The joint effects of mixture exposure to multiple meteorological factors on step count: A panel study in China.

ABSTRACT: The public health burden of increasing extreme weather events has been well documented. However, the influence of meteorological factors on physical activity remains limited. Existing mixture effect methods cannot handle cumulative lag effects. Therefore, we developed quantile g-computation Distributed lag non-linear model (QG-DLNM) by embedding a DLNM into quantile g-computation to allow for the concurrent consideration of both cumulated lag effects and mixture effects. We gathered repeated measurement data from Henan Province in China to investigate both the individual impact of meteorological factor on step counts using a DLNM, and the joint effect using the QG-DLNM. We projected future step counts linked to changes in temperature and relative humidity driven by climate change under thr

SUBMITTER: Lin Z 

PROVIDER: S-EPMC12323698 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature