Using Tai Chi and Qigong to Treat Back Pain: An Application of Artificial Intelligence to Traditional Chinese Medicine

Abstract Tai Chi and Qigong are safe, effective, non-pharmacological interventions for chronic low back pain (CLBP). This comprehensive review, synthesized with the assistance of artificial intelligence, integrates evidence from 16 studies (randomized controlled trials, systematic reviews, meta-analyses, and qualitative investigations) published between 2011 and 2025. Across diverse populations, including older adults, Tai Chi (Yang, Chen, and other styles) and Qigong consistently produced moderate-to-large reductions in pain intensity (pooled effect sizes ranging from SMD –0.64 to –1.75 and WMD –1.09 to –1.27 on 0–10 scales) and disability (SMD up to –1.96), together with improvements in physical function, quality of life, balance, gait, and psychological variables such as pain catastrophizing. Interventions lasting 6–36 weeks (typically 40–60 min sessions, 2–5 times/week, ≥30 total sessions preferred) showed the strongest and most sustained benefits, with very low rates of adverse events. Mechanisms appear multifactorial, involving enhanced neuromuscular coordination, core stability, proprioception, anti-inflammatory effects, stress reduction, mindfulness, and, from the traditional Chinese medicine perspective, improved circulation and cultivation of Qi. Despite limitations such as study heterogeneity, small sample sizes in some trials, and variable methodological quality, the overall body of evidence supports the clinical integration of Tai Chi and Qigong, either alone or as adjuncts to conventional care, into multidisciplinary management of chronic low back pain. Standardized protocols, longer-term follow-up studies, and direct head-to-head comparisons with other active exercises are recommended to further optimize prescription parameters.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-02-05
DOI
https://doi.org/10.5281/zenodo.18492168
Primary Topic
Biofield Effects and Biophysics
Type
article
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article

Using Tai Chi and Qigong to Treat Back Pain: An Application of Artificial Intelligence to Traditional Chinese Medicine

Robert W. McGee
Zenodo (CERN European Organization for Nuclear Research)
Biofield Effects and Biophysics
article

Using Tai Chi and Qigong to Treat Back Pain: An Application of Artificial Intelligence to Traditional Chinese Medicine

Robert W. McGee
article en

Abstract

Abstract Tai Chi and Qigong are safe, effective, non-pharmacological interventions for chronic low back pain (CLBP). This comprehensive review, synthesized with the assistance of artificial intelligence, integrates evidence from 16 studies (randomized controlled trials, systematic reviews, meta-analyses, and qualitative investigations) published between 2011 and 2025. Across diverse populations, including older adults, Tai Chi (Yang, Chen, and other styles) and Qigong consistently produced moderate-to-large reductions in pain intensity (pooled effect sizes ranging from SMD –0.64 to –1.75 and WMD –1.09 to –1.27 on 0–10 scales) and disability (SMD up to –1.96), together with improvements in physical function, quality of life, balance, gait, and psychological variables such as pain catastrophizing. Interventions lasting 6–36 weeks (typically 40–60 min sessions, 2–5 times/week, ≥30 total sessions preferred) showed the strongest and most sustained benefits, with very low rates of adverse events. Mechanisms appear multifactorial, involving enhanced neuromuscular coordination, core stability, proprioception, anti-inflammatory effects, stress reduction, mindfulness, and, from the traditional Chinese medicine perspective, improved circulation and cultivation of Qi. Despite limitations such as study heterogeneity, small sample sizes in some trials, and variable methodological quality, the overall body of evidence supports the clinical integration of Tai Chi and Qigong, either alone or as adjuncts to conventional care, into multidisciplinary management of chronic low back pain. Standardized protocols, longer-term follow-up studies, and direct head-to-head comparisons with other active exercises are recommended to further optimize prescription parameters.

Zenodo (CERN European Organization for Nuclear Research)
Fayetteville State University (US)
Openalex Percentile: Top 63%
Biofield Effects and Biophysics
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