AMERICAN JOURNAL OF EPIDEMIOLOGY

Copyright © 1990 by The Johns Hopkins University School of Hygiene and Public Health All rights reserved

Vol. 132, No. 4 Printed in U.S A.

A META-ANALYSIS OF PHYSICAL ACTIVITY IN THE PREVENTION OF CORONARY HEART DISEASE JESSE A. BERLIN12 AND GRAHAM A. COLDITZ13 Berlin, J. A. (U. of Pennsylvania School of Medicine, Clinical Epidemiology Unit, Philadelphia, PA 19104-6095), and G. A. Colditz. A meta-analysis of physical activity in the prevention of coronary heart disease. Am J Epidemiol 1990; 132:61228. Evidence for an independent role of increased physical activity in the primary prevention of coronary heart disease has grown in recent years. The authors apply the techniques of meta-analysis to data extracted from the published literature by Powell et al. (Ann Rev Public Health 1987;8:253-87), as well as more recent studies addressing this relation, in order to make formal quantitative statements and to explore features of study design that influence the observed relation between physical activity and coronary heart disease risk. They find, for example, a summary relative risk of death from coronary heart disease of 1.9 (95% confidence interval 1.6-2.2) for sedentary compared with active occupations. The authors also find that methodologically stronger studies tend to show a larger benefit of physical activity than less well-designed studies. coronary disease; exercise; meta-analysis

Evidence for an independent role of increased physical activity in the primary prevention of coronary heart disease has grown in recent years. In most recent major

reviews (1-8) the authors have concluded that physically active people are at lower risk for coronary heart disease than those who are inactive. Recently, Powell et al. (9) produced a thorough review of this topic, combining approaches that had been used Received for publication February 17, 1989, and in in earlier reviews. In their review, they final form March 7, 1990 present extensive tables that document 1 Technology Assessment Group, Harvard School study characteristics, including an overall of Public Health, Boston, MA. 2 University of Pennsylvania School of Medicine, rating of study quality, and summarize the Section of General Internal Medicine, Clinical Epi- relative risk information reported for each demiology Unit, Philadelphia, PA. 3 Channing Laboratory, Harvard Medical School, of the studies. The authors stop short, howBoston, MA. ever, of using formal techniques of metaReprint requests to Dr. Jesse A. Berlin, University analysis to make quantitative summary of Pennsylvania School of Medicine, Clinical Epidemiology Unit, 331 R Nursing Education Building, statements about the relation between physical activity and the incidence of corPhiladelphia, PA 19104-6095. This work was supported by research grant onary heart disease. A subsequent work HS05936 from the National Center for Health Services Research and Health Care Technology Assess- (10), concluding that exercise is an economment and by HL 35464 from the National Institutes ically favorable risk-reduction strategy of Health. when compared with other preventive or The authors wish to thank Drs. Kenneth Powell, Sander Greenland, and Moyses Szklo for helpful com- therapeutic interventions for coronary ments on earlier versions of this paper. Unpublished heart disease, used a median relative risk data were kindly provided by Ralph D'Agostino and presented by Powell et al. (9) in making Albert Belanger for the Framingham Study and by calculations. In this paper, we apply the Paul Sorlie for the Puerto Rico Heart Health Program. 612

META-ANALYSIS OF PHYSICAL ACTIVITY AND CORONARY HEART DISEASE

techniques of meta-analysis to the data collected by Powell et al., as well as more recent studies addressing this relation, in order to make more formal quantitative statements and to explore features of study design that may influence the observed relation between physical activity and coronary heart disease. Meta-analysis has been defined as "the statistical analysis of a collection of analytic results for the purpose of integrating the findings" (11). Although many of the applications of meta-analysis have been in the social sciences, it has gained greater popularity as a tool for summarizing the results of randomized controlled trials of medical and surgical therapies (12). MacMahon and Hutchison (13) used techniques of meta-analysis to combine epidemiologic data on intra-uterine exposure and risk of leukemia in 1964. More recently, meta-analytic methods have again been used in the review of epidemiologic studies of the relations between occupational exposures or life-style characteristics and disease (14-16). We apply the techniques developed in these earlier quantitative reviews to the analysis of the relative risk information reported in studies of the relation between physical activity and coronary heart disease. Specifically, we address the question of whether physical activity relates to coronary heart disease and examine study characteristics that may influence conclusions on this question in individual investigations. METHODS

Several major sources of variability among the studies of physical activity and coronary heart disease must be addressed before combining the published results. Some studies involve activity levels at work, and others focus on leisure-time activity or a combination of work and leisure activity levels. Using these different definitions of physical activity may be measuring quite different levels of energy expenditure over varying periods of time.

