Strandberg Johan, Pini Alessia, Häger Charlotte K, Schelin Lina
Department of Mathematics and Mathematical Statistics, Umeå University, Umeå, Sweden.
Department of Statistical Sciences, Università Cattolica del Sacro Cuore, Milan, Italy.
Front Bioeng Biotechnol. 2021 May 14;9:645014. doi: 10.3389/fbioe.2021.645014. eCollection 2021.
Three-dimensional human motion analysis provides in-depth understanding in order to optimize sports performance or rehabilitation following disease or injury. Recent developments of statistical methods for functional data allow for novel ways to analyze often complex biomechanical data. Even so, for such methods as well as for traditional well-established statistical methods, the interpretations of the results may be influenced by analysis choices made prior to the analysis. We evaluated the consequences of three such choices when comparing one-leg vertical hop (OLVH) performance in individuals who had ruptured their anterior cruciate ligament (ACL), to that of asymptomatic controls, and also athletes. Kinematic data were analyzed using a statistical approach for functional data, targeting entire curve data. This was done not only for one joint at a time but also for multiple lower limb joints and movement planes simultaneously using a multi-aspect methodology, testing for group differences while also accounting for covariates. We present the results of when an individual representative curve out of three available was either: (1) a mean curve (), (2) a curve from the highest hop (), or (3) a curve describing the variability (), as a representation of performance stability. We also evaluated choice of sample leg comparison; e.g., ACL-injured leg compared to either the dominant or non-dominant leg of asymptomatic groups. Finally, we explored potential outcome effects of different combinations of included joints. There were slightly more pronounced group differences when using compared to , while the specifics of the observed differences depended on the outcome variable. For there were less significant group differences. Generally, there were more disparities throughout the hop movement when comparing the injured leg to the dominant leg of controls, resulting in e.g., group differences for trunk and ankle kinematics, for both and . When the injured leg was instead compared to the non-dominant leg of controls, there were trunk, hip and knee joint differences. For a more stringent comparison, we suggest considering to compare the injured leg to the non-dominant leg. Finally, the multiple-joint analyses were coherent with the single-joint analyses. The direct effects of analysis choices can be explored interactively by the reader in the Supplementary Material. To summarize, the choices definitively have an impact on the interpretation of a hop test results commonly used in rehabilitation following knee injuries. We therefore strongly recommend well-documented methodological analysis choices with regards to comparisons and representative values of the measures of interests.
三维人体运动分析有助于深入了解情况，从而优化运动表现或在患病或受伤后进行康复。功能数据统计方法的最新进展为分析通常复杂的生物力学数据提供了新途径。即便如此，对于此类方法以及传统的成熟统计方法而言，结果的解释可能会受到分析前所做分析选择的影响。我们评估了三种此类选择在比较前交叉韧带（ACL）断裂个体与无症状对照组以及运动员的单腿垂直跳（OLVH）表现时的影响。运动学数据采用针对功能数据的统计方法进行分析，以整条曲线数据为目标。这不仅一次针对一个关节进行，还同时使用多方面方法针对多个下肢关节和运动平面进行，在考虑协变量的同时测试组间差异。我们展示了从三条可用曲线中选取一条个体代表性曲线时的结果，该曲线分别为：(1) 平均曲线（），(2) 最高跳跃的曲线（），或 (3) 描述变异性的曲线（），以此作为表现稳定性的一种表示。我们还评估了样本腿比较的选择；例如，将ACL损伤腿与无症状组的优势腿或非优势腿进行比较。最后，我们探究了所纳入关节不同组合的潜在结果影响。与 使用 相比时，组间差异略显明显，而观察到的差异细节取决于结果变量。对于 ，组间差异不太显著。总体而言，将受伤腿与对照组的优势腿进行比较时，在整个跳跃动作中差异更多，例如在 和 时，躯干和踝关节运动学方面存在组间差异。当将受伤腿与对照组的非优势腿进行比较时，则存在躯干、髋关节和膝关节差异。为了进行更严格的比较，我们建议考虑将受伤腿与非优势腿进行比较。最后，多关节分析与单关节分析一致。读者可在补充材料中交互式地探索分析选择的直接影响。总之，这些选择肯定会对膝关节损伤后康复中常用的跳跃测试结果的解释产生影响。因此，我们强烈建议在比较和感兴趣测量指标的代表性值方面，对方法分析选择进行详细记录。
