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

Abstract Background: Anxiety and depression are highly prevalent, particularly among older adults, cancer patients, and individuals with chronic diseases. Tai Chi and Qigong, traditional Chinese mind-body practices, have been increasingly studied as non-pharmacological interventions. Objective: To synthesize current evidence from systematic reviews and randomized trials on the efficacy of Tai Chi and Qigong for reducing anxiety and depression symptoms. Methods: Thirty-six high-quality studies and meta-analyses published primarily between 2019 and 2025 were reviewed with the assistance of artificial intelligence (Grok) to extract effect sizes, optimal protocols, mechanisms, and clinical recommendations. Results: Tai Chi and Qigong consistently produced moderate-to-large reductions in depressive symptoms (standardized mean differences [SMD] –0.56 to –1.23) and anxiety symptoms (SMD –0.45 to –2.13). Benefits were observed across older adults, cancer survivors, patients with cardiovascular disease, COPD, stroke, diabetes, substance use disorders, and perinatal populations. Optimal dosing appears to be 40–60 minutes, 3–4 times weekly, for ≥12 weeks. Proposed mechanisms include reduced inflammation, enhanced brain connectivity, improved serotonin metabolism, and increased parasympathetic activity. Conclusion: Tai Chi and Qigong are safe, effective, evidence-based adjunctive therapies for anxiety and depression. They should be routinely integrated into psychiatric, geriatric, oncological, and primary-care settings as complementary or alternative interventions, especially when conventional treatments are poorly tolerated or insufficient.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-01-25
DOI
https://doi.org/10.5281/zenodo.18369237
Primary Topic
Biofield Effects and Biophysics
Type
article
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article

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

Robert W. McGee
article en

Abstract

Abstract Background: Anxiety and depression are highly prevalent, particularly among older adults, cancer patients, and individuals with chronic diseases. Tai Chi and Qigong, traditional Chinese mind-body practices, have been increasingly studied as non-pharmacological interventions. Objective: To synthesize current evidence from systematic reviews and randomized trials on the efficacy of Tai Chi and Qigong for reducing anxiety and depression symptoms. Methods: Thirty-six high-quality studies and meta-analyses published primarily between 2019 and 2025 were reviewed with the assistance of artificial intelligence (Grok) to extract effect sizes, optimal protocols, mechanisms, and clinical recommendations. Results: Tai Chi and Qigong consistently produced moderate-to-large reductions in depressive symptoms (standardized mean differences [SMD] –0.56 to –1.23) and anxiety symptoms (SMD –0.45 to –2.13). Benefits were observed across older adults, cancer survivors, patients with cardiovascular disease, COPD, stroke, diabetes, substance use disorders, and perinatal populations. Optimal dosing appears to be 40–60 minutes, 3–4 times weekly, for ≥12 weeks. Proposed mechanisms include reduced inflammation, enhanced brain connectivity, improved serotonin metabolism, and increased parasympathetic activity. Conclusion: Tai Chi and Qigong are safe, effective, evidence-based adjunctive therapies for anxiety and depression. They should be routinely integrated into psychiatric, geriatric, oncological, and primary-care settings as complementary or alternative interventions, especially when conventional treatments are poorly tolerated or insufficient.

Zenodo (CERN European Organization for Nuclear Research)
Fayetteville State University (US)
Good health and well-being
Openalex Percentile: Top 79%
Biofield Effects and Biophysics
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