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通过投影寻踪回归从驱动方法推导出自动调节平台的有效性和可靠性。

Validity and reliability of deriving the autoregulatory plateau through projection pursuit regression from driven methods.

机构信息

Cerebrovascular Concussion Lab, Faculty of Kinesiology, University of Calgary, Calgary, Alberta, Canada.

Sport Injury Prevention Research Centre, Faculty of Kinesiology, University of Calgary, Calgary, Alberta, Canada.

出版信息

Physiol Rep. 2024 Jan;12(2):e15919. doi: 10.14814/phy2.15919.

Abstract

To compare the construct validity and between-day reliability of projection pursuit regression (PPR) from oscillatory lower body negative pressure (OLBNP) and squat-stand maneuvers (SSMs). Nineteen participants completed 5 min of OLBNP and SSMs at driven frequencies of 0.05 and 0.10 Hz across two visits. Autoregulatory plateaus were derived at both point-estimates and across the cardiac cycle. Between-day reliability was assessed with intraclass correlation coefficients (ICCs), Bland-Altman plots with 95% limits of agreement (LOA), coefficient of variation (CoV), and smallest real differences. Construct validity between OLBNP-SSMs were quantified with Bland-Altman plots and Cohen's d. The expected autoregulatory curve with positive rising and negative falling slopes were present in only ~23% of the data. The between-day reliability for the ICCs were poor-to-good with the CoV estimates ranging from ~50% to 70%. The 95% LOA were very wide with an average spread of ~450% for OLBNP and ~350% for SSMs. Plateaus were larger from SSMs compared to OLBNPs (moderate-to-large effect sizes). The cerebral pressure-flow relationship is a complex regulatory process, and the "black-box" nature of this system can make it challenging to quantify. The current data reveals PPR analysis does not always elicit a clear-cut central plateau with distinctive rising/falling slopes.

摘要

比较基于振动式下体负压(OLBNP)和深蹲-站立动作(SSMs)的投影寻踪回归(PPR)的构建效度和日内可靠性。19 名参与者在两次访问中分别以 0.05 和 0.10Hz 的驱动频率完成了 5 分钟的 OLBNP 和 SSM。在两点估计和整个心动周期中都得出了自动调节平台。日内可靠性评估采用组内相关系数(ICCs)、95%一致性界限(LOA)的 Bland-Altman 图、变异系数(CoV)和最小真实差异。OLBNP-SSMs 之间的构建效度通过 Bland-Altman 图和 Cohen's d 进行量化。只有约 23%的数据呈现出预期的自动调节曲线,具有正上升和负下降斜率。ICC 的日内可靠性从差到好,CoV 估计值范围从约 50%到 70%。95%LOA 非常宽,OLBNP 的平均分布约为 450%,SSMs 的平均分布约为 350%。与 OLBNP 相比,SSM 产生的平台更大(中等至大效应量)。脑压流关系是一个复杂的调节过程,该系统的“黑盒”性质使得量化变得具有挑战性。目前的数据显示,PPR 分析并不总是能引出一个具有明显上升/下降斜率的清晰中央平台。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee4b/10805621/81af68c95191/PHY2-12-e15919-g001.jpg

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