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LIBRIS Formathandbok  (Information om MARC21)
FältnamnIndikatorerMetadata
00004591naa a2200433 4500
001oai:DiVA.org:umu-222371
003SwePub
008240314s2024 | |||||||||||000 ||eng|
024a https://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-2223712 URI
024a https://doi.org/10.1080/15502724.2023.22968632 DOI
040 a (SwePub)umu
041 a engb eng
042 9 SwePub
072 7a ref2 swepub-contenttype
072 7a art2 swepub-publicationtype
100a Hammad, Grégoryu Sleep & Chronobiology Group, GIGA – CRC in Vivo Imaging, University of Liège, Liège, Belgium; Chair of Neurogenetics, Institute of Human Genetics, University Hospital, Technical University of Munich, Munich, Germany4 aut
2451 0a Open-source python module for the analysis of personalized light exposure data from wearable light loggers and dosimeters
264 c 2024
264 1b Taylor & Francis,c 2024
338 a electronic2 rdacarrier
520 a 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 various 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 series of an individual’s light exposure. There is growing interest in relating light exposure patterns to health outcomes, requiring analytic techniques to summarize light exposure properties. Building on the previously published Python-based pyActigraphy module, here we introduce the module pyLight. This module allows users to extract light exposure data recordings from a wide range of devices. It also includes software tools to clean and filter the data, and to compute common metrics for quantifying and visualizing light exposure data. For this tutorial, we demonstrate the use of pyLight in one example dataset with the following processing steps: (1) loading, accessing and visual inspection of a publicly available dataset, (2) truncation, masking, filtering and binarization of the dataset, (3) calculation of summary metrics, including time above threshold (TAT) and mean light timing above threshold (MLiT). The pyLight module paves the way for open-source, large-scale automated analyses of light-exposure data.
650 7a MEDICIN OCH HÄLSOVETENSKAPx Medicinska och farmaceutiska grundvetenskaperx Neurovetenskaper0 (SwePub)301052 hsv//swe
650 7a MEDICAL AND HEALTH SCIENCESx Basic Medicinex Neurosciences0 (SwePub)301052 hsv//eng
653 a analysis
653 a dosimetry
653 a Light exposure
653 a light loggers
653 a open-source software
653 a python
700a Wulff, Katharinau Umeå universitet,Wallenberg centrum för molekylär medicin vid Umeå universitet (WCMM),Institutionen för molekylärbiologi (Medicinska fakulteten),Institutionen för molekylärbiologi (Teknisk-naturvetenskaplig fakultet)4 aut0 (Swepub:umu)kawu0003
700a Skene, Debra J.u Chronobiology, Faculty of Health and Medical Sciences, University of Surrey, Guildford, United Kingdom4 aut
700a Münch, Mirjamu Centre for Chronobiology, Psychiatric Hospital of the University of Basel, Basel, Switzerland; Transfaculty Platform for Molecular and Cognitive Neuroscience, University of Basel, Basel, Switzerland4 aut
700a Spitschan, Manuelu Translational Sensory & Circadian Neuroscience, Max Planck Institute for Biological Cybernetics, Tübingen, Germany; TUM School of Medicine & Health, Technical University of Munich, Munich, Germany; TUM Institute for Advanced Study, Technical University of Munich, Garching, Germany4 aut
710a Sleep & Chronobiology Group, GIGA – CRC in Vivo Imaging, University of Liège, Liège, Belgium; Chair of Neurogenetics, Institute of Human Genetics, University Hospital, Technical University of Munich, Munich, Germanyb Wallenberg centrum för molekylär medicin vid Umeå universitet (WCMM)4 org
773t LEUKOS The Journal of the Illuminating Engineering Society of North Americad : Taylor & Francisx 1550-2724x 1550-2716
856u https://doi.org/10.1080/15502724.2023.2296863y Fulltext
856u https://umu.diva-portal.org/smash/get/diva2:1844678/FULLTEXT01.pdfx primaryx Raw objecty fulltext:print
8564 8u https://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-222371
8564 8u https://doi.org/10.1080/15502724.2023.2296863

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