Methods for monitoring the human circadian rhythm in free-living

Open Access
Author:
Description:
Our internal clock, the circadian clock, determines at which time we have our best cognitive abilities, are physically strongest, and when we are tired. Circadian clock phase is influenced primarily through exposure to light. A direct pathway from the eyes to the suprachiasmatic nucleus, where the circadian clock resides, is used to synchronise the circadian clock to external light-dark cycles. In modern society, with the ability to work anywhere at anytime and a full social agenda, many struggle to keep internal and external clocks synchronised. Living against our circadian clock makes us less efficient and poses serious health impact, especially when exercised over a long period of time, e.g. in shift workers. Assessing circadian clock phase is a cumbersome and uncomfortable task. A common method, dim light melatonin onset testing, requires a series of eight saliva samples taken in hourly intervals while the subject stays in dim light condition from 5 hours before until 2 hours past their habitual bedtime. At the same time, sensor-rich smartphones have become widely available and wearable computing is on the rise. The hypothesis of this thesis is that smartphones and wearables can be used to record sensor data to monitor human circadian rhythms in free-living. To test this hypothesis, we conducted research on specialised wearable hardware and smartphones to record relevant data, and developed algorithms to monitor circadian clock phase in free-living. We first introduce our smart eyeglasses concept, which can be personalised to the wearers head and 3D-printed. Furthermore, hardware was integrated into the eyewear to recognise typical activities of daily living (ADLs). A light sensor integrated into the eyeglasses bridge was used to detect screen use. In addition to wearables, we also investigate if sleep-wake patterns can be revealed from smartphone context information. We introduce novel methods to detect sleep opportunity, which incorporate expert knowledge to filter and fuse classifier outputs. Furthermore, ...
Year of Publication:
2020-01-09
Document Type:
doctoralthesis ; doc-type:doctoralThesis ; [Doctoral and postdoctoral thesis]
Language:
eng
Subjects:
Tagesrhythmus ; ddc:004
DDC:
004 Data processing & computer science ; 571 Physiology & related subjects (computed)
Rights:
https://www.ub.uni-passau.de/fileadmin/dokumente/einrichtungen/universitaetsbibliothek/oeffentlich/formulare/Einverstaendniserklaerung_OPUS_Passau_de.pdf ; info:eu-repo/semantics/openAccess
URL:
https://opus4.kobv.de/opus4-uni-passau/frontdoor/index/index/docId/760
https://nbn-resolving.org/urn:nbn:de:bvb:739-opus4-7607
https://opus4.kobv.de/opus4-uni-passau/files/760/Thesis_Final.pdf
Content Provider:
Universität Passau: OPUS Dokumentenserver
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