Dreaming during the Covid-19 pandemic: Computational assessment of dream reports reveals mental suffering related to fear of contagion

Abstract

The current global threat brought on by the Covid-19 pandemic has led to widespread social isolation, posing new challenges in dealing with metal suffering related to social distancing, and in quickly learning new social habits intended to prevent contagion. Neuroscience and psychology agree that dreaming helps people to cope with negative emotions and to learn from experience, but can dreaming effectively reveal mental suffering and changes in social behavior? To address this question, we applied natural language processing tools to study 239 dream reports by 67 individuals, made either before the Covid-19 outbreak or during the months of March and April, 2020, when lockdown was imposed in Brazil following the WHO’s declaration of the pandemic. Pandemic dreams showed a higher proportion of anger and sadness words, and higher average semantic similarities to the terms “contamination” and “cleanness”. These features seem to be associated with mental suffering linked to social isolation, as they explained 40% of the variance in the PANSS negative subscale related to socialization (p = 0.0088). These results corroborate the hypothesis that pandemic dreams reflect mental suffering, fear of contagion, and important changes in daily habits that directly impact socialization.

Dreaming in Adolescents During the COVID-19 Health Crisis: Survey Among a Sample of European School Students

“…Moreover, a shift toward more negative dreams was found, which was directly related to the subjective stress in waking life, for example, social distancing affected mental health (Barrett, 2020;Iorio et al, 2020;Mota et al, 2020;Schredl and Bulkeley, 2020). In a similar way, the frequency of nightmares increased during the pandemic in both clinical (Gupta, 2020;Sierro et al, 2020) and normative samples of adults (Musse et al, 2020;Pérez-Carbonell et al, 2020;Scarpelli et al, 2021).…”

“…Recent studies showed indeed that dreaming in adults has undergone significant changes during the COVID-19 pandemic (Barrett, 2020;Mota et al, 2020;Gorgoni et al, 2021;Wang et al, 2021). Regarding dream recall in particular, several studies (Bottary et al, 2020;Pappa et al, 2020;Gorgoni et al, 2021) indicated a self-reported increase in dream recall due to the pandemic; this could be explained by the longer sleep duration during home confinement (Martínez-Lezaun et al, 2020) or by changes in sleep patterns due to home working (Altena et al, 2020;Cellini et al, 2020).…”

Pandemic Nightmares: COVID-19 Lockdown Associated With Increased Aggression in Female University Students' Dreams

“…Studies within a population with more demographic homogeneity may highlight the shared experience of pandemic lockdown, as similarities of lifestyle, age, and finances may result in further commonalities within dream content. Additionally, dream content research during the pandemic has used predominately quantitative analysis (Barrett, 2020 ; Mota et al, 2020 ; Schredl and Bulkeley, 2020 ); little attention has been given to qualitative themes to explore individual experiences. The University of Toronto student body is highly diverse, comprised of 21% international students from 168 countries (University of Toronto Office of Planning Budget, 2020 ).…”

“…While the general effect of stress on sleep and dreaming has been thoroughly investigated (Payne and Nadel, 2004 ; Âkerstedt, 2006 ), the pandemic's global reach allows comparison of individual responses to a shared external stress that may shape common sleep patterns and dreams. Studies have employed social network analyses to explore pandemic dream content in Finland (Pesonen et al, 2020 ); dream diaries of university students collected over 2-week periods in Canada (MacKay and DeCicco, 2020 ), large online surveys in the U.S.A (Schredl and Bulkeley, 2020 ) and across the globe (Barrett, 2020 ); and natural language processing methodology to analyze dream reports in Brazil (Mota et al, 2020 ).…”

The application of artificial intelligence and data integration in COVID-19 studies: a scoping review

“…In the lone study that integrated heterogeneous data for modeling, Abdalla et al integrated 43 sociodemographic variables from multiple sources (eg, Census Bureau, US Department of Agriculture, Centers for Disease Control and Prevention) and built elastic net models to examine how sociodemographics impacted county-level social distancing ( Table 4 ). 130 Of the remaining studies, 1 used ANN to perform a drive-through mass vaccination simulation, 138 while the other 4 used NLP methods and tools on various research topics, including cross-lingual clinical deidentification in electronic health records (EHRs), 139 dream reports analysis, 140 drug safety analysis by mining the FDA adverse event system, 141 COVID-19 clinical concept (signs and symptoms) identification, and normalization in EHRs. 142 …”