On-field Head Acceleration Exposure Measurement Using Instrumented Mouthguards: Missing Data Imputation for Complete Exposure Analysis.

IF 3 2区 医学 Q3 ENGINEERING, BIOMEDICAL
David Luke, Zaryan Masood, Daniel Bondi, Chaokai Zhang, Rebecca Kenny, Adam Clansey, Paul van Donkelaar, Alexander Rauscher, Songbai Ji, Lyndia Wu
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引用次数: 0

Abstract

Purpose: Accurate quantification of head acceleration event (HAE) exposure is critical for investigating brain injury risk in contact sports athletes. However, missing HAEs may be unavoidable in real-world data collection. This study introduces missing data imputation methods to estimate complete video- and sensor-based HAE exposure.

Methods: We captured and verified university men's ice hockey HAEs using video and instrumented mouthguards (iMGs) in one varsity season (nathletes = 27, ngames = 31). A statistical mapping technique was first introduced to impute missing video-based HAEs during away games with limited camera angles. We then applied multiple imputation to impute missing iMG-based HAEs using captured data, including the complete video-based HAE exposure. This enabled estimation of complete exposure data at a per-athlete level over all games of the season.

Results: Among 591 athlete-games, 45% did not have any recorded iMG data. We find that data imputation increased the median values of per-athlete-season video- and iMG-based HAE counts by 10% and 69%, respectively. Consequently, common head kinematics- and brain deformation-based cumulative exposure metrics also increased substantially (median per-athlete-season cumulative peak linear acceleration by 95%, peak angular acceleration by 109%, and corpus callosum strain by 69%).

Conclusion: This study highlights the potential underestimation of exposure metrics due to missing HAEs and fills a critical gap in sports HAE exposure research. Future studies should incorporate missing data imputation methods for more accurate estimation of HAE exposure in investigating acute and long-term brain trauma risks.

现场头部加速度暴露测量使用仪器护齿:缺失的数据输入完全暴露分析。
目的:准确量化头部加速度事件(HAE)暴露对于研究接触性运动运动员的脑损伤风险至关重要。然而,在现实世界的数据收集中,缺少HAEs可能是不可避免的。本研究介绍了缺失数据输入方法来估计基于视频和传感器的完整HAE暴露。方法:在一个大学赛季(非运动员27人,比赛31场)中,我们使用视频和仪器护齿(iMGs)捕获并验证了大学男子冰球HAEs。首先引入了一种统计映射技术,用于在有限摄像机角度的客场比赛中计算基于视频的HAEs缺失。然后,我们使用捕获的数据,包括完整的基于视频的HAE曝光,应用多重imputation来推测缺失的基于img的HAE。这样就可以估计每个运动员在本赛季所有比赛中的完整暴露数据。结果:在591场运动员比赛中,45%没有任何记录的iMG数据。我们发现,数据输入使每个运动员赛季视频和基于img的HAE计数的中位数分别增加了10%和69%。因此,常见的基于头部运动学和大脑变形的累积暴露指标也大幅增加(每个运动员赛季累积峰值线性加速度中位数增加95%,峰值角加速度中位数增加109%,胼胝体应变中位数增加69%)。结论:本研究强调了由于缺少HAE而可能低估暴露指标,填补了运动HAE暴露研究的关键空白。未来的研究应纳入缺失数据的输入方法,以便在调查急性和长期脑外伤风险时更准确地估计HAE暴露。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Annals of Biomedical Engineering
Annals of Biomedical Engineering 工程技术-工程:生物医学
CiteScore
7.50
自引率
15.80%
发文量
212
审稿时长
3 months
期刊介绍: Annals of Biomedical Engineering is an official journal of the Biomedical Engineering Society, publishing original articles in the major fields of bioengineering and biomedical engineering. The Annals is an interdisciplinary and international journal with the aim to highlight integrated approaches to the solutions of biological and biomedical problems.
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