Approaches to Improve Causal Inference in Physical Activity Epidemiology

It is not always clear whether physical activity is causally related to health outcomes, or whether the associations are induced through confounding or other biases. Randomized controlled trials of physical activity are not feasible when outcomes of interest are rare or develop over many years. Thus...

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Bibliographic Details
Published in:Journal of physical activity & health 2020-01, Vol.17 (1), p.80-84
Main Authors: Lynch, Brigid M, Dixon-Suen, Suzanne C, Ramirez Varela, Andrea, Yang, Yi, English, Dallas R, Ding, Ding, Gardiner, Paul A, Boyle, Terry
Format: Article
Language:English
Subjects:
Epidemiology
Exercise
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Summary:It is not always clear whether physical activity is causally related to health outcomes, or whether the associations are induced through confounding or other biases. Randomized controlled trials of physical activity are not feasible when outcomes of interest are rare or develop over many years. Thus, we need methods to improve causal inference in observational physical activity studies. We outline a range of approaches that can improve causal inference in observational physical activity research, and also discuss the impact of measurement error on results and methods to minimize this. Key concepts and methods described include directed acyclic graphs, quantitative bias analysis, Mendelian randomization, and potential outcomes approaches which include propensity scores, g methods, and causal mediation. We provide a brief overview of some contemporary epidemiological methods that are beginning to be used in physical activity research. Adoption of these methods will help build a stronger body of evidence for the health benefits of physical activity.
ISSN:1543-3080
1543-5474
DOI:10.1123/jpah.2019-0515