Guest guest Posted January 11, 2005 Report Share Posted January 11, 2005 Hi All, It seems to be based on the below that our chance of dying from all causes is dependent on both our fitness level and our exercise level. The pdf is available for the below Am J Med. 2004 Dec 15;117(12):912-8. Fitness versus physical activity patterns in predicting mortality in men. Myers J, Kaykha A, S, Abella J, Zaheer N, Lear S, Yamazaki T, Froelicher V. ... 6213 men referred for exercise testing .... 842 ... follow-up of 5.5 +/- 2 years. RESULTS: Expressing the data by age-adjusted quartiles, exercise capacity was a stronger predictor of mortality than was activity pattern (hazard ratio = 0.56; 95% confidence interval [CI]: 0.38 to 0.83; P < 0.001). In a multivariate analysis that considered clinical characteristics, risk factors, exercise test data, and activity patterns, exercise capacity (HR per quartile = 0.62; CI: 0.47 to 0.82; P < 0.001) and energy expenditure from adulthood recreational activity (HR per quartile = 0.72; 95% CI: 0.58 to 0.89; P = 0.002) were the only significant predictors of mortality; these two variables were stronger predictors than established risk factors such as smoking, hypertension, obesity, and diabetes. Age-adjusted mortality decreased per quartile increase in exercise capacity (HR for very low capacity = 1.0; HR for low = 0.59; HR for moderate = 0.46; HR for high = 0.28; P < 0.001) and physical activity (HR for very low activity = 1.0; HR for low = 0.63; HR for moderate = 0.42; HR for high = 0.38; P < 0.001). A 1000-kcal/wk increase in activity was approximately similar to a 1 metabolic equivalent increase in fitness; both conferred a mortality benefit of 20%. CONCLUSION: Exercise capacity determined from exercise testing and energy expenditure from weekly activity outperform other clinical and exercise test variables in predicting all-cause mortality. PMID: 15629729 [PubMed - in process] Increasing evidence of the association between physical inactivity and cardiovascular or all-cause mortality (1) has led health authorities around the world to make physical activity promotion part of broad health care policy goals (1, 2 and 3). More recent studies (3, 4, 5 and 6) have observed strong associations between physical fitness, measured by a maximal exercise test, and survival from cardiovascular and noncardiovascular causes. Physical fitness is related to physical activity patterns, and thus current physical activity guidelines generally consider fitness a surrogate measure of physical activity. However, other attributes, such as genetics, subclinical disease, and behavioral and environmental factors, determine individual fitness levels (7). There has been some recent debate as to whether daily physical activity patterns largely determine one¡¯s fitness level and therefore its inverse association with mortality, or whether fitness level predicts mortality independently from activity pattern (8 and 9). In addition, while these issues have been studied largely in asymptomatic populations (10, 11, 12 and 13), less is known about these associations in patients with existing cardiovascular disease. In the present study, we assessed all-cause mortality using fitness measured in subjects referred for exercise testing for clinical reasons, and quantified adulthood physical activity patterns by questionnaire. Our objectives were to compare the independent contributions of fitness and physical activity patterns to overall mortality, to determine the predictive power of fitness and activity patterns as compared with other clinical and exercise test variables, and to assess the interaction between fitness and activity in predicting mortality. Results In the convenience subgroup, the mean (¡ÀSD) follow-up period was 5.5 ¡À 2.0 years, and the average annual mortality was 2%. A total of 1256 deaths occurred during follow-up in the total group of subjects (n = 6213) undergoing exercise testing; 89 occurred among those in the subgroup who had physical activity patterns assessed. No major complications occurred during testing, although sustained ventricular tachycardia occurred during 1.3% of the exercise tests. In the subgroup, 230 patients had ischemic responses to exercise: 42 had ¡Ý 1.0-mm horizontal or downsloping ST depression, 130 had angina during exercise, and angina was the main reason for stopping in 58. The prevalence of these responses was similar between the total group of subjects and the subgroup. The current sample in which exercise testing and physical activity patterns were assessed was compared with the remainder of the group of subjects referred for exercise testing (n = 5371). Demographic, historical, and clinical characteristics were generally similar, including for age and medication use, although small differences were observed in the prevalence of stroke, heart