613

Such differences could have quite marked physiologic effects. We therefore separate these two general types of activity. We address this issue in our primary analysis by pooling results separately from studies of work-related activity and studies of leisuretime activity. The few studies that use both work and leisure activity are grouped with the leisure studies, leaving the occupational studies as a distinct group (9). Even within a given type of activity, the actual degree of activity, especially for the so-called "physically active" group, varies from study to study. Paffenbarger et al. (17), in an earlier review, suggest that the reason that some studies fail to show a protective relation between activity and coronary heart disease lies in insufficient differences in actual activity level between the "active" and "inactive" groups under study. This lack of differential activity levels might be problematic when both active and inactive groups are highly active or when both groups are inactive. Some studies included in the review by Powell et al. present separate relative risk estimates for a "moderate" activity comparison group and a "sedentary" comparison group, with the "highly active" group as the reference for both. We provide summary relative risks, therefore, for three sets of studies. In the first summary, we combine the relative risks from the comparisons of the vigorous activity groups with the moderate activity groups, for only those studies that present separate relative risks for moderate and sedentary comparison groups. In the second summary, we combine the relative risks from the studies that do not separate moderate from sedentary comparison groups. In the third summary, we combine the relative risks from the studies that separate moderate and sedentary comparison groups, using only the sedentary groups. These separate analyses serve, in effect, as an assessment of a dose-response relation between activity level and risk for coronary heart disease.

614

BERLIN AND COLDITZ

An additional obstacle to pooling the results from the individual studies is the variety of coronary heart disease outcomes reported from the previous studies. These include coronary heart disease incidence, coronary heart disease death, myocardial infarction, myocardial infarction plus sudden death combined, angina pectoris, and congestive heart failure (usually combined with another category). For most studies that report a relative risk for lack of physical activity with respect to total coronary heart disease, the relative risk includes the information on all of the separate outcomes combined. The etiology of these different categories of coronary disease may differ, as may the contribution of physical activity. We address this issue by developing separate summaries reflecting different levels of disease severity: coronary heart disease incidence, coronary heart disease death, myocardial infarction, myocardial infarction and sudden death combined, and angina pectoris. Assessment of study quality To explore the relation between study quality and study outcome, we developed a scoring system for quality. We based this directly on data provided by Powell et al. (9), who describe in detail their assessment of the quality of three components of study design: measurement of activity, measurement of disease status, and epidemiologic methods. For each article, these authors described the quality of each component and assign a quality of "unsatisfactory," "satisfactory," or "good." We assigned a value of 0 to the rating unsatisfactory, 1 to satisfactory, and 2 to good, for each of three components, and summed the scores for each study to produce a total quality score which can range from 0 to 6. For example, a study with ratings of unsatisfactory for activity assessment and good for both other components was given a score of 4 (0 + 2 + 2 = 4) (e.g., references 18, 19). We also developed a second, more objective, quality score, based on ratings of sep-

arate sub-components for each of the three major components of quality. Powell et al. describe seven separate desirable features of a physical activity measure, four desirable components of a coronary heart disease measure, and five desirable aspects of the epidemiologic methods, for a total of 16 components. The authors rated each component in each study as to its presence or absence with a " •" for "no or uncertain," a "+" for present "in part," and a "++" for "yes." We awarded a single point for every "+" appearing (thus, a "++" rating received two points). The possible scores on this scale, then, ranged from 0 to 32. It was not uncommon for two studies with the same objective score for a component to receive different ratings from Powell et al. For example, one study (20) reported on two cohorts of workers in London: postal workers and busmen. The subscores for the physical activity measure on both cohorts were 4 out of a possible 14, yet the measure for the postal workers was rated unsatisfactory while the measure for busmen was rated satisfactory. Statistical methods To pool relative risks from several studies, we adapted a method developed for combining event rate differences in clinical trials (11). This method has previously been used to combine standardized mortality ratios from occupational cohort studies (15). Because the log scale is symmetric and the variance of the log relative risk is well understood, we first take the log of the relative risk before combining results from the individual studies. The pooling method gives essentially a weighted average of the log relative risks from the individual studies, where the weight depends, in part, on the inverse of the variance of the log relative risk (11). In effect, larger studies are given more importance in the summary measure than smaller studies. This random effects method, however, allows for heterogeneity among study results that may remain, even after strati-

META-ANALYSIS OF PHYSICAL ACTIVITY AND CORONARY HEART DISEASE

fication on type of activity (work vs. leisyre), outcome (coronary heart disease, coronary heart disease death, etc.), degree of activity (moderate vs. sedentary), and quality score. That is, it does not assume homogeneity of relative risks across studies. Heterogeneity may result, for example, when study populations, activity levels (or their measurement), or epidemiologic methods vary extensively from study to study. Substantial heterogeneity may remain, however, even after stratification on important study attributes. The method we use produces a relatively larger variance (and, hence, wider confidence intervals) for summary estimates of heterogeneous studies than for summaries of homogeneous studies. This should seem intuitively reasonable, since we would not expect to have the same confidence in a summary of very diverse results as we have in one of very similar results. The technique is analogous to the widely used Mantel-Haenszel method (21) for combining odds ratios. An exception is that the Mantel-Haenszel method assumes homogeneous odds ratios and does not compensate for heterogeneity by building wider confidence intervals. We perform and include results from a chisquare test for heterogeneity along with all summary estimates. It is important to be aware that summarization in the presence of extreme heterogeneity may be somewhat misleading and certainly does not obviate the need to explore the sources of the heterogeneity. To examine further the hypotheses that aspects of study design are related to study results, we performed weighted least squares, fixed effects regressions of the log relative risk on several study attributes. These were: whether the relative risk was adjusted for at least age or not, whether the study measured work or leisure activity (both kinds of studies were included in a single analysis), and each quality score, subjective and objective, separately. We also performed regressions using the separate components of each of the quality

