Objective: A habitual sedentary lifestyle is associated with adverse health outcomes; however, the predictors of sedentary behaviors have not been sufficiently explored to inform the development and delivery of effective interventions to reduce sedentary behaviors. This study examined whether reports of symptoms of depression could predict weekly time spent in sedentary behaviors (i.e., television watching, computer use) 4years later.
Method: Self-reported symptoms of depression were assessed at age 20years (2007-08), and television watching time and computer use were assessed at age 24years (2011-12) in 761 adults (45% men) participating in the Nicotine Dependence in Teens study. Data were analyzed using linear regression analysis, with separate models for men and women.
Results: After controlling for past sedentary behavior, symptoms of depression at age 20years predicted more computer use 4years later in men (R(2)=.21, β=.13, p<.05), but not in women. Symptoms of depression did not predict television watching.
Conclusions: Results highlight the need to distinguish between types of sedentary behaviors as their predictors may differ. Further, they provide support for the hypothesis that psychological factors, in this case symptoms of depression, may relate to select sedentary behaviors in young men.
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http://dx.doi.org/10.1016/j.ypmed.2013.12.003 | DOI Listing |
J Adolesc Health
January 2025
Laboratory of Cognitive Control and Brain Healthy, Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, China; Department of Psychiatry, Zhujiang Hospital, Southern Medical University, Guangzhou, China. Electronic address:
Purpose: Subthreshold depression refers to a condition involving clinically significant depressive symptoms that fall short of meeting the diagnostic criteria for major depressive disorder (MDD). Identifying risk and protective factors associated with the progression of subthreshold depression in early life is essential for timely prevention. However, there is limited research on this topic among early adolescents.
View Article and Find Full Text PDFJ Voice
January 2025
Department of Surgery, UMONS Research Institute for Health Sciences and Technology, University of Mons (UMons), Mons, Belgium; Division of Laryngology and Bronchoesophagology, Department of Otolaryngology Head Neck Surgery, EpiCURA Hospital, Baudour, Belgium; Department of Otolaryngology-Head and Neck Surgery, Foch Hospital, School of Medicine, UFR Simone Veil, Université Versailles Saint-Quentin-en-Yvelines (Paris Saclay University), Paris, France; Department of Otolaryngology, Elsan Hospital, Paris, France. Electronic address:
Background: Voice analysis has emerged as a potential biomarker for mood state detection and monitoring in bipolar disorder (BD). The systematic review aimed to summarize the evidence for voice analysis applications in BD, examining (1) the predictive validity of voice quality outcomes for mood state detection, and (2) the correlation between voice parameters and clinical symptom scales.
Methods: A PubMed, Scopus, and Cochrane Library search was carried out by two investigators for publications investigating voice quality in BD according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statements.
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.
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