Tai Ji Quan Roadmap Charts Path to Next-Generation Exercise Medicine for Falls Prevention
PRESS RELEASE
22 September 2026
Tai Ji Quan Roadmap Charts Path to Next-Generation Exercise Medicine for Falls Prevention
New review outlines clinical, digital trial, and AI strategies to bring tai ji quan into routine fall-prevention care
A new review in the Journal of Sport and Health Science proposes a translational roadmap for turning tai ji quan into a scalable, evidence-based form of exercise medicine for preventing falls among older adults. The article highlights strong evidence for tai ji quan’s safety, effectiveness, and cost value, while outlining three priorities for broader impact: clinical workflow integration, decentralized virtual trials, and responsibly governed Augmented AI.
Falls remain a major and growing public health challenge for aging populations worldwide. Although tai ji quan (also known as tai chi) has long been studied as a mind–body exercise with benefits for strength, balance, mobility, and confidence, its full integration into routine healthcare remains limited. In a new review, Fuzhong Li of the Oregon Research Institute argues that the field is ready to move beyond proving efficacy and toward building systems that can deliver evidence-based tai ji quan reliably, equitably, and at scale.
The review summarizes more than three decades of research showing that tai ji quan can reduce falls and improve mobility among older adults, particularly when tested in adequately powered randomized controlled trials. Reported effects across major trials and meta-analyses support tai ji quan as a safe, low-cost, and clinically useful intervention. The article also notes that tai ji quan is included in public health and clinical recommendations, including falls-prevention guidance and evidence-based intervention compendia.
Despite this evidence base, Li emphasizes that tai ji quan remains only loosely connected to everyday clinical care. To close this evidence-to-practice gap, the review proposes a three-pillar roadmap. The first pillar calls for embedding tai ji quan into routine clinical workflows, beginning with systematic fall-risk screening and referral, followed by program linkage, coordinated delivery, outcome feedback to clinicians, and sustainability mechanisms such as reimbursement and incentives.
The second pillar focuses on decentralized, virtual randomized controlled trials. Digital health technologies—including telehealth platforms, wearable sensors, electronic consent, and remote data capture—could make tai ji quan research more accessible to people who face transportation barriers, mobility limitations, weather constraints, or limited access to community exercise programs. The review cites emerging evidence that virtual tai ji quan interventions can be feasible and safe when supported by careful adaptation, participant orientation, instructor training, and remote safety monitoring.
The third pillar introduces an Augmented-AI framework, emphasizing collaboration between human expertise and AI-enabled technologies. Potential applications include identifying fall risk, supporting referral decisions, personalizing exercise prescriptions, monitoring movement quality, tracking adherence, and communicating outcomes back to clinicians. The review stresses that these tools must be developed with strong governance, including privacy protections, model validation, bias and drift monitoring, and human oversight.
Li argues that tai ji quan is especially well suited for this transformation because it integrates physical, sensorimotor, and cognitive elements in a structured and reproducible movement system. These characteristics create opportunities for digital delivery, remote monitoring, AI-assisted feedback, and individualized progression. The proposed roadmap may also serve as a broader model for translating other evidence-based exercise interventions into technology-enabled healthcare delivery for chronic conditions associated with aging.
Reference
| Title of original paper: | Transforming tai ji quan into next-generation, evidence-based exercise medicine: A translational roadmap for falls prevention |
| Journal: | Journal of Sport and Health Science |
| DOI: | 10.1016/j.jshs.2026.101169 |
Additional information
| Latest Article Publication Date: | 31 August 2026 |
| Method of Research: | Literature review |
| Subject of Research: | People |
| Conflicts of Interest Statement: | The author is the founder and owner of Exercise Alternatives, LLC. The author declares no financial competing interest. Given his role as associate editor, FL had no involvement in the peer review of this article and had no access to information regarding its peer review. Full responsibility for the editorial process for this article was delegated to another journal editor. |
About Dr. Fuzhong Li from Oregon Research Institute
Dr. Fuzhong Li is affiliated with Oregon Research Institute in Springfield, Oregon, USA. His work focuses on developing, evaluating, and translating tai ji quan-based interventions for falls prevention, mobility, cognition, and healthy aging.
Funding information
This work was supported by grants R01AG074045-01A1 and R01AG081206-01 from the National Institute on Aging. The funder had no role in the writing of the manuscript.
Media contact
Name: Kathryn Madden
e-mail: kathryn@ori.org
Image
Title: Integrated care model for AI-enabled tai ji quan-based falls prevention
Caption: The figure illustrates a future tai ji quan-based falls-prevention ecosystem linking research and evidence generation, Augmented-AI infrastructure, core AI functions, and clinical care delivery.
Credit: Dr. Fuzhong Li from Oregon Research Institute, USA
Image source link: https://www.sciencedirect.com/science/article/pii/S2095254626000608?via%3Dihub#abs0003
License type: CC BY-NC-ND 4.0
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