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Peer-reviewedCC BYPLOS One

Allostatic (over) load measurement: Workflow and repository

  • Shawna Beese1,2
  • Jason Cross1,2
  • David Rice3
  • Trey L DeJong2,3
  1. 1College of Agricultural, Human, and Natural Resources Sciences, Washington State University, Pullman, Washington, United States of America.
  2. 2CHORDS Lab, Washington State University, Bellingham, Washington, United States of America.
  3. 3Center for Interdisciplinary Statistical Education and Research (CISER), Washington State University, Pullman, Washington, United States of America.
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Abstract

Researchers have long studied allostatic (over)load as an estimated measure of individual cumulative stress over a lifetime. Often called the overall ‘wear and tear’ from social and environmental stressors, allostatic (over)load shows promise as a practical indicator of general health trends in community settings. This data processing workflow aims to document our overall approach and reasoning when calculating allostatic (over)load for data analysis and knowledge sharing. The included repository features an R script for generating datasets using this workflow from the following data sources: All of Us Research Program data repository. Health and Retirement Study (HRS). National Health and Nutrition Examination Survey (NHANES). Our allostatic (over)load measurement process, along with the linked repository, provides a reproducible workflow to process secondary data and offers insights into protocol-driven measurement practices in community environments.

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Cite

BibTeXRIS
BibTeX
@article{beese2026allostatic,
  title = {{Allostatic (over) load measurement: Workflow and repository}},
  author = {Shawna Beese and Jason Cross and David Rice and Trey L DeJong},
  journal = {PLOS One},
  year = {2026},
  month = oct,
  doi = {10.1371/journal.pone.0360639},
  url = {https://doi.org/10.1371/journal.pone.0360639},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - Allostatic (over) load measurement: Workflow and repository
AU  - Shawna Beese
AU  - Jason Cross
AU  - David Rice
AU  - Trey L DeJong
PY  - 2026
DA  - 2026/10/21
JO  - PLOS One
DO  - 10.1371/journal.pone.0360639
UR  - https://doi.org/10.1371/journal.pone.0360639
AB  - Researchers have long studied allostatic (over)load as an estimated measure of individual cumulative stress over a lifetime. Often called the overall ‘wear and tear’ from social and environmental stressors, allostatic (over)load shows promise as a practical indicator of general health trends in community settings. This data processing workflow aims to document our overall approach and reasoning when calculating allostatic (over)load for data analysis and knowledge sharing. The included repository features an R script for generating datasets using this workflow from the following data sources: All of Us Research Program data repository. Health and Retirement Study (HRS). National Health and Nutrition Examination Survey (NHANES). Our allostatic (over)load measurement process, along with the linked repository, provides a reproducible workflow to process secondary data and offers insights into protocol-driven measurement practices in community environments.
SN  - 1932-6203
ER  -