Approaches to improve causal inference in physical activity epidemiology

Brigid M. Lynch, Suzanne C. Dixon-Suen, Andrea Ramirez Varela, Yi Yang, Dallas R. English, Ding Ding, Paul A. Gardiner, Terry Boyle

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

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.

LanguageEnglish
Pages80-84
Number of pages5
JournalJournal of Physical Activity and Health
Volume17
Issue number1
DOIs
Publication statusPublished - 1 Jan 2020

Keywords

  • Biostatistics
  • Causal inference
  • Methods
  • Potential outcomes approach

ASJC Scopus subject areas

  • Orthopedics and Sports Medicine

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