615

scores: assessment of disease, measurement of activity, and epidemiologic methods. The weight assigned to each study was the inverse of the variance of the log relative risk. For each study, we chose only one outcqme to include in the regression model (so as to maintain independence among observations, which is one of the major assumptions of regression). In order of preference, the outcome chosen from a study was coronary heart disease, coronary heart disease death, or myocardial infarction and sudden death together. Studies that did not examine one of these three outcomes were not included in this analysis. We estimated parameters for the model with all three main effects terms, the three models with all pairs of main effects, and the three univariate models. Several specific rules were adopted for combining results. When more than one published report was generated using the same cohort and the same outcome, only the most recently published results were used. For example, two studies pf occupational activity both reported on the same outcomes in the same cohort (20, 22). If earlier studies reported on an outcome not examined in a later study of the same cohort, the result from the earlier study was used only for the relevant outcome (e.g., references 20 and 22, 23 and 24). To use as much of the available information as possible, we included studies that reported on women separately (25, 26). We recognize that results for men and women may differ, but the number of studies that included only women was inadequate to examine this hypothesis. For the studies of "various" kinds of activity, we tried to approximate "total activity" by using the combined "work and leisure" results when these were available from a study for a particular outcome (26-28). For some studies and some outcomes, the leisure results were used in preference to the work activity results (26, 29). Since we had already performed a separate analysis of occupational activity, we did not wish to be redundant.

616

BERLIN AND COLDITZ

In addition to these general rules, we also adopted some technical rules. To convert confidence intervals into estimates of the variance of the log relative risk, we first transformed the interval to the log scale. The lower endpoint was subtracted from the upper endpoint to compute the length of the confidence interval. Under the (admittedly convenient but not always true) assumption that the 95 percent confidence interval had been constructed by adding and subtracting 1.96 times the standard error of the log relative risk, we divided the interval length by 3.92 to obtain an approximate standard error. This was then squared to provide an estimate of variance. This procedure was modified for the few 90 percent confidence intervals that were provided. For several studies, only a relative risk and a p value were provided. The p value was first converted to the corresponding two-sided z statistic (from a table of standard normal variates). Assuming that the log relative risk is approximately normally distributed, we set the calculated z statistic equal to the log relative risk divided by its unreported standard error. The simple equation could then be solved for the standard error. In cases where the p value was reported, for example, as "p < 0.05," we made the most conservative assumption that the p value was equal to 0.05, and would assign a z statistic of 1.96. Some studies included in review by Powell et al. (9) were excluded from our analysis because they did not report a relative risk or a confidence interval or both. This meant that we excluded the only two studies (the Framingham Study and the Puerto Rico Heart Health Program) included by Powell et al. that would have received a quality score of 6 (30, 31). We were able to obtain unpublished data from the authors of both of those studies for inclusion in a separate analysis. By using a MEDLINE search and by checking the reference lists of those papers already retrieved, we found several studies

of nonoccupational activity that have appeared since the publication of Powell et al. (9) and that met their inclusion criteria. In a separate analysis, these studies were combined for the appropriate outcomes. Because we did not feel we could exactly replicate the scoring system used by Powell et al., we did not include the most recent studies in the stratified analyses involving measurement of quality scores. These studies were, however, included in several regression analyses by treating their quality scores as missing data. An indicator variable for presence of the quality score (0 = absent, 1 = present) was entered, along with a product term of this indicator and the quality score, with no main effect for quality score. RESULTS

Summaries of the characteristics of and findings from 27 cohorts in which the relation between physical activity and coronary heart disease was investigated appear in table 1 (occupational activity) and table 2 (other forms of activity). The data in these tables represent the unique results reported from cohorts for which estimates of the relative risk for lack of physical activity were accompanied by either confidence intervals or p values. The eight recently published studies that we identified are included in table 2 without quality scores. The quality scores of the studies of nonoccupational activity tend to be higher than those of the occupational studies, particularly with respect to the evaluation of activity level. All of the nonoccupational studies have either a good or satisfactory rating for the activity evaluation; only six of 18 occupational studies have such measures of activity. The objective quality score was higher, on average, in the nonoccupational studies as well. Not surprisingly, because the presence of statistical adjustment formed part of that rating scale, the objective quality score was higher for studies

617

META-ANALYSIS OF PHYSICAL ACTIVITY AND CORONARY HEART DISEASE

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that used adjustment than for those that did not. We first pooled the relative risk for each cardiovascular outcome for high activity compared with moderate activity and for high activity compared with low activity when comparison groups were separated by activity level in the original studies. In a third analysis, we pooled the relative risks for those studies that presented only a single comparison. We pooled studies separately within the occupational and nonoccupational activity categories (see tables 3-5). In these tables, we report the chisquare statistic from the test for heterogeneity of relative risks for each pooled relative risk. Several patterns emerge from the data. Many of the pooled relative risks are statistically significant and accordingly their confidence intervals exclude 1.0. Statistical significance is not generally apparent using the moderate comparison groups for occupational activity. The summaries of studies reporting on angina pectoris are consistently not statistically significant. The combined results from comparisons with a moderate activity group give lower relative risks than those produced by pooling studies with sedentary or mixed comparison groups for each cardiovascular outcome. These results are consistent with an inverse doseresponse relation; increasing physical activity is associated with a decrease in risk of coronary heart disease. Several recent studies, not included in Powell et al. (9), are included in the summary relative risks for coronary heart disease and coronary heart disease death in table 5. The addition of a substantial number of new studies to these analyses does not materially alter the pooled relative risks, although the confidence intervals tend to be narrower when more studies are pooled. When unpublished data from the Framingham and Puerto Rico studies are included in the summary of coronary heart disease incidence, the pooled relative risks are 1.1 (95 percent confidence interval (CI)

to

s,
5 kcal/

Work category 4 (most active)

Work, less than 80% seated

Work, less than 80% seated

Work, heavy

Work, heavy

CHD

CHD death

Work, heavy

MI, SD

CHD

Work, medium and light Letter carriers &5 years on job Work, heavy

Farmers

Category 3 Category 1 Category 3 Category 2 (none in category 1) Moderate and light tasks (£5 kcal/min)

1 6 (1.2-2.2)

1.4 (0.6-3.6) 1.9 (0.9-4.0) 1.8 (0.8-4.1) 3.1 (1.2-7.5) 1.2 (0.4-3.6) 2.0 (0.6-6.0) 1.9 (1.3-2.8) 1.6 (1.2-2.1) 2.6 (1.8-3.6) 2.5 (1.8-3.4) 2.0 (1.2-3.3) 3.5 (2.1-6.0) 1.8 (0.6-5.1) 3.0 (0.3-29.4) 0.7 (0.3-1.6) 1.1 (0.4-3.1) 1.1 (0.2-8.4) 2.4 (0.3-18.5)

Moderate Sedentary Moderate Sedentary Moderate Sedentary Moderate Sedentary At least 80% seated At least 80% seated

2.8 (1.0-7.8)

Postal clerks >5 years on job

Sedentary

1.1 (0.6-2.2)

1.4 (1.1-1.7) 2.0 (1.7-2.5) 1.2 (0.6-2.4) 1.1 (0.5-2.4) 1.2 (0.7-1.9) 1.8 (1.2-2.7) 1.3 (0.8-2.1)

Switchmen Clerks Sedentary clerks Nonfarmers

Relative risk (95% CI$)

Low activity group

5

Age Age Age Age

14

2

5

None

Age

23

14

19

17

14

2

None None

4

3

Age Age Age Age Age Age

3

16

4

None

None

14

19

15

15

21

18

Objective quality score 1|

3

4

Age

None

2

Age

2

3

Age

Adjustments

Subjective quality score§

• Adapted from Powell et al. (9). t Abbreviations: AP) angina pectoris; CHD, coronary heart disease; CHF, congestive heart failure; MI, myocardial infarction; SD, sudden death. t CI, confidence interval. § Subjective quality scores were based on assessments of quality by Powell et al. (9) of three components of study design: measurement of activity, measurement of disease status, and epidemiologic methods. Each component was rated as "unsatisfactory," "satisfactory," or "good" by these authors. We assigned numerical values of 0, 1, or 2 to the three levels of quality, respectively. Thus, the subjective scores could range from 0 to 6. || Objective quality scores were based on ratings by Powell et al. (9) of separate subcomponents for each of the three major components of study design. Possible values of the objective score ranged from 0 to 32.

5,229

172,459

Italian railroad employees (43)

Israeli kibbutzim female residents (25)

1,215

Greek islands residents (41, 42)

5,288

1,712

Italy residents (18, 19)

Israeli kibbutzim male residents (25)

1,371

Yugoslavia residents (39, 40)

784

1,664

Chicago Utility company employees (37) Washington, DC postal workers (38)

AP

MI, SD AP, coronary insufficiency CHD, CHF

20,000

Switchmen

CHD death MI, SD

North Dakota residents (36)

Section men

High activity group

CHD death

Outcomef

191,609

Cohort size

US railroad workers (34, 35)

Cohort (reference no.)

TABLE 1—Continued

Occupational cohort studies used in the meta-analysis*

D

r z > Z a o o

50

3

oo

61,000

3,154

1,741

3,978

New York health insurance subscribers (27)

San Francisco corporate employees (29) San Francisco Federal employees (29) North Karelia, Finland male residents (26)

15,088

CHD death

8,125

Gothenberg, Sweden residents (51)

Western Finland residents (52)

CHD

2,779

Los Angeles firemen and policemen (50)

3,688

CHD

17,944

British civil servants (48, 49)

North Karelia, Finland female residents (26)

CHD

16,936

CHD

MI

MI

AP

CHD, CHF, conduction defects

MI, SD

MI, SD

CHD death

AP

MI

Nonfatal

CHD death (ref#23)

CHD death (ref. #23)

CHD

Outcomet

1,989

Cohort size

Chicago Western Electric employees (47) Harvard alumni (24)

Cohort (reference no.) Low activity group

Work and leisure high activity

High work and high leisure High work and high leisure High work and high leisure

Fable continues

High/low or low/ high Low/low Low activity

High/low or low/ high Low/low

Leisure, sport Nonparticipant participant >2,000 kcal/wk 2,000 kcal/wk 500-1,999 kcal/wk in leisure activity >2,000 kcal/wk 2,000 kcal/wk z a o o

o

O

«< CO

"0

•n

O

CO

>

s

CHD

CHD death

2,014

3,043

2,363

636

Oslo, Norway residents (55)

