JMIR mHealth and uHealth (Jul 2020)

Intra-Rater and Inter-Rater Reliability of Tongue Coating Diagnosis in Traditional Chinese Medicine Using Smartphones: Quasi-Delphi Study

  • Wang, Zhi Chun,
  • Zhang, Shi Ping,
  • Yuen, Pong Chi,
  • Chan, Kam Wa,
  • Chan, Yi Yi,
  • Cheung, Chun Hoi,
  • Chow, Chi Ho,
  • Chua, Ka Kit,
  • Hu, Jun,
  • Hu, Zhichao,
  • Lao, Beini,
  • Leung, Chun Chuen,
  • Li, Hong,
  • Zhong, Linda,
  • Liu, Xusheng,
  • Liu, Yulong,
  • Liu, Zhenjie,
  • Lun, Xin,
  • Mo, Wei,
  • Siu, Sheung Yuen,
  • Xiong, Zhoujian,
  • Yeung, Wing Fai,
  • Zhang, Run Yun,
  • Zhang, Xuebin

Journal volume & issue
Vol. 8, no. 7
p. e16018


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BackgroundThere is a growing trend in the use of mobile health (mHealth) technologies in traditional Chinese medicine (TCM) and telemedicine, especially during the coronavirus disease (COVID-19) outbreak. Tongue diagnosis is an important component of TCM, but also plays a role in Western medicine, for example in dermatology. However, the procedure of obtaining tongue images has not been standardized and the reliability of tongue diagnosis by smartphone tongue images has yet to be evaluated. ObjectiveThe first objective of this study was to develop an operating classification scheme for tongue coating diagnosis. The second and main objective of this study was to determine the intra-rater and inter-rater reliability of tongue coating diagnosis using the operating classification scheme. MethodsAn operating classification scheme for tongue coating was developed using a stepwise approach and a quasi-Delphi method. First, tongue images (n=2023) were analyzed by 2 groups of assessors to develop the operating classification scheme for tongue coating diagnosis. Based on clinicians’ (n=17) own interpretations as well as their use of the operating classification scheme, the results of tongue diagnosis on a representative tongue image set (n=24) were compared. After gathering consensus for the operating classification scheme, the clinicians were instructed to use the scheme to assess tongue features of their patients under direct visual inspection. At the same time, the clinicians took tongue images of the patients with smartphones and assessed tongue features observed in the smartphone image using the same classification scheme. The intra-rater agreements of these two assessments were calculated to determine which features of tongue coating were better retained by the image. Using the finalized operating classification scheme, clinicians in the study group assessed representative tongue images (n=24) that they had taken, and the intra-rater and inter-rater reliability of their assessments was evaluated. ResultsIntra-rater agreement between direct subject inspection and tongue image inspection was good to very good (Cohen κ range 0.69-1.0). Additionally, when comparing the assessment of tongue images on different days, intra-rater reliability was good to very good (κ range 0.7-1.0), except for the color of the tongue body (κ=0.22) and slippery tongue fur (κ=0.1). Inter-rater reliability was moderate for tongue coating (Gwet AC2 range 0.49-0.55), and fair for color and other features of the tongue body (Gwet AC2=0.34). ConclusionsTaken together, our study has shown that tongue images collected via smartphone contain some reliable features, including tongue coating, that can be used in mHealth analysis. Our findings thus support the use of smartphones in telemedicine for detecting changes in tongue coating.