The aims of this study were to test the Norwegian version of Goldberg's 30-item General Health Questionnaire (GHQ-30) in a group of older, care-dependent individuals living at home; to describe self-reported mental health; and to relate mental health to receiving home nursing, home help, and family care. A sample of 234 home nursing patients in Norway aged 75 years and older was interviewed. Mental state was assessed using the GHQ-30. Reliability and validity were calculated with Spearman's rank correlations, Cronbach's alpha coefficient, and Mann-Whitney U-test. The factor analysis was performed using the principal components analysis with varimax rotation and Kaiser normalization. Demographic characteristics and amounts of formal and family care were recorded, and descriptive statistics and stepwise multiple regression were used in the analyses. Cronbach's alpha coefficient for the GHQ was 0.92. The item-total correlations were generally acceptable. For items concerning depression and anxiety, the item-total correlations ranged from r(s)= 0.60 to 0.77. The factors extracted in the factor analysis explained 70% of the variance in the group. Females <85 years of age living in urban areas were associated with reduced mental health. There were no associations between general mental health and the amounts of formal and family care provided.
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Brain
January 2025
Translational Neuroimaging Laboratory, Montreal Neurological Institute, H3A 2B4, Montreal, Canada.
Plasma phosphorylated tau biomarkers open unprecedented opportunities for identifying carriers of Alzheimer's disease pathophysiology in early disease stages using minimally invasive techniques. Plasma p-tau biomarkers are believed to reflect tau phosphorylation and secretion. However, it remains unclear to what extent the magnitude of plasma p-tau abnormalities reflects neuronal network disturbance in the form of cognitive impairment.
View Article and Find Full Text PDFJMIR Res Protoc
January 2025
Data and Web Science Group, School of Business Informatics and Mathematics, University of Manneim, Mannheim, Germany.
Background: The rapid evolution of large language models (LLMs), such as Bidirectional Encoder Representations from Transformers (BERT; Google) and GPT (OpenAI), has introduced significant advancements in natural language processing. These models are increasingly integrated into various applications, including mental health support. However, the credibility of LLMs in providing reliable and explainable mental health information and support remains underexplored.
View Article and Find Full Text PDFJMIR Ment Health
January 2025
Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States.
Background: Mental health concerns have become increasingly prevalent; however, care remains inaccessible to many. While digital mental health interventions offer a promising solution, self-help and even coached apps have not fully addressed the challenge. There is now a growing interest in hybrid, or blended, care approaches that use apps as tools to augment, rather than to entirely guide, care.
View Article and Find Full Text PDFJMIR Form Res
January 2025
School of Psychology, Ulster University, Coleraine, United Kingdom.
Background: Psychologists have developed frameworks to understand many constructs, which have subsequently informed the design of digital mental health interventions (DMHIs) aimed at improving mental health outcomes. The science of happiness is one such domain that holds significant applied importance due to its links to well-being and evidence that happiness can be cultivated through interventions. However, as with many constructs, the unique ways in which individuals experience happiness present major challenges for designing personalized DMHIs.
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