US railroad workers (56)

Belgian physical fitness study participants (57)

423

8,838

4,121

Upper tertile of total activity Work and leisure, high activity Framingham activity indexes upper tertile

CHD

CHD

High total

Upper (fourth) quartile of kcal/expenditure Upper quartile of leisure activity

CHD

MI

CHD death

High physical fitness based on exercise test Upper tertile physical fitness Upper quartile of physical fitness

High activity group

1.45 (1.14-1.89) 2.33 (1.01-5.26) 1.27 (1.01-1.61) 1.20 (1.00-1.44) 1 41 (1.04-1.91) 0.95 (0.67-1.37)

Low activity Moderate activity Lowest tertile Middle tertile

Lowest tertile Middle tertile

0.90 (0.40-2.04)

Middle two quartiles Light and sedentary 1.3 (0.89-1.89)

0.69 (0.25-1.93)

Age

Age, sex

Age, BP, CH, SM, other Age, SM, other

None

Age, BP, CH, SM

1.28 (0.99-1.63) 1.05 (1.00-1.11)

Lowest quartile

Age

1.5 (0.6-3.8)

Middle two quartiles Lowest quartile Third quartile

Age

Age, BP, CH, SM, other

Age, BP, CH, SM, other

Adjustments?

4.8 (2.2-10.7)

1.5 (1.09-2.04) 1.2 (1.04-1.43)

2.7 (1.4-5.1)

Relative risk (95% CI+)

Lowest quartile

Lowest tertile

Low fitness

Low activity group

6

6

Subjective quality score ||

26

26

Objective quality scorell

* Adapted from Powell et al. (9). t Abbreviations: AP, angina pectoris; CHD, coronary heart disease; CHF, congestive heart failure; MI, myocardial infarction; SD, sudden death. $ CI, confidence interval. § Abbreviations: BP, blood pressure; CH, cholesterol; SM, smoking. || Subjective quality scores were based on assessments of quality by Powell et al. (9) of three components of study design: measurement of activity, measurement of disease status, and epidemiologic methods. Each component was rated as "unsatisfactory," "satisfactory," or "good" by these authors. We assigned numerical values of 0, 1, or 2 to the three levels of quality, respectively. Thus, the subjective scores could range from values 0 to 6. 11 Objective quality scores were based on ratings by Powell et al. (9) of separate subcomponents for each of the three major components of study design. Possible values of the objective score ranged from 0 to 32. ** Unpublished data provided by R. D'Agostino and A. Belanger and analyzed by the authors using logistic regression. t t Unpublished data provided by P. Sorlie and analyzed by the authors using logistic regression.

Puerto Rico residents (31)tt

Honolulu heart study participants (59) Ages 45-64 Ages 65+ Framingham, Massachusetts residents (30, 60)**

7,221

CHD death

12,138

MRFIT trial participants (54)

Finnish men (58)

CHD death

4,276

Lipid Research Clinics Prevalence Study Participants (53)

Outcomet

Cohort size

Cohort (reference no.)

CHD

TABLE 2—Continued

Nonoccupational cohort studies used in the meta-analysis*

s

o o

w

META-ANALYSIS OF PHYSICAL ACTIVITY AND CORONARY HEART DISEASE TABLE 3

Pooled relative risks from studies of occupational activity and risk of heart disease Outcome*

No. of studies

Relative risk (95% CI)

x'for heterogeneity}

A. Relative risks for high activity compared with moderate activity groups from studies that reported both moderate and sedentary comparison groups CHD 4 1.1 (0.9-1.3) 2.79 CHD death 5 1.4 (1.2-1.8) 5.09 MI 1 1.3 (0.9-1.9) MI + SD 1 1.8(0.8-4.1) AP 1 0.6(0.4-0.9) B. Relative risks for high activity compared with low activity groups from studies that did not separate moderate and sedentary comparison groups CHD 2 1.4 (0.9-2.3) 1.10 CHD death 6 1.5 (1.1-2.0) 11.45 MI 3 2.4 (1.8-3.2) 0.35 MI + SD 2 1.6 (1.0-2.4) 1.20 AP 5 1.6 (0.9-2.7) 22.49 C. Relative risks for high activity compared with sedentary groups from studies that reported both moderate and sedentary comparison groups CHD 4 1.4 (1.0-1.8) 3.31 CHD death 5 1.9 (1.6-2.2) 2.89 MI 1 1.4 (0.9-2.1) MI + SD 1 3.1 (1.2-7.5) AP 1 0.8(0.5-1.2) * Abbreviations: AP, angina pectoris; CHD, coronary heart disease; MI, myocardial infarction; SD, sudden death. t CI, confidence interval. $ Degrees of freedom for x2 are one less than the number of studies.

