Approaches to Improve Causal Inference in Physical Activity Epidemiology
in Journal of Physical Activity and HealthClick name to view affiliation
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Background: 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. Methods: 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. Results: 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. Conclusions: 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.
Lynch, Dixon-Suen, Yang, and English are with the Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia. Lynch, Yang, and English are also with the Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia. Lynch is also with the Physical Activity Laboratory, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia. Ramirez Varela is with the Postgraduate Program in Epidemiology, Federal University of Pelotas, Pelotas, Brazil; and the Postgraduate Program in Epidemiology, University of Los Andes, Bogota, Colombia. Ding is with the Prevention Research Collaboration, Sydney School of Public Health, University of Sydney, Sydney, NSW, Australia; and The Charles Perkins Centre, University of Sydney, Sydney, NSW, Australia. Gardiner is with the Centre for Health Services Research, The University of Queensland, Brisbane, QLD, Australia; and Mater Research Institute, The University of Queensland, Brisbane, QLD, Australia. Boyle is with the Australian Centre for Precision Health, School of Health Sciences, University of South Australia, Adelaide, SA, Australia.
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