Methods for strengthening causal inference in physical activity epidemiology studies
| Method | Example | Strength | Limitation |
| Lifecourse approach | Use of prospective studies to investigate PA levels at different ages and how they might differently affect lifespan | Useful for assessing temporal associations; ability to adjust for the respective outcome measures at baseline (where possible) makes it possible to disentangle prospective associations from tracking effects | Logistically demanding as it requires repeat assessments; residual confounding; measurement error in exposure, outcome and covariables; selection bias |
| Cross-context comparison | Comparison of associations between voluntary leisure-time PA and compulsory occupational PA; PA across different cultures or dissimilar countries | Exploring residual confounding; reliable findings if estimates are similar across different contexts (where the confounding structure in these settings is likely to differ) | Assumptions about different confounding structures may not be correct; variables in different studies might be measured with varying accuracy and generalisability |
| Sibling comparison | MZ or DZ twin comparisons among siblings discordant for PA | Using MZ best controls for familial background and genetic confounding, compared with DZ (or siblings), where 50% of genetic information is shared | Assumes a stable family environment; confounding by factors not perfectly shared by siblings; reverse causation still possible |
| Mendelian randomisation | The use of genetic variants associated with exercise and fitness, incorporated into a Mendelian randomisation analysis, whereby genotype serves as an instrumental variable for PA | Genetic instruments are not subject to confounding from environmental or lifestyle factors, are not influenced by the outcome, do not change over time and are measured with high accuracy | Low power; lack of instruments; pleiotropy and linkage disequlibrium; population stratification; canalisation |
| Objective measures of the exposure and biomarkers | The use of objective measures of PA (eg, accelerometry data), fitness (eg, VO2 max) or the incorporation of other biomarkers (eg, DNA methylation) | More precise measurement of underlying risk factor reduces measurement error in documented PA and problems with regression dilution bias; biomarkers may serve as surrogate endpoints in PA trials and longitudinal studies without long-term follow-up | Does not evade problems of confounding or reverse causation |
DZ, dizygotic; MZ, monozygotic; PA, physical activity.