women
. Circ Cardiovasc Qual Outcomes. 2010; 3 (2): 128–134.
74. Lloyd-Jones DM, Hong Y, Labarthe D, et al.on behalf of the American Heart Association Strategic Planning Task Force and Statistics Committee. Defining and setting national goals for cardiovascular health promotion and disease reduction: the American Heart Association’s Strategic Impact Goals through 2020 and beyond. Circulation. 2010; 121 (4): 586–613.
75. McGuire KA, Janssen I, Ross R. Ability of physical activity to predict cardiovascular disease beyond commonly evaluated cardiometabolic risk factors
. Am J Cardiol. 2009; 104 (11): 1522–1526.
76. Kamm-Steigelman L, Kimble LP, Dunbar S, Sowell RL, Bairan A. Religion, relationships and mental health in midlife women
following acute myocardial infarction. Issues Ment Health Nurs. 2006; 27 (2): 141–159.
77. White ML, Groh CJ. Depression
and quality of life
after a myocardial infarction. J Cardiovasc Nurs. 2007; 22 (2): 138–144.
78. Ye S, Muntner P, Shimbo D, et al. Behavioral mechanisms, elevated depressive symptoms, and the risk for myocardial infarction or death in individuals with coronary heart disease. J Am Coll Cardiol. 2013; 61 (6): 622–630.
79. Ruo B, Rumsfeld JS, Hlatky MA, et al. Depressive symptoms and health-related quality of life
. The heart and soul study. JAMA. 2003; 290 (2): 215–221.
80. Finkelstein EA, Khavjou OA, Mobley LR, Haney DM, Will JC. Racial/ethnic disparities in coronary heart disease risk factors
among WISEWOMAN enrollees. J Womens Health (Larchmt). 2004; 13 (5): 503–518.
81. Frasure-Smith N, Lesperance F. Depression
and cardiac risk: present status and future directions. Heart. 2010; 96 (3): 173–176.
82. Lichtman JH, Bigger JT, Blumenthal JA, et al. Depression
and coronary heart disease: recommendations for screening, referral, and treatment: a science advisory from the American Heart Association. Circulation. 2008; 118 (17): 1768–1775.
83. Blumenthal JA, Sherwood A, Babyak MA, et al. Exercise and pharmacological treatment of depressive symptoms in patients with coronary heart disease. J Am Coll Cardiol. 2012; 60 (12): 1053–1063.
Keywords:Copyright © 2015 Wolters Kluwer Health, Inc. All rights reserved
depression; quality of life; risk factors; women