P079 Words matter: Dream content may predict psychological distress
M. Burge, H. Meaklim, M. Turner, I. Dunican, R. Menzies, D. Cunnington
SLEEP Advances October 1, 2024 DOI: 10.1093/sleepadvances/zpae070.161 (opens in new tab) via Semantic Scholar
Summary
AI-generated from the abstractSpecific words used to describe dream content are associated with higher levels of anxiety and stress. In a cohort of 217 participants who completed the Depression Anxiety Stress Scale 21 and described their last remembered dream, no significant predictors were found for depression. However, a principal component high in frequencies of visual and auditory perception, motion, and space language significantly increased the odds of severe and higher anxiety (Tjur R² = 0.04). A component high in frequencies of substances, sexual, food, and motion language significantly increased the odds of moderate and higher stress (Tjur R² = 0.09). Reviewing dream content may provide additional insights for clinicians.
Study at a glance
| Characteristics | Observational cohort Peer reviewed |
|---|---|
| Sample size | 217 |
| Population | Participants aged 19-83 (149 female) |
| Keywords | Psychology |
| Key finding | Specific dream content language categories are associated with higher anxiety and stress, but not depression. |
Abstract
Abstract Introduction Dream content analysis is not a routine part of a clinical sleep assessment. However, dreams are an important part of the sleep experience and may provide insights into other health domains. This study investigated the relationship between descriptions of dream content and psychological distress. Methods The study cohort consisted of 217 participants (149 female, age range: 19-83) who completed the Depression Anxiety Stress Scale 21 (DASS-21) and described their last remembered dream. Dream content was analysed using 26 categories from the Linguistic Inquiry and Word Count 22, including emotion, pro-social vs. conflict, lifestyle, health, drives, and perception language. Principal components analysis (PCA) identified 18 principle components (PC) that explained 90% of the variance in these categories. These components were used to predict severe and higher scores of depression and anxiety and moderate and higher scores of stress through logistic regression models. Stepwise regression using the backward method, based on Akaike Information Criterion scores, was employed to select the best models. Results The analysis found no significant predictors for depression. However, a PC high in frequencies of visual and auditory perception, motion, and space language and a PC high in frequencies of substances, sexual, food, and motion language significantly increased the odds of severe and higher anxiety (Tjur R² = 0.04) and moderate and higher stress (Tjur R² = 0.09). Discussion These findings suggest specific words used to describe dream content are associated with higher levels of anxiety and stress. Reviewing dream content may provide additional insights for clinicians.