P079 Words matter: Dream content may predict psychological distress

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.