Italy and Belgium have been among the first western countries to face the Coronavirus disease 2019 (COVID-19) emergency, imposing a total lockdown over the entire national territories.These limitations have proven effective in slowing down the spread of the infection. However, the benefits obtained in public health have come with huge costs in terms of social, economic, and psychological well-being. In the current study, we aimed at investigating how the period of home confinement affected self-reported sleep characteristics in Italians and Belgians, with special regard to sleep timing and subjective quality. Using an online survey we collected data from 2272 participants, 1622 Italians (Mage=34.1±13.6 years, 1171 F), and 650 Belgian (Mage=43.0±16.8 years, 509 F). Participants reported their sleep pattern (e.g., bedtime, risetime) and perceived sleep quality during and, retrospectively, before the lockdown. During the lockdown, sleep timing was significantly delayed, time spent in bed increased, and sleep quality was markedly impaired in both Italians and Belgians. The most vulnerable individualsappeared to be women, subjects experiencing a more negative mood, and those perceiving the pandemic situation as highly stressful. However, the two samples differed in the subgroups most affected by the changes, possibly because of the different welfare systems of the two countries. In fact, in the Italian sample sleep quality and timing underwent significant modifications especially in unemployed participants, whereas in the Belgian sample this category was the one who suffered less from the restrictions. Considering that the novel coronavirus has spread across the whole globe, involving countries with different types of health and welfare systems, understanding which policy measures have the most effective protecting role on physical and mental health is of primary importance.
Over the past 40 years, actigraphy has been used to study rest-activity patterns in circadian rhythm and sleep research. Furthermore, considering its simplicity of use, there is a growing interest in the analysis of large population-based samples, using actigraphy. Here, we introduce pyActigraphy, a comprehensive toolbox for data visualization and analysis including multiple sleep detection algorithms and rest-activity rhythm variables. This open-source python package implements methods to read multiple data formats, quantify various properties of rest-activity rhythms, visualize sleep agendas, automatically detect rest periods and perform more advanced signal processing analyses. The development of this package aims to pave the way towards the establishment of a comprehensive open-source software suite, supported by a community of both developers and researchers, that would provide all the necessary tools for in-depth and large scale actigraphy data analyses.
Neuroimaging and genetics studies have advanced our understanding of the neurobiology of sleep and its disorders. However, individual studies usually have limitations to identifying consistent and reproducible effects, including modest sample sizes, heterogeneous clinical characteristics and varied methodologies. These issues call for a large-scale multi-centre effort in sleep research, in order to increase the number of samples, and harmonize the methods of data collection, preprocessing and analysis using pre-registered well-established protocols. The Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) consortium provides a powerful col-How to cite this article: Tahmasian M, Aleman A, Andreassen OA, et al. ENIGMA-Sleep: Challenges, opportunities, and the road map.
Over the past 40 years, actigraphy has been used to study rest-activity patterns in circadian rhythm and sleep research. Furthermore, considering its simplicity of use, there is a growing interest in the analysis of large population-based samples, using actigraphy. Here, we introduce pyActigraphy, a comprehensive toolbox for data visualization and analysis including multiple sleep detection algorithms and rest-activity rhythm variables. This open-source python package implements methods to read multiple data formats, quantify various properties of rest-activity rhythms, visualize sleep agendas, automatically detect rest periods and perform more advanced signal processing analyses. The development of this package aims to pave the way towards the establishment of a comprehensive open-source software suite, supported by a community of both developers and researchers, that would provide all the necessary tools for in-depth and large scale actigraphy data analyses.Required MetadataCurrent code version
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