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Insomnia and Depression: Japanese Hospital Workers Questionnaire Survey

OBJECTIVES: This study aimed to identify a correlation between insomnia and the occurrence of depression among Japanese hospital employees using the data obtained from a self-reported questionnaire. METHODS: A self-administered questionnaire on sleeping patterns, depression, fatigue, lifestyle-relat...

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Detalles Bibliográficos
Autores principales: Koyama, Fumihiko, Yoda, Takeshi, Hirao, Tomohiro
Formato: Online Artículo Texto
Lenguaje:English
Publicado: De Gruyter Open 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5757352/
https://www.ncbi.nlm.nih.gov/pubmed/29318183
http://dx.doi.org/10.1515/med-2017-0056
Descripción
Sumario:OBJECTIVES: This study aimed to identify a correlation between insomnia and the occurrence of depression among Japanese hospital employees using the data obtained from a self-reported questionnaire. METHODS: A self-administered questionnaire on sleeping patterns, depression, fatigue, lifestyle-related diseases, and chronic pain was given to 7690 employees aged 20-60 years, and 5,083 employees responded. RESULTS: An insomnia score of >2 was observed in 840 (13%) respondents. Chronic insomnia correlated significantly with gender, occupation, overtime work, metabolic syndrome, chronic pain, fatigue, and depression. Moreover, significant negative effects on depression scores were observed in males aged 30-39 (partial regression coefficient: b=0.357, p=0.016), females aged 20-29 (b=0.494, p<0.001), male administrative staff (b=0.475, p=0.003), males with metabolic syndrome (b=0.258, p=0.023), and both genders with chronic insomnia (male; b=0.480, p<0.001: female; b=0.485, p<0.001), and fatigue (male; b=1.180, p<0.001: female; b=1.151, p<0.001). DISCUSSION: Insomnia is a risk factor for depression and for other lifestyle-related diseases. The insomnia score may be useful in preventative care settings because it is associated with a wide spectrum of diseases and serves as a valuable marker for early detection of depression. Thus, our future studies will focus on establishing a method for early detection of depression symptoms among workers across various job profiles.