0662 Digital sleep-health surveillance: Population nightmare frequency is observable in social media
Remington Mallett, Laura Sowin, Michelle Carr
Sleep May 1, 2023 DOI: 10.1093/sleep/zsad077.0662 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Observational study using digital health surveillance of social media posts Peer reviewed |
|---|---|
| Population | Public posts from the r/Dreaming subreddit |
| Topics | Dreaming |
| Key findings | The percentage of dreams posted on Reddit identified as nightmares was higher after the WHO's COVID-19 pandemic declaration than before, and anxiety in posted dreams also increased, with weekly dream-anxiety changes positively correlated with the percentage of COVID-19 news headlines. The authors argue this social media surveillance approach may offer a low-cost, real-time method for monitoring population sleep health. |
Abstract
Nightmares are a critical component of multidimensional sleep health. Frequent nightmare recallers suffer reduced sleep length, depressed mood, and are more likely to be diagnosed with a psychiatric disorder in the future. Reliable population estimates of nightmare frequency are difficult to obtain and often restricted to rigid timepoints (e.g., once per year). Thus, there is a need for low-cost methods to track population levels of nightmare frequency and their dynamic changes over time. In the current study, we tracked dream content and nightmare frequency using a popular new approach – digital health surveillance – that analyzes language of public and freely-accessible social media posts to track population characteristics. To evaluate whether social media could detect known changes in population sleep health, we extracted posts from r/Dreaming, a popular subreddit dedicated to dream sharing. Prior survey studies have shown a reliable increase in nightmare frequency during the first wave of the COVID-19 pandemic. Thus, we quantified the amount of nightmares posted on Reddit surrounding the World Health Organization’s (WHO) declaration of a COVID-19 as a global pandemic. Nightmares were identified using a word-search algorithm that identified nightmare-related words in a post title. Nightmare frequency was compared before and after the WHO declaration using a chi-squared analysis. The percentage of dreams posted on Reddit that were identified as nightmares was higher after the WHO declaration than before (p < .05). Additionally, the amount of anxiety in all dreams posted on Reddit was higher after the WHO declaration than before (p < .05). Weekly change in dream anxiety was positively correlated with the percentage of COVID-19 news headlines (p < .05). We observed an increase in nightmares shared on Reddit immediately following the WHO’s declaration of COVID-19 as a global pandemic. This novel approach to tracking nightmares might offer the field of sleep medicine a low-cost and real-time system for monitoring population sleep health. Other recent work suggests this method might be viable for tracking other components of sleep health.
Comparable studies
Other observational and cohort studies on dreaming, most cited first.
| Study | Year | Design | Participants |
|---|---|---|---|
| Lucid Dreaming: a State of Consciousness with Features of Both Waking and Non-Lucid Dreaming Student volunteers trained to become lucid and signal lucidity with horizontal eye... | 2009 | Observational study | n = 6 |
| Nightmare Prevalence, Nightmare Distress, and Self-Reported Psychological Disturbance Young adults with a mean age of 20 years | 2002 | Prospective observational study | n = 116 |
| Long-term effects of extreme situational stress on sleep and dreaming Survivors of traumatic events who complained of sleep disturbances, compared with... | 1987 | Case-control study | n = 11 |
| The relationship of nightmare frequency and nightmare distress to well‐being Adults who reported dreams | 2004 | Observational cohort | n = 147 |
| Dreaming in NREM Sleep: A High-Density EEG Study of Slow Waves and Spindles Healthy human subjects | 2018 | Observational cohort | n = 14 |