Objectives: Autism is difficult to identify in adults due to lack of validated self-report questionnaires. We compared the effectiveness of the autism-spectrum quotient (AQ) and the Ritvo autism-Asperger's diagnostic scale-revised (RAADS-R) questionnaires in adult mental health services in two English counties.
Methods: A subsample of adults who completed the AQ and RAADS-R were invited to take part in an autism diagnostic observation schedule (ADOS Module 4) assessment with probability of selection weighted by scores on the questionnaires.
Results: There were 364 men and 374 women who consented to take part. Recorded diagnoses were most commonly mood disorders (44%) and mental and behavioural disorders due to alcohol/substance misuse (19%), and 4.8% (95% CI [2.9, 7.5]) were identified with autism (ADOS Module 4 10+). One had a pre-existing diagnosis of autism; five (26%) had borderline personality disorders (all female) and three (17%) had mood disorders. The AQ and RAADS-R had fair test accuracy (area under receiver operating characteristic [ROC] curve 0.77 and 0.79, respectively). AQ sensitivity was 0.79 (95% CI [0.54, 0.94]) and specificity was 0.77 (95% CI [0.65, 0.86]); RAADS-R sensitivity was 0.75 (95% CI [0.48, 0.93]) and specificity was 0.71 (95% CI [0.60, 0.81]).
Conclusions: The AQ and RAADS-R can guide decisions to refer adults in mental health services to autism diagnostic services.
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http://dx.doi.org/10.1002/mpr.1814 | DOI Listing |
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.
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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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