failure, and myocardial infarction (Table 1). Table 1. Comparison of Demographic and Clinical Characteristics of the Study Sample and the Larger Patient Group Referred for Exercise Testing during the Study Period Characteristic Study Sample (n = 842) Other Referrals (n = 5371) P Value Mean ¡À SD or Number (%) Age (years) 58.9 ¡À 11.7 58.9 ¡À 11.2 0.96 Duration of follow-up (years) 4.6 ¡À 1.8 6.9 ¡À 4.0 <0.001 Height (inches) 69.0 ¡À 3.4 69.2 ¡À 3.2 0.30 Weight (lbs) 193.4 ¡À 37.6 189.8 ¡À 37.6 0.01 Medications Digoxin 23 (3) 321 (6) <0.001 Calcium antagonist 221 (26) 1519 (28) 0.48 Beta-blocker 147 (18) 1048 (20) 0.32 Nitrate 142 (17) 1344 (25) <0.001 Antihypertensive 185 (22) 1343 (25) 0.15 History Atrial fibrillation 31 (4) 813 (15) 0.29 Pulmonary disease 75 (9) 367 (7) 0.02 Stroke 43 (5) 185 (3) 0.01 Claudication 37 (4) 304 (6) 0.19 Typical angina 141 (17) 1213 (23) 0.04 Myocardial infarction 204 (24) 1658 (31) <0.001 Heart failure 46 (6) 481 (9.0) 0.001 Coronary bypass surgery 74 (8.8) 507 (9.4) 0.75 Percutaneous coronary intervention 68 (8) 324 (6) 0.01 Physical activity versus physical fitness in predicting mortality Physical fitness was poorly related to energy expenditure from adulthood physical activity (r = 0.09). Age-adjusted univariate predictors of mortality, in rank order, were peak exercise capacity, recreational energy expenditure during adulthood, recreational energy expenditure over the last year, and energy expended from blocks walked and flights of stairs climbed per week (Table 2). Although energy expended from occupational activity (P = 0.13) and the combination of occupational and recreational activity (P = 0.16) were associated with approximately 15% reductions in mortality per quartile increase, they did not appear to be strong predictors of survival. Age-adjusted multivariate predictors of survival, in rank order, were exercise capacity, followed by energy expenditure from recreational activity during adulthood. Past occupational energy expenditure was not associated with survival by multivariate analysis. A 1000-kcal/wk increase in adulthood activity was approximately equal to an increase of 1 MET in fitness; both conferred a survival benefit of 20%. Table 2. Age-Adjusted Predictors of Mortality among Measures of Activity and Fitness* Variable Univariate Analysis Multivariate Analysis** Hazard Ratio (95% Confidence Interval) P Value Hazard Ratio (95% Confidence Interval) P Value Exercise capacity 0.53 (0.41¨C0.69) <0.001 0.56 (0.38¨C0.83) <0.001 Recreational activity/week, lifetime 0.70 (0.56¨C0.87) <0.001 0.68 (0.49¨C0.95) 0.02 Recreational activity/week, last year 0.77 (0.61¨C0.98) 0.03 ¡ª Blocks walked/flights of stairs climbed per week 0.74 (0.55¨C0.98) 0.04 0.78 (0.56¨C1.09) 0.13 Occupational activity/week, lifetime 0.85 (0.69¨C1.06) 0.13 ¡ª Combined occupational and recreational activities/week 0.86 (0.71¨C 1.05) 0.16 ¡ª * Data are from proportional hazards analysis, categorized by quartiles. Activity data calculated from questionnaire, expressed in kcal/wk; exercise capacity is expressed in quartiles of metabolic equivalents calculated from peak treadmill speed and grade. ** Adjusted for age. In age-adjusted analyses, exercise capacity and weekly energy expenditure were stronger predictors of mortality than historical data, other exercise test responses, and risk factors such as hypertension, hyperlipidemia, diabetes, and obesity (Table 3). Each quartile increase in exercise capacity was associated with an overall 38% reduction in mortality, whereas each quartile increase in energy expenditure from weekly activity was associated with an overall 28% reduction in mortality. However, the reduction in mortality risk between quartiles was not linear; the largest reduction occurred between the least fit or least active group and the next least fit or active group, with smaller differences observed between the other groups. Table 3. Age-Adjusted Multivariate Predictors of Mortality among Clinical Variables, Fitness Level, and Physical Activity Patterns* Variable Hazard Ratio (95% Confidence Interval) P Value Fitness level Very low (reference)** 1.0 ¨C Low 0.59 (0.52¨C0.68) <0.001 Moderate 0.46 (0.39¨C0.55) <0.001 High 0.28 (0.23¨C0.34) <0.001 Activity level Sedentary (reference) 1.0 ¨C Low 0.63 (0.36¨C1.10) 0.10 Moderate 0.42 (0.23¨C0.78) <0.01 High 0.38 (0.19¨C0.73) <0.01 Cardiovascular disease 1.62 (0.96¨C2.73) 0.06 History of smoking 1.58 (0.86¨C2.87) 0.15 History of hypertension 1.32 (0.82¨C2.13) 0.31 Family history of coronary artery disease 1.22 (0.74¨C2.02) 0.50 Diabetes 1.26 (0.70¨C2.27) 0.52 Obesity 0.81 (0.47¨C1.38) 0.47 Cholesterol level >220 mg/dL 1.08 (0.61¨C1.90) 0.79 MET = metabolic equivalent. * Activity data expressed as quartiles in kcal/wk of adulthood recreational activity; exercise capacity expressed in quartiles of METs; all