Pandemic Dreams: Network Analysis of Dream Content During the COVID-19 Lockdown

Anu-Katriina Pesonen; Jari Lipsanen; Risto Halonen; Marko Elovainio; Nils Sandman; Juha-Matti Mäkelä; Minea Antila; Deni Béchard; Hanna M Ollila; Liisa Kuula

doi:10.3389/fpsyg.2020.573961

Pandemic Dreams: Network Analysis of Dream Content During the COVID-19 Lockdown

Front Psychol. 2020 Oct 1:11:573961. doi: 10.3389/fpsyg.2020.573961. eCollection 2020.

Authors

Anu-Katriina Pesonen¹, Jari Lipsanen², Risto Halonen¹, Marko Elovainio¹, Nils Sandman^{3

4}, Juha-Matti Mäkelä¹, Minea Antila¹, Deni Béchard⁵, Hanna M Ollila^{6

7

8

9}, Liisa Kuula¹

Affiliations

¹ Sleepwell Research Program, Faculty of Medicine, University of Helsinki, Helsinki, Finland.
² Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Helsinki, Finland.
³ Department of Psychology and Speech-Language Pathology, University of Turku, Turku, Finland.
⁴ Department of Social Psychology, University of Helsinki, Helsinki, Finland.
⁵ Visiting Researcher, Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland.
⁶ Department of Psychiatry and Behavioral Sciences, Stanford University, Palo Alto, CA, United States.
⁷ Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, United States.
⁸ Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, United States.
⁹ Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland.

Abstract

We used crowdsourcing (CS) to examine how COVID-19 lockdown affects the content of dreams and nightmares. The CS took place on the sixth week of the lockdown. Over the course of 1 week, 4,275 respondents (mean age 43, SD = 14 years) assessed their sleep, and 811 reported their dream content. Overall, respondents slept substantially more (54.2%) but reported an average increase of awakenings (28.6%) and nightmares (26%) from the pre-pandemic situation. We transcribed the content of the dreams into word lists and performed unsupervised computational network and cluster analysis of word associations, which suggested 33 dream clusters including 20 bad dream clusters, of which 55% were pandemic-specific (e.g., Disease Management, Disregard of Distancing, Elderly in Trouble). The dream-association networks were more accentuated for those who reported an increase in perceived stress. This CS survey on dream-association networks and pandemic stress introduces novel, collectively shared COVID-19 bad dream contents.

Keywords: COVID-19; cluster; crowdsourcing; dream; network analysis; nightmare; sleep; stress.