Examining the factor structure, reliability, and validity of the disturbing dreams and nightmare severity index (DDNSI) consequences sub-component

Background: The Disturbing Dreams and Nightmares Severity Index (DDNSI) is commonly used when assessing the experience of nightmares. It comprises two parts examining i) chronicity and ii) nightmare consequences. The primary aim of the present study was to explore the dimensional structure of the optional and currently unvalidated nightmare consequences component using exploratory factor analysis. Internal reliability and construct validity were also examined. A secondary aim explored the relationships between nightmare chronicity and perceived consequences with measures of anxiety, depression, stress, self-efficacy, and insomnia. Methods: A cross-sectional survey was conducted with complete data from N = 757 students from six UK-based universities. Participants completed the chronicity and consequences components of the DDNSI, alongside the Sleep Condition Indicator, Patient Health Questionnaire-9, Generalized Anxiety Disorder-7, Perceived Stress Scale, and General Self-Efficacy Scale. Results: Two nightmare consequences factors emerged; ‘Sleep-Interference’ (four items; α =.848), and ‘Psychosocial Well-being’ (six items; α =.946). Significantly moderate correlations were observed between the two emerging factors and the nightmare chronicity component, as well as with insomnia, anxiety, depression, perceived stress, and self-efficacy. Perceived ‘Sleep-Interference’ (β =−.241) was the strongest predictor of insomnia, and ‘Psychosocial wellbeing’ was the strongest predictor of anxiety (β =.688) depression (β =.804) perceived stress and lower self-efficacy. Conclusions: The perceived nightmare consequences component of the DDSNI is a multidimensional construct comprising two internally consistent and distinct, but related dimensions. The potential importance of distinguishing between types of perceived nightmare consequences and the associations with mental health outcomes in a student population is highlighted.