Global and large-scale study of complex patterns in high-resolution sleep activity data

Sigriður Svala Jónasdóttir

Research output: Book/ReportPh.D. thesis

154 Downloads (Pure)


Sleep is a complex physiological process influenced by intrinsic and extrinsic factors. Everyday, each person on earth sleeps and if they do not, it will impact them. For that reason sleep has been studied across the population at large scale, but the research has been limited by self-reported and subjective data-sets known to recall biases. Some of the key metrics and methods were developed to cater to these types of data, but today sleep recording technology has been revolutionised and wearable devices enable objective recordings in-situ over long period of time. With rising numbers of wearable device owners and studies using this technology, I see a great potential to accelerate our understanding of human sleep in modern society. This study sets out to develop new methods and metrics appropriate for multi-night recordings of sleep in-situ. Furthermore, I investigate whether
current knowledge regarding sleep patterns persist when explored with a global, large-scale and highresolution sleep activity data-set, but also seek to expand on some the fundamental knowledge. I find detailed sleep trajectories to have complex and multidimensional patterns across the population. I introduce new features and visualisation methods, and a novel data-driven metric which may be indicative of whether individual physiological sleep needs are met or not. Furthermore, I study age-related changes in sleep timing, duration and life-stage dependent gender differences. I find novel and unprecedented results regarding associated changes in sleep due to travel, and show that regional policy and cultural context exerts strong influence on sleep behavior.
Original languageEnglish
PublisherTechnical University of Denmark
Number of pages264
Publication statusPublished - 2021


Dive into the research topics of 'Global and large-scale study of complex patterns in high-resolution sleep activity data'. Together they form a unique fingerprint.

Cite this