The novel effectiveness of Tai Chi on cardiopulmonary fitness among stroke patients in the recovery phase: a study protocol for a randomized controlled trial

Tan, Tianyang; Meng, Yanyan; Li, Xinyu; Bai, Ru; Wang, Chengchao; Lyu, Jiaxuan; Yan, Kang; Li, Meng; Zhang, Chaoyang; Wei, Yulong

doi:10.1186/s13063-021-05565-2

Cited by 5 publications

(1 citation statement)

References 39 publications

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“…Therefore its safety has been well evaluated 3 4. Recent studies indicate that Tai Chi improves cardio-pulmonary function5 6 and prevents elders from falling 7 8. In patients with knee osteoarthritis (KOA), Tai Chi effectively enhances their strength, balance9 10 and proprioception,11 thereby alleviating knee joint stiffness and pain 12…”

Section: Introductionmentioning

confidence: 99%

Prediction of knee joint pain in Tai Chi practitioners: a cross-sectional machine learning approach

Xing,

Su,

Liu

et al. 2023

BMJ Open

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ObjectiveTo build a supervised machine learning-based classifier, which can accurately predict whether Tai Chi practitioners may experience knee pain after years of exercise.DesignA prospective approach was used. Data were collected using face-to-face through a self-designed questionnaire.SettingSingle centre in Shanghai, China.ParticipantsA total of 1750 Tai Chi practitioners with a course of Tai Chi exercise over 5 years were randomly selected.MeasuresAll 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.ResultsA 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.ConclusionCatBoost-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.

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Section: Introductionmentioning

confidence: 99%

Prediction of knee joint pain in Tai Chi practitioners: a cross-sectional machine learning approach

Xing,

Su,

Liu

et al. 2023

BMJ Open

View full text Add to dashboard Cite

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Effect of Taichi Chuan on health-related physical fitness in adults: A systematic review with meta-analysis

Soh

Nasiruddin

et al. 2023

Complementary Therapies in Medicine

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Modified tai chi movement training based on sEMG and movement analysis on improving upper extremities motor function: a protocol for a clinical randomised controlled trial

Li,

et al. 2024

BMJ Open

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IntroductionStroke survivors often face motor dysfunction, increasing fall risk. Lower extremity muscle weakness is a key factor affecting walking ability. Tai chi (TC) has been shown to improve muscle strength and mobility in patients with stroke more effectively than traditional walking training. However, existing TC programmes for stroke rehabilitation are often too simplified and fail to fully use TC’s benefits. Additionally, subjective assessment scales are time-consuming and prone to bias. This study proposes integrating TC’s early movement features with neurodevelopmental therapy, using surface electromyography and inertial measurement unit (IMU) sensors to thoroughly analyse diverse TC movements. Tailored exercises, based on stroke-induced impairments, will be objectively assessed through biomechanical analysis.Methods and analysisThe study unfolds in two phases. The initial phase employs the IMU sensor and electromyography to objectively analyse TC’s biomechanics, informing personalised rehabilitation plans aligned with distinct movement impairments. The second phase adopts a randomised, single-blind, parallel controlled trial design involving 60 patients with stroke randomly assigned to either the intervention or control group. The intervention group undergoes biomechanics-based TC training alongside routine rehabilitation for 12 weeks, practicing the 24-form TC three times weekly. The control group engages in routine rehabilitation thrice weekly for the same duration. Primary and secondary outcomes, including kinematic/dynamic data, surface electromyography, motion analysis, comprehensive the international classification of functioning, disability and health Core Set for Stroke, Modified Barthel Index and Fugl-Meyer Assessment, will be evaluated at baseline and post-intervention.Ethics and disseminationThe study has received approval from the Ethics Committee of Zhongda Hospital Southeast University (2023ZDSYLL378-P01). All prospective participants will receive comprehensive information regarding the study protocol, and their informed consent will be obtained before their participation. Additionally, the trial will be registered with the Chinese Clinical Trial Registry to ensure transparency and compliance with research regulations. Results from this study will be disseminated through peer-reviewed journals, conference presentations and public databases to ensure wide accessibility and to contribute to the advancement of medical knowledge.Protocol version2.0 (14 June 2024).Trial registration numberwww.chictr.org.cn, identifier ChiCTR2400080158.

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The novel effectiveness of Tai Chi on cardiopulmonary fitness among stroke patients in the recovery phase: a study protocol for a randomized controlled trial

Cited by 5 publications

References 39 publications

Prediction of knee joint pain in Tai Chi practitioners: a cross-sectional machine learning approach

Prediction of knee joint pain in Tai Chi practitioners: a cross-sectional machine learning approach

Effect of Taichi Chuan on health-related physical fitness in adults: A systematic review with meta-analysis

Modified tai chi movement training based on sEMG and movement analysis on improving upper extremities motor function: a protocol for a clinical randomised controlled trial

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