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Clinical Review | Clinician's Corner

Cardiorespiratory Fitness as a Quantitative Predictor of All-Cause Mortality and Cardiovascular Events in Healthy Men and Women:  A Meta-analysis FREE

Satoru Kodama, MD, PhD; Kazumi Saito, MD, PhD; Shiro Tanaka, PhD; Miho Maki, MS; Yoko Yachi, RD, MS; Mihoko Asumi, MS; Ayumi Sugawara, RD; Kumiko Totsuka, RD; Hitoshi Shimano, MD, PhD; Yasuo Ohashi, PhD; Nobuhiro Yamada, MD, PhD; Hirohito Sone, MD, PhD
[+] Author Affiliations

Author Affiliations: Department of Internal Medicine, University of Tsukuba Institute of Clinical Medicine, Ibaraki (Drs Kodama, Saito, Shimano, Yamada, and Sone, and Mss Maki, Yachi, Asumi, Sugawara, and Totsuka); Department of Clinical Trial, Design, and Management, Translational Research Center, Kyoto University Hospital, Kyoto (Dr Tanaka); and Department of Biostatistic, Epidemiology, and Preventive Health Sciences, University of Tokyo, Tokyo (Dr Ohashi), Japan.


JAMA. 2009;301(19):2024-2035. doi:10.1001/jama.2009.681.
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Published online

Context Epidemiological studies have indicated an inverse association between cardiorespiratory fitness (CRF) and coronary heart disease (CHD) or all-cause mortality in healthy participants.

Objective To define quantitative relationships between CRF and CHD events, cardiovascular disease (CVD) events, or all-cause mortality in healthy men and women.

Data Sources and Study Selection A systematic literature search was conducted for observational cohort studies using MEDLINE (1966 to December 31, 2008) and EMBASE (1980 to December 31, 2008). The Medical Subject Headings search terms used included exercise tolerance, exercise test, exercise/physiology, physical fitness, oxygen consumption, cardiovascular diseases, myocardial ischemia, mortality, mortalities, death, fatality, fatal, incidence, or morbidity. Studies reporting associations of baseline CRF with CHD events, CVD events, or all-cause mortality in healthy participants were included.

Data Extraction Two authors independently extracted relevant data. CRF was estimated as maximal aerobic capacity (MAC) expressed in metabolic equivalent (MET) units. Participants were categorized as low CRF (<7.9 METs), intermediate CRF (7.9-10.8 METs), or high CRF (≥10.9 METs). CHD and CVD were combined into 1 outcome (CHD/CVD). Risk ratios (RRs) for a 1-MET higher level of MAC and for participants with lower vs higher CRF were calculated with a random-effects model.

Data Synthesis Data were obtained from 33 eligible studies (all-cause mortality, 102 980 participants and 6910 cases; CHD/CVD, 84 323 participants and 4485 cases). Pooled RRs of all-cause mortality and CHD/CVD events per 1-MET higher level of MAC (corresponding to 1-km/h higher running/jogging speed) were 0.87 (95% confidence interval [CI], 0.84-0.90) and 0.85 (95% CI, 0.82-0.88), respectively. Compared with participants with high CRF, those with low CRF had an RR for all-cause mortality of 1.70 (95% CI, 1.51-1.92; P < .001) and for CHD/CVD events of 1.56 (95% CI, 1.39-1.75; P < .001), adjusting for heterogeneity of study design. Compared with participants with intermediate CRF, those with low CRF had an RR for all-cause mortality of 1.40 (95% CI, 1.32-1.48; P < .001) and for CHD/CVD events of 1.47 (95% CI, 1.35-1.61; P < .001), adjusting for heterogeneity of study design.

Conclusions Better CRF was associated with lower risk of all-cause mortality and CHD/CVD. Participants with a MAC of 7.9 METs or more had substantially lower rates of all-cause mortality and CHD/CVD events compared with those with a MAC of less 7.9 METs.

Figures in this Article

Coronary heart disease (CHD) is a major cause of disability and premature death throughout the world.1 Epidemiological studies have demonstrated an inverse association between physical fitness and the incidence of CHD or all-cause mortality in healthy or asymptomatic participants. Physical fitness is typically expressed as cardiorespiratory fitness (CRF) and is assessed by exercise tolerance testing2; however, it is rare for clinicians to consider CRF when evaluating future risk of CHD.3

A major reason for lack of consideration of CRF as a marker of CHD risk may be that the quantitative association of CRF for cardiovascular risk is not well established. The degree of risk reduction associated with each incremental higher level of CRF, the criteria for low CRF, and the magnitude of risk associated with low CRF have been inconsistent among studies. Our goal of this meta-analysis was to systematically review the quantitative relationship between CRF and all-cause mortality and CHD or cardiovascular disease (CVD) events in healthy individuals.

Search Strategy

The meta-analysis was conducted according to the checklist of the Meta-analysis of Observational Studies in Epidemiology.4 We performed a systematic literature search of MEDLINE (1966 to December 31, 2008) and EMBASE (1980 to December 31, 2008) for observational cohort studies. Three search themes were combined using the Boolean operator and. The first keywords were related to CRF (combined exploded versions of the Medical Subject Headings [MeSH] as follows: exercise tolerance OR exercise test OR exercise/physiology OR physical fitness OR oxygen consumption); the second keywords were related to the outcome of this meta-analysis (combined unexploded version of MeSH [cardiovascular diseases] or the exploded version of MeSH [myocardial ischemia]) or the following text words (mortality OR mortalities OR death OR fatality OR fatal OR incidence* OR event* OR morbidity); and the third keywords were related to risk estimates (combined text words as follows: regression analysis OR regression model* OR statistical regression* OR logistic regression* OR logit regression* OR logistic model* OR logit model* OR Cox model OR hazard model OR odds ratio* OR ORs OR relative odds OR risk ratio* OR relative risk* OR RRs). We also included studies published in non-English language. In addition, we searched the reference lists of all identified relevant publications.

Inclusion and Exclusion Criteria

We included papers if (1) CRF was assessed by an exercise stress test; (2) the association of CRF with all-cause mortality and with CHD or CVD was evaluated; (3) CRF could be assessed as maximal aerobic capacity (MAC), expressed in units of metabolic equivalents (METs), which is defined as the ratio of intensity of physical activity to that of sitting at rest; and (4) risk ratios (RRs) and their corresponding 95% confidence intervals (CIs) relating to each category of MAC were reported or could be calculated. We excluded studies that were intended only for patients having a specific disease that presented a major risk factor, such as diabetes, hypertension, and familial hypercholesteremia, as well as studies that included patients with CHD or chronic heart failure.

To avoid double counting of a cohort, study selection was limited to a single set of results when multiple publications were available for a single observational study. The first priority for selection was the study with the longest follow-up and the second was the study with full cohort analysis covering the largest number of participants among articles from a single cohort. We conducted 2 separate meta-analyses for risk of all-cause mortality and CHD or CVD in relation to CRF. When an individual study provided data on both CHD or myocardial infarction (MI) and CVD,57 priority for data abstraction was given to CVD because CVD is more comprehensive than CHD and MI. Similarly, if data on both events and deaths were provided,6,8,9 priority was given to events.

