It is straightforward enough to prove that exercise extends healthy (but not average or overall) life span in studies of mice. It is far less straightforward to demonstrate that same proof in human epidemiological data. We can't put humans into carefully controlled groups stratified by life-long differences in exercise and follow them from birth to death, as is the case for mice. As a consequence, near all studies of physical activity and longevity produce only correlations, as there is no practical way to derive causation given the data to hand. It is felt that these correlations likely reflect causation because of the extensive animal studies and the essential similarities of biochemistry between the mammalian species involved, but that isn't the same thing as a rigorous determination. The editorial here is a discussion of this point; the authors look at the limitations and challenges that face any attempt to generate better evidence in support of the generally accepted proposition that exercise causes extended healthy life span in humans.
While epidemiological findings show that increased physical activity (PA) lengthens the life span, it has been argued that intervention studies do not support PA causing a reduced risk of death, and that limitations in previous observational studies may have led to spurious conclusions. This coincides with the publication of findings from the large-scale Prospective Urban Rural Epidemiologic (PURE) study of 130,843 participants, which identified a graded lower rate of mortality among those individuals achieving moderate and high levels of PA compared with those with low PA. While this study is undeniably an impressive endeavour, collecting prospective data on participants from 17 countries, the findings are, as so often, unable to fully assert a causal (rather than correlational) role for PA levels in reducing mortality.
Epidemiological study designs are vulnerable to limitations that may skew or distort observational associations and impede the distinction between correlation and causation. Such distortions of observed relationships may arise due to confounding by measured/unmeasured lifestyle, behavioural, and biological factors (such as higher fitness, lower body mass index (BMI), genetic variation, and socioeconomic factors) correlated with both PA and longevity. If not appropriately accounted for, confounding factors make the ascertainment of underlying causal mechanisms and pathways exceptionally complex. Such was illustrated by the noted London busmen study, where confounding by baseline adiposity biased findings that bus conductors had lower risk of coronary heart disease than their less-active driver counterparts.
The possibility of reverse causation may also lead to misinterpretation of observed associations. For example, the notion that reducing PA increases the risk of becoming overweight/obese is as plausible as the reverse, where being overweight/obese renders PA difficult. Studies of older adults or those with many comorbidities are particularly vulnerable to reverse causation. For example, aged individuals who are healthy enough to participate in PA due to a lack of chronic illness will seemingly have a reduced risk of death compared with their less-fit peers. Furthermore, comparing estimates of risk for physically demanding versus sedentary occupations may suffer reverse causation, particularly when high fitness and good health are criteria for recruitment into such physically demanding occupations.
Related to this, in the setting of evaluating potential causes of mortality, both selection and survival biases, which influence participation rates in epidemiological studies, can also lead to distortion of associations among respondents. In these cases, the population under study (and therefore the observed associations) may differ from the population not selected or who were unable/unwilling to participate (due to morbidity or lack of interest in surveys relating to health).