The relationship between moderate to vigorous physical activity and metabolic syndrome: a Bayesian measurement error approach

Daniel Ries and Alicia Carriquiry

Journal of Applied Statistics, 2023, vol. 50, issue 10, 2246-2266

Abstract: Metabolic Syndrome (MetS) is a serious condition that can be an early warning sign of heart disease and Type 2 diabetes. MetS is characterized by having elevated levels of blood pressure, cholesterol, waist circumference, and fasting glucose. There are many articles in the literature exploring the relationship between physical activity and MetS, but most do not consider the measurement error in the physical activity measurements nor the correlations among the MetS risk factors. Furthermore, previous work has generally treated MetS as binary, rather than directly modeling the risk factors on their measured, continuous space. Using data from the National Health and Nutrition Examination Survey (NHANES), we explore the relationship between minutes of moderate to vigorous physical activity (MVPA) and MetS risk factors. We construct a measurement error model for the accelerometry data, and then model its relationship between MetS risk factors with nonlinear seemingly unrelated regressions, incorporating dependence among MetS risk factors. The novel features of this model give the medical research community a new way to understand relationships between MVPA and MetS. The results of this approach present the field with a different modeling perspective than previously taken and suggest future avenues of scientific discovery.

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:taf:japsta:v:50:y:2023:i:10:p:2246-2266

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DOI: 10.1080/02664763.2022.2073336

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