We combined CHD and CVD into 1 outcome (CHD/CVD), which included studies whose outcome was a CVD event, CVD death, CHD event, or CHD death, because the number of eligible studies included was limited. Although criteria for the end point in CHD varied from study to study, the end points that we specified as CHD outcome in our meta-analysis were (1) death from MI; (2) death from CHD including MI; and (3) a CHD event, a term which meant either death from CHD, sudden cardiac death, occurrence of nonfatal CHD, or nonfatal MI. Additionally, we included studies whose outcome was either CVD death (ie, encompassing death from cardiovascular causes other than CHD) or CVD events (ie, lumping together fatal and nonfatal CVD).

Data Abstraction

Data abstracted were the first author's name, year of publication, country of origin, specific outcomes, duration of follow-up, methods for outcome assessment, instrument or methods for measurement of CRF, whether maximal exercise testing (defined as instructing participants to continue exercise until their maximal workload) was conducted, mean of participants' age, proportion of men, number of participants and number of new cases (ie, deaths or events) during the observational periods, adjusted variables, and whether participants with abnormal electrocardiogram findings (ie, ST elevation/depression) during exercise testing were included. Two of our investigators (S. Kodama and H. Sone) independently reviewed each published paper and extracted relevant information. Any disagreement was resolved by consensus.

In studies using CRF as a categorical variable, we standardized all reported RRs into comparison of the risk of the lower CRF group with that in the higher CRF group. Therefore, when the lowest CRF group was referent, we converted the reported RR into its reciprocal. When a study provided several RRs, such as unadjusted and adjusted RRs, the most completely adjusted RR was used. The standard error (SE) of each RR was derived from 95% CIs or P values. If data related to RR and its corresponding SE were not provided, their value was directly calculated using data on the number of participants (P) and new cases (C) of risk and the reference (ref) groups in each comparison, using the equation:

RR = [(Crisk/Prisk)/(Cref/Pref)], SE2 = [(1/Crisk)−(1/Prisk)]+[(1/Cref)−(1/Pref)].

The MAC was calculated from the exercise workload at the termination of exercise testing and relative exercise intensity (ie, proportion of the workload to MAC). The exercise workload was converted into MET units (1 MET corresponds to 3.5 mL/min/kg of oxygen consumption [O2]), according to the Metabolic Calculation Handbook by the American College of Sports Medicine.10 Relative exercise intensity was estimated using a linear equation according to Swain et al11:

heart rate at exercise/maximal heart rate = 0.64 × (O2 at exercise/maximal O2).

For some specific exercise stress tests, the MAC was directly estimated using the prediction equation determined by a previous validation study for each protocol of the exercise test (the Balke treadmill test,12,13 the modified Bruce test,14 and the Canadian Home Fitness test15).

When exposure was expressed as a range, we converted it into point estimates expressed as average exposure using the midpoint of the range except for the lowest and highest fit group. If data on the average value were not available, it was estimated by the assumption that the MAC levels of the study population had a normal distribution using the mean value and its SD of each study sample. This assumption is consistent with a prior study.16 However, if the SD was not available, we assumed that its value equaled 2 METs, according to the statement of the American Heart Association.17

After converting all exposures into MET units, we additionally adjusted MET units for age and sex. According to a Statement for Healthcare Professionals From the American Heart Association,17 we assumed that the MAC is 2 METs lower in women than in men and that for each year of aging, it decreased by 0.1 MET based on a prior study.18 Finally, we represented CRF as the adjusted MAC under the assumption that all participants were 50-year-old men in the analyses described below.

Dose-Response and Categorical Analyses

We first performed dose-response analyses by summarizing how much risk reduction could be predicted per incremental increase in CRF. The study-specific RR for each higher MET (corresponding to 1-km/h higher running/jogging speed) in MAC, if not reported, was estimated by regressing the natural logarithm of the RR (lnRR) according to each CRF category against its corresponding mean MAC value, using the method described by Greenland and Longnecker.19

We then performed categorical analyses to summarize the risk of all-cause mortality and CHD/CVD for low CRF. We assigned every RR reported in each study to 1 of the following 3 comparisons based on the CRF level of risk and reference group: (1) low vs high CRF, (2) low vs intermediate CRF, and (3) intermediate vs high CRF. This method is based on a previous meta-analysis of the relationship between activity level and stroke risk.20 For studies that presented risk estimates for more than 2 CRF categories, the ranges of the adjusted MAC of the lowest, highest, and in-between categories defined by each study were 5.5 to 7.8, 11.0 to 15.2, and 7.9 to 10.7 METs, respectively; except that in 2 studies,21,22 the second highest category of CRF was more than 11.0 METs and, in 1 study,7 the highest category of CRF was 10.6 METs.

To avoid overlap of the CRF range of the 3 categories, we defined low, intermediate, and high CRF as less than 7.9 METs, 7.9 to 10.8 METs, and 10.9 METs or more, respectively. Consequently, we could assign every RR in each study to 1 of the 3 predefined subgroups with 2 exceptions. In 2 studies,21,22 the mean MAC values for both the highest and the second highest category were the same as the high CRF category (defined by ≥10.9 METs). Therefore, RR data for comparison between 2 CRF categories could not be included in our categorical analysis for these 2 studies.

Statistical Analysis

The pooled RRs for a 1-MET higher level of MAC and the lower CRF in comparison with the higher CRF within each of the 3 comparisons were estimated by using a fixed-effects or random-effects model.23 If significant heterogeneity of RRs that was tested by calculating the I2 statistic24 was present, we chose the pooled estimates from the random-effects model because it is better than the fixed-effects model and it explains between-study heterogeneity.

To examine the effect of study characteristics on risk reduction per 1-MET higher level of MAC, sensitivity analyses were conducted for the possible confounders (mean age [≥50 years or not], sex [only men or not], adjustment for smoking [yes or no], adjustment for multiple confounders, defined as adjustment for >3 factors among obesity, hypertension, total cholesterol or low-density lipoprotein cholesterol, high-density lipoprotein cholesterol and diabetes [yes or no], mean follow-up [≥12 years or <12 years], instrument for assessing CRF [ergometer or others], and maximal exercise testing [yes or no]). To examine the extent to which between-study heterogeneity was explained by these study characteristics, we additionally conducted linear multiple regression analyses by simultaneously entering these confounders as explanatory variables.

Categorical analyses were repeated with multiadjustment for the prespecified confounders to consider the potential heterogeneity of study characteristics among the subgroups (ie, low vs high CRF, low vs intermediate CRF, and intermediate vs high CRF). Tests of interaction were performed to assess whether the association between CRF and the study outcomes varied across these 3 subgroups.

The Begg and Egger tests25,26 were used for assessment of publication bias (ie, the tendency for positive associations to be published and negative or null associations to be unpublished). We also followed the Duval and Tweedie “trim and fill” procedure27 as a method of adjustment for suspected publication bias. This method considers the possibility of hypothetical “missing” studies that might exist, imputes their RRs, and recalculates a pooled RR that incorporates the hypothetical missing studies as though they actually existed.