1.0-1.3) for moderate comparison groups and 1.3 (95 percent CI 1.1-1.5) for sedentary comparison groups. These studies were not included in the analysis of the studies reviewed by Powell et al. (9) because the original published reports did not provide the requisite data. Heterogeneity among the studies that we combined is fairly high for some analyses. Extremely high heterogeneity is evident in the two summaries of angina pectoris results that include more than one study. Both are characterized by highly significant heterogeneity and by confidence intervals that include the null value of 1.0. This is of note since the point estimate of relative

621

risk of 1.6 for the five occupational activity studies with mixed comparison groups is not very close to the null. If we compute the simple, inverse-variance weighted averages of the same five angina studies, ignoring heterogeneity, the pooled relative risk is 1.9 (95 percent CI 1.5-2.3). Because this confidence interval excludes 1.0, we might draw much different qualitative conclusions from the pooling method that ignores heterogeneity among studies. The inclusion of 1.0 in the random effects confidence interval implies only that the mean relative risk may be null, but it may still be implausible that all of the individual TABLE 4

Pooled relative risks from studies of nonoccupational activity and risk of heart disease, including only those studies reviewed by Powell et al. (9) Outcome*

No. of studies

Relative risk (95% Clf)

x2 for heterogeneity}

A. Relative risks for high activity compared with moderate activity groups from studies that reported both moderate and sedentary comparison groups CHD No studies CHD death 1 1.3 (1.0-1.7) MI 2 1.4 (1.1-1.7) 0.77 MI + SD No studies AP No studies B. Relative risks for high activity compared with low activity groups from studies that did not separate moderate and sedentary comparison groups CHD 5 1.6 (1.3-1.8) 4.47 CHD death 2 1.9 (1.0-3.4) 2.87 MI 1 1.3 (1.0-1.7) MI + SD 2 2.3 (1.5-3.6) 0.01 AP 2 1.1 (0.4-3.0) 11.90 C. Relative risks for high activity compared with sedentary groups from studies that reported both moderate and sedentary comparison groups CHD No studies CHD death 1 1.6 (1.2-2.2) MI 2 2.9 (1.9-4.5) 1.51 MI + SD No studies AP No studies * Abbreviations: AP, angina pectoris; CHD, coronary heart disease; MI, myocardial infarction; SD, sudden death. t CI, confidence interval. X Degrees of freedom for x 2 are one less than the number of studies.

622

BERLIN AND COLDITZ TABLE 5

Pooled relative risks from studies of nonoccupational activity and risk of heart disease, including recent studies not reviewed by Powell et al. (9) (only those outcomes for which new studies were published are summarized) Outcome*

No. of studies

Relative risk (95% CIt)

x2 for heterogeneity^

A. Relative risks for high activity compared with moderate activity groups from studies that reported both moderate and sedentary comparison groups CHD 1 0.9 (0.4-2.0) CHD death 3 1.1 (1.0-1.3) 2.94 B. Relative risks for high activity compared with low activity groups from studies that did not separate moderate and sedentary comparison groups CHD 9 1.5(1.4-1.7) 9.32 CHD death 3 1.6(1.1-2.4) 4.31 C. Relative risks for high activity compared with sedentary groups from studies that reported both moderate and sedentary comparison groups CHD 1 0.7 (0.3-1.9) CHD death 4 1.7 (1.2-2.3) 10.04 MI 3 2.1(1.1-4.4) 23.33 * Abbreviations: CHD, coronary heart disease; MI, myocardial infarction. t CI, confidence interval. t Degrees of freedom for x2 a r e one less than the number of studies.

effects are null. The fixed effect confidence interval, which excludes 1.0, implies that it is, in fact, unlikely that all of the studyspecific relative risks (the individual random effects) are 1.0. We next explored the impact of study design on the relative risk observed by the original investigators. We first categorized the studies into two groups, higher quality scores (from 3 to 5) and lower quality scores (0 to 2). With only one exception, when we pool occupational studies using coronary heart disease, coronary heart disease death, or angina pectoris separately according to quality score, we observe higher relative risk estimates for all outcomes among the studies with higher quality scores than among those with lower quality scores (table 6). A further effect of separating studies on the basis of quality score is a reduction of the heterogeneity. For all of the pooled

relative risks for the coronary heart disease outcomes, the heterogeneity is reduced to very low levels, suggesting that some of the heterogeneity of outcomes in the overall pooled estimates resulted from an association between quality score and outcome. For angina pectoris, heterogeneity is low for the studies with lower quality scores after stratification. The heterogeneity among the higher quality score studies is still quite high (x 2 for heterogeneity = 9.46, 2 degrees of freedom, p = 0.009), but it might arguably be considered less statistically significant compared with the unstratified result (x 2 = 22.49 on 4 degrees of freedom, p = 0.0002). Overall, the pooled relative risks for strenous physical activity compared with sedentary, for the studies of coronary heart disease outcomes with higher quality scores, are in the 1.8 range, are statistically significant, and accordingly exclude 1.0 from their confidence intervals. In contrast, the pooled relative risks against sedentary comparison groups, for the coronary heart disease studies with lower quality scores, are in the null range. A similar pattern holds when moderate activity groups are used in comparisons with the strenuous activity groups, except that the pooled risk ratios are not as large as those for the sedentary comparison groups (b and c in table 6). Regression results The regression of the log relative risk on various study characteristics (relative risk adjusted or not, occupational versus other activity, and quality score) confirms the impression that quality score relates to study outcome. Although all three factors were significant predictors of the log relative risk in univariate regressions (p < 0.05), the only significant predictor in any of the multiple regressions was quality score, using either the subjective or the objective measure. In the multivariate models, the parameter estimates for quality score were not substantially changed in magnitude by the inclusion of other vari-

META-ANALYSIS OF PHYSICAL ACTIVITY AND CORONARY HEART DISEASE

623

TABLE 6

Summary relative risks from studies of occupational activity and risk of coronary heart disease incidence and death Satisfactory! Outcome*

N o of

studies

Relative risk (95% CI$)

Unsatisfactoryt x 2 for heterogeneity?

