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.