Two-sided P ≤ .05 was considered statistically significant, except for the test of publication bias for which the recommended levels are P ≤ .10.28 Data were analyzed using STATA version 10 (STATA Corp, College Station, Texas).

Literature Search and Study Characteristics

Figure 1 shows the number of studies that were identified and excluded at different stages of the selection process. A total of 33 studies59,16,21,22,2953 were included in our meta-analysis. Characteristics of the 33 selected studies comprising 102 980 participants (range, 486-25 341) and 6910 cases (range, 26-941) for all-cause mortality and 84 323 participants (range, 302-20 278) and 4485 cases (range, 10-1512) for CHD/CVD are shown in Table 1. Twenty-one studies5,6,8,9,16,21,22,29,30,32,36,38,39,42,4447,50,51,53 reported all-cause mortality and 24 studies59,21,22,3137,3941,43,46,4852 reported CVD/CHD. Mean age and follow-up duration ranged from 37 to 57 years and 1.1 to 26 years, respectively. Eight studies8,33,37,39,45,46,49,52 were used for the dose-response analyses only and 4 studies9,16,40,44 were used for the categorical analyses only. In 20 studies,5,79,16,21,22,30,32,33,3739,4446,48,50,52,53 RRs were adjusted for smoking and in 9 studies,79,16,33,39,46,50,52 there were multiple study confounders (see eTable).

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Figure 1. Selection of Articles for Meta-analysis
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CHD indicates coronary heart disease; CRF, cardiorespiratory fitness; CVD, cardiovascular disease; METs, metabolic equivalents; and RRs, risk ratios.

Table Graphic Jump LocationTable 1. Characteristics of Studies Included in the Meta-analysis
Dose-response Analyses

Figure 2 shows the pooled estimates for the reduction in risk of all-cause mortality and CHD/CVD per higher MET of exercise capacity. Pooled RRs of all-cause mortality and CHD/CVD per 1-MET higher level of MAC were 0.87 (95% CI, 0.84-0.90) and 0.85 (95% CI, 0.82-0.88), respectively. There was evidence of statistical heterogeneity of RRs across studies (I2 = 82.3%; P < .001 for all-cause mortality; I2 = 74.7%; P < .001 for CHD/CVD).

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Figure 2. Meta-analysis of All-Cause Mortality and CHD/CVD per 1-MET Higher Level of MAC
Graphic Jump Location

CHD indicates coronary heart disease; CI, confidence interval; CVD, cardiovascular disease; MAC, maximal aerobic capacity; MET, metabolic equivalent; RR, risk ratio. Area of each square is proportional to study weight.

Table 2 shows the results of analyses investigating the associations of study characteristics on each outcome. Quiz Ref IDThe finding of risk reduction per higher MET for all-cause mortality and CHD/CVD was consistently significant in all of the stratified analyses. However, studies with a follow-up of at least 12 years had weaker associations with study outcomes compared with those that had follow-up of less than 12 years for all-cause mortality (P = .08) and CHD/CVD events (P = .004). The associations between CRF and risk of CHD/CVD events were stronger in studies that used an ergometer for assessing CRF (P = .009) or conducted maximal exercise testing (P = .02) and were weaker in studies that were adjusted for smoking (P = .006) or multiple metabolic factors (P = .06). However, these study characteristics did not influence the associations between MAC and risk of all-cause mortality.

Table Graphic Jump LocationTable 2. Stratified Analyses of Pooled RR of All-Cause Mortality and CVD/CHD for Each MET Higher Level of Maximal Aerobic Capacity

Multiple regression analyses in which all the study characteristics listed in Table 2 were entered as independent variables indicated that study characteristics significantly explained the heterogeneity of the RRs per 1-MET higher level of MAC (all-cause mortality, 79% of total variance; P = .01; and CHD/CVD, 67% of total variance; P = .01). After adjustment for these study characteristics, there were neither significant differences in risk estimates of CHD/CVD between CHD and CVD (0.89; 95% CI, 0.86-0.92 and 0.89; 95% CI, 0.87-0.90, respectively; P = .99) nor between CHD or CVD death and CHD or CVD events (0.88; 95% CI, 0.86-0.90 and 0.90; 95% CI, 0.88-0.91, respectively; P = .27).

Categorical Analyses

We performed categorical analyses to summarize the risk of all-cause mortality and CHD/CVD for 3 subgroups (low vs high CRF [Figure 3], low vs intermediate CRF [Figure 4], and intermediate vs high CRF [Figure 5]). After adjustment for heterogeneity of study characteristics and Quiz Ref IDcompared with high and intermediate CRF, respectively, the pooled RRs for the association of low CRF with all-cause mortality were 1.70 (95% CI, 1.51-1.92) and 1.56 (95% CI, 1.39-1.75), respectively. After adjustment for heterogeneity and compared with high and intermediate CRF, respectively, the pooled RRs for the association of low CRF with CHD/CVD events were 1.40 (95% CI, 1.32-1.48) and 1.47 (95% CI, 1.35-1.61), respectively. The pooled RRs for the associations of intermediate CRF with all-cause mortality and CHD/CVD events compared with high CRF were 1.13 (95% CI, 1.04-1.22) and 1.07 (95% CI, 1.01-1.13), respectively. However, tests of the interaction indicated that these estimates for comparisons between intermediate and high risk were significantly lower than for those between low vs high CRF and low vs intermediate CRF (P < .001 for any comparisons). Tests of interaction also indicated that there were no significant differences in risk estimates for low vs high CRF compared with low vs intermediate CRF (all-cause mortality, P = .28; CHD/CVD, P = .33).

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Figure 3. Meta-analysis of All-Cause Mortality and CHD/CVD for Individuals With Low vs High CRF
Graphic Jump Location

CHD indicates coronary heart disease; CI, confidence interval; CRF, cardiorespiratory fitness; CVD, cardiovascular disease; MET, metabolic equivalent; NA, not available; RR, risk ratio. Low and high CRF categories were defined as less than 7.9 METs and 10.9 METs or more of maximal aerobic capacity, respectively, under the assumption that all participants were 50-year-old men. Crude and adjusted RR indicate RRs before and after adjustment for study heterogeneity among the subgroups, respectively.

Place holder to copy figure label and caption
Figure 4. Meta-analysis of All-Cause Mortality and CHD/CVD for Individuals With Low vs Intermediate CRF
Graphic Jump Location

CHD indicates coronary heart disease; CI, confidence interval; CRF, cardiorespiratory fitness; CVD, cardiovascular disease; Int, intermediate; MET, metabolic equivalent; NA, not available; RR, risk ratio. Low and intermediate CRF categories were defined as less than 7.9 METs and 7.9 to 10.8 METs of maximal aerobic capacity, respectively, under the assumption that all participants were 50-year-old men. Crude and adjusted RR indicate RRs before and after adjustment for study heterogeneity among the subgroups, respectively. The words first, second, third, and fourth in brackets represent comparisons between the lowest CRF category and the highest, second, third, or fourth CRF category in the relevant study.

