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Three types of university students with subthreshold depression characterized by distinctive cognitive behavioral skills.

Nao ShiraishiMasatsugu SakataRie ToyomotoKazufumi YoshidaYan LuoYukako NakagamiAran TajikaHidemichi SugaHiroshi ItoMichihisa SumiTakashi MutoHiroshi IchikawaMasaya IkegawaTakafumi WatanabeEthan SahkerTeruhisa UwatokoHisashi NomaMasaru HorikoshiTaku IwamiToshiaki A Furawaka
Published in: Cognitive behaviour therapy (2023)
Subthreshold depression impairs young people's quality of life and places them at greater risk of developing major depression. Cognitive behavioral therapy (CBT) is an evidence-based approach for addressing such depressive states. This study identified subtypes of university students with subthreshold depression and revealed discrete profiles of five CBT skills: self-monitoring, cognitive restructuring, behavioral activation, assertive communication, and problem solving. Using data from the Healthy Campus Trial (registration number: UMINCTR-000031307), a hierarchical clustering analysis categorized 1,080 students into three clusters: Reflective Low-skilled, Non-reflective High-skilled, and Non-reflective Low-skilled students. Non-reflective Low-skilled students were significantly more depressed than other students ( p  < .001). The severity of depression seemed to be related to the combination of self-monitoring skills and other CBT skills. Considering the high prevalence of poor self-monitoring skills in persons with autism, the most severe depression was observed in the significant association between Non-reflective Low-skilled students and autistic traits ( p  = .008). These findings suggest that subthreshold depression can be categorized into three subtypes based on CBT skill profiles. The assessment of autistic traits is also suggested when we provide CBT interventions for Non-reflective Low-skilled students.
Keyphrases
  • depressive symptoms
  • high school
  • sleep quality
  • acute care
  • medical students
  • randomized controlled trial
  • physical activity
  • single cell
  • bipolar disorder
  • deep learning
  • big data