Chinese Journal of Tissue Engineering Research ›› 2026, Vol. 30 ›› Issue (16): 4038-4044.doi: 10.12307/2026.335

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Relationship between spatio-temporal gait characteristics and fall risk in stroke patients

Xu Feng1, Gu Dongyang1, Zhu Zihao2, 3, Li Qiujie2, 3, Wan Xianglin2, 3   

  1. 1Department of Rehabilitation, People’s Hospital of Queshan, Zhumadian 463200, Henan Province, China; 2School of Sport Science, Beijing Sport University, Beijing 100084, China; 3Key Laboratory for Performance Training & Recovery of General Administration of Sport of China, Beijing 100084, China
  • Received:2025-06-06 Accepted:2025-08-27 Online:2026-06-08 Published:2025-11-26
  • Contact: Wan Xianglin, MD, Associate professor, Doctoral supervisor, School of Sport Science, Beijing Sport University, Beijing 100084, China; Key Laboratory for Performance Training & Recovery of General Administration of Sport of China, Beijing 100084, China
  • About author:Xu Feng, Associate chief physician, Department of Rehabilitation, People’s Hospital of Queshan, Zhumadian 463200, Henan Province, China
  • Supported by:
    Fundamental Research Funds for the Central Universities of China, No. 2024TNJN008 (to LQJ)

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Abstract: BACKGROUND: Abnormalities in the spatio-temporal gait characteristics of stroke patients are closely associated with their risk of falling.
OBJECTIVE: To construct a model for assessing the fall risk of stroke patients based on spatio-temporal gait parameters, thereby providing a foundation for refining the fall risk assessment system and optimizing fall prevention strategies for such patients.
METHODS: Thirty-four stroke patients with unilateral hemiplegia who were discharged after recovery were recruited. These patients were divided into two groups based on whether they had a history of falls within 6 months prior to testing: the faller group (with a history of falls) and the non-faller group (without a history of falls). The Qualisys infrared motion capture system was employed to collect the coordinates of surface markers on the patients’ bodies during walking, from which temporal-spatial gait parameters were calculated. Univariate analysis was used to compare the spatio-temporal gait parameters between the two groups, and logistic regression analysis was conducted to establish a regression model for assessing the fall risk of patients.
RESULTS AND CONCLUSION: (1) A total of 34 eligible participants were included in the study, comprising 19 individuals in the faller group and 15 in the non-faller group. (2) The faller group exhibited a significantly higher percentage of stance phase on the unaffected side compared with the non-faller group (P < 0.05). Additionally, the faller group had significantly lower values for the percentage of swing phase on the unaffected side, stride length on the unaffected side, stride length on the affected side, double support time, and walking speed compared with the non-faller group (P < 0.05). (3) The variables ultimately included in the logistic regression model were the percentage of stance phase on the unaffected side and double support time (P < 0.05). The overall accuracy of this model was 79.4%, with a sensitivity of 73.7% and specificity of 86.7%. To conclude, the percentage of stance phase on the unaffected side and double support time during walking in stroke patients can effectively assess fall risks. The fall risk assessment model for stroke patients constructed based on spatio-temporal gait parameters can provide clinicians with a basis for screening patients at high risk of falls and implementing early preventive treatments.

Key words: stroke, spatio-temporal gait characteristics, fall history, risk factors, regression model, gait, fall prevention, retrospective study

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