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Sleep quality moderates the association between psychotic-like experiences and suicidal ideation among help-seeking university students.

ABSTRACT: Suicide is a leading cause of death for young adults, and college-enrolled students are at markedly high risk for suicide. Psychotic-like experiences (PLEs) and sleep difficulties are prevalent among college students and have been linked to increased suicidal ideation (SI). This cross-sectional study examined the relation between PLEs and SI, moderated by sleep quality, in a sample of 442 students at a university counseling center. The Behavioral Health Measure-43 (BHM-43) was used to evaluate mental health symptoms, including sleep quality and SI. The PRIME Screen-Revised was used to measure PLEs. Regression results indicated that higher PRIME scores statistically predicted greater SI. There was a significant interaction between PRIME and sleep quality in predicting SI. Among individuals with greater sleep difficulties, PLEs were positively, significantly associated with SI. The PRIME was not a significant predictor of SI at lower levels of sleep difficulties (i.e. better sleep quality). This interaction effect remained significant when controlling for age and the BHM-43 depression and bipolar subscales. Findings suggest that sleep difficulties may be linked to increased SI for individuals with PLEs, and better sleep may be protective. Further research is needed to explore treatment targeting PLEs and/or sleep to mitigate suicide risk among university students.

SUBMITTER: Thompson EC

PROVIDER: S-EPMC8482876 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:AimsProspective studies on the mental health of university students highlighted a major concern. Specifically, young adults in academia are affected by markedly worse mental health status than their peers or adults in other vocations. This situation predisposes to exacerbated disability-adjusted life-years.MethodsWe enroled 1,388 students at the baseline, 557 of whom completed follow-up after 6 months, incorporating their demographic information and self-report questionnaires on depressive, anxiety and obsessive-compulsive symptoms. We applied multiple regression modelling to determine associations - at baseline - between demographic factors and self-reported mental health measures and supervised machine learning algorithms to predict the risk of poorer mental health at follow-up, by leveraging the demographic and clinical information collected at baseline.ResultsApproximately one out of five students reported severe depressive symptoms and/or suicidal ideation. An association of economic worry with depression was evidenced both at baseline (when high-frequency worry odds ratio = 3.11 [1.88-5.15]) and during follow-up. The random forest algorithm exhibited high accuracy in predicting the students who maintained well-being (balanced accuracy = 0.85) or absence of suicidal ideation but low accuracy for those whose symptoms worsened (balanced accuracy = 0.49). The most important features used for prediction were the cognitive and somatic symptoms of depression. However, while the negative predictive value of worsened symptoms after 6 months of enrolment was 0.89, the positive predictive value is basically null.ConclusionsStudents' severe mental health problems reached worrying levels, and demographic factors were poor predictors of mental health outcomes. Further research including people with lived experience will be crucial to better assess students' mental health needs and improve the predictive outcome for those most at risk of worsening symptoms.

Project description:Psychotic experiences (PEs) are prevalent in the general population, particularly in adolescents. PEs are associated with various negative outcomes such as psychotic, depressive, anxiety and post-traumatic stress disorders and suicidal behavior. Recent studies in the general population have suggested that what makes PEs relevant is not so much the experiences per se, but their association with non-psychotic comorbidity and other transdiagnostic domains. Thus, there is a need for a better understanding of how PEs exist in a larger psychopathological context in adolescents. In the present study we aimed to explore this, using latent profile analysis (LPA) to identify different patterns in which PEs, psychiatric symptoms and psychological processes co-occur. LPA was conducted using data from an adolescent general population subsample (n = 335) with PEs. We conducted LPA, using measures of PEs, psychiatric symptoms and behaviors (depression, anxiety post-traumatic stress disorder and suicidal behavior) and cognitive and affective processes of entrapment/defeat and emotional regulation as manifest variables. We found that the best fit was obtained with a four-class solution that distinguished primarily between different levels of overall severity: “low symptomatology” (19.1%), “mild-moderate symptomatology” (39.4%), “moderate symptomatology” (33.7%); “high symptomatology” (7.8%). Levels of depression, post-traumatic stress symptoms and defeat/entrapment were most differentiated between classes. The high symptomatology group showed the highest scores in all psychiatric symptoms suicidal ideation, and emotional/cognitive domains, except in cognitive reappraisal. This group also showed the highest usage of emotional suppression. Our results suggest that the assessment of mental health risk in adolescents should be aware that PEs exist in a broad context of other domains of psychopathology and transdiagnostic cognitive and affective processes.

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Sleep quality moderates the association between psychotic-like experiences and suicidal ideation among help-seeking university students.

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