首页> 外文期刊>Journal of athletic training >Trunk and Lower Extremity Movement Patterns, Stress Fracture Risk Factors, and Biomarkers of Bone Turnover in Military Trainees
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Trunk and Lower Extremity Movement Patterns, Stress Fracture Risk Factors, and Biomarkers of Bone Turnover in Military Trainees

机译:行李箱和下肢运动模式,压力骨折危险因素,军事学员的骨质营业额的生物标志物

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Context Military service members commonly sustain lower extremity stress fractures (SFx). How SFx risk factors influence bone metabolism is unknown. Understanding how SFx risk factors influence bone metabolism may help to optimize risk-mitigation strategies. Objective To determine how SFx risk factors influence bone metabolism. Design Cross-sectional study. Setting Military service academy. Patients or Other Participants Forty-five men (agepre = 18.56 ± 1.39 years, heightpre = 176.95 ± 7.29 cm, masspre = 77.20 ± 9.40 kg; body mass indexpre = 24.68 ± 2.87) who completed Cadet Basic Training (CBT). Individuals with neurologic or metabolic disorders were excluded. Intervention(s) We assessed SFx risk factors (independent variables) with (1) the Landing Error Scoring System (LESS), (2) self-reported injury and physical activity questionnaires, and (3) physical fitness tests. We assessed bone biomarkers (dependent variables; procollagen type I amino-terminal propeptide [PINP] and cross-linked collagen telopeptide [CTx-1]) via serum. Main Outcome Measure(s) A markerless motion-capture system was used to analyze trunk and lower extremity biomechanics via the LESS. Serum samples were collected post-CBT; enzyme-linked immunosorbent assays determined PINP and CTx-1 concentrations, and PINP?:?CTx-1 ratios were calculated. Linear regression models demonstrated associations between SFx risk factors and PINP and CTx-1 concentrations and PINP?:?CTx-1 ratio. Biomarker concentration mean differences with 95% confidence intervals were calculated. Significance was set a priori using α ≤ .10 for simple and α ≤ .05 for multiple regression analyses. Results The multiple regression models incorporating LESS and SFx risk factor data predicted the PINP concentration (R2 = 0.47, P = .02) and PINP?:?CTx-1 ratio (R2 = 0.66, P = .01). The PINP concentration was increased by foot internal rotation, trunk flexion, CBT injury, sit-up score, and pre- to post-CBT mass changes. The CTx-1 concentration was increased by heel-to-toe landing and post-CBT mass. The PINP?:?CTx-1 ratio was increased by foot internal rotation, lower extremity sagittal-plane displacement (inversely), CBT injury, sit-up score, and pre- to post-CBT mass changes. Conclusions Stress fracture risk factors accounted for 66% of the PINP?:?CTx-1 ratio variability, a potential surrogate for bone health. Our findings provide insight into how SFx risk factors influence bone health. This information can help guide SFx risk-mitigation strategies.
机译:背景下的军事服务成员通常维持下肢应力骨折(SFX)。 SFX风险因素如何影响骨代谢是未知的。了解SFX风险因素如何影响骨代谢可能有助于优化风险缓解策略。目的确定SFX风险因素如何影响骨代谢。设计横断面研究。制定军事服务学院。患者或其他参与者四十五名男子(Agepregre = 18.56±1.39岁,高度= 176.95±7.29厘米,Masspre = 77.20±9.40公斤;身体质量索引= 24.68±2.87)谁完成了Cadet基础培训(CBT)。排除了具有神经系统或代谢障碍的个体。干预我们评估了SFX风险因素(独立变量),其中(1)着陆误差评分系统(较少),(2)自我报告的伤害和身体活动问卷,(3)身体健康测试。我们评估了骨骼生物标志物(依赖变量; Procollagen I型氨基 - 末端肽[PINP]并通过血清交联胶原蛋白[CTX-1])。主要结果措施是无标记运动捕获系统,用于通过较少的方式分析躯干和下肢生物力学。收集CBT的血清样品;酶联免疫吸附测定测定的PINP和CTX-1浓度,并计算出CTX -1比率。线性回归模型在SFX风险因素和PINP和CTX-1浓度和脊布之间演示了关联?:?CTX-1比率。生物标志物浓度计算了置信区间的平均差异。用于多元回归分析的简单和α≤05,使用α≤.10设置显着性。结果包含较少和SFX风险因子数据的多元回归模型预测PINP浓度(R2 = 0.47,p = .02)和贴图?:?CTX-1比率(R2 = 0.66,P = .01)。 PINP浓度增加了脚内旋转,树干屈曲,CBT损伤,仰卧起标,并预先发布了CBT质量变化。通过脚跟到脚趾着陆和后CBT质量增加CTX-1浓度。 PINP?:?CTX-1的比例通过脚内旋转增加,下肢矢状 - 平面位移(成反比),CBT损伤,仰卧起标,并预先接受CBT质量变化。结论应激骨折危险因素占PINP的66%?:CTX-1比率变异性,骨骼健康的潜在替代品。我们的研究结果提供了洞察SFX风险因素如何影响骨骼健康。此信息可以帮助指导SFX风险缓解策略。

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