other variables are dichotomous. ** <5.0 METs. Kaplan-Meier survival curves applying commonly recognized indexes for exercise capacity (¡Ü5 METs vs. >5 METs for the entire referred group of 6213 subjects) and energy expenditure from physical activity (>2000 kcal/wk vs. subjects reporting no activity for the subgroup) showed that both higher exercise capacity and activity were associated with improved survival (Figure 1). Subjects with a higher exercise capacity or greater levels of activity had progressively lower mortality (P < 0.001; Figure 2). Age-adjusted interactions between fitness and activity, and the respective hazard ratios associated with being fit or active, demonstrated that being comparatively fit or active was associated with >50% reductions in mortality risk, regardless of categorization at the time of assessment (Figure 3). For example, among the least fit subjects (those achieving <5 METs), being relatively active (>1500 kcal/wk) was associated with a 68% reduction in mortality. Alternatively, among the least active subjects (those reporting no activity), being relatively fit was associated with a 55% reduction in mortality. Discussion Our results demonstrate that both exercise capacity and energy expenditure from adulthood recreational physical activity are inversely associated with all-cause mortality in patients referred for exercise testing. These two variables were stronger predictors of mortality than other clinical and exercise data; in multivariate analyses, they outperformed established risk factors such as smoking, hypertension, hyperlipidemia, and diabetes. However, exercise capacity was a stronger predictor of mortality than were measures of recent or adulthood habitual physical activity, supporting the concept that physical fitness is a stronger predictor than activity level (8). Previous studies demonstrating an association between physical activity pattern or exercise tolerance and health outcomes generally involved apparently healthy cohorts (10, 11, 12 and 13), and few such analyses have been performed in more clinically relevant populations, such as patients referred for exercise testing for clinical reasons, which was the sample we studied. The extent to which the benefits of physical activity on health and longevity are mediated through one¡¯s fitness level has been debated (8 and 9). We found a correlation of 0.09, suggesting independence of these two measures. This association is lower than that reported previously (27), in which the correlation ranged between 0.30 and 0.60. We also observed a comparatively large reduction in mortality (72%) between the most and least fit subjects. Although comparisons with previous studies are complicated by different approaches to assessing activity, classification of groups, and other methodological differences, our results contrast with those of the majority of studies demonstrating differences in mortality in the order of 50% between the most and least fit groups (28). We observed a less dramatic but nevertheless strong gradient for the reduction in mortality as physical activity increased. Indeed, fitness more strongly predicted mortality than did activity pattern as evidenced by both univariate and multivariate analyses. This concurs with a recent summary of eight fitness and 30 activity cohorts (8), in which fitness was a considerably stronger predictor of cardiovascular events. However, the strengths of the mortality gradients for fitness and activity that we observed were more similar to one another than those in most previous studies. Few studies have addressed both fitness and physical activity in the same sample with other clinical and risk factor data, although available data generally suggest that fitness level more strongly predicts outcomes compared with physical activity patterns (8 and 28). There may be several reasons why this is the case. First, the quantification of fitness is more objective than activity. Fitness is generally determined directly from symptom or sign-limited exercise testing, whereas activity level is dependent on subject recollection, as well as on the judiciousness with which subjects respond and other limitations associated with questionnaires (29). Second, the strength of exercise capacity in stratifying risk, although only recently appreciated (30 and 31), is increasingly being recognized among both healthy (2, 4, 5, 8 and 11) and clinically referred subjects (4, 6, 32, 33 and 34). For example, in recent studies performed at the Cleveland Clinic (34), the Mayo Clinic (6 and 33), and the Veterans Administration (4), exercise capacity more strongly predicted cardiovascular events, all-cause mortality, or both, than did other clinical and exercise test variables. Previous studies have observed that the dose-response relation between fitness or activity and the