Place holder to copy figure label and caption
Figure 5. Meta-analysis of All-Cause Mortality and CHD/CVD for Individuals With Intermediate vs High CRF
Graphic Jump Location

CHD indicates coronary heart disease; CI, confidence interval; CRF, cardiorespiratory fitness; CVD, cardiovascular disease; Int, intermediate; MET, metabolic equivalent; NA, not available; RR, risk ratio. Intermediate and high CRF categories were defined as 7.9 to 10.8 METs and 10.9 METs or more of maximal aerobic capacity, respectively, under the assumption that all participants were 50-year-old men. Crude and adjusted RR indicate RRs before and after adjustment for study heterogeneity among the subgroups, respectively. The words second, third, and fourth in brackets represent comparisons between the second, third, or fourth highest CRF category and the highest CRF category in the relevant study.

Publication Bias

In risk estimates per 1-MET higher level of MAC, there was a statistically significant publication bias according to Egger test (all-cause mortality, P = .002; CHD/CVD, P = .02). However, adjustment for publication bias by the trim and fill procedure could not detect hypothetical negative unpublished studies that could influence the study. In some of the categorical analyses, statistically significant publication bias was also observed in risk estimates after adjustment for heterogeneity of study characteristics (pooled RR of all-cause mortality for low vs high CRF and low vs intermediate CRF, P = .03 by Egger test and P = .03 by Begg test, respectively; pooled RR of CHD/CVD for low vs intermediate CRF, P < .001 by Egger test). After incorporating the hypothetical studies using trim and fill methods, the risk estimates were attenuated in risk of all-cause mortality for low vs high CRF (RR, 1.48; 95% CI, 1.31-1.68) and low vs intermediate CRF (RR, 1.35; 95% CI, 1.18-1.54), and CHD/CVD for low vs high CRF (RR, 1.38; 95% CI, 1.30-1.45), which suggested the existence of potentially negative studies. Nevertheless, these biases did not change the general conclusions.

Our meta-analysis is the first to our knowledge to quantify CRF as measured by METs, which is a standard scale for expressing exercise workload, and its relationship to all-cause mortality and CHD or CVD events in healthy men and women. According to the dose-response analyses, a 1-MET higher level of MAC was associated with 13% and 15% decrements in risk of all-cause mortality and CHD/CVD, respectively. From the clinical viewpoint, these values may be considerable. For example, based on risk estimates of the components of metabolic syndrome according to the National Cholesterol Education Program,54Quiz Ref IDthese findings suggest that a 1-MET higher level of MAC is comparable to a 7-cm, 5-mm Hg, 1-mmol/L, and 1-mmol/L decrement in waist circumference,55 systolic blood pressure,56 triglyceride level (in men),57 and fasting plasma glucose,58 respectively, and a 0.2-mmol/L increment in high-density lipoprotein cholesterol.59 It is possible that prediction of CHD risk could be improved by including CRF with already established risk factors for CHD.

In categorical analyses, Quiz Ref IDindividuals with low CRF (<7.9 METs in MAC) had a substantially higher risk of all-cause mortality and CHD/CVD compared with those with intermediate and high CRF (7.9-10.8 and ≥10.9 METs in MAC, respectively). These risk estimates were higher than those for individuals with intermediate CRF compared with those with high CRF, according to the test of interaction. These analyses suggest that a minimal CRF of 7.9 METs may be important for significant prevention of all-cause mortality and CHD/CVD. A previous review suggested that physical activity yielding 1000 kcal energy expenditure per week is needed for significant risk reduction of all-cause mortality.60 However, using CRF may be preferable to using physical activity as risk predictors because 1 prior study61 suggested that physical fitness was more strongly correlated with CHD than physical activity.

According to the results reported herein, the minimum CRF level that is associated with significantly lower event rates for men and women is approximately 9 and 7 METs (at 40 years old), 8 and 6 METs (at 50 years), and 7 and 5 METs (at 60 years), respectively. This means that women and men younger than 60 years would need to complete stage I (1.7 mph at gradient 10°) and stage II (2.5 mph at gradient 12°), respectively, of the standard Bruce protocol, which is one of the most commonly used treadmill tests in clinical settings.14 If the CRF level is Quiz Ref IDexpressed in terms of walking speed, men around 50 years of age must be capable of continuous walking at a speed of 4 mph and women must continuously walk at 3 mph for prevention of CHD,17 with the assumption that the anaerobic threshold is 50% to 60% of MAC.62 It is possible that consideration of low CRF as a major coronary risk factor could be put into practical use in the clinical setting through identification of low exercise tolerance by exercise stress testing or in daily life by the speed at which a person can walk before experiencing exhaustion.

Some cross-sectional population studies have suggested that higher aerobic fitness is associated with more favorable coronary or cardiovascular risk factor profiles63,64; therefore, the association between CRF and the risk of all-cause mortality and CHD/CVD could potentially be explained by residual confounding by established risk factors. Our sensitivity analyses indicated that a weaker association was observed between a 1-MET higher level of MAC and risk reduction of CHD/CVD, but not all-cause mortality, in studies with adjustment for smoking or more comprehensive risk factors. This finding suggests that better CRF is independently associated with longevity, while the inverse association between CRF and risk of CHD/CVD is explained partly by established coronary risk factors.

Limitations of this meta-analysis must be considered. First, a possible misclassification bias might affect our results. Misclassification bias could occur in transforming the reported CRF data into MET units. However, all of the prediction equations used in our analyses for estimating MAC have already been validated and are commonly used. Another possible misclassification bias is due to the fact that the definitions of low, intermediate, and high CRF were fundamentally based on study-specific CRF classifications, which varied from study to study but were not based on a standard cutoff. Fortunately, we could assign every exposure in each study to 1 of the 3 categories, which did not overlap with few exceptions, although MAC values in each category are approximately 1 MET smaller than those based on a general standard (eg, data from the National Health and Nutrition Examination Survey65). Therefore, the possibility of misclassification bias due to those 2 reasons should be limited. Second, Begg or Egger tests suggested publication bias. However, trim and fill analyses to incorporate potentially existing negative studies did not change the general result, although the risk estimates were moderately attenuated. Nevertheless, this possibility was not fully excluded by this analysis.

Based on the findings of our meta-analysis, we suggest for future research (1) further development of a CHD prediction algorithm (eg, Framingham Scores66) that would consider both CRF and the classical coronary risk factors to allow physicians to use CRF as a major risk factor in clinical settings; (2) cost-effectiveness of exercise testing for assessing CRF from the viewpoint of primary prevention of all-cause mortality and CHD; and (3) a clinical trial to determine whether an intervention that improves CRF by exercise reduces the risk of all-cause mortality and CHD.

In conclusion, better CRF was associated with lower risk of all-cause mortality and CHD/CVD. A 1-MET higher level of MAC was associated with a 13% and 15% risk reduction of all-cause mortality and CHD/CVD, respectively. The minimal MAC value for substantial risk reduction in persons aged 50 (SD, 10) years was estimated to be 8 (SD, 1) METs for men and 6 (SD, 1) METs for women. We suggest that CRF, which can be readily assessed by an exercise stress test, could be useful for prediction of CHD/CVD and all-cause mortality risk in a primary care medical practice.

