Ai enhanced tai chi rehabilitation for substance use disorder with clinical evidence and predictive modeling for relapse prevention

Ariyanto, Nova and Abd Rahman, Siti Fatimah (2025) Ai enhanced tai chi rehabilitation for substance use disorder with clinical evidence and predictive modeling for relapse prevention. Journal of Machine Intelligence in Healthcare, 1 (2). pp. 100-112. ISSN 3090-9511 E-ISSN 3090-9503

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Abstract

Substance use disorder (SUD) remains a significant global health issue, with relapse rates exceeding 60% in some compulsory rehabilitation centers despite structured interventions. Recent research suggests that mind–body exercises, like Tai Chi, can lessen cravings and enhance psychological well-being, though their current use is often standardized, subjective, and not personalized. This study aimed to evaluate the clinical benefits of Tai Chi within a compulsory rehabilitation setting and to develop an AI-enhanced framework for individualized relapse prevention. A randomized controlled trial (RCT) involving 168 participants diagnosed with SUD compared a Tai Chi intervention group with a standard physical education control group. Psychometric outcomes were measured using validated scales, while a conceptual AI framework using a CNN-LSTM architecture was simulated with multimodal inputs that combined psychometric indicators and motion-based features. The RCT showed significant reductions in craving, depression, and anxiety, along with improved self-control in the Tai Chi group compared to controls. Mediation analysis indicated that psychological symptoms partially explained the link between craving and relapse risk. The AI simulation achieved 82% accuracy and an area under the ROC curve of 0.85, with craving and depression identified as key predictors. These results provide initial evidence that integrating Tai Chi with AI-driven monitoring can transform exercise-based rehabilitation into a closed-loop, adaptive system capable of delivering personalized feedback and early relapse warnings. This combined approach has the potential to enhance the scalability, accuracy, and effectiveness of institutional rehab programs for SUD

Item Type:	Article (Journal)
Uncontrolled Keywords:	Substance use disorder, Tai Chi, artificial intelligence, rehabilitation, relapse prediction, human activity recognition, psychometrics, deep learning
Subjects:	K Law > KBP Islamic Law > KBP1 Islamic law.Shariah.Fiqh > KBP490 Furūʻ al-fiqh. Substantive law. Branches of law. > KBP3075 Public health Q Science > Q Science (General) R Medicine > RA Public aspects of medicine > RA0421 Public health. Hygiene. Preventive Medicine T Technology > T Technology (General) T Technology > T Technology (General) > T175 Industrial research. Research and development T Technology > TJ Mechanical engineering and machinery
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Education > Department of Language & Literacy
Depositing User:	Dr Siti Fatimah Abd Rahman
Date Deposited:	25 Sep 2025 16:27
Last Modified:	25 Sep 2025 16:27
URI:	http://irep.iium.edu.my/id/eprint/123392

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