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A Prediction Model for Acute Core and Lower Extremity Injuries in Division 1 Collegiate Football Players.

机译:1级大学足球运动员急性核心和下肢损伤的预测模型。

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摘要

Context: Various intrinsic factors such as high exposure, poor endurance of core muscles, previous injury, strength deficits, suboptimal neurocognitive function, and orthopedic abnormalities have been found as predictors for sprains and strains among collegiate football players.;Objective: Assess the applicability of pre-participation assessments as predictors of core or lower extremity injury.;Design: Cohort Study.;Setting: National Collegiate Athletic Association Division I football program.;Patients or Other Participants: Athletes who underwent mandatory pre-participation examinations before preseason football training over two consecutive seasons (n=225).;Main Outcome Measure(s): Associations between preseason protocols and injury incidence for core and lower extremity injuries were established for 225 players using three different injury definitions; all injuries reported (ALL), limited participation (LP), and removed (OUT). Receiver operating characteristic analysis was used to establish cut-points that classified cases as high-risk or low-risk for injury incidence. Logistic regression and Cox regression analyses were used to identify a multivariable prediction model for injury.;Results: A 4-factor model (FM) for ALL identified ≥2 Positive Factors for differentiating between injured and uninjured athletes (P<.001, OR=3.21; 90% CI 1.98, 5.20 , Sens=77.3%, Spec=48.5%). A 3-FM for LP identified ≥1 Positive Factors to be the criteria ( P<.004, OR=2.41; 90% CI 1.41, 4.10, Sens=82.8%, Spec=33.3%). A 3-FM identified ≥2 Positive Factors for OUT to be the criteria ( P<.012, OR=2.25; 90% CI 1.27, 4.00, Sens=75.4%, Spec=42.3%). A 4-FM identified =4 Factors to be the standard for injury in the previous season (P<.001, OR=8.61; 90% CI 4.00, 18.53, Sens=58.5%, Spec= 85.9%). A 4-FM identified ≥3 Factors for subsequent injuries during both years (P<.011, OR=8.40; 90% CI 2.00, 35.70, Sens=44.4%, Spec=91.3%).;Conclusions: Injury definition appears to be important for identifying risk factors for football injuries. Additionally, there are modifiable risk factors that can be determined from previous season injury and for athletes who are injured in consecutive years.;Key Words: injury prediction, injury prevention, injury risk, Core Stability, Reaction Time, Predictive Modeling.
机译:背景:高暴露度,核心肌肉耐力差,先前的损伤,力量不足,神经认知功能欠佳和骨科异常等各种内在因素已被发现是大学橄榄球运动员扭伤和劳损的预测指标;目的:评估以下因素:参与前的评估,以预测核心或下肢的伤害;设计:队列研究;设置:国家大学体育协会第一分会足球项目;患者或其他参与者:在进行季前橄榄球训练之前接受强制性参与前检查的运动员连续两个赛季(n = 225)。主要结果指标:季前赛规程与核心和下肢伤害的伤害发生率之间的关联是使用三种不同的伤害定义为225名球员建立的;报告所有伤害(全部),有限参与(LP)和清除(OUT)。接受者操作特征分析用于确定切入点,这些切入点将伤害发生的案例分类为高风险或低风险。结果:用ALL的4因子模型(FM)识别出≥2个阳性因子,以区分受伤和未受伤的运动员(P <.001,OR = 3.21; 90%CI 1.98,5.20,Sens = 77.3%,Spec = 48.5%)。 LP的3-FM确定≥1个阳性因子为标准(P <.004,OR = 2.41; 90%CI 1.41,4.10,Sens = 82.8%,Spec = 33.3%)。 3-FM将OUT的≥2个积极因素确定为标准(P <.012,OR = 2.25; 90%CI 1.27,4.00,Sens = 75.4%,Spec = 42.3%)。 4-FM将= 4个因素确定为上赛季受伤的标准(P <.001,OR = 8.61; 90%CI 4.00,18.53,Sens = 58.5%,Spec = 85.9%)。 4-FM确定了两个年度中造成后续伤害的≥3个因素(P <.011,OR = 8.40; 90%CI 2.00,35.70,Sens = 44.4%,Spec = 91.3%).;结论:伤害定义似乎是对于确定足球受伤的危险因素很重要。此外,还有一些可修改的危险因素,可以根据上一赛季的受伤情况以及连续几年受伤的运动员确定。关键词:伤害预测,伤害预防,伤害风险,核心稳定性,反应时间,预测模型。

著录项

  • 作者

    McDonald, Alexandra.;

  • 作者单位

    University of Arkansas.;

  • 授予单位 University of Arkansas.;
  • 学科 Kinesiology.;Health sciences.;Physical therapy.
  • 学位 M.S.
  • 年度 2016
  • 页码 112 p.
  • 总页数 112
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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