EXERCISE AND ALL CAUSE MORTALITY

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EXERCISE AND ALL CAUSE MORTALITY All-cause mortality is the documentation of any and all causes of death. The top ten causes of death in the US is illustrated to the right. Cardiovascular disease leads the list. Cancer is a close second. Pulmonary disease and diabetes also top the list. A common underlying disease for all of these diseases is obesity. Morbidity is the incidence of the disease whereas mortality is death from the disease. Epidemiologic research in physical activity and heart disease began in the late 1940's. A significant inverse relationship was found between physical activity and risk of coronary heart disease and the associated risk factors. These findings were consistent and strong. Recently, the epidemiologic research has been expanded to observe more modern chronic disease. Study Study Design Duration Measurement of Physical Activity London Postal Workers Clerks vs. Delivery 1948-1950 London Transport Busmen Morris, J. N . , Heady , J. A . , Raffle, P . A . B . , Roberts, C . G . , Parks, J . W. 1 953. Coronary heart disease and physical activity of work. Lancet 2 : 1 053-57, 1 1 1 1 -20 Drivers vs. Ticket Takers 1949-1958 Uniform size Los Angeles Civil Servants Chapman, 1. M . , Goerke, L. S . , Dixon, W . , Loveland, D. B . , Phillips, E . , 1957. The clinical status of a population group in Los Angeles under observation for two to three years. Am. J. Public Health 47:33-42 Occupational activity 1949-1954 Job title: National Office of Vital Statistics Sedentary Light Medium Heavy Doubtful US Railroad Workers Taylor, H . L., Kelpetar, E . , Keys, A . , Parlin, W . , B lackburn, H . , e t al. 1962. Death rates among physically active and sedentary employees of the railroad industry. Am. Public Health 52: 1697- 1707 Clerks vs. Switchmen vs. Section men 1954-1956 Framingham MA Kannel , W. B. 1 978. Recent findings of the Framingham Study. Resident Staff Phys. 24:56-7 1 City Population 1948-present Physical Activity Index: 24 Hour Report Sleeping (1 MET) Sitting or standing (1.1 METs) Walking (1.5 METs) Gardenting (2.4 METs) Heavy work (5 METs) Longshoremen Paffenbarger, R . S. Jr. , Hale, W. E . , Brand, R . J . , Hyde, R. T. 1977. Workenergy level, personal characteristics, and fatal heart attack: A birth-cohort effect. Am. J. Epidemiol. 1 05:200- 1 3 Occupational Activity 1951-1972 Weekly averages Number of city blocks walked Number of stairs climbed Minutes in sports Harvard Alumni Paffenbarger, R . S. Jr. , Wing, A. L. , Hyde, R. T. 1 978. Physical activity as an index of heart attack risk in college alumni. Am. J. Epidemiol. 108: 161- 75 Athletes vs. non-athletes 1962-1978 Powell, K.E., P.D. Thompson, C.J. Caspersen and J.S. Kendrick. Physical activity and the incidence of coronary heart disease. Ann Rev Public Health, 8:253-287, 1987. If the relationship between risk and activity were graphed for these epidemiological studies, the relationships would be similar to those plotted to the left. That is, the largest change in risk is found between the sedentary to some activity. One of the more classic series of studies was carried out by Blair and associates from the Cooper Institute in Dallas Texas, USA. The first study was reported in 1989. Blair and colleagues observed the relationship between physical fitness, measured by time on a treadmill test, and all-cause mortality in a group of 10,244 men and 3,120 women who visited the Cooper Clinic for fitness evaluations. They followed these people over an eight-year period and found the causes of death for 240 men and 43 women who died during the eight-year period. Blair, S.N. et al, Physical fitness and all-cause mortality: A prospective study of healthy men and women. JAMA 626:2395-2401, 1989 Each lifestyle variable was classified in normal, borderline or high categories. The criteria for the high categories are listed to the right. They classified physical fitness into five categories. The lowest being a "one" and the highest being a "five". High Categories of the Lifestyle Variables and Risk Factors Cholesterol 240 mg/dl Systolic Blood Pressure > 140 mmHg Body Mass Index >27.2 kg/m2 Smoking Family History Glucose >140 mg/dl These relationships were plotted in three dimensional graphs, as illustrated on the right. The X axisof the graphs is the fitness categories from one to five. However, they were condensed into three fitness categories, 1, 2-3, and 4-5. The Y axis is the relative risk. The higher the risk, the more likely the disease manifests. The Z axis is the lifestyle variable, the yellow being the lowest, the pink being the borderline, and the blue being the highest. The findings were consistent for each lifestyle variable. As expected, the risk of death increased for each lifestyle variable as it progressed from normal to borderline to high (looking back, parallel to the Z axis). However, the risk within each lifestyle variable decreased as physical fitness increased (looking across, parallel to the X axis). The lowest risk being in the highest fitness category for the "normal" lifestyle group and the highest risk being in the lowest fitness category for the "high" lifestyle group.

