Background

Predictors of recovery in patellofemoral pain syndrome (PFPS) currently used in prognostic models are scalar in nature, despite many physiological measures originally lying on the functional scale. Traditional modelling techniques cannot harness the potential predictive value of functional physiological variables.

Research question

What is the classification performance of PFPS status of a statistical model when using functional ground reaction force (GRF) time-series?

Methods

Thirty-one individuals (control = 17, PFPS = 14) performed maximal countermovement jumps, on two force plates. The three-dimensional components of the GRF profiles were time-normalized between the start of the eccentric phase and take-off, and used as functional predictors. A statistical model was developed using functional data boosting (FDboost), for binary classification of PFPS statuses (control vs PFPS). The area under the Receiver Operating Characteristic curve (AUC) was used to quantify the model’s ability to discriminate the two groups.

Results

The three predictors of GRF waveform achieved an average out-of-bag AUC of 93.7 %. A 1 % increase in applied medial force reduced the log odds of being in the PFPS group by 0.68 at 87 % of jump cycle. In the AP direction, a 1 % reduction in applied posterior force increased the log odds of being classified as PFPS by 1.10 at 70 % jump cycle. For the vertical GRF, a 1 % increase in applied force reduced the log odds of being classified in the PFPS group by 0.12 at 44 % of the jump cycle.

Significance

Using simple functional GRF variables collected during functionally relevant task, in conjunction with FDboost, produced clinically interpretable models that retain excellent classification performance in individuals with PFPS. FDboost may be an invaluable tool to be used in longitudinal cohort prognostic studies, especially when scalar and functional predictors are collected.

中文翻译：

---

使用地面力剖面对患有或不患有pa股股骨疼痛综合征的个体进行分类-开发使用功能数据增强的方法。

背景

尽管许多生理指标最初都在功能范围内，但目前在预后模型中使用的pa股疼痛综合征（PFPS）恢复的预测指标本质上是标量。传统的建模技术无法利用功能性生理变量的潜在预测价值。

研究问题

使用功能地面反作用力（GRF）时间序列时，统计模型的PFPS状态的分类性能如何？

方法

31个人（对照组= 17，PFPS = 14）在两个测力板上进行了最大的反跳。GRF轮廓的三维分量在偏心阶段开始和起飞之间经过时间标准化，并用作功能预测器。使用功能数据增强（FDboost）开发了统计模型，用于PFPS状态的二进制分类（对照vs PFPS）。接收器工作特性曲线（AUC）下的面积用于量化模型区分两组的能力。

结果

GRF波形的三个预测因子实现了平均袋外AUC为93.7％。施加的内力增加1％，则在跳跃周期为87％时，PFPS组的对数几率降低了0.68。在AP方向上，后施加力降低1％，则在70％的跳跃周期将被归类为PFPS的对数几率提高1.10。对于垂直GRF，施加力增加1％时，在跳跃周期的44％时，PFPS组中被分类的对数几率降低了0.12。

意义

使用在功能相关任务期间收集的简单功能GRF变量，并与FDboost结合，产生了可在临床上解释的模型，该模型在PFPS个体中保留了出色的分类性能。FDboost可能是用于纵向队列预后研究的宝贵工具，尤其是在收集了标量和功能预测指标的情况下。