To further the understanding of long-term sequelae as a result of repetitive head impacts in sports, in vivo head impact exposure data are critical to expand on existing evidence from animal model and laboratory studies. Recent technological advances have enabled the development of head impact sensors to estimate the head impact exposure of human subjects in vivo. Previous research has identified the limitations of filtering algorithms to process sensor data. In addition, observer and/or video confirmation of sensor-recorded events is crucial to remove false positives. The purpose of the current study was to conduct a systematic review to determine the proportion of published head impact sensor data studies that used filtering algorithms, observer confirmation and/or video confirmation of sensor-recorded events to remove false positives. Articles were eligible for inclusion if collection of head impact sensor data during live sport was reported in the methods section. Descriptive data, confirmation methods and algorithm use for included articles were coded. The primary objective of each study was reviewed to identify the primary measure of exposure, primary outcome and any additional covariates. A total of 168 articles met the inclusion criteria, the publication of which has increased in recent years. The majority used filtering algorithms (74%). The majority did not use observer and/or video confirmation for all sensor-recorded events (64%), which suggests estimates of head impact exposure from these studies may be imprecise.

为了进一步了解运动中重复头部撞击导致的长期后遗症，体内头部撞击暴露数据对于扩展来自动物模型和实验室研究的现有证据至关重要。最近的技术进步使头部撞击传感器的发展能够估计人体受试者在体内的头部撞击暴露. 先前的研究已经确定了过滤算法处理传感器数据的局限性。此外，传感器记录事件的观察者和/或视频确认对于消除误报至关重要。当前研究的目的是进行系统审查，以确定已发布的头部撞击传感器数据研究的比例，这些研究使用过滤算法、观察者确认和/或传感器记录事件的视频确认来消除误报。如果在方法部分报告了现场运动期间头部撞击传感器数据的收集，则文章有资格纳入。对纳入文章的描述性数据、确认