Computer methods and programs in biomedicine · 2026
Effects of tibial fracture-induced gait alterations on healing outcomes: Implications for patient-specific rehabilitation strategies.
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Ding Qianjun, Li Lunjian, Miramini Saeed, Shi Shuangmin, Ebeling Peter, Wei Yongping, Zhang Lihai
Аннотация
BACKGROUND AND OBJECTIVE: Partial weight-bearing (PWB) exercise is critical in fracture rehabilitation. However, the effects of tibial fracture-induced gait alterations on musculoskeletal loading conditions, particularly regarding body weights (BWs) and walking speeds, remain unclear. These patient-specific gait deviations can substantially modify mechanical stimuli at the fracture site, thereby affecting healing trajectories. This study aims to evaluate how fracture-altered gait mechanics influence loading conditions during rehabilitation to inform patient-specific strategies.
METHOD: A gait-based biomechanical model was developed to simulate PWB walking in patients with tibial fractures, based on changes in ground reaction forces (GRFs) compared to the uninjured limb. Fracture-induced effects on joint and muscle loading rates were quantified and integrated into the fracture healing model, simulating mesenchymal stem cell (MSC)-mediated tissue differentiation under various walking speeds and BWs.
RESULTS: Tibial fracture-induced gait changes increased peak loading rates at knee and ankle joints by 0.9-1.3 BW/s and 0.8-1.2 BW/s, respectively, as speed rises from 0.6 to 1.0v0(v0 = 5 km/h). The tibialis posterior and rectus femoris exhibited the largest increase in peak loading rates, by 92 % and 220 %, respectively. Ignoring fracture effect could underestimate the mechanical stimulation and risk of fracture non-union. There exists an optimal walking speed for patient-specific BW to promote endochondral ossification, while controlling fibrous tissue formation (e.g., walking at 0.6 v0 for a 65 kg patient under 30 % PWB).
CONCLUSION: This study offers clinically relevant insights to assist physiotherapists in prescribing effective, patient-specific gait rehabilitation strategies to enhance tibial fracture healing during early recovery.
METHOD: A gait-based biomechanical model was developed to simulate PWB walking in patients with tibial fractures, based on changes in ground reaction forces (GRFs) compared to the uninjured limb. Fracture-induced effects on joint and muscle loading rates were quantified and integrated into the fracture healing model, simulating mesenchymal stem cell (MSC)-mediated tissue differentiation under various walking speeds and BWs.
RESULTS: Tibial fracture-induced gait changes increased peak loading rates at knee and ankle joints by 0.9-1.3 BW/s and 0.8-1.2 BW/s, respectively, as speed rises from 0.6 to 1.0v0(v0 = 5 km/h). The tibialis posterior and rectus femoris exhibited the largest increase in peak loading rates, by 92 % and 220 %, respectively. Ignoring fracture effect could underestimate the mechanical stimulation and risk of fracture non-union. There exists an optimal walking speed for patient-specific BW to promote endochondral ossification, while controlling fibrous tissue formation (e.g., walking at 0.6 v0 for a 65 kg patient under 30 % PWB).
CONCLUSION: This study offers clinically relevant insights to assist physiotherapists in prescribing effective, patient-specific gait rehabilitation strategies to enhance tibial fracture healing during early recovery.