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Bibliometric and visual analysis of circadian rhythms in depression from 2004 to 2024.
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Introduction: Understanding the intricate relationship between circadian rhythms and depression is crucial for developing effective interventions and treatments for individuals affected by depression. Circadian rhythms regulate various physiological and behavioral processes, while depression manifests as persistent feelings of sadness and disturbances in sleep, appetite, and energy levels. Emerging research suggests a significant interplay between circadian rhythm disruption and depression, highlighting the need for comprehensive analysis in this area. Methodology: A bibliometric and visual analysis of literature on circadian rhythms in depression from 2004 to 2024 was conducted using the Web of Science Core Collection. Data were analyzed using bibliometric tools including VOSviewer, CiteSpace, and Bibliometrix to identify publication trends, geographical distribution, authorship patterns, institutional collaborations, journal preferences, keyword co-occurrence, and highly cited references. Results: Analysis revealed a steady increase in publications and citations related to circadian rhythms in depression. The United States emerged as the leading contributor, with strong global collaborations. Key journals included Chronobiology International and Journal of Affective Disorders. Top keywords included circadian rhythm, depression, sleep, melatonin, and bipolar disorder. The most cited article is a review titled "Practice parameters for the indications for polysomnography and related procedures: An update for 2005". Conclusions: This study offers a comprehensive overview of research on circadian rhythms in depression, highlighting key trends, contributors, and interdisciplinary intersections. [ABSTRACT FROM AUTHOR]
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Copyright of Annals of General Psychiatry is the property of BioMed Central and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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