Spatiotemporal light exposure modeling for environmental circadian misalignment and solar jetlag

Author:

VoPham Trang12ORCID,Ton Mimi23ORCID,Weaver Matthew D.45ORCID

Affiliation:

1. Epidemiology Program, Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington

2. Department of Epidemiology, University of Washington, Seattle, Washington

3. Cancer Prevention Program, Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington

4. Division of Sleep and Circadian Disorders, Departments of Medicine and Neurology, Brigham and Women’s Hospital, Boston, Massachusetts

5. Division of Sleep Medicine, Harvard Medical School, Boston, Massachusetts

Abstract

Background: Light exposure is the most powerful resetting signal for circadian rhythms. The objective of this study was to develop and validate a high-resolution geospatial light exposure model that measures environmental circadian misalignment (or solar jetlag) as the mismatch between the social clock and sun clock, which occurs from geographic variation in light exposure leading to delayed circadian phase from relatively less morning light exposure and greater evening light exposure with increasing westward position within a time zone. Methods: The light exposure model (30 m2 spatial resolution) incorporated geospatial data across the United States on time zones, elevation (using Google Earth Engine), sunrise time, and sunset time to estimate solar jetlag scores (higher values indicate higher environmental circadian misalignment). The validation study compared the light exposure model in 2022, which was linked with geocoded residential addresses of n = 20 participants in Boston, MA (eastern time zone position) and Seattle, WA (western time zone position) using a geographic information system, with illuminance values captured from wearable LYS light sensors and with sun times from the Solar Calculator. Results: Western versus eastern positions within a time zone were associated with higher solar jetlag scores from the light exposure model (P < 0.01) and relatively larger differences in sunset time measured using light sensors (social clock) and the Solar Calculator (sun clock) (P = 0.04). Conclusion: We developed and validated a geospatial light exposure model, enabling high spatiotemporal resolution and comprehensive characterization of geographic variation in light exposure potentially impacting circadian phase in epidemiologic studies.

Publisher

Ovid Technologies (Wolters Kluwer Health)

Reference60 articles.

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