No. of studies

Relative risk (95% CI$)

2 x for heterogeneity?

A. Relative risks for high activity compared with moderate activity groups from studies that reported both moderate and sedentary comparison groups CHD 2 1.3 (0.7-2.6) 0.05 2 0.9 (0.4-1.8) 2.40 CHD death 2 1.6(1.2-2.1) 1.88 3 1.2(0.8-1.8) 2.27 B. Relative risks for high activity compared with low activity groups from studies that did not separate moderate and sedentary comparison groups CHD 1 1.8 (0.9-3.3) 1 1.1 (0.6-2.2) CHD death 5 1.7 (1.3-2.2) 2.64 1 1.1 (0.9-1.2) AP 3 2.2 (1.3-3.9) 9.46 2 0.9 (0.3-2.2) 3.39 C. Relative risks for high activity compared with sedentary groups from studies that reported both moderate and sedentary comparison groups CHD 2 1.9 (1.0-3.6) 0.00 2 1.0 (0.5-2.3) 1.77 CHD death 2 1.8(1.5-2.3) 1.66 3 1.9(1.4-2.6) 1.22 * Abbreviation: CHD, coronary heart disease; AP, angina pectoris. t Satisfactory: subjective quality score >2. Unsatisfactory: subjective quality score 0-2. Subjective quality scores were based on assessments of quality by Powell et al. (9) of three components of study design: measurement of activity, measurement of disease status, and epidemiologic methods. Each component was rated as "unsatisfactory," "satisfactory," or "good" by these authors. We assigned numerical values of 0, 1, or 2 to the three levels of quality, respectively. Thus the subjective scores could range from 0 to 6. Objective scores were based on ratings by Powell et al. (9) of separate subcomponents for each of the three major components of study design. Possible values of the objective score ranged from 0 to 32. t CI, confidence interval. § Degrees of freedom for x 2 are one less than the number of studies.

ables in the model compared with the esti- The association also held for the objective mate from the univariate model for quality quality score (slope = 0.0457, SE = 0.0123, score. In addition, the coefficients for the p = 0.001). The relative risk predicted using variables other than quality score were the objective quality score would be 0.7 for small relative to the coefficient for quality a study with an objective score of zero and score. The regression equation, based on 3.1 for a study with a perfect objective score 27 studies, for the most parsimonious of 32. It is of note, in terms of the degree model is: of extrapolation involved in these predicted risks that, among the published results, the = °° 2 9 2 + °' 1341 * (subjective highest observed subjective score was 5 out of a possible 6, and the highest observed The standard error of the slope for qual- objective score was only 23 out of a posity score is 0.0273 (p = 0.0001), suggesting sible 32. a highly significant linear relation between In multiple regressions involving the inquality score and log relative risk. Using dividual components of the quality scores this equation, the predicted relative risk for and the adjustment and activity type varia study with a quality score of zero is 1.0 ables, only the quality score for epidemio(essentially no association between physi- logic methods was a statistically significant cal activity and risk of coronary heart dis- predictor of the log relative risk. The criease), and for a study with a quality score teria for the evaluation of epidemiologic of 6 the relative risk is predicted to be 2.3. methods used by Powell et al. (9) included

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whether physical activity status clearly preceded the period observed for outcome events, whether the analysis was adjusted for covariates, and whether loss to followup was less than 20 percent. The fact that the quality score contained information on the adjustment for covariates may partially explain why the adjustment variable was not predictive when the quality score was also in the model. We performed several regressions that included the more recent studies that were not reviewed by Powell et al., using a missing data algorithm to take into account the absence of quality scores for the newer studies. The qualitative conclusions about which covariates were predictive of relative risk were identical to those from the models involving fewer studies, i.e., quality score was the only significant predictor in the multiple regression. DISCUSSION

The results of this meta-analysis are consistent with an association between lack of physical activity and increased risk of coronary heart disease. This association is generally stronger when the "high activity" group in a study is compared with a sedentary group rather than when the comparison group has a moderate activity level. This pattern of association supports a doseresponse relation between physical activity and protection from coronary heart disease. This quantitative dose-response relation supports the argument by Paffenbarger et al. (17) that the explanation for a lack of association between increased activity and decreased coronary heart disease risk in some studies is the relatively low activity level in the so-called "active" group. It should be noted that a lack of apparent difference between activity groups could also stem from measurement error that is large relative to among-person variability in physical activity. Nevertheless, even perfect measurement of activity levels that are close together could yield no association