Corresponding Author: Hirohito Sone, MD, PhD, Department of Internal Medicine, University of Tsukuba Institute of Clinical Medicine, 3-2-7 Miya-machi, Mito, Ibaraki 310-0015, Japan (hsone@md.tsukuba.ac.jp).

Author Contributions: Dr Sone had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Study concept and design: Kodama, Saito, Maki, Yamada, Sone.

Acquisition of data: Kodama, Yachi, Sugawara, Totsuka.

Analysis and interpretation of data: Kodama, Tanaka, Asumi, Shimano, Ohashi, Yamada, Sone.

Drafting of the manuscript: Kodama, Maki, Sone.

Critical revision of the manuscript for important intellectual content: Kodama, Saito, Tanaka, Yachi, Asumi, Sugawara, Totsuka, Shimano, Ohashi, Yamada, Sone.

Statistical analysis: Kodama, Saito, Tanaka, Ohashi, Sone.

Obtained funding: Sone.

Administrative, technical, or material support: Kodama, Saito, Tanaka, Maki, Yachi, Asumi, Sugawara, Totsuka, Shimano, Ohashi, Sone.

Study supervision: Yamada, Sone.

Financial Disclosures: None reported.

Funding/Support: Drs Kodama and Sone are recipients of a Grant-in-Aid for Scientific Research and Postdoctoral Research Fellowship, respectively, both from the Japan Society for the Promotion of Science (JSPS). This study was also supported by Ministry of Health, Labor and Welfare, Japan.

Role of the Sponsors: The research organizations providing funding support did not have any role in the design and conduct of the study, in the correction, management, analysis, and interpretation of the data, or in the preparation, review, or approval of the manuscript.

