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Physical activity and longevity: how to move closer to causal inference
  1. Kaitlin H Wade,
  2. Rebecca C Richmond,
  3. George Davey Smith
  1. Medical Research Council Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
  1. Correspondence to Dr Kaitlin H Wade, Integrative Epidemiology Unit, University of Bristol, Bristol BS8 2BN, UK; Kaitlin.Wade{at}bristol.ac.uk

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Kujala provides an insightful review contesting epidemiological findings that increased physical activity (PA) lengthens the life span,1 arguing that intervention (randomised controlled trial (RCT) and experimental) studies do not support PA causing a reduced risk of death and highlighting several limitations in previous observational studies that may have led to spurious conclusions.

The review coincides with the publication of findings from the large-scale Prospective Urban Rural Epidemiologic (PURE) study (n=130 843), which identified a graded lower rate of mortality among those individuals achieving moderate and high levels of PA compared with those with low PA (HR 0.80; 95% CI 0.74 to 0.87 and 0.65; 95% CI 0.60 to 0.70; P for trend <0.0001).2 While this study is undeniably an impressive endeavour, collecting prospective data on participants from 17 countries, the conclusion to support increased forms of PA levels for all individuals (irrespective of age, gender or country of origin) has major public health implications. The findings are, as so often, qualified by the study, being unable to fully assert a causal (rather than correlational) role for PA levels in reducing mortality.

Kujala emphasises how 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 the exposure (here, PA) and outcome (here, longevity). If not appropriately accounted for, confounding factors make the ascertainment of underlying causal mechanisms and pathways exceptionally complex. Such was illustrated by Jerry Morris’ London busmen study revisited by Kujala, where confounding by baseline adiposity biased findings that bus conductors had lower risk of coronary heart disease than their less-active driver counterparts …

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