Thus, low fitness appears to be associated with increased mortality and morbidity. As it turns out, the low fitness category was just as strong as the other lifestyle factors that had previously been associated with all cause mortality. That is, physical inactivity appeared to be just as strong as high cholesterol, high blood pressure, smoking, family history, blood glucose, and body mass index, in all cause mortality. The most significant decrease in risk of disease was found between fitness quintiles one and two. This has been interpreted as very little change in physical fitness can make a significant reduction in risk of disease. Many of the public policies on physical activity for the general public have been based on these findings. So, if we can encourage the public to move from fitness quintile #1 to fitness quintile #2 we will make a significant change in the health of this country. This change represents a change in max METS from <6 to >7 METS. In summary, the important findings were: Physical inactivity is directly related to disease The risk of physical inactivity is just as strong as High Cholesterol High Blood Pressure Smoking Diabetes Family History A small increase in physical activity should make a significant reduction in risk These findings were consistent with other epidemiologic studies, despite how physical activity was measured. However, the "selection vs protection" argument still can be made with any of these data. Was it just a coincidence that the low risk people were healthy enough to exercise or did physical activity really protect them from disease? To solve the selection vs protection issue, the question becomes, What happens to the risk when you increase your fitness? Will an increase in fitness result in a reciprocal decrease risk? In an attempt to answer this question, Blair and colleagues observed the data in a different manor. They found that 9777 men who had been measured twice during the course of five years. These men were divided into four categories: Blair, SN, et al. Changes in Physical Fitness and All-Cause Mortality. JAMA 273:1093-1098, 1995 those who were unfit for both test periods (un-un) those who were unfit at the first, but fit at the second (un-fit) those who were fit at the first, but unfit at the second (fit-un) those who were fit at both test periods (fit-fit) Needless to say, they found that those who remained unfit still exhibited high risk of disease (Un-Un) those who became fit decreased their risk to 56% of prior risk (Un-Fit) those who became unfit exhibited low risk of 52% of the high-risk group (Fit-Un) those who maintained their fitness, exhibited the lowest risk at 33% of the high-risk group (Fit-Fit) Paffenbarger and colleagues performed the same type of evaluation with the classic Harvard Alumni and Longshoremen Studies. They classified subjects as Sedentary (Sed) and Active (Act) instead of fit and unfit. They reported similar findings to Blair and colleagues, except for the group that changed from being active to sedentary (Act-Sed). Unlike Blair's group (Fit-Un), this group increased their risk of disease above the group that had remained sedentary (Sed-Sed or Un-Un). Blair and Paffenbarger measured different physical activity variables. Blair measured fitness whereas Paffenbarger measured physical activity. Thus, changing fitness categories from unfit to fit or moving from sedentary to active, apparently reduces the risk of all-cause mortality. The epidemiological research in physical activity does not appear to reflect the selection vs protection effect.

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