Open-source Python module for the analysis of personalized light exposure data

Abstract

Light exposure fundamentally influences human physiology and behavior, with light being the most important zeitgeber of the circadian system. Throughout the day, people are exposed to a variety of different scenes differing in light level, spectral composition and spatio-temporal properties. Personalized light exposure can be measured through wearable light loggers and dosimeters, including wrist-worn actimeters containing light sensors, yielding time courses of person-centric light exposure. There is a growing interest in relating light exposure patterns to health outcomes, requiring analytic techniques to summarize light exposure time courses. Building on the previously published pyActigraphy package, we introduce the package pyLight. pyLight allows users to extract light exposure data recordings from a wide range of devices, clean and filter the data, and compute common metrics for quantifying and summarising light exposure extracted from a literature survey. We demonstrate the use of the module in three examples: (1) loading, accessing and visually inspection a dataset, (2) truncation, masking, filtering and binarization of data set, (3) calculation of summary metrics, including time above threshold (TAT) and mean light timing above threshold (MLit). The pyLight package paves the way for the large-scale programmatic analysis of light-exposure data.

Human-Centric Lighting Research and Policy in the Melanopsin Age

“…Making data findable, accessible, interoperable, and reusable (FAIR) ( Wilkinson et al, 2016 ) must be a key priority. The development of common measures for data collection ( Farber et al, 2023 ), common data models, metadata descriptors, reporting standards for light interventions ( Knoop et al, 2019 ; Spitschan et al, 2023 ; Spitschan, Stefani, et al, 2019 ; Veitch & Knoop, 2020 ), and analysis pipelines ( Hammad et al, 2023 ; Spitschan et al, 2021 ) must underpin large-scale data integration efforts. This requires dedicated effort, such as the ongoing professionalization of research software engineering and its integration as a core tool in contemporary scientific research ( Horsfall et al, 2023 ).…”