Associations of autistic traits, sleep/circadian factors, and mental health
Deshpande, N.; Nair, S.; Taylor, E.; Van Someren, E.; Chellappa, S. L.
Show abstract
BackgroundAutistic individuals experience a heightened risk of depression and lower quality of life; however, it remains to be established whether disrupted sleep and circadian factors mediate this increased risk. ObjectivesWe assessed whether disruption of self-reported sleep and circadian factors mediate the associations of autistic traits with depression symptom severity and quality of life. Methods838 participants (mean: 52.8 [SD = 1.3] years, 70% females) from a large-scale observational survey (Netherlands Sleep Registry) completed the Autism Quotient Scale (AQ), Hospital Anxiety and Depression Scale Cantril Ladder quality of life, Insomnia Severity Index, Pittsburgh Sleep Quality Index, and the Munich Chronotype Questionnaire. Results\Higher autistic traits were associated with a trend for higher depression symptom severity (p = 0.06), significantly lower quality of life (p < 0.001), higher insomnia severity (p < 0.001), lower sleep quality (p < 0.001), a trend for late chronotype (r = 0.04, p = 0.06), but not social jetlag (r = 0.02, p = 0.21). Insomnia severity and chronotype partly mediated the association of autistic traits and depression symptom severity (standardized beta = -0.02, 95% CI = [-0.04, 0.00]), and the association of autistic traits and quality of life (standardized beta = -0.02, 95% CI = [-0.04, 0.00]). ConclusionAutistic traits were associated with depression severity and lower quality of life, mediated by insomnia symptom severity and chronotype. Future studies targeting insomnia complaints and late chronotype in this population may help alleviate their mental health complaints and increase quality of life.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- The effect of sleep continuity disruption on multimodal emotion processing and regulation: a laboratory-based, randomized, controlled experiment in good sleepers 95%
- Two years after lockdown: longitudinal trajectories of sleep disturbances and mental health over the COVID-19 pandemic and the effects of age, gender, and chronotype 94%
- Sleeping under the waves: a longitudinal study across the contagion peaks of COVID-19 pandemic in Italy 94%
Similar papers in this journal
- Unstable sleep and rest-activity rhythms in adolescents at-risk for bipolar disorder: links to mood symptoms and the effect of sleep stabilization 94%
- Associations Between Daily Outdoor Temperature and Subjective Real-time Ratings of Emotional States and Sleep in Mood Disorder Subtypes 92%
- Strong Genetic Overlaps Between Dimensional and Categorical Models of Bipolar Disorders in a Family Sample 91%
Similar papers in this journal
- Characterizing Sleep Disorders in an Autism-Specific Collection of Electronic Health Records 93%
- Effects of mindfulness meditation and Acceptance and Commitment Therapy in patients with obstructive sleep apnea with residual excessive sleepiness: A randomized controlled pilot study 92%
- Effects of cognitive behavioral therapy for insomnia on subjective and objective measures of sleep and cognition 92%
Similar papers in this journal
- Sleep to remember, sleep to protect: increased sleep spindle and theta activity predict fewer intrusive memories after analogue trauma 93%
- Investigating the contributions of circadian pathway and insomnia risk genes to autism and sleep disturbances 92%
- LSD increases sleep duration the night after microdosing 90%
Similar papers in this journal
- Mental-health before and during the COVID-19 pandemic in adults with neurodevelopmental disorders 91%
- Clinical properties of the Short Mood and Feelings Questionnaire: Development of a free calculator based on a Brazilian High-Risk Cohort Study 90%
- Elevated levels of hoarding in ADHD: a special link with inattention 90%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.