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Terry Boyle, Jane Heyworth, Fiona C. Bull and Lin Fritschi

Background:

One of the convenient ways to achieve recommended levels of physical activity is through ‘active transport,’ such as walking or cycling to and from work or school. Although studies have shown that participants can reliably recall information about recent transport-related physical activity, it is not known if the reliability remains high when asking about lifetime behavior. This study tested the reliability of questions that collect information about transport-related physical activity performed over the lifetime.

Methods:

Participants were asked to complete self-administered questions about transport-related physical activity on 2 separate occasions. The questions asked about cycling and walking to and from work and/or school during 3 age periods: 15−24 years, 25−39 years, and 40 years and above. A lifetime average was also calculated for cycling, walking, and total activity.

Results:

There was fair to good test-retest reliability of the age-period specific questions for transport-related cycling (ICCs from 0.65−0.74), walking (ICCs from 0.44−0.58), and total activity (ICCs from 0.57−0.66). The reliability of the lifetime averages were also fair to good (ICCs from 0.58−0.70).

Conclusions:

The questions tested in this study have moderate reliability, and appear to be useful questions for measuring lifetime transport-related physical activity.

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Brigid M. Lynch, Andrea Ramirez Varela and Terry Boyle

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

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.