risk of heart disease or mortality is generally shaped such that relatively greater health benefits occur at the lower rather than higher end of the spectrum (1, 2, 8 and 11). Hence, greater health benefits would occur by increasing physical activity among the most sedentary or least fit persons. Indeed, the various consensus documents on physical activity and health generally acknowledge that ¡°the greatest potential for reduced mortality is in the sedentary who become more active¡± (35). Our findings concur with these observations. We found that approximately 40% of the reduction in total mortality occurred between the least fit or least active and the next least fit or least active groups, suggesting that levels of fitness or regular activity that are achievable by most adults are sufficient to achieve a significant reduction in mortality. The interactions that we observed between fitness or activity and mortality are provocative from a public health perspective. Being comparatively unfit was associated with a higher mortality risk even among those who were active, and being relatively inactive was associated with a higher mortality risk regardless of fitness level. No deaths were observed among subjects who were both fit (>10 METs) and active (>1500 kcal/wk). Importantly, regardless of how subjects were classified in terms of fitness or activity status, being more fit or more active was associated with a substantial reduction in mortality. Our study has several limitations. Although dose-response relations between fitness or activity and mortality have been shown to be similar between men and women (2), our sample did not include women. Our sample was comparatively small, but our data confirm results from larger studies that evaluated activity status or fitness separately (10, 11 and 23). As with any questionnaire approach, the responses were dependent on subject recollection and how attentive subjects may have been in their responses. In addition, we had information only on all-cause mortality, and not on specific causes of death. Finally, answering the question of whether fitness or physical activity is more important in terms of health outcomes by multivariate analysis necessitates that they be independent, and this could not be determined from the present study. In summary, low exercise capacity determined from exercise testing and low energy expenditure from weekly activity were associated with higher mortality risk in men, even more strongly than that of established risk markers such as smoking, hypertension, diabetes, previous myocardial infarction, or a history of heart failure. An approximate 1000-kcal/wk increase in activity, a modest amount achievable by most adults, confers a 20% survival benefit, similar to that which would occur by increasing fitness by 1 MET. Of the two measures, exercise capacity predicted mortality more strongly than did activity pattern. Being unfit carried a marked increase in risk even among persons who were comparatively active; likewise, being inactive was associated with a higher risk even among those who were relatively fit. Given the strong inverse association between fitness and mortality in the present and other recent studies, increasing fitness should be a priority when reviewing test results with patients. In addition, because physical activity in part develops physical fitness, increasing physical activity should remain an important health care policy objective. Cheers, Al Pater. Quote Link to comment Share on other sites More sharing options...
Guest guest Posted January 11, 2005 Report Share Posted January 11, 2005 Hi Al: What I would REALLY like to see is a study of the kind described below, but done on HEALTHY people. Even better, a study that included subjects with CRON-like CVD risk factor data - some of whom exercise and some who do not - to determine to what extent, if any, exercise can benefit people like us. It seems to me that the study below shows, principally, that when sick people are sent for exercise testing (which is where the subjects for this study were found) the ones who are the sickest (so sick they have been unable to exercise for quite some time so are very unfit) die the soonest. That should not come as a surprise. Indeed, I would even say that this study was a waste of time and money, trying to prove what ought to be obvious. IMO this study does not in any way demonstrate that HEALTHY people who exercise more will live longer. But I am certainly interested in, and open minded to the possibility that, exercise enables healthy people to live longer. Unfortunately, this study did not address that issue. [The study was done: " in subjects referred for exercise testing for clinical reasons " - I take that to mean that they were already sick enough to go to their doctor, with some of them, like Clinton, already 90% occluded.] We need more studies, like the WUSTL study, done on people with established CRON health test data. But I doubt we are going to see it for a long time. It seems that it is considered more appropriate to do studies on people who have never taken any interest in preserving their health (and are hardly likely to start now) than in trying to help those who already have shown they will respond positively if there is evidence they can take steps to improve their health - such as those on CRON. Makes no sense to me. But then much that goes on in the world these days makes no sense to me. Rodney. --- In , " old542000 " <apater@m...