between lack of activity and coronary heart disease risk. The data we combined from previous cohort studies indicate that the protective effect of physical activity lies in prevention of the occurrence of major cardiovascular events, rather than in the reduction of the severity of events that do occur. If we pool results only from studies with higher quality scores that either employ a sedentary comparison group or do not specify the activity level in the comparison group, the relative risks for both coronary heart disease and coronary heart disease death fall in the range of 1.8. If the effect of exercise were only to reduce the severity of events that do occur, we would expect to see a higher relative risk for coronary heart disease death than for overall coronary heart disease incidence, a pattern that we do not observe for the studies with higher quality scores. In support of the claim made by Powell et al. (9), we also found that studies with higher quality scores tend to show higher relative risks than those with lower quality scores. The results of our regression analyses suggest that the association may be due primarily to an association between the quality of the epidemiologic methods used in the studies and the log relative risk. It is also of note, however, that the pooled relative risk of angina pectoris, the most easily misclassified of the outcomes examined, is not significantly elevated in any group of studies. This suggests that misclassification of disease may also contribute to the reduced relative risk observed in studies with lower quality scores. Powell et al. defined explicit criteria for all of their measures of study quality. For example, to be rated highly for epidemiologic methods, a study had to provide evidence that the physical activity status was determined for a period prior to the onset of coronary heart disease. Several other reasonably objective criteria for epidemiologic methods had to be met. In addition, the more objective quality score was also

META-ANALYSIS OF PHYSICAL ACTIVITY AND CORONARY HEART DISEASE

predictive of study outcome. Bias in the classification of coronary heart disease in the original studies is unlikely to explain the observed results because the associations did not increase in magnitude with decreasing objectivity of the categorization of severity of coronary heart disease (i.e., from coronary death to angina pectoris). Many of the studies of physical activity and heart disease, particularly the studies of occupational activity, did not or could not adjust results for confounding variables other than age. In several studies, however, results are presented with adjustment for age alone, and for age with other covariates, including cholesterol (e.g., references 50, 52, 54, and 56). In these studies, multivariate adjustment using regression methods has only a small impact on either the magnitude or the statistical significance of the regression term for physical activity, and physical activity remains an independent predictor of the risk of coronary heart disease even after multivariate adjustment for other risk factors. Thus, while it may not be possible to make a definitive statement about the role of adjustment for risk factors other than age, this adjustment does not appear to alter our conclusions about the protective effect of physical activity against coronary heart disease.

625

can be explained by available covariates. Nevertheless, the random effects summary can be appropriate for taking into account excess variability that remains even after stratification on important covariates. The results we present, in fact, highlight another of the strengths of meta-analysis, that is, the ability to explore the sources of heterogeneity. Both stratification of studies into those with "higher" and "lower" quality scores and regression analyses in which we included components of quality score, adjustment of the data during analysis, and type of physical activity, show the influence of quality score not only on the relative risk observed but also on the level of heterogeneity. For coronary heart disease, coronary heart disease death, and angina pectoris, pooled results from both the "higher" and "lower" quality studies show lower variability among studies than when we combined all results. The regression of log relative risk on quality score further indicates that the relation between quality score and relative risk is both systematic and quantifiable. It is possible that the results we have observed are influenced by the selective publication of statistically significant or epidemiologically popular results showing a relation between lack of physical activity Our analysis demonstrates the impor- and coronary heart disease. This phenomtance of not ignoring heterogeneity of re- enon, known as publication bias, has been sults among component studies of a meta- discussed in the social sciences (61, 62) and analysis. Results for several summaries, has been demonstrated to be a potentially particularly angina pectoris, are highly het- serious problem in the reporting of cancer erogeneous. With such high heterogeneity, clinical trials (63, 64). While it is difficult it is clearly not advisable to use a summary to rule out publication bias in the current estimate that assumes homogeneity of rel- situation, several factors argue against this ative risks across studies. This is a point bias being serious: made in most advanced epidemiologic text1) Many of the studies included in the metabooks. The angina studies conflict as to analysis, although showing relative risks favorwhether physical activity is protective or ing exercise as a preventive measure, are not statistically significant. not, a point that is apparent even without 2) Begg and Berlin (65) show that publication statistics. Thus, even the use of a random bias is more extreme for nonrandomized trials effects summary may be deceptive in the than for randomized trials. If we believe that case of the angina studies. Any summary part of the bias is related to poorer study quality and the consequent reluctance by both authors could be considered deceptive to the extent and editors to publish nonsignificant results, we that it masks important heterogeneity that should see an inverse relation between quality

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score and size of the observed relative risk. Instead, we see the opposite association. 3) A scatter plot of study weights against log relative risks (not shown), similar to the "funnel plot" that has been suggested (66) as a qualitative assessment of publication bias, does not reveal any striking evidence of publication bias.

The association between lack of physical activity and risk of heart disease implies that sedentary life-styles could be having a considerable public health impact. Cardiovascular disease is the leading cause of death in the United States (67). Among adults over age 34, one study estimates that over 60 percent are sedentary (68). This combination of a common disease and a common exposure suggests that a substantial number of deaths from cardiovascular disease might be avoidable. We have presented convincing evidence that physical activity has a protective effect against coronary heart disease. This can and should have great importance in setting public health priorities and goals for future research. We have not demonstrated that the benefits of physical activity are independent of levels of other risk factors for coronary heart disease. There is evidence, for example, presented by Paffenbarger et al. (23), that those at low risk (e.g., college alumni who had low systolic blood pressure) may not derive as much benefit from exercise as those at high risk. It is possible that physical activity may modify the harmful effects of other risk factors. Such a subgroup analysis was not possible in this meta-analysis because the data necessary were not available in published reports. Based on these data, we conclude that lack of physical activity is a potentially modifiable risk factor for coronary heart disease that should receive greater emphasis in the current efforts to reduce the impact of disease on society. REFERENCES

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