This study aimed to develop a new measure of sleep acceptance, the Sleep Acceptance Scale (SAS), and evaluate its psychometric properties. Participants were 1419 adults, with and without insomnia symptoms, who completed the SAS and several other questionnaires related to sleep, anxiety, and depression. Results from parallel analysis and exploratory factor analysis suggested that the SAS consisted of two latent factors: Avoidance and Distress. Confirmatory factor analysis found that the 2-factor model provided a good fit and had high factor loadings. The SAS had moderate to high internal consistency and showed construct validity by being positively correlated with measures of insomnia, anxiety, and depression and negatively correlated with a measure of sleep problem acceptance. Overall, the study provides preliminary evidence that the SAS is a valid tool for assessing the acceptance of sleep problems.

Insomnia is a common problem that affects a significant portion of the population. Dysfunctional beliefs and attitudes about sleep have been identified as important factors contributing to developing and maintaining insomnia. In this study, we developed a Brazilian-Portuguese version of the dysfunctional beliefs and attitudes about sleep scale (DBAS-16) and conducted a comprehensive psychometric evaluation using latent variable and psychometric network frameworks. Participants (N = 1,386) were between 18 and 59 years old, with and without insomnia complaints. Using Confirmatory Factor Analysis (CFA) with dynamic fit indices, we found that the original DBAS-16 structure was replicated in our sample with moderate fit quality. We also found support for configural, metric, and scalar longitudinal invariance (14 days) but not for metric invariance across groups of good and bad sleepers. Unique Variable Analysis applied to half of our sample data (n = 693) identified three redundant items suitable for exclusion (1. Need 8 hours of sleep, 3. Consequences of insomnia on health, and 15. Medication as a solution). Additionally, Exploratory Graph Analysis (EGA) identified two dimensions with excellent structural stability, replicated when EGA was applied to the other half of the sample. Using CFA, we found that the EGA model fit significantly better than the proposed theoretical model, endorsing an alternative DBAS dimensionality. Our findings support the use of the DBAS-16 with a Brazilian-Portuguese-speaking population. Further, we found that after excluding locally dependent variables, two dimensions may better represent dysfunctional beliefs and attitudes about sleep.

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