> wrote: > > Hi All, > > It seems to be based on the below that > our chance of dying from all causes is > dependent on both our fitness level > and our exercise level. > > The pdf is available for the below > > Am J Med. 2004 Dec 15;117(12):912-8. > Fitness versus physical activity patterns in predicting mortality in > men. > Myers J, Kaykha A, S, Abella J, Zaheer N, > Lear S, Yamazaki T, Froelicher V. > ... 6213 men referred for exercise testing > ... 842 ... follow-up of 5.5 +/- 2 years. RESULTS: Expressing the data > by age-adjusted quartiles, exercise capacity was a stronger predictor > of > mortality than was activity pattern (hazard ratio = 0.56; 95% > confidence > interval [CI]: 0.38 to 0.83; P < 0.001). In a multivariate analysis > that > considered clinical characteristics, risk factors, exercise test > data, and > activity patterns, exercise capacity (HR per quartile = 0.62; CI: > 0.47 to 0.82; > P < 0.001) and energy expenditure from adulthood recreational > activity (HR per > quartile = 0.72; 95% CI: 0.58 to 0.89; P = 0.002) were the only > significant > predictors of mortality; these two variables were stronger predictors > than > established risk factors such as smoking, hypertension, obesity, and > diabetes. > Age-adjusted mortality decreased per quartile increase in exercise > capacity (HR > for very low capacity = 1.0; HR for low = 0.59; HR for moderate = > 0.46; HR for > high = 0.28; P < 0.001) and physical activity (HR for very low > activity = 1.0; > HR for low = 0.63; HR for moderate = 0.42; HR for high = 0.38; P < > 0.001). A > 1000-kcal/wk increase in activity was approximately similar to a 1 > metabolic > equivalent increase in fitness; both conferred a mortality benefit of > 20%. > CONCLUSION: Exercise capacity determined from exercise testing and > energy > expenditure from weekly activity outperform other clinical and > exercise test > variables in predicting all-cause mortality. > PMID: 15629729 [PubMed - in process] > > Increasing evidence of the association between physical inactivity > and cardiovascular or all-cause mortality (1) has led health > authorities around the world to make physical activity promotion part > of broad health care policy goals (1, 2 and 3). More recent studies > (3, 4, 5 and 6) have observed strong associations between physical > fitness, measured by a maximal exercise test, and survival from > cardiovascular and noncardiovascular causes. Physical fitness is > related to physical activity patterns, and thus current physical > activity guidelines generally consider fitness a surrogate measure of > physical activity. However, other attributes, such as genetics, > subclinical disease, and behavioral and environmental factors, > determine individual fitness levels (7). There has been some recent > debate as to whether daily physical activity patterns largely > determine one¡¯s fitness level and therefore its inverse association > with mortality, or whether fitness level predicts mortality > independently from activity pattern (8 and 9). In addition, while > these issues have been studied largely in asymptomatic populations > (10, 11, 12 and 13), less is known about these associations in > patients with existing cardiovascular disease. > In the present study, we assessed all-cause mortality using > fitness measured in subjects referred for exercise testing for > clinical reasons, and quantified adulthood physical activity patterns > by questionnaire. Our objectives were to compare the independent > contributions of fitness and physical activity patterns to overall > mortality, to determine the predictive power of fitness and activity > patterns as compared with other clinical and exercise test variables, > and to assess the interaction between fitness and activity in > predicting mortality. > Results > In the convenience subgroup, the mean (¡ÀSD) follow-up period was 5.5 > ¡À 2.0 years, and the average annual mortality was 2%. A total of 1256 > deaths occurred during follow-up in the total group of subjects (n = > 6213) undergoing exercise testing; 89 occurred among those in the > subgroup who had physical activity patterns assessed. No major > complications occurred during testing, although