Human Performance Research Centre, Faculty of Health, University of Technology Sydney (UTS), Driver Avenue, Moore Park, Sydney, NSW, 2021, Australia.
School of Mathematical and Physical Sciences, University of Technology Sydney (UTS), Sydney, NSW, Australia.
Sports Med. 2021 Mar;51(3):581-592. doi: 10.1007/s40279-020-01378-6. Epub 2020 Dec 17.
AIM: The aim of this study was to examine the associations between the injury risk and the acute (AL) to chronic (CL) workload ratio (ACWR) by substituting the original CL with contrived values to assess the role of CL (i.e., the presence and implications of statistical artefacts). METHODS: Using previously published data, we generated a contrived ACWR by dividing the AL by fixed and randomly generated CLs, and we compared these results to real data. We also reproduced previously reported subgroup analyses, including dichotomising players' data above and below the median CL. Our analyses follow the same, previously published modelling approach. RESULTS: The analyses with original data showed effects compatible with higher injury risk for ACWR only (odd ratios, OR: 2.45, 95% CI 1.28-4.71). However, we observed similar effects by dividing AL by the "contrived" fixed and randomly generated CLs: OR 1.95 (1.18-3.52) dividing by 1510 (average CL); and OR ranging from 1.16 to 2.07, using random CL 1.53 (mean). Random ACWRs reduced the variance relative to the original AL and further inflated the ORs (mean OR 1.89, from 1.42 to 2.70). ACWR causes artificial reclassification of players compared to AL alone. Finally, neither ACWR nor AL alone confer a meaningful predictive advantage to an intercept-only model, even within the training sample (c-statistic 0.574/0.544 vs. 0.5 in both ACWR/AL and intercept-only models, respectively). DISCUSSION: ACWR is a rescaling of the explanatory variable (AL, numerator), in turn magnifying its effect estimates and decreasing its variance despite conferring no predictive advantage. Other ratio-related transformations (e.g., reducing the variance of the explanatory variable and unjustified reclassifications) further inflate the OR of AL alone with injury risk. These results also disprove the etiological theory behind this ratio and its components. We suggest ACWR be dismissed as a framework and model, and in line with this, injury frameworks, recommendations, and consensus be updated to reflect the lack of predictive value of and statistical artefacts inherent in ACWR models.
目的：本研究旨在通过替代原始慢性负荷（CL）值来检验损伤风险与急性（AL）至慢性（CL）工作量比（ACWR）之间的关联，以评估 CL 的作用（即统计伪影的存在和影响）。
方法：我们使用之前发表的数据，通过将 AL 除以固定和随机生成的 CL 来生成一个虚构的 ACWR，并将这些结果与真实数据进行比较。我们还复制了之前报道的亚组分析，包括将球员数据分为高于和低于中位数 CL 的两个组别。我们的分析遵循相同的、之前发表的建模方法。
结果：使用原始数据进行的分析显示，ACWR 仅与更高的损伤风险相关（比值比，OR：2.45，95%置信区间 1.28-4.71）。然而，当我们将 AL 除以虚构的固定和随机生成的 CL 时，我们观察到了类似的效果：除以 1510（平均 CL）时，OR 为 1.95（1.18-3.52）；当使用随机 CL 1.53（平均值）时，OR 范围为 1.16 至 2.07。随机 ACWR 相对于原始 AL 降低了方差，并进一步放大了 OR（平均 OR 1.89，1.42-2.70）。与仅使用 AL 相比，ACWR 会人为地重新分类球员。最后，无论是 ACWR 还是 AL 本身，即使在训练样本中，也无法为仅包含截距的模型提供有意义的预测优势（ACWR/AL 和仅包含截距的模型的 C 统计量分别为 0.574/0.544 和 0.5）。
讨论：ACWR 是解释变量（AL，分子）的重新缩放，尽管它没有提供预测优势，但它会放大其效应估计值并降低其方差。其他与比率相关的转换（例如，降低解释变量的方差和不合理的重新分类）会进一步放大 AL 单独与损伤风险之间的 OR。这些结果也否定了该比率及其组成部分背后的病因理论。我们建议摒弃 ACWR 作为框架和模型，并且，为了与这一观点一致，应更新损伤框架、建议和共识，以反映出 ACWR 模型缺乏预测价值和固有的统计伪影。
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