Bayrak Ahmet
Faculty of Health Science, Department of Physical Therapy and Rehabilitation, Selcuk University, Injury prediction and prediction, Konya, Türkiye.
BMC Sports Sci Med Rehabil. 2025 Jul 28;17(1):217. doi: 10.1186/s13102-025-01262-8.
This study aimed to examine the injury profile of a professional football team over five consecutive seasons and assess the predictive value of the Functional Movement Screen (FMS), offering insights to optimize injury prevention strategies in professional football.
Injury data for 169 players between the 2016-2017 and 2020-2021 seasons were recorded, including the number of missed training sessions, injury severity, and injury types. Descriptive statistics were used to analyze these factors. The relationship between preseason FMS composite scores, asymmetry findings, and injury profiles was assessed using Variance Inflation Factor (VIF) and Logistic Regression Analysis.
Over the five seasons, the injury incidence was 7.76 injuries per 1,000 training hours (95% CI: 7.59-7.93), 15.47 injuries per 1,000 match hours (95% CI: 15.23-15.71), and 8.9 injuries per 1,000 combined hours (95% CI: 8.72- 9.0). Injury data, including severity, type, and training or match absence, were meticulously recorded and analyzed. The study established an injury profile for players over five consecutive seasons but found that FMS was ineffective in predicting injuries, either within individual seasons or across the entire period. This suggests that the FMS may not be a reliable tool for forecasting injury risk in high-performance football.
The injury frequency was 8.9 per 1,000 h of exposure, with 26% of injuries classified as severe, leading to over 28 missed training days per injury. Given that the FMS test battery alone did not reliably predict injury risk, we recommend its use in combination with other multifactorial screening methods to enhance the accuracy of injury risk assessment. Hamstring injuries were the most common, while goalkeepers primarily experienced back issues. Factors such as age, height, and body mass may influence injury risk. These findings underscore the need for multifaceted injury prevention programs that consider a wider range of risk factors beyond FMS scores, including age, height, and body mass, to effectively manage and reduce the risk of injuries in professional football. Additionally, these insights can assist technical staff in managing training absences and planning player availability more effectively.
本研究旨在调查一支职业足球队连续五个赛季的伤病情况，并评估功能性动作筛查（FMS）的预测价值，为优化职业足球的伤病预防策略提供见解。
记录了2016 - 2017赛季至2020 - 2021赛季期间169名球员的伤病数据，包括错过的训练课次数、伤病严重程度和伤病类型。使用描述性统计分析这些因素。使用方差膨胀因子（VIF）和逻辑回归分析评估季前FMS综合评分、不对称结果与伤病情况之间的关系。
在五个赛季中，每1000个训练小时的伤病发生率为7.76次伤病（95%置信区间：7.59 - 7.93），每1000个比赛小时的伤病发生率为15.47次伤病（95%置信区间：15.23 - 15.71），每1000个总小时的伤病发生率为8.9次伤病（95%置信区间：8.72 - 9.0）。对包括严重程度、类型以及训练或比赛缺勤情况在内的伤病数据进行了详细记录和分析。该研究确立了球员连续五个赛季的伤病情况，但发现FMS在预测单个赛季内或整个期间的伤病方面均无效。这表明FMS可能不是预测高水平足球伤病风险的可靠工具。
每1000小时的暴露时长中伤病频率为8.9次，26%的伤病被归类为严重伤病，每次伤病导致超过28天的训练缺勤。鉴于仅靠FMS测试组不能可靠地预测伤病风险，我们建议将其与其他多因素筛查方法结合使用，以提高伤病风险评估的准确性。腘绳肌伤病最为常见，而守门员主要经历背部问题。年龄、身高和体重等因素可能影响伤病风险。这些发现强调了需要多方面的伤病预防计划，该计划要考虑到除FMS评分之外更广泛的风险因素，包括年龄、身高和体重在内，以有效管理和降低职业足球中的伤病风险。此外，这些见解可以帮助技术人员更有效地管理训练缺勤情况并规划球员的可上场情况。