sustained ventricular > tachycardia occurred during 1.3% of the exercise tests. In the > subgroup, 230 patients had ischemic responses to exercise: 42 had ¡Ý > 1.0-mm horizontal or downsloping ST depression, 130 had angina during > exercise, and angina was the main reason for stopping in 58. The > prevalence of these responses was similar between the total group of > subjects and the subgroup. The current sample in which exercise > testing and physical activity patterns were assessed was compared > with the remainder of the group of subjects referred for exercise > testing (n = 5371). Demographic, historical, and clinical > characteristics were generally similar, including for age and > medication use, although small differences were observed in the > prevalence of stroke, heart failure, and myocardial infarction (Table > 1). > Table 1. > Comparison of Demographic and Clinical Characteristics of the > Study Sample and the Larger Patient Group Referred for Exercise > Testing during the Study Period Characteristic Study Sample (n = 842) > Other Referrals (n = 5371) P Value > Mean ¡À SD or Number (%) > Age (years) 58.9 ¡À 11.7 58.9 ¡À 11.2 0.96 > Duration of follow-up (years) 4.6 ¡À 1.8 6.9 ¡À 4.0 <0.001 > Height (inches) 69.0 ¡À 3.4 69.2 ¡À 3.2 0.30 > Weight (lbs) 193.4 ¡À 37.6 189.8 ¡À 37.6 0.01 > Medications > Digoxin 23 (3) 321 (6) <0.001 > Calcium antagonist 221 (26) 1519 (28) 0.48 > Beta-blocker 147 (18) 1048 (20) 0.32 > Nitrate 142 (17) 1344 (25) <0.001 > Antihypertensive 185 (22) 1343 (25) 0.15 > History > Atrial fibrillation 31 (4) 813 (15) 0.29 > Pulmonary disease 75 (9) 367 (7) 0.02 > Stroke 43 (5) 185 (3) 0.01 > Claudication 37 (4) 304 (6) 0.19 > Typical angina 141 (17) 1213 (23) 0.04 > Myocardial infarction 204 (24) 1658 (31) <0.001 > Heart failure 46 (6) 481 (9.0) 0.001 > Coronary bypass surgery 74 (8.8) 507 (9.4) 0.75 > Percutaneous coronary intervention 68 (8) 324 (6) 0.01 > Physical activity versus physical fitness in predicting > mortality > Physical fitness was poorly related to energy expenditure from > adulthood physical activity (r = 0.09). Age-adjusted univariate > predictors of mortality, in rank order, were peak exercise capacity, > recreational energy expenditure during adulthood, recreational energy > expenditure over the last year, and energy expended from blocks > walked and flights of stairs climbed per week (Table 2). Although > energy expended from occupational activity (P = 0.13) and the > combination of occupational and recreational activity (P = 0.16) were > associated with approximately 15% reductions in mortality per > quartile increase, they did not appear to be strong predictors of > survival. Age-adjusted multivariate predictors of survival, in rank > order, were exercise capacity, followed by energy expenditure from > recreational activity during adulthood. Past occupational energy > expenditure was not associated with survival by multivariate > analysis. A 1000-kcal/wk increase in adulthood activity was > approximately equal to an increase of 1 MET in fitness; both > conferred a survival benefit of 20%. > Table 2. > Age-Adjusted Predictors of Mortality among Measures of Activity > and Fitness* Variable Univariate Analysis Multivariate Analysis** > Hazard Ratio (95% Confidence Interval) P Value Hazard > Ratio (95% Confidence Interval) P Value > Exercise capacity 0.53 (0.41¨C0.69) <0.001 0.56 (0.38¨C0.83) <0.001 > Recreational activity/week, lifetime 0.70 (0.56¨C0.87) <0.001 0.68 > (0.49¨C0.95) 0.02 > Recreational activity/week, last year 0.77 (0.61¨C0.98) 0.03 ¡ª > Blocks walked/flights of stairs climbed per week 0.74 (0.55¨C0.98) > 0.04 0.78 (0.56¨C1.09) 0.13 > Occupational activity/week, lifetime 0.85 (0.69¨C1.06) 0.13 ¡ª > Combined occupational and recreational activities/week 0.86 (0.71¨C > 1.05) 0.16 ¡ª > * Data are from proportional hazards analysis, categorized by > quartiles. Activity data calculated from questionnaire, expressed in > kcal/wk; exercise capacity is expressed in quartiles of metabolic > equivalents calculated from peak treadmill speed and grade. > ** Adjusted for age. > In age-adjusted analyses, exercise capacity and weekly energy > expenditure were stronger predictors of mortality than historical > data, other exercise test responses, and risk factors such as > hypertension, hyperlipidemia, diabetes, and obesity (Table 3). Each > quartile increase in exercise capacity was associated with an overall > 38% reduction in mortality, whereas each quartile increase in energy > expenditure from weekly activity was associated with an overall 28% > reduction in mortality. However, the reduction in mortality risk > between quartiles was not linear; the largest reduction occurred > between the least fit or least active group and the next least fit or > active group, with smaller differences observed between the other > groups. > Table 3. > Age-Adjusted Multivariate Predictors of Mortality among Clinical > Variables, Fitness Level, and Physical Activity Patterns* Variable > Hazard Ratio (95% Confidence Interval) P Value > Fitness level > Very low (reference)** 1.0 ¨C > Low 0.59 (0.52¨C0.68) <0.001 > Moderate 0.46 (0.39¨C0.55) <0.001 > High 0.28 (0.23¨C0.34) <0.001 > Activity level > Sedentary (reference) 1.0 ¨C > Low 0.63 (0.36¨C1.10) 0.10 > Moderate 0.42 (0.23¨C0.78) <0.01 > High 0.38 (0.19¨C0.73) <0.01 > Cardiovascular disease 1.62 (0.96¨C2.73) 0.06 > History of smoking 1.58 (0.86¨C2.87) 0.15 > History of hypertension 1.32 (0.82¨C2.13) 0.31 > Family history of coronary artery disease 1.22 (0.74¨C2.02) 0.50 > Diabetes 1.26 (0.70¨C2.27) 0.52 > Obesity 0.81 (0.47¨C1.38) 0.47 > Cholesterol level >220 mg/dL 1.08 (0.61¨C1.90) 0.79 > MET = metabolic equivalent. > * Activity data expressed as quartiles in kcal/wk of adulthood > recreational activity; exercise capacity expressed in quartiles of > METs; all other variables are dichotomous. > ** <5.0 METs. > Kaplan-Meier survival curves applying commonly recognized indexes > for exercise capacity (¡Ü5 METs vs. >5 METs for the entire referred > group of 6213 subjects) and energy expenditure from physical activity > (>2000 kcal/wk vs. subjects reporting no activity for the subgroup) > showed that both higher exercise capacity and activity were > associated with improved survival (Figure 1). Subjects with a higher > exercise capacity or greater levels of activity had progressively > lower mortality (P < 0.001; Figure 2). Age-adjusted interactions > between fitness and activity, and the respective hazard ratios > associated with being fit or active, demonstrated that being > comparatively fit or active was associated with >50% reductions in > mortality risk, regardless of categorization at the time of > assessment (Figure 3). For example, among the least fit subjects > (those achieving <5 METs), being relatively active (>1500 kcal/wk) > was associated with a 68% reduction in mortality. Alternatively, > among the least active subjects (those reporting no activity), being > relatively fit was associated with a 55% reduction in mortality. > Discussion > Our results demonstrate that both exercise capacity and energy > expenditure from adulthood recreational physical activity are > inversely associated with all-cause mortality in patients referred > for exercise testing. These two variables were stronger predictors of > mortality than other clinical and exercise data; in multivariate > analyses, they outperformed established risk factors such as smoking, > hypertension, hyperlipidemia, and diabetes. However, exercise > capacity was a stronger predictor of mortality than were measures of > recent or adulthood habitual physical activity, supporting the > concept that physical fitness is a stronger predictor than activity > level (8). Previous studies demonstrating an association between > physical activity pattern or exercise tolerance and health outcomes > generally involved apparently healthy cohorts (10, 11, 12 and 13), > and few such analyses have been performed in more clinically relevant > populations, such as patients referred for exercise testing for > clinical reasons, which was the sample we studied. > The extent to which the benefits of physical activity on health > and longevity are mediated through one¡¯s fitness level has been > debated (8 and 9). We found a correlation of 0.09, suggesting > independence of these two measures. This association is lower than > that reported previously (27), in which the correlation ranged > between 0.30 and 0.60. We also observed a comparatively large > reduction in mortality (72%) between the most and least fit subjects. > Although comparisons with previous studies are complicated by > different approaches to assessing activity, classification of groups, > and other methodological differences, our results contrast with those > of the majority of studies demonstrating differences in mortality in > the order of 50% between the most and least fit groups (28). > We observed a less dramatic but nevertheless strong gradient for > the reduction in mortality as physical activity increased. Indeed, > fitness more strongly predicted mortality than did activity pattern > as evidenced by both univariate and multivariate analyses. This > concurs with a recent summary of eight fitness and 30 activity > cohorts (8), in which fitness was a considerably stronger predictor > of cardiovascular events. However, the strengths of the mortality > gradients for fitness and activity that we observed were more similar > to one another than those in most previous studies. > Few studies have addressed both fitness and physical activity in > the same sample with other clinical and risk factor data, although > available data generally suggest that