Objective: To assess the validity of the Hospital Anxiety and Depression Scale (HADS) for depression and anxiety screening in primary care patients in Colombia.
Methods: A criterion validity study was conducted with 243 adults that had completed the HADS and were later assessed using the MINI as a gold standard. Cronbach's alpha, McDonald's omega and factor structure were applied through confirmatory factor analysis (CFA). ROC curve analysis and Youden's statistic were used to determine the cut-off point.
Results: Cronbach's α was reported to be 0.85 and 0.82 for McDonalds' ω. CFA supported a two-factor solution demonstrating satisfactory fit. Root mean square error of approximation = 0.04, Comparative Fix Index (CFI) and Tucker-Lewis Index (TLI) = 0.97. For HADS-A, the cut-off point was determined as 6 associated with a sensitivity of 0.76, a specificity of 0.72 and Youden's index of 0.50. The ABC was 0.81. For HADS-D, the cut-off point was determined as 4 associated with a sensitivity of 0.78, a specificity of 0.74 and Youden's index of 0.53. The ABC was 0.82.
Conclusion: The HADS is a valid and reliable instrument for anxiety and depression screening in adult patients of primary healthcare services in Colombia.
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http://dx.doi.org/10.1016/j.genhosppsych.2021.01.014 | DOI Listing |
Neurosci Biobehav Rev
January 2025
Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands.
During most dreams, the dreamer does not realize that they are in a dream. In contrast, lucid dreaming allows to become aware of the current state of mind, often accompanied by considerable control over the ongoing dream episode. Lucid dreams can happen spontaneously or be induced through diverse behavioural, cognitive or technological strategies.
View Article and Find Full Text PDFJ Affect Disord
January 2025
Department of Psychiatry and Psychotherapy, University of Marburg, Germany; Center for Mind, Brain and Behavior (CMBB), University of Marburg, Germany.
Background: Major depressive disorder (MDD) comes along with an increased risk of recurrence and poor course of illness. Machine learning has recently shown promise in the prediction of mental illness, yet models aiming to predict MDD course are still rare and do not quantify the predictive value of established MDD recurrence risk factors.
Methods: We analyzed N = 571 MDD patients from the Marburg-Münster Affective Disorder Cohort Study (MACS).
J Affect Disord
January 2025
Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA; Department of Medicine, Duke University, Durham, NC, USA; Duke Institute of Brain Sciences, Duke University, Durham, NC, USA. Electronic address:
Metabolomics provides powerful tools that can inform about heterogeneity in disease and response to treatments. In this exploratory study, we employed an electrochemistry-based targeted metabolomics platform to assess the metabolic effects of three randomly-assigned treatments: escitalopram, duloxetine, and Cognitive-Behavioral Therapy (CBT) in 163 treatment-naïve outpatients with major depressive disorder. Serum samples from baseline and 12 weeks post-treatment were analyzed using targeted liquid chromatography-electrochemistry for metabolites related to tryptophan, tyrosine metabolism and related pathways.
View Article and Find Full Text PDFJ Affect Disord
January 2025
Lusófona University, HEI-Lab: Digital Human-Environment Interaction Labs, Portugal. Electronic address:
Assessing Fear of Birth Scale's (FOBS) psychometric properties in the perinatal period using multicountry data is a step toward effectively screen clinically significant fear of childbirth (FOC) in maternal healthcare settings. FOBS psychometric properties were analyzed in women in the perinatal period using data from Australia, Germany, Lithuania, Poland, and Portugal. FOBS' reliability, criterion (known group and convergent), concurrent, predictive, and clinical validity were analyzed.
View Article and Find Full Text PDFJ Affect Disord
January 2025
Center for Anti-racism, Social Justice & Public Health, New York University School of Global Public Health, New York, NY, USA; Department of Biostatistics, New York University School of Global Public Health, New York, NY, USA. Electronic address:
Background: A knowledge gap exists in understanding the role of social isolation as a determinant of mental health among hybrid employees during the COVID-19 era.
Methods: Using 2024 Household Pulse Survey data, we investigated the relationship between social isolation and mental health among US hybrid employees. We assessed depression symptoms using the Patient Health Questionnaire-2 and anxiety symptoms using the Generalized Anxiety Disorder-2.
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