Exercise seems to be effective in reducing depression itself, as well as the risk of relapse. This study evaluated whether standardized guided exercise therapy (GET) in comparison with self-organized activity (SOA) is an effective augmentation therapy in depressive adults. A total of 111 inpatients (66.7% women; mean age, 45.05 ± 12.19 years) with major depression were randomly assigned to either GET or SOA. Interventions were performed three times a week, with each session lasting 50 minutes. Both GET and SOA exerted effects even after a short-term application of 6 weeks. GET was superior to SOA in reducing depression symptom severity, as measured by the Hamilton Depression Scale (p = 0.017), specifically improving suicidality (p = 0.028) as well as time (p = 0.003) and severity of diurnal variation (p = 0.027). The findings support the beneficial role of adjuvant GET in patients with major depression as a feasible treatment in a psychiatric short-term inpatient setting.
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http://dx.doi.org/10.1097/NMD.0000000000001240 | DOI Listing |
J Affect Disord
January 2025
Department of Neurology, The First Affiliated Hospital of Anhui Medical University, 218 Jixi Road, Anhui Province, Hefei 230022, China; Department of Neurology, The Second Affiliated Hospital of Anhui Medical University, Hefei 230601, China; Department of Psychology and Sleep Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei 230601, China. Electronic address:
Background: Electroconvulsive therapy (ECT) is an effective treatment for patients with major depressive disorder (MDD), but the underlying neuromodulatory mechanisms remain largely unknown. Functional stability represents a newly developed method based on the dynamic functional connectivity framework. This study aimed to explore ECT-evoked changes in functional stability and their relationship with clinical outcomes.
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
University of Ottawa Institute of Mental Health Research, University of Ottawa, Ottawa, Canada. Electronic address:
Aim: Major depressive disorder (MDD) is characterized by altered activity in various higher-order regions like the anterior cingulate and prefrontal cortex. While some findings also show changes in lower-order sensory regions like the occipital cortex in MDD, the latter's exact neural and temporal, e.g.
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