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PubMed   |  Link to Article
Stroup DF, Berlin JA, Morton SC,  et al.  Meta-analysis of observational studies in epidemiology: a proposal for reporting: Meta-analysis Of Observational Studies in Epidemiology (MOOSE) group.  JAMA. 2000;283(15):2008-2012
PubMed   |  Link to Article
Slattery ML, Jacobs DR Jr. Physical fitness and cardiovascular disease mortality: the US Railroad Study.  Am J Epidemiol. 1988;127(3):571-580
PubMed
Miller GJ, Cooper JA, Beckles GL. Cardiorespiratory fitness, all-cause mortality, and risk of cardiovascular disease in Trinidadian men: the St James survey.  Int J Epidemiol. 2005;34(6):1387-1394
PubMed   |  Link to Article
Sui X, LaMonte MJ, Blair SN. Cardiorespiratory fitness as a predictor of nonfatal cardiovascular events in asymptomatic women and men.  Am J Epidemiol. 2007;165(12):1413-1423
PubMed   |  Link to Article
Laukkanen JA, Rauramaa R, Salonen JT, Kurl S. The predictive value of cardiorespiratory fitness combined with coronary risk evaluation and the risk of cardiovascular and all-cause death.  J Intern Med. 2007;262(2):263-272
PubMed   |  Link to Article
Laukkanen JA, Rauramaa R, Kurl S. Exercise workload, coronary risk evaluation and the risk of cardiovascular and all-cause death in middle-aged men.  Eur J Cardiovasc Prev Rehabil. 2008;15(3):285-292
PubMed   |  Link to Article
American College of Sports Medicine.  ACSM's Metabolic Calculations Handbook. Philadelphia, PA: Lippincott Williams & Wilkins; 2006
Swain DP, Abernathy KS, Smith CS, Lee SJ, Bunn SA. Target heart rates for the development of cardiorespiratory fitness.  Med Sci Sports Exerc. 1994;26(1):112-116
PubMed
Pollock ML, Bohannon RL, Cooper KH,  et al.  A comparative analysis of four protocols for maximal treadmill stress testing.  Am Heart J. 1976;92(1):39-46
PubMed   |  Link to Article
American Heart Association.  Exercise Testing and Training of Apparently Healthy Individuals: A Handbook for Physicians. New York, NY: American Heart Association; 1972
American College of Sports Medicine.  ACSM's Health-Related Physical Fitness Assessment Manual2nd ed. Philadelphia, PA: Lippincott Williams & Wilkins; 2007
Jetté M, Campbell J, Mongeon J, Routhier R. The Canadian Home Fitness Test as a predictor for aerobic capacity.  Can Med Assoc J. 1976;114(8):680-682
PubMed
Gulati M, Pandey DK, Arnsdorf MF,  et al.  Exercise capacity and the risk of death in women: the St James Women Take Heart Project.  Circulation. 2003;108(13):1554-1559
PubMed   |  Link to Article
Fletcher GF, Balady G, Froelicher VF, Hartley LH, Haskell WL, Pollock ML. Exercise standards: a statement for health care professionals from the American Heart Association: Writing Group.  Circulation. 1995;91(2):580-615
PubMed   |  Link to Article
Wilson TM, Tanaka H. Meta-analysis of the age-associated decline in maximal aerobic capacity in men: relation to training status.  Am J Physiol Heart Circ Physiol. 2000;278(3):H829-H834
PubMed
Greenland S, Longnecker MP. Methods for trend estimation from summarized dose-response data, with applications to meta-analysis.  Am J Epidemiol. 1992;135(11):1301-1309
PubMed
Wendel-Vos GC, Schuit AJ, Feskens EJ,  et al.  Physical activity and stroke: a meta-analysis of observational data.  Int J Epidemiol. 2004;33(4):787-798
PubMed   |  Link to Article
Stevens J, Cai J, Evenson KR, Thomas R. Fitness and fatness as predictors of mortality from all causes and from cardiovascular disease in men and women in the lipid research clinics study.  Am J Epidemiol. 2002;156(9):832-841
PubMed   |  Link to Article
Stevens J, Evenson KR, Thomas O, Cai J, Thomas R. Associations of fitness and fatness with mortality in Russian and American men in the lipids research clinics study.  Int J Obes Relat Metab Disord. 2004;28(11):1463-1470
PubMed   |  Link to Article
DerSimonian R, Laird N. Meta-analysis in clinical trials.  Control Clin Trials. 1986;7(3):177-188
PubMed   |  Link to Article
Higgins JP, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses.  BMJ. 2003;327(7414):557-560
PubMed   |  Link to Article
Begg CB, Mazumdar M. Operating characteristics of a rank correlation test for publication bias.  Biometrics. 1994;50(4):1088-1101
PubMed   |  Link to Article
Egger M, Davey Smith G, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test.  BMJ. 1997;315(7109):629-634
PubMed   |  Link to Article
Duval S, Tweedie R. Trim and fill: a simple funnel-plot-based method of testing and adjusting for publication bias in meta-analysis.  Biometrics. 2000;56(2):455-463
PubMed   |  Link to Article
Sterne JA, Gavaghan D, Egger M. Publication and related bias in meta-analysis: power of statistical tests and prevalence in the literature.  J Clin Epidemiol. 2000;53(11):1119-1129
PubMed   |  Link to Article
Aijaz B, Babuin L, Squires RW,  et al.  Long-term mortality with multiple treadmill exercise test abnormalities: comparison between patients with and without cardiovascular disease.  Am Heart J. 2008;156(4):783-789
PubMed   |  Link to Article
Aktas MK, Ozduran V, Pothier CE, Lang R, Lauer MS. Global risk scores and exercise testing for predicting all-cause mortality in a preventive medicine program.  JAMA. 2004;292(12):1462-1468
PubMed   |  Link to Article
Allen WH, Aronow WS, Goodman P, Stinson P. Five-year follow-up of maximal treadmill stress test in asymptomatic men and women.  Circulation. 1980;62(3):522-527
PubMed   |  Link to Article
Arraiz GA, Wigle DT, Mao Y. Risk assessment of physical activity and physical fitness in the Canada Health Survey mortality follow-up study.  J Clin Epidemiol. 1992;45(4):419-428
PubMed   |  Link to Article
Balady GJ, Larson MG, Vasan RS, Leip EP, O'Donnell CJ, Levy D. Usefulness of exercise testing in the prediction of coronary disease risk among asymptomatic persons as a function of the Framingham risk score.  Circulation. 2004;110(14):1920-1925
PubMed   |  Link to Article
Bruce RA, DeRouen TA, Hossack KF. Value of maximal exercise tests in risk assessment of primary coronary heart disease events in healthy men: five years' experience of the Seattle Heart Watch Study.  Am J Cardiol. 1980;46(3):371-378
PubMed   |  Link to Article
Cumming GR, Samm J, Borysyk L, Kich L. Electrocardiographic changes during exercise in asymptomatic men: 3-year follow-up.  Can Med Assoc J. 1975;112(5):578-581
PubMed
Erikssen G, Liestol K, Bjornholt J, Thaulow E, Sandvik L, Erikssen J. Changes in physical fitness and changes in mortality.  Lancet. 1998;352(9130):759-762
PubMed   |  Link to Article
Erikssen G, Bodegard J, Bjornholt JV, Liestol K, Thelle DS, Erikssen J. Exercise testing of healthy men in a new perspective: from diagnosis to prognosis.  Eur Heart J. 2004;25(11):978-986
PubMed   |  Link to Article
Farrell SW, Cheng YJ, Blair SN. Prevalence of the metabolic syndrome across cardiorespiratory fitness levels in women.  Obes Res. 2004;12(5):824-830
PubMed   |  Link to Article
Gulati M, Arnsdorf MF, Shaw LJ,  et al.  Prognostic value of the duke treadmill score in asymptomatic women.  Am J Cardiol. 2005;96(3):369-375
PubMed   |  Link to Article
Gulati M, Black HR, Shaw LJ,  et al.  The prognostic value of a nomogram for exercise capacity in women.  N Engl J Med. 2005;353(5):468-475
PubMed   |  Link to Article
Gyntelberg F, Lauridsen L, Schubell K. Physical fitness and risk of myocardial infarction in Copenhagen males aged 40-59: a five- and seven-year follow-up study.  Scand J Work Environ Health. 1980;6(3):170-178
PubMed   |  Link to Article
Hein HO, Suadicani P, Gyntelberg F. Physical fitness or physical activity as a predictor of ischemic heart disease? a 17-year follow-up in the Copenhagen Male Study.  J Intern Med. 1992;232(6):471-479
PubMed   |  Link to Article
Jouven X, Empana JP, Schwartz PJ, Desnos M, Courbon D, Ducimetiere P. Heart-rate profile during exercise as a predictor of sudden death.  N Engl J Med. 2005;352(19):1951-1958
PubMed   |  Link to Article
Kampert JB, Blair SN, Barlow CE, Kohl HW III. Physical activity, physical fitness, and all-cause and cancer mortality: a prospective study of men and women.  Ann Epidemiol. 1996;6(5):452-457
PubMed   |  Link to Article
Katzmarzyk PT, Church TS, Janssen I, Ross R, Blair SN. Metabolic syndrome, obesity, and mortality: impact of cardiorespiratory fitness.  Diabetes Care. 2005;28(2):391-397
PubMed   |  Link to Article
Mora S, Redberg RF, Cui Y,  et al.  Ability of exercise testing to predict cardiovascular and all-cause death in asymptomatic women: a 20-year follow-up of the lipid research clinics prevalence study.  JAMA. 2003;290(12):1600-1607
PubMed   |  Link to Article
Myers J, Prakash M, Froelicher V, Do D, Partington S, Atwood JE. Exercise capacity and mortality among men referred for exercise testing.  N Engl J Med. 2002;346(11):793-801
PubMed   |  Link to Article
Peters RK, Cady LD Jr, Bischoff DP, Bernstein L, Pike MC. Physical fitness and subsequent myocardial infarction in healthy workers.  JAMA. 1983;249(22):3052-3056
PubMed   |  Link to Article
Rywik TM, O'Connor FC, Gittings NS, Wright JG, Khan AA, Fleg JL. Role of nondiagnostic exercise-induced ST-segment abnormalities in predicting future coronary events in asymptomatic volunteers.  Circulation. 2002;106(22):2787-2792
PubMed   |  Link to Article
Sandvik L, Erikssen J, Thaulow E, Erikssen G, Mundal R, Rodahl K. Physical fitness as a predictor of mortality among healthy, middle-aged Norwegian men.  N Engl J Med. 1993;328(8):533-537
PubMed   |  Link to Article
Sawada S, Muto T. Prospective study on the relationship between physical fitness and all-cause mortality in Japanese men [in Japanese].  Nippon Koshu Eisei Zasshi. 1999;46(2):113-121
PubMed
Sobolski J, Kornitzer M, De Backer G,  et al.  Protection against ischemic heart disease in the Belgian Physical Fitness Study: physical fitness rather than physical activity?  Am J Epidemiol. 1987;125(4):601-610
PubMed
Villeneuve PJ, Morrison HI, Craig CL, Schaubel DE. Physical activity, physical fitness, and risk of dying.  Epidemiology. 1998;9(6):626-631
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National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III).  Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) final report.  Circulation. 2002;106(25):3143-3421
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de Koning L, Merchant AT, Pogue J, Anand SS. Waist circumference and waist-to-hip ratio as predictors of cardiovascular events: meta-regression analysis of prospective studies.  Eur Heart J. 2007;28(7):850-856
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Lewington S, Clarke R, Qizilbash N, Peto R, Collins R.Prospective Studies Collaboration.  Age-specific relevance of usual blood pressure to vascular mortality: a meta-analysis of individual data for one million adults in 61 prospective studies.  Lancet. 2002;360(9349):1903-1913
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Hokanson JE, Austin MA. Plasma triglyceride level is a risk factor for cardiovascular disease independent of high-density lipoprotein cholesterol level: a meta-analysis of population-based prospective studies.  J Cardiovasc Risk. 1996;3(2):213-219
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Coutinho M, Gerstein HC, Wang Y, Yusuf S. The relationship between glucose and incident cardiovascular events: a metaregression analysis of published data from 20 studies of 95 783 individuals followed for 12.4 years.  Diabetes Care. 1999;22(2):233-240
PubMed   |  Link to Article
Gordon DJ, Probstfield JL, Garrison RJ,  et al.  Highdensity lipoprotein cholesterol and cardiovascular disease: four prospective American studies.  Circulation. 1989;79(1):8-15
PubMed   |  Link to Article
Lee IM, Skerrett PJ. Physical activity and all-cause mortality: what is the dose-response relation?  Med Sci Sports Exerc. 2001;33(6):(suppl)  S459-S471, S493-S494
PubMed
Talbot LA, Morrell CH, Metter EJ, Fleg JL. Comparison of cardiorespiratory fitness vs leisure time physical activity as predictors of coronary events in men aged < or = 65 years and >65 years.  Am J Cardiol. 2002;89(10):1187-1192
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American College of Sports Medicine.  Guidelines for Exercise Testing and Prescription. 6th ed. Baltimore, MD: Lippincott Williams & Wilkins; 2000:25-27, 147, 303
Conway TL, Cronan TA. Smoking, exercise, and physical fitness.  Prev Med. 1992;21(6):723-734
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Borodulin K, Laatikainen T, Lahti-Koski M,  et al.  Associations between estimated aerobic fitness and cardiovascular risk factors in adults with different levels of abdominal obesity.  Eur J Cardiovasc Prev Rehabil. 2005;12(2):126-131
PubMed   |  Link to Article
Sanders LF, Duncan GE. Population-based reference standards for cardiovascular fitness among US adults: NHANES 1999-2000 and 2001-2002.  Med Sci Sports Exerc. 2006;38(4):701-707
PubMed   |  Link to Article
Grundy SM, Balady GJ, Criqui MH,  et al.  Primary prevention of coronary heart disease: guidance from Framingham: a statement for health care professionals from the AHA Task Force on Risk Reduction. American Heart Association.  Circulation. 1998;97(18):1876-1887
PubMed   |  Link to Article

