Journal of Oriental Neuropsychiatry (동의신경정신과학회지)

The Korean Society of Oriental Neuropsychiatry (대한한방신경정신과학회)
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Can Data-Driven Analysis Demonstrate the Plausibility of Traditional Medical Typology?

  • Chae, Han (School of Korean Medicine, Pusan National University) ;
  • Lee, Siwoo (KM Data Division, Korea Institute of Oriental Medicine) ;
  • Lee, Soo Jin (Department of Psychology, Kyungsung University)
  • Received : 2021.11.15
  • Accepted : 2021.12.02
  • Published : 2021.12.30
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Abstract

Objectives: Although medical typologies based on indigenous biopsychological ideas have been described, their integrity has been questioned due to its theory-driven nature in categorization. Therefore, studies on the Sasang typology, a temperament-based traditional Korean medicine, are needed to examine whether it is possible to classify types of specific biopsychological profiles using data-driven analysis. Methods: Psychological measures of the Eastern Sasang Personality Questionnaire (SPQ) and Western NEO-Personality Inventory (NEO-PI) along with physical measures and Sasang types were acquired from 2,049 participants. Latent groups based on the SPQ and NEO-PI subscale scores were extracted using Latent Profile Analysis. Their psychosomatic features were then compared with those of Sasang types. Results: Three SPQ-based latent groups showed distinctive psychological and physical features consistent with those of Sasang types. However, four NEOPI-based latent groups presented only psychological features. Furthermore, SPQ-High and SPQ-Low latent groups demonstrated similar psychosomatic profiles to those of So-Yang and So-Eum Sasang types, respectively. Conclusions: This study illustrates that biopsychological profiles of Sasang types are supported by psychosomatic features of latent groups based on SPQ of Eastern psychology, signifying that the categorization of Sasang typology have acceptable validity and reliability.

Keywords

Acknowledgement

This study was conducted with the support of BIG project of KIOM, grant no. KSN2022120.

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