Cardiovascular diseases are the number one cause of death globally.1 In 2010, approximately 800 000 people died of cardiovascular diseases in the United States, accounting for one-third of all deaths.2 The major modifiable contributors to cardiovascular disease mortality are high blood pressure, tobacco use, unhealthy diet and obesity, insufficient physical activity, and abnormal glucose levels.3 Other risk factors associated with heart disease include dyslipidemia and harmful use of alcohol,1 as well as emerging risk factors such as depression4 and anxiety.5
The abovementioned risk factors also worsen prognosis in patients with established heart disease or after acute coronary syndrome. Depression, which is evident in nearly 20% of these patients,6 is associated with a 2.25-fold risk of all-cause mortality after myocardial infarction7 and has been recommended by the American Heart Association as a risk factor for poor prognosis following acute coronary syndrome.8 The risk for all-cause mortality in patients suffering from heart failure is increased by 51% in the presence of depression.9
Insufficient physical activity leads to poor fitness, which, in patients with established heart disease or after acute coronary syndrome, is also associated with poor prognosis. Fitness can be objectively assessed by determining exercise capacity using a symptom-limited exercise test. Low exercise capacity in these patients is associated with a more than 2-fold risk of all-cause mortality compared with high exercise capacity.10
Given the abovementioned clinical significance of depression and exercise capacity in this population, expected outcomes for patients enrolled in cardiac rehabilitation programs include the absence of clinically significant depressive symptoms and a clinically significant increase in exercise capacity.11 A potential interaction between these 2 variables may therefore be of substantial clinical importance in assessment, risk stratification, goal setting, therapeutic intervention, and rehabilitation. Studies assessing a correlation between depressive symptoms and exercise capacity in patients suffering from heart disease are few; however, this correlation can be assessed in every study that reports measures of depressive symptoms and exercise capacity, regardless of whether this is the purpose of the study or not. We therefore conducted a meta-analysis including studies from which correlations between depressive symptoms and exercise capacity could be calculated.
Studies published between January 2000 and September 2013 were inspected. The restriction to recent studies was decided on the rationale that access to the raw database of the studies in order to calculate correlations would be needed and the availability of such databases would diminish with time. Experimental and observational studies on adult patients with coronary artery disease, heart failure, congenital heart disease, and implantable cardioverter defibrillator (ICD), alone and with comorbid depression (mild, moderate, or major), published and unpublished in English were eligible for inclusion. Studies on patients with comorbid depression were eligible in order for the total range of depressive symptoms to be used for calculating the correlation between depressive symptoms and exercise capacity. Studies that reported correlation(s) between depressive symptoms and exercise capacity, as well as studies from which such correlations could be calculated, were included. Depressive symptoms were measured using validated depression scales. Exercise capacity was measured (1) in the case of cardiopulmonary exercise testing, using a symptom-limited exercise test and reported as peak oxygen uptake (
O2peak); (2) with treadmill exercise testing, estimated metabolic equivalents (METs) at maximum workload or in watts with cycle ergometer testing; and (3) total distance covered in the case of incremental shuttle walk testing (ISWT). Heart transplantation studies were excluded.
A systematic computerized literature search in the databases of PubMed, Cochrane Library, Google Scholar, and ProQuest, covering articles from January 2000 to September 2013, was conducted. ProQuest contains unpublished theses, the inclusion of which reduces publication bias. References of included studies were also reviewed, and principal investigators of included studies were asked to provide relevant published or unpublished studies.
The titles and abstracts (PubMed), titles (Google Scholar), abstracts (ProQuest), and full text (Cochrane Library) of studies were searched using the following search terms: exercise AND depress*; card* AND depress*; heart AND depress*; stress test* AND depress*; aerobic capacity AND depress*; fitness AND depress*; oxygen uptake AND depress*;
O2max AND depress*;
O2peak AND depress*; and rehabilitation AND depress*
All search terms were followed by NOT menopause NOT pregnant NOT postpartum NOT postnatal NOT fibromyalgia NOT Parkinson's NOT stroke NOT sclerosis NOT spinal cord NOT lumbar NOT low back NOT spondylitis NOT osteoarthritis NOT fracture NOT musculoskeletal NOT cancer NOT bipolar NOT binge NOT adolescent* NOT rat, except in the Google Scholar search.