Figures

Place holder to copy figure label and caption
Figure 1. Selection of Articles for Meta-analysis
Graphic Jump Location

CHD indicates coronary heart disease; CRF, cardiorespiratory fitness; CVD, cardiovascular disease; METs, metabolic equivalents; and RRs, risk ratios.

Place holder to copy figure label and caption
Figure 2. Meta-analysis of All-Cause Mortality and CHD/CVD per 1-MET Higher Level of MAC
Graphic Jump Location

CHD indicates coronary heart disease; CI, confidence interval; CVD, cardiovascular disease; MAC, maximal aerobic capacity; MET, metabolic equivalent; RR, risk ratio. Area of each square is proportional to study weight.

Place holder to copy figure label and caption
Figure 3. Meta-analysis of All-Cause Mortality and CHD/CVD for Individuals With Low vs High CRF
Graphic Jump Location

CHD indicates coronary heart disease; CI, confidence interval; CRF, cardiorespiratory fitness; CVD, cardiovascular disease; MET, metabolic equivalent; NA, not available; RR, risk ratio. Low and high CRF categories were defined as less than 7.9 METs and 10.9 METs or more of maximal aerobic capacity, respectively, under the assumption that all participants were 50-year-old men. Crude and adjusted RR indicate RRs before and after adjustment for study heterogeneity among the subgroups, respectively.

Place holder to copy figure label and caption
Figure 4. Meta-analysis of All-Cause Mortality and CHD/CVD for Individuals With Low vs Intermediate CRF
Graphic Jump Location

CHD indicates coronary heart disease; CI, confidence interval; CRF, cardiorespiratory fitness; CVD, cardiovascular disease; Int, intermediate; MET, metabolic equivalent; NA, not available; RR, risk ratio. Low and intermediate CRF categories were defined as less than 7.9 METs and 7.9 to 10.8 METs of maximal aerobic capacity, respectively, under the assumption that all participants were 50-year-old men. Crude and adjusted RR indicate RRs before and after adjustment for study heterogeneity among the subgroups, respectively. The words first, second, third, and fourth in brackets represent comparisons between the lowest CRF category and the highest, second, third, or fourth CRF category in the relevant study.

Place holder to copy figure label and caption
Figure 5. Meta-analysis of All-Cause Mortality and CHD/CVD for Individuals With Intermediate vs High CRF
Graphic Jump Location

CHD indicates coronary heart disease; CI, confidence interval; CRF, cardiorespiratory fitness; CVD, cardiovascular disease; Int, intermediate; MET, metabolic equivalent; NA, not available; RR, risk ratio. Intermediate and high CRF categories were defined as 7.9 to 10.8 METs and 10.9 METs or more of maximal aerobic capacity, respectively, under the assumption that all participants were 50-year-old men. Crude and adjusted RR indicate RRs before and after adjustment for study heterogeneity among the subgroups, respectively. The words second, third, and fourth in brackets represent comparisons between the second, third, or fourth highest CRF category and the highest CRF category in the relevant study.

Tables

Table Graphic Jump LocationTable 1. Characteristics of Studies Included in the Meta-analysis
Table Graphic Jump LocationTable 2. Stratified Analyses of Pooled RR of All-Cause Mortality and CVD/CHD for Each MET Higher Level of Maximal Aerobic Capacity

