We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.
To save content items to your account,
please confirm that you agree to abide by our usage policies.
If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account.
Find out more about saving content to .
To save content items to your Kindle, first ensure [email protected]
is added to your Approved Personal Document E-mail List under your Personal Document Settings
on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part
of your Kindle email address below.
Find out more about saving to your Kindle.
Note you can select to save to either the @free.kindle.com or @kindle.com variations.
‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi.
‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.
Adverse Childhood Experiences (ACEs) are known to increase risk of mental health challenges, and sleep is known to decrease risk. We investigated whether adequate sleep duration and sleep regularity would moderate the impact of ACE exposure on mental health risk.
Methods:
We conducted secondary cross-sectional analyses on the 2020-2021 waves of the National Survey of Children’s Health (NSCH; N = 92,669). Logistic and ordinal regressions explored impact of ACEs (total, household, community, and single) and sleep (duration and irregularity) and related interactions on mental health diagnosis and symptom severity.
Results:
Known main effects for ACEs and sleep on mental health were replicated. Interactions between ACE exposure and sleep factors were not clinically significant, although some were statistically significant due to large sample, such that adequate duration was associated with marginally increased risk of mental health diagnosis (Omnibus B = 0.048, p < 0.0001) and greater bedtime irregularity was associated with marginally decreased risk (Omnibus B = -0.030, p < 0.001).
Discussion:
Dichotomous and categorical assessments of sleep health may not be sensitive to interaction effects, compared with continuous data. Examining mental health symptoms (rather than diagnosis status) may also allow for nuanced understanding of potential interactions.
Subjective cognition is a predictor of cognitive decline and previous work has identified age, education and depression as predictors of subjective cognition. This study aimed to investigate whether several sleep characteristics were associated with subjective cognition above-and-beyond known predictors.
Methods:
Participants (N=3284, Mage=42.7 years, 48.5% female) completed an online study that included the Patient Health Questionnaire-4 (PHQ-4), Insomnia Severity Index (ISI), RU-SATED, Sleep Regularity Questionnaire (SRQ) and the 6-item PROMIS Cognitive Function. A 3-step hierarchical regression model predicted PROMIS Cognition scores, with Step 1 including age and education as predictors, Step 2 including age, education, and PHQ-4 scores, and Step 3 including all previous variables and sleep variables.
Results:
In Step 1 (R2=.03), age and education were significant predictors, while in Step 2 (R2=.36), PHQ-4 and education were significant, and age was no longer significant. In Step 3 (R2=.48), PHQ-4, ISI, RU-SATED, and SRQ scores were significant, while age and education were not significant. All steps accounted for a significant increase in variance (p’s<.001).
Conclusions:
Sleep characteristics were associated with subjective cognition above-and-beyond known predictors of age, education and mood. Further research is needed to investigate whether changes in sleep characteristics are associated with changes in subjective cognition.
Recommend this
Email your librarian or administrator to recommend adding this to your organisation's collection.