fitness level more strongly > predicts outcomes compared with physical activity patterns (8 and > 28). There may be several reasons why this is the case. First, the > quantification of fitness is more objective than activity. Fitness is > generally determined directly from symptom or sign-limited exercise > testing, whereas activity level is dependent on subject recollection, > as well as on the judiciousness with which subjects respond and other > limitations associated with questionnaires (29). Second, the strength > of exercise capacity in stratifying risk, although only recently > appreciated (30 and 31), is increasingly being recognized among both > healthy (2, 4, 5, 8 and 11) and clinically referred subjects (4, 6, > 32, 33 and 34). For example, in recent studies performed at the > Cleveland Clinic (34), the Mayo Clinic (6 and 33), and the Veterans > Administration (4), exercise capacity more strongly predicted > cardiovascular events, all-cause mortality, or both, than did other > clinical and exercise test variables. > Previous studies have observed that the dose-response relation > between fitness or activity and the risk of heart disease or > mortality is generally shaped such that relatively greater health > benefits occur at the lower rather than higher end of the spectrum > (1, 2, 8 and 11). Hence, greater health benefits would occur by > increasing physical activity among the most sedentary or least fit > persons. Indeed, the various consensus documents on physical activity > and health generally acknowledge that ¡°the greatest potential for > reduced mortality is in the sedentary who become more active¡± (35). > Our findings concur with these observations. We found that > approximately 40% of the reduction in total mortality occurred > between the least fit or least active and the next least fit or least > active groups, suggesting that levels of fitness or regular activity > that are achievable by most adults are sufficient to achieve a > significant reduction in mortality. > The interactions that we observed between fitness or activity and > mortality are provocative from a public health perspective. Being > comparatively unfit was associated with a higher mortality risk even > among those who were active, and being relatively inactive was > associated with a higher mortality risk regardless of fitness level. > No deaths were observed among subjects who were both fit (>10 METs) > and active (>1500 kcal/wk). Importantly, regardless of how subjects > were classified in terms of fitness or activity status, being more > fit or more active was associated with a substantial reduction in > mortality. > Our study has several limitations. Although dose-response > relations between fitness or activity and mortality have been shown > to be similar between men and women (2), our sample did not include > women. Our sample was comparatively small, but our data confirm > results from larger studies that evaluated activity status or fitness > separately (10, 11 and 23). As with any questionnaire approach, the > responses were dependent on subject recollection and how attentive > subjects may have been in their responses. In addition, we had > information only on all-cause mortality, and not on specific causes > of death. Finally, answering the question of whether fitness or > physical activity is more important in terms of health outcomes by > multivariate analysis necessitates that they be independent, and this > could not be determined from the present study. > In summary, low exercise capacity determined from exercise testing > and low energy expenditure from weekly activity were associated with > higher mortality risk in men, even more strongly than that of > established risk markers such as smoking, hypertension, diabetes, > previous myocardial infarction, or a history of heart failure. An > approximate 1000-kcal/wk increase in activity, a modest amount > achievable by most adults, confers a 20% survival benefit, similar to > that which would occur by increasing fitness by 1 MET. Of the two > measures, exercise capacity predicted mortality more strongly than > did activity pattern. Being unfit carried a marked increase in risk > even among persons who were comparatively active; likewise, being > inactive was associated with a higher risk even among those who were > relatively fit. Given the strong inverse association between fitness > and mortality in the present and other recent studies, increasing > fitness should be a priority when reviewing test results with > patients. In addition, because physical activity in part develops > physical fitness, increasing physical activity should remain an > important health care policy objective. > > Cheers, Al Pater. Quote Link to comment Share on other sites More sharing options...
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