References

Rosamond W, Flegal K, Friday G,  et al; American Heart Association Statistics Committee and Stroke Statistics Subcommittee.  Heart disease and stroke statistics–2007 update: a report from the American Heart Association Statistics Committee and Stroke Statistics Subcommittee.  Circulation. 2007;115(5):e69-e171
PubMed   |  Link to Article
Noonan V, Dean E. Submaximal exercise testing: clinical application and interpretation.  Phys Ther. 2000;80(8):782-807
PubMed
Wilson PW, 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
PubMed   |  Link to Article
Stroup DF, Berlin JA, Morton SC,  et al.  Meta-analysis of observational studies in epidemiology: a proposal for reporting: Meta-analysis Of Observational Studies in Epidemiology (MOOSE) group.  JAMA. 2000;283(15):2008-2012
PubMed   |  Link to Article
Slattery ML, Jacobs DR Jr. Physical fitness and cardiovascular disease mortality: the US Railroad Study.  Am J Epidemiol. 1988;127(3):571-580
PubMed
Miller GJ, Cooper JA, Beckles GL. Cardiorespiratory fitness, all-cause mortality, and risk of cardiovascular disease in Trinidadian men: the St James survey.  Int J Epidemiol. 2005;34(6):1387-1394
PubMed   |  Link to Article
Sui X, LaMonte MJ, Blair SN. Cardiorespiratory fitness as a predictor of nonfatal cardiovascular events in asymptomatic women and men.  Am J Epidemiol. 2007;165(12):1413-1423
PubMed   |  Link to Article
Laukkanen JA, Rauramaa R, Salonen JT, Kurl S. The predictive value of cardiorespiratory fitness combined with coronary risk evaluation and the risk of cardiovascular and all-cause death.  J Intern Med. 2007;262(2):263-272
PubMed   |  Link to Article
Laukkanen JA, Rauramaa R, Kurl S. Exercise workload, coronary risk evaluation and the risk of cardiovascular and all-cause death in middle-aged men.  Eur J Cardiovasc Prev Rehabil. 2008;15(3):285-292
PubMed   |  Link to Article
American College of Sports Medicine.  ACSM's Metabolic Calculations Handbook. Philadelphia, PA: Lippincott Williams & Wilkins; 2006
Swain DP, Abernathy KS, Smith CS, Lee SJ, Bunn SA. Target heart rates for the development of cardiorespiratory fitness.  Med Sci Sports Exerc. 1994;26(1):112-116
PubMed
Pollock ML, Bohannon RL, Cooper KH,  et al.  A comparative analysis of four protocols for maximal treadmill stress testing.  Am Heart J. 1976;92(1):39-46
PubMed   |  Link to Article
American Heart Association.  Exercise Testing and Training of Apparently Healthy Individuals: A Handbook for Physicians. New York, NY: American Heart Association; 1972
American College of Sports Medicine.  ACSM's Health-Related Physical Fitness Assessment Manual2nd ed. Philadelphia, PA: Lippincott Williams & Wilkins; 2007
Jetté M, Campbell J, Mongeon J, Routhier R. The Canadian Home Fitness Test as a predictor for aerobic capacity.  Can Med Assoc J. 1976;114(8):680-682
PubMed
Gulati M, Pandey DK, Arnsdorf MF,  et al.  Exercise capacity and the risk of death in women: the St James Women Take Heart Project.  Circulation. 2003;108(13):1554-1559
PubMed   |  Link to Article
Fletcher GF, Balady G, Froelicher VF, Hartley LH, Haskell WL, Pollock ML. Exercise standards: a statement for health care professionals from the American Heart Association: Writing Group.  Circulation. 1995;91(2):580-615
PubMed   |  Link to Article
Wilson TM, Tanaka H. Meta-analysis of the age-associated decline in maximal aerobic capacity in men: relation to training status.  Am J Physiol Heart Circ Physiol. 2000;278(3):H829-H834
PubMed
Greenland S, Longnecker MP. Methods for trend estimation from summarized dose-response data, with applications to meta-analysis.  Am J Epidemiol. 1992;135(11):1301-1309
PubMed
Wendel-Vos GC, Schuit AJ, Feskens EJ,  et al.  Physical activity and stroke: a meta-analysis of observational data.  Int J Epidemiol. 2004;33(4):787-798
PubMed   |  Link to Article
Stevens J, Cai J, Evenson KR, Thomas R. Fitness and fatness as predictors of mortality from all causes and from cardiovascular disease in men and women in the lipid research clinics study.  Am J Epidemiol. 2002;156(9):832-841
PubMed   |  Link to Article
Stevens J, Evenson KR, Thomas O, Cai J, Thomas R. Associations of fitness and fatness with mortality in Russian and American men in the lipids research clinics study.  Int J Obes Relat Metab Disord. 2004;28(11):1463-1470
PubMed   |  Link to Article
DerSimonian R, Laird N. Meta-analysis in clinical trials.  Control Clin Trials. 1986;7(3):177-188
PubMed   |  Link to Article
Higgins JP, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses.  BMJ. 2003;327(7414):557-560
PubMed   |  Link to Article
Begg CB, Mazumdar M. Operating characteristics of a rank correlation test for publication bias.  Biometrics. 1994;50(4):1088-1101
PubMed   |  Link to Article
Egger M, Davey Smith G, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test.  BMJ. 1997;315(7109):629-634
PubMed   |  Link to Article
Duval S, Tweedie R. Trim and fill: a simple funnel-plot-based method of testing and adjusting for publication bias in meta-analysis.  Biometrics. 2000;56(2):455-463
PubMed   |  Link to Article
Sterne JA, Gavaghan D, Egger M. Publication and related bias in meta-analysis: power of statistical tests and prevalence in the literature.  J Clin Epidemiol. 2000;53(11):1119-1129
PubMed   |  Link to Article
Aijaz B, Babuin L, Squires RW,  et al.  Long-term mortality with multiple treadmill exercise test abnormalities: comparison between patients with and without cardiovascular disease.  Am Heart J. 2008;156(4):783-789
PubMed   |  Link to Article
Aktas MK, Ozduran V, Pothier CE, Lang R, Lauer MS. Global risk scores and exercise testing for predicting all-cause mortality in a preventive medicine program.  JAMA. 2004;292(12):1462-1468
PubMed   |  Link to Article
Allen WH, Aronow WS, Goodman P, Stinson P. Five-year follow-up of maximal treadmill stress test in asymptomatic men and women.  Circulation. 1980;62(3):522-527
PubMed   |  Link to Article
Arraiz GA, Wigle DT, Mao Y. Risk assessment of physical activity and physical fitness in the Canada Health Survey mortality follow-up study.  J Clin Epidemiol. 1992;45(4):419-428
PubMed   |  Link to Article
Balady GJ, Larson MG, Vasan RS, Leip EP, O'Donnell CJ, Levy D. Usefulness of exercise testing in the prediction of coronary disease risk among asymptomatic persons as a function of the Framingham risk score.  Circulation. 2004;110(14):1920-1925
PubMed   |  Link to Article
Bruce RA, DeRouen TA, Hossack KF. Value of maximal exercise tests in risk assessment of primary coronary heart disease events in healthy men: five years' experience of the Seattle Heart Watch Study.  Am J Cardiol. 1980;46(3):371-378
PubMed   |  Link to Article
Cumming GR, Samm J, Borysyk L, Kich L. Electrocardiographic changes during exercise in asymptomatic men: 3-year follow-up.  Can Med Assoc J. 1975;112(5):578-581
PubMed
Erikssen G, Liestol K, Bjornholt J, Thaulow E, Sandvik L, Erikssen J. Changes in physical fitness and changes in mortality.  Lancet. 1998;352(9130):759-762
PubMed   |  Link to Article
Erikssen G, Bodegard J, Bjornholt JV, Liestol K, Thelle DS, Erikssen J. Exercise testing of healthy men in a new perspective: from diagnosis to prognosis.  Eur Heart J. 2004;25(11):978-986
PubMed   |  Link to Article
Farrell SW, Cheng YJ, Blair SN. Prevalence of the metabolic syndrome across cardiorespiratory fitness levels in women.  Obes Res. 2004;12(5):824-830
PubMed   |  Link to Article
Gulati M, Arnsdorf MF, Shaw LJ,  et al.  Prognostic value of the duke treadmill score in asymptomatic women.  Am J Cardiol. 2005;96(3):369-375
PubMed   |  Link to Article
Gulati M, Black HR, Shaw LJ,  et al.  The prognostic value of a nomogram for exercise capacity in women.  N Engl J Med. 2005;353(5):468-475
PubMed   |  Link to Article
Gyntelberg F, Lauridsen L, Schubell K. Physical fitness and risk of myocardial infarction in Copenhagen males aged 40-59: a five- and seven-year follow-up study.  Scand J Work Environ Health. 1980;6(3):170-178
PubMed   |  Link to Article
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PubMed   |  Link to Article
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