AI Article Synopsis

  • Compositional data analysis (CoDA) helps researchers look at how different daily activities like sleeping, sitting, and exercising are connected and affect health, which has become more popular over the last ten years.
  • A review of studies using CoDA found that while many did a good job explaining their methods, they were inconsistent in sharing other important details.
  • Out of 102 studies examined, things like how they measured activities and reported finding were done differently, showing a need for better and clearer reporting practices.

Article Abstract

Background: Compositional data analysis (CoDA) techniques are well suited for examining associations between 24-h movement behaviors (i.e., sleep, sedentary behavior, physical activity) and indicators of health given they recognize these behaviors are co-dependent, representing relative parts that make up a whole day. Accordingly, CoDA techniques have seen increased adoption in the past decade, however, heterogeneity in research reporting practices may hinder efforts to synthesize and quantify these relationships via meta-analysis. This systematic review described reporting practices in studies that used CoDA techniques to investigate associations between 24-h movement behaviors and indicators of health.

Methods: A systematic search of eight databases was conducted, in addition to supplementary searches (e.g., forward/backward citations, expert consultation). Observational studies that used CoDA techniques involving log-ratio transformation of behavioral data to examine associations between time-based estimates of 24-h movement behaviors and indicators of health were included. Reporting practices were extracted and classified into seven areas: (1) methodological justification, (2) behavioral measurement and data handling strategies, (3) composition construction, (4) analytic plan, (5) composition-specific descriptive statistics, (6) model results, and (7) auxiliary information. Study quality and risk of bias were assessed by the National Institutes of Health Quality Assessment Tool for Observational Cohort and Cross-sectional Studies.

Results: 102 studies met our inclusion criteria. Reporting practices varied considerably across areas, with most achieving high standards in methodological justification, but inconsistent reporting across all other domains. Some items were reported in all studies (e.g., how many parts the daily composition was partitioned into), whereas others seldom reported (e.g., definition of a day: midnight-to-midnight versus wake-to-wake). Study quality and risk of bias was fair in most studies (85%).

Conclusions: Current studies generally demonstrate inconsistent reporting practices. Consistent, clear and detailed reporting practices are evidently needed moving forward as the field of time-use epidemiology aims to accurately capture and analyze movement behavior data in relation to health outcomes, facilitate comparisons across studies, and inform public health interventions and policy decisions. Achieving consensus regarding reporting recommendations is a key next step.

Supplementary Information: The online version contains supplementary material available at 10.1186/s44167-024-00062-8.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11446952PMC
http://dx.doi.org/10.1186/s44167-024-00062-8DOI Listing

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