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Published on: December 9, 2014
Identifying sub-health symptom clusters among urban older adults: exploring associations with Tai Chi exercise and
Tongtong Hao1, Juntong Yuan1, Ying He1
1China Wushu School, Beijing Sport University, Beijing, China.
Background:
Sub-health status in urban older adults is multidimensional and heterogeneous, potentially influencing fall risk. This study identifies sub-health symptom clusters and explores how Tai Chi's benefits vary across these groups.
Methods:
From December 2025 to February 2026, 612 sub-healthy older adults in Beijing were analyzed. Latent Profile Analysis (LPA) identified symptom patterns. Differences in demographics and fall risk indicators (fall rate and ABC scale) were compared across clusters. Stratified analysis assessed the association between Tai Chi exercise and fall risk within each cluster.
Results:
Three clusters were identified: Low Physical-Psychological (C1, 21.6%), Balanced Average (C2, 43.9%), and Cognitive-Social Advantage (C3, 34.5%). Significant demographic gradients existed across groups (all p < 0.001). Fall risk also showed a significant gradient: C1 had the highest fall rate (67.4%) and lowest ABC score (56.8 ± 9.6), while C3 had the lowest fall rate (7.6%) and highest ABC score (88.9 ± 6.5). Stratified analysis revealed that Tai Chi practitioners across all clusters had significantly lower fall rates and higher ABC scores than non-practitioners. Notably, C1 showed the greatest benefits, with the largest mean difference in ABC scores (14.6 points) and the most pronounced disparity in fall rates (12.2% vs. 92.3%).
Conclusion:
Sub-health status in urban older adults exhibits significant heterogeneity, with gradient differences in fall risk and Tai Chi benefits. Older adults in the Low Physical-Psychological group face the highest risk but derive the most significant gains from Tai Chi. Community health management should integrate sub-health profiling to develop personalized interventions. Prioritizing Tai Chi for health-impaired individuals can ensure precise resource allocation and maximize fall prevention outcomes.
