Eckart Adam C, Ghimire Pragya Sharma, Stavitz James
Department of Health and Human Performance, College of Health Professions and Human Services, Kean University, 1000 Morris Avenue, Union, NJ 07083, USA.
Department of Athletic Training, College of Health Professions and Human Services, Kean University, 1000 Morris Avenue, Union, NJ 07083, USA.
Sports (Basel). 2024 Apr 28;12(5):123. doi: 10.3390/sports12050123.
Popular movement-based injury risk screens were shown to lack predictive precision, leading to interest in multifactorial models. Furthermore, there is a lack of research regarding injury risk assessment for those currently or planning to be recreationally active. This study aims to provide injury risk insights by analyzing multifactorial injury risk models and associated clinical measures in the U.S. population. Data related to injury, inflammatory markers, physical functioning, body composition, physical activity, and other variables from 21,033 respondents were extracted from NHANES. Odds ratios for self-reported injury were calculated for single predictors and risk models. Case-control and principal component analyses (PCA) were conducted to elucidate confounders and identify risk factor clusters, respectively. Receiver operating characteristic analysis was used to test the precision of a risk factor cluster to identify pain points and functional difficulties. Sociodemographic, individual, and lifestyle factors were strongly associated with higher odds of injury. Increases in fibrinogen and C-reactive protein were significantly associated with all risk groups. Membership to the high-risk group (age over 40, obesity, no muscle-strengthening activities, sedentary lifestyle, and low back pain) predicted at least one functional difficulty with 67.4% sensitivity and 87.2% specificity. In the injury group, bone turnover markers were higher, yet confounded by age, and there was a significantly higher prevalence of self-reported osteoporosis compared to the control. In males, low testosterone was associated with injury, and high estradiol was associated with pain and functional difficulties. In females, high follicle-stimulating hormone was associated with functional difficulties. PCA revealed four high-risk profiles, with markers and activities showing distinct loadings. A comprehensive approach to injury risk assessment should consider the nexus of aging, lifestyle, and chronic disease to enhance tailored injury prevention strategies, fostering safe and effective physical activity participation and reducing the burden of musculoskeletal disorders.
基于运动的常见损伤风险筛查显示缺乏预测精度,这引发了人们对多因素模型的兴趣。此外,对于目前正在进行或计划进行休闲活动的人群,缺乏关于损伤风险评估的研究。本研究旨在通过分析美国人群中的多因素损伤风险模型和相关临床指标,提供损伤风险见解。从美国国家健康与营养检查调查(NHANES)中提取了21,033名受访者与损伤、炎症标志物、身体功能、身体成分、身体活动及其他变量相关的数据。计算了单一预测因素和风险模型的自我报告损伤比值比。分别进行病例对照分析和主成分分析(PCA)以阐明混杂因素并识别风险因素集群。使用受试者工作特征分析来测试风险因素集群识别痛点和功能障碍的精度。社会人口统计学、个体和生活方式因素与较高的损伤几率密切相关。纤维蛋白原和C反应蛋白的升高与所有风险组均显著相关。高危组(年龄超过40岁、肥胖、无肌肉强化活动、久坐不动的生活方式和腰痛)成员预测至少一种功能障碍的敏感性为67.4%,特异性为87.2%。在损伤组中,骨转换标志物较高,但受年龄混杂影响,且自我报告的骨质疏松症患病率显著高于对照组。在男性中,低睾酮与损伤相关,高雌二醇与疼痛和功能障碍相关。在女性中,高促卵泡激素与功能障碍相关。PCA揭示了四种高危特征,标志物和活动显示出不同的负荷。损伤风险评估的综合方法应考虑衰老、生活方式和慢性病之间的联系,以加强针对性的损伤预防策略,促进安全有效的身体活动参与,并减轻肌肉骨骼疾病的负担。