STUDY SELECTION AND DATA COLLECTION
All potentially relevant studies, judged from title and abstract, were either acquired in full text or their authors were contacted and asked whether an exercise capacity metric (
O2peak, METs, maximum watts, and total distance covered in the ISWT) and a depression scale score had been reported. Studies reporting one of the abovementioned exercise capacity metrics and a depression scale score or correlation between them were included. Authors of such studies were contacted, and the raw data or the correlations were requested.
The correlation between depressive symptoms and exercise capacity was the only data item and was either reported or calculated from the included studies. One correlation coefficient per study was calculated and included in the analysis. If a study had more than 1 group, the correlation for the total cohort was calculated. If a study had more than 1 measurement, the correlation at baseline was calculated.
RISK OF BIAS IN INDIVIDUAL STUDIES
To reduce the risk of selective outcome reporting and to eliminate the bias of lack of blinding of the participants and researchers in prospective studies, the correlation between depressive symptoms and exercise capacity at baseline only was reported or calculated.
Pearson (or Spearman, in case of nonparametric data) correlation coefficients between a depression scale score and an exercise capacity metric were used to create the summary measure. A random-effects model was used, since there is no current knowledge of the effect of moderators (eg, gender, age) on the true effect size.
PLANNED METHOD OF ANALYSIS
The method of Hedges and Vevea12 was used for synthesizing the correlation coefficients. In this method, the correlations are converted to the Fisher z scale and the analysis is performed using the transformed values. Then, the results, such as summary effect and 95% CIs, are converted back to correlations. This method of converting correlations to Fisher z values was chosen to eliminate the bias in the correlation variance, since it depends strongly on the correlation itself.
Any excessive inconsistency (between-studies variation) is considered to reflect true differences between studies and is termed heterogeneity. Heterogeneity was assessed using the Cochran Q χ2 test and using I2, which represents the percentage of the total variation that is attributed to true differences between studies. Confidence (uncertainty) intervals for I2 were also calculated. MIX 2.0 (Biostat, Englewood, NJ) and Comprehensive Meta-Analysis software (Biostat, Englewood, NJ) were used for statistical analysis.
RISK OF BIAS ACROSS STUDIES
Publication bias was assessed visually on a funnel plot (effect size by the inverse of its standard error) and statistically using the Egger test, the Begg and Mazumdar rank correlation test, and the Trim and Fill method. It should be mentioned, however, that other factors such as study quality and true heterogeneity can produce asymmetry in funnel plots. Duplication was avoided by juxtaposing author names, study sample size, gender, and age. In cases of ambiguity, the authors were contacted. Availability bias (selective inclusion of studies that are easily accessible to the researcher), with regard to omitting studies from which the required data were difficult to acquire, was addressed by contacting all authors of the study, by regular (biweekly) followups, and by collecting data for a period of 12 months. The Rosenthal Fail-safe N statistic was also used to calculate the number of missing studies that would render the summary effect nonsignificant.
Prespecified exclusion sensitivity analysis was performed to assess the effect of exclusion of each study (one at a time) on the summary effect. Post hoc subgroup analyses were performed using a Q-test based on analysis of variance for random-effects model with pooled estimate of τ2 (between-studies dispersion) with the following moderators: gender, with 2 levels: male and female; disease with 2 levels: coronary artery disease and heart failure; exercise test with 4 levels: cardiopulmonary exercise test, simple treadmill test, watts-based cycle ergometer test, and ISWT. Prespecified meta-regression was performed to assess the effect of age on the effect sizes.
A total of 59 studies were included in the analysis. The search of all databases provided a total of 23 508 citations. Of these, 21 627 studies were discarded after reading the title and/or abstract, since it was clear that they did not meet the criteria. The remaining 1881 studies were acquired in full text or their authors were contacted and asked whether depression and exercise capacity had been assessed. Of these, 1543 and 229 studies were discarded because there was no assessment of depression and/or exercise capacity and because exercise capacity was measured using a submaximal test (eg, 6-minute walk test), respectively. Of the remaining 109 eligible studies, 16 studies were duplicates or shared the same sample whereas 34 studies were not included because it was impossible to acquire the data from their authors (Figure 1).
Fifty-eight published studies and 1 unpublished thesis13 were included, totaling 25 733 participants. Eleven studies reported a correlation between depressive symptoms and exercise capacity, and authors of the remaining 48 studies were contacted and provided the raw data or the correlations. Studies were experimental and observational, and the majority included patients with coronary artery disease and heart failure. One study included patients with congenital heart disease,14 and 2 studies included patients with ICD15,16 (Table 1).
RISK OF BIAS WITHIN STUDIES
Typical sources of bias within studies, such as lack of concealment of randomization and patient blinding, were not assessed because this meta-analysis used only the baseline data for the total cohort. However, data collector blinding at baseline was assessed because it may affect baseline measurements. Baseline measurements that were conducted before randomization were considered collector blind. Of the 26 studies in which collector blinding was applicable, 15 included collector blinding. The results can be seen in Table 2.
RESULTS OF INDIVIDUAL STUDIES
Effect sizes, including sample size, 95% CI, z-statistic, and study weights for the individual studies are reported in Table 3.
SYNTHESES OF RESULTS
The summary correlation coefficient was −0.15 with a 95% CI from −0.17 to −0.12 (Figure 2). Heterogeneity was statistically significant (I2 = 64% with a 95% CI from 52 to 73; Q = 161; df = 58; P < .001).
RISK OF BIAS ACROSS STUDIES
The visual impression of the heterogeneity funnel plot (effect size by the inverse of its standard error) showed no sign of publication bias (Figure 3). This was confirmed statistically using the Egger test (intercept was −0.51 with a 95% CI from −1.15 to 0.13, and a 1-tailed P = .06) and the Begg and Mazumdar rank correlation test (Kendall τ was −0.05 with a 1-tailed P = .29). The Rosenthal Fail-safe N was 4681. The Trim and Fill method using the MIX software did not suggest inputting studies; however, the Comprehensive Meta-Analysis software suggested inputting 4 studies, adjusting the summary effect to −0.14 with a 95% CI from −0.17 to −0.12.
Exclusion sensitivity analysis (the effect of exclusion of each study to the summary effect) did not show clinically significant findings. The greatest positive shift was after the exclusion of the studies of Jolly et al17 or Mroszczyk-McDonald et al18 (summary effect −0.14 with a 95% CI from −0.17 to −0.12), and the greatest negative shift was after the exclusion of several studies and affected only the 95% CI (summary effect −0.15 with a 95% CI from −0.18 to −0.13).
The subgroup analysis based on gender did not show statistically significant results (Q = 0.05; df = 1; P = .8). The subgroup analysis based on disease did not show statistically significant results (Q = 0.07; df = 1; P = .8). The subgroup analysis based on the exercise test did not show statistically significant results (Q = 5.3; df = 3; P = .15).
The slope of the meta-regression of participant age on the effect sizes was not statistically significant (the slope was −0.001 with a P value of .2).
The results of this study showed a modest correlation (r = −0.15; 95% CI, −0.17 to −0.12) between depressive symptoms and exercise capacity in patients with heart disease that was irrelevant to gender, disease type, and type of exercise test.
One plausible explanation of this relationship is behavioral. The higher the depressive symptoms, the weaker was the motivation to engage in physical activity, which can result in reduced exercise capacity. Indeed, there is evidence that physical activity is associated with depression19 and that depression may be a significant risk factor for developing a sedentary lifestyle.20 Another plausible explanation of the relationship between depressive symptoms and exercise capacity is biological. There is strong evidence that exercise improves cognitive abilities and enhances brain neuroplasticity.21 Levels of brain-derived neurotrophic factor, which is involved in cellular analogues of memory formation, plasticity, and cell proliferation, are reduced in depression and can be increased with aerobic exercise.22 Moreover, inflammation is present in depression23 whereas levels of inflammatory markers, such as C-reactive protein, are inversely associated with cardiorespiratory fitness in patients with heart disease.24
The association between depressive symptoms and exercise capacity in patients with heart disease has implications regarding patient assessment and treatment plan. Clinicians should consider that patients with poor exercise capacity may tend to have elevated depressive symptoms and thus may consider conducting a more thorough psychosocial evaluation of this patient group; they may also be prepared to anticipate depression-related challenges, such as poor adherence to treatment, including rehabilitation.25 Furthermore, clinicians may consider elevated depressive symptoms in the differential diagnosis of poor exercise capacity, in addition to other factors, such as age, function of cardiovascular and pulmonary systems, body weight, carbohydrate intake, and physical activity,26 in patients with heart disease. Finally, it may be possible that the reduced exercise capacity observed in patients with elevated depressive symptoms is a result of suboptimal exercise test performance due to poor motivation, which could prevent the detection of potential ischemia.27 Perhaps, a cardiopulmonary exercise test, in which performance of the subject can be objectively assessed by using the respiratory exchange ratio ≥1.1 as a test termination criterion,28 would be more appropriate for patients with heart disease and elevated depressive symptoms.
The association between depressive symptoms and exercise capacity in patients with heart disease also has implications regarding patient care. Because of the linear relationship between depressive symptoms and exercise capacity, it should be considered that even elevated depressive symptoms (not major depressive disorder) may affect exercise capacity. This is of clinical importance because the prevalence of subclinical depression is significantly larger than that of a major depressive disorder in patients with heart disease29 and it may predict mortality30 after myocardial infarction. Even small reductions in exercise capacity may affect mood. This is of clinical importance, especially not only for cardiac rehabilitation programs where the majority of patients entering cardiac rehabilitation exhibit poor exercise capacity31 but also for all patients with heart disease where exercise capacity is a strong predictor of mortality.10 Conversely, even small increases in exercise capacity may improve mood and reduce depression-related increased mortality in patients with coronary heart disease32 and heart failure.33
Furthermore, the abovementioned association warrants a multidimensional approach to treating depression and improving exercise capacity in patients with heart disease. Regardless of whether there is unidirectional causation, bidirectional causation, or the influence of other variable(s), conducting research on the effects of treating the one on the other is warranted. Improving exercise capacity through exercise interventions may reduce depressive symptoms. There is evidence that exercise is superior to placebo in reducing depressive symptoms in patients with coronary artery disease34; however, more research is needed in order for the optimum frequency, intensity, type, and duration of exercise to be determined, as well as for assessing whether the effect is transient or longer-lasting. Reducing depressive symptoms might improve exercise capacity. There is evidence that psychological and pharmacological interventions for depression reduce depressive symptoms in patients with coronary artery disease,35 but, to our knowledge, their effects on exercise capacity have not been studied.
Finally, the association between depressive symptoms and exercise capacity in patients with heart disease further supports the status of depression as a risk factor for poor prognosis following acute coronary syndrome8 and it adds to the pool of evidence that links depression to worse prognosis in patients with heart failure, since depression is associated with an established strong and independent predictor of mortality in this patient group. The abovementioned clinical and prognostic implications of this association also support the American Heart Association's recommendation for depression screening in patients with coronary heart disease36 and advocates for the extension of this recommendation to patients with heart failure.
It was not possible to assess the effects of potential moderators of the association between depressive symptoms and exercise capacity, such as age, gender, body mass index, physical activity, and medication, in within-study level because raw data were not available for every study. Between-study analysis was only possible for gender and age, using subgroup analysis and meta-regression, respectively.
Within- and across-studies bias should be taken into consideration. Blinding of data collectors was performed in 15 of 26 applicable studies. Only studies in English language were included, and, although publication bias was not significant, it should be mentioned that 34 eligible studies (see Supplemental Digital Content 1, available at: http://links.lww.com/JCRP/A27) were not included in the analysis because it was impossible to acquire the necessary additional data from their authors.
In summary, patients with heart disease and elevated depressive symptoms may tend to have reduced exercise capacity, and vice versa. This finding has implications for patient assessment, plan of care, treatment, rehabilitation, and prognosis. It also encourages research on the effects of improving depression on exercise capacity, and vice versa. The effects of potential moderators and mediators need to be explored.
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