Prevalence of insomnia and its association with quality of life in caregivers of psychiatric inpatients during the COVID-19 pandemic: a network analysis

Impact of the COVID-19 Pandemic on Subjective Well-Being and Quality of Life: A Comprehensive Bibliometric and Thematic Analysis

This study employs bibliometric and thematic analysis to evaluate the growing body of research on subjective well-being and quality of life during the COVID-19 pandemic. The findings underscore the pandemic's profound impacts on global health, evidenced by a notable increase in studies addressing mental health and quality of life, fostered by international scientific collaboration. Keyword analysis reveals critical themes, including the pandemic's influence on mental health, physical activity, and social support systems. This research provides valuable insights into the long-term consequences of the pandemic and highlights adaptive strategies for managing future crises. By identifying key trends and research gaps, the study serves as an essential resource for academics, policymakers, and public health practitioners, offering a roadmap for future investigations.

Healthcare needs and expectations of family members caring for mental healthcare users in South Africa

BACKGROUND

 In South Africa, many mental healthcare users reside in rural areas and 91% of them live with their family members. Exploring and describing the needs of family members caring for mental healthcare users is important to determine their expectations of the healthcare system.

OBJECTIVES

 The study aimed to explore and describe the healthcare needs and expectations of family members caring for mental healthcare users in Vhembe district, Limpopo province, South AfricaMethod: A qualitative, descriptive and contextual design was used to collect data from 16 purposively selected family members caring for mental healthcare users. Data were collected through semi-structured face-to-face interviews, and analysed using thematic data analysis to develop themes and categories.

RESULTS

 The results of the interviews yielded two themes, healthcare needs and expectations of family members. The needs included physical healthcare, psychological healthcare, and social, financial, educational and spiritual needs. The healthcare expectations were divided into two sub-themes: (1) expectations from the healthcare systems, and (2) expectations from healthcare professionals.

CONCLUSION

 The study showed that support from community members and healthcare providers could reduce the burden of care perceived by family members. Results confirmed the need for developing a family psychoeducational programme adapted to the South African context to meet the family members' needs. Quantitative research on healthcare needs with a larger sample of family members is recommended.Contribution: The study may contribute to mental health nursing practice and education. Nursing support based on the needs of family members may enhance their well-being and caregiving abilities. The importance of tailor-made psychoeducational family support should be emphasised in nursing education.

Machine learning and Bayesian network analyses identifies associations with insomnia in a national sample of 31,285 treatment-seeking college students

BACKGROUND

A better understanding of the relationships between insomnia and anxiety, mood, eating, and alcohol-use disorders is needed given its prevalence among young adults. Supervised machine learning provides the ability to evaluate which mental disorder is most associated with heightened insomnia among U.S. college students. Combined with Bayesian network analysis, probable directional relationships between insomnia and interacting symptoms may be illuminated.

METHODS

The current exploratory analyses utilized a national sample of college students across 26 U.S. colleges and universities collected during population-level screening before entering a randomized controlled trial. We used a 4-step statistical approach: (1) at the disorder level, an elastic net regularization model examined the relative importance of the association between insomnia and 7 mental disorders (major depressive disorder, generalized anxiety disorder, social anxiety disorder, panic disorder, post-traumatic stress disorder, anorexia nervosa, and alcohol use disorder); (2) This model was evaluated within a hold-out sample. (3) at the symptom level, a completed partially directed acyclic graph (CPDAG) was computed via a Bayesian hill-climbing algorithm to estimate potential directionality among insomnia and its most associated disorder [based on SHAP (SHapley Additive exPlanations) values)]; (4) the CPDAG was then tested for generalizability by assessing (in)equality within a hold-out sample using structural hamming distance (SHD).

RESULTS

Of 31,285 participants, 20,597 were women (65.8%); mean (standard deviation) age was 22.96 (4.52) years. The elastic net model demonstrated clinical significance in predicting insomnia severity in the training sample [R2 = .44 (.01); RMSE = 5.00 (0.08)], with comparable performance in the hold-out sample (R2 = .33; RMSE = 5.47). SHAP values indicated that the presence of any mental disorder was associated with higher insomnia scores, with major depressive disorder as the most important disorder associated with heightened insomnia (mean |SHAP|= 3.18). The training CPDAG and hold-out CPDAG (SHD = 7) suggested depression symptoms presupposed insomnia with depressed mood, fatigue, and self-esteem as key parent nodes.

CONCLUSION

These findings provide insights into the associations between insomnia and mental disorders among college students and warrant further investigation into the potential direction of causality between insomnia and depression.

TRIAL REGISTRATION

Trial was registered on the National Institute of Health RePORTER website (R01MH115128 || 23/08/2018).

Machine Learning and Bayesian Network Analyses Identifies Psychiatric Disorders and Symptom Associations with Insomnia in a national sample of 31,285 Treatment-Seeking College Students

Background

A better understanding of the structure of relations among insomnia and anxiety, mood, eating, and alcohol-use disorders is needed, given its prevalence among young adults. Supervised machine learning provides the ability to evaluate the discriminative accuracy of psychiatric disorders associated with insomnia. Combined with Bayesian network analysis, the directionality between symptoms and their associations may be illuminated.

Methods

The current exploratory analyses utilized a national sample of college students across 26 U.S. colleges and universities collected during population-level screening before entering a randomized controlled trial. Firstly, an elastic net regularization model was trained to predict, via repeated 10-fold cross-validation, which psychiatric disorders were associated with insomnia severity. Seven disorders were included: major depressive disorder, generalized anxiety disorder, social anxiety disorder, panic disorder, post-traumatic stress disorder, anorexia nervosa, and alcohol use disorder. Secondly, using a Bayesian network approach, completed partially directed acyclic graphs (CPDAG) built on training and holdout samples were computed via a Bayesian hill-climbing algorithm to determine symptom-level interactions of disorders most associated with insomnia [based on SHAP (SHapley Additive exPlanations) values)] and were evaluated for stability across networks.

Results

Of 31,285 participants, 20,597 were women (65.8%); mean (standard deviation) age was 22.96 (4.52) years. The elastic net model demonstrated clinical significance in predicting insomnia severity in the training sample [R2 = .449 (.016); RMSE = 5.00 [.081]), with comparable performance in accounting for variance explained in the holdout sample [R2 = .33; RMSE = 5.47). SHAP indicated the presence of any psychiatric disorder was associated with higher insomnia severity, with major depressive disorder demonstrated to be the most associated disorder. CPDAGs showed excellent fit in the holdout sample and suggested that depressed mood, fatigue, and self-esteem were the most important depression symptoms that presupposed insomnia.

Conclusion

These findings offer insights into associations between psychiatric disorders and insomnia among college students and encourage future investigation into the potential direction of causality between insomnia and major depressive disorder.

Trial registration

Trial may be found on the National Institute of Health RePORTER website: Project Number: R01MH115128-05.