Prediction of knee joint pain in Tai Chi practitioners: a cross-sectional machine learning approach
Author: Hua Xing#1, Xiaojie Su#1, Yushan Liu2, Yang Chen2, Yubin Ju1, Zhiran Kang1, Wuquan Sun1, Fei Yao3, Lijun Yao4, Li Gong5
Affiliation:
1 Department of Tuina, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
2 Shanghai Key Lab of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai, China.
3 School of Acupuncture and Tuina, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
4 Clinical Research Center for Mental Disorders, Shanghai Pudong New Area Mental Health Center, Tongji University School of Medicine, Shanghai, China lyao@tongji.edu.cn yyyygongli@shutcm.edu.cn.
5 Department of Tuina, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China lyao@tongji.edu.cn yyyygongli@shutcm.edu.cn.
Conference/Journal: BMJ Open
Date published: 2023 Aug 1
Other:
Volume ID: 13 , Issue ID: 8 , Pages: e067036 , Special Notes: doi: 10.1136/bmjopen-2022-067036. , Word Count: 348
Objective:
To build a supervised machine learning-based classifier, which can accurately predict whether Tai Chi practitioners may experience knee pain after years of exercise.
Design:
A prospective approach was used. Data were collected using face-to-face through a self-designed questionnaire.
Setting:
Single centre in Shanghai, China.
Participants:
A total of 1750 Tai Chi practitioners with a course of Tai Chi exercise over 5 years were randomly selected.
Measures:
All participants were measured by a questionnaire survey including personal information, Tai Chi exercise pattern and Irrgang Knee Outcome Survey Activities of Daily Living Scale. The validity of the questionnaire was analysed by logical analysis and test, and the reliability of this questionnaire was mainly tested by a re-test method. Dataset 1 was established by whether the participant had knee pain, and dataset 2 by whether the participant's knee pain affected daily living function. Then both datasets were randomly assigned to a training and validating dataset and a test dataset in a ratio of 7:3. Six machine learning algorithms were selected and trained by our dataset. The area under the receiver operating characteristic curve was used to evaluate the performance of the trained models, which determined the best prediction model.
Results:
A total of 1703 practitioners completed the questionnaire and 47 were eliminated for lack of information. The total reliability of the scale is 0.94 and the KMO (Kaiser-Meyer-Olkin measure of sampling adequacy) value of the scale validity was 0.949 (>0.7). The CatBoost algorithm-based machine-learning model achieved the best predictive performance in distinguishing practitioners with different degrees of knee pain after Tai Chi practice. 'Having knee pain before Tai Chi practice', 'knee joint warm-up' and 'duration of each exercise' are the top three factors associated with pain after Tai Chi exercise in the model. 'Having knee pain before Tai Chi practice', 'Having Instructor' and 'Duration of each exercise' were most relevant to whether pain interfered with daily life in the model.
Conclusion:
CatBoost-based machine learning classifier accurately predicts knee pain symptoms after practicing Tai Chi. This study provides an essential reference for practicing Tai Chi scientifically to avoid knee pain.
Keywords: Adult orthopaedics; Knee; SPORTS MEDICINE.
PMID: 37527889 DOI: 10.1136/bmjopen-2022-067036
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