Leporace Gustavo, Guadagnin Eliane C, Carpes Felipe P, Gustafson Jonathan, Gonzalez Felipe F, Chahla Jorge, Metsavaht Leonardo
Instituto Brasil de Tecnologias da Saúde, Rio de Janeiro, Brazil.
Departamento de Diagnóstico por Imagem, Escola Paulista de Medicina, Universidade Federal de São Paulo, São Paulo, Brazil.
Sports Health. 2025 May 20:19417381251338267. doi: 10.1177/19417381251338267.
Running biomechanics can influence injury risk, but whether the combined effect of different biomechanical factors can be identified by individual running profiles remains unclear. Here, we identified distinct biomechanical profiles among healthy runners, examined lower limb mechanical load characteristics, and evaluated potential implications for injury risk.
Multiple factors would serve as a common denominator allowing identification of specific patterns.
Cross-sectional.
Level 2.
Step cadence, stance time, vertical oscillation, duty factor, vertical stiffness, peak ground reaction force (GRF), and anteroposterior, lateral, and vertical smoothness were determined from 3-dimensional kinematic data from 79 healthy runners using a treadmill at 2.92 m/s. Principal component analysis, self-organizing maps, and K-means clustering techniques delineated distinct biomechanical running profiles. Mutual information analysis, Kruskal-Wallis, and Pearson's Chi-squared tests were conducted.
Five biomechanical profiles (P1-P5) demonstrated different running mechanical characteristics: P1 exhibited low cumulative and peak mechanical load due to a combination of high duty factor, low step cadence, and longer stance time; P2 showed characteristics associated with the lowest peak mechanical load due to reduced peak GRF and greater smoothness; P3 and P5 showed contrasting running patterns, but maintained moderate smoothness and peak GRF; and P4 exhibited the highest peak mechanical load, driven by high GRF, low duty factor, and high vertical oscillation.
The 5 profiles appear to be associated with different lower limb load patterns, highlighting previously unrecognized connections between biomechanical variables during running. Some variables contribute to increased peak and cumulative load, whereas others help reduce it, underscoring the complex interplay of biomechanical factors in running.
Identifying distinct running profiles can help clinicians better understand individual variations in mechanical load and injury risk, thus informing targeted interventions, such as personalized training adjustments or rehabilitation programs, to prevent injuries and enhance performance in runners.
跑步生物力学可影响受伤风险，但不同生物力学因素的综合作用能否通过个体跑步特征来识别仍不清楚。在此，我们确定了健康跑步者之间不同的生物力学特征，研究了下肢机械负荷特征，并评估了对受伤风险的潜在影响。
多种因素将作为一个共同标准，允许识别特定模式。
横断面研究。
2级。
利用跑步机以2.92米/秒的速度对79名健康跑步者进行三维运动学数据采集，测定步频、支撑时间、垂直振荡、负荷率、垂直刚度、地面反作用力峰值（GRF）以及前后、横向和垂直方向的平滑度。主成分分析、自组织映射和K均值聚类技术描绘了不同的生物力学跑步特征。进行了互信息分析、Kruskal-Wallis检验和Pearson卡方检验。
五种生物力学特征（P1 - P5）表现出不同的跑步机械特征：P1由于高负荷率、低步频和更长的支撑时间，表现出低累积和峰值机械负荷；P2由于GRF峰值降低和平滑度更高，表现出与最低峰值机械负荷相关的特征；P3和P5表现出相反的跑步模式，但保持适度的平滑度和GRF峰值；P4由于高GRF、低负荷率和高垂直振荡，表现出最高的峰值机械负荷。
这五种特征似乎与不同的下肢负荷模式相关，突出了跑步过程中生物力学变量之间以前未被认识到的联系。一些变量会导致峰值和累积负荷增加，而其他变量则有助于降低负荷，强调了跑步中生物力学因素的复杂相互作用。
识别不同的跑步特征可帮助临床医生更好地理解机械负荷和受伤风险的个体差异，从而为有针对性的干预措施提供依据，如个性化训练调整或康复计划，以预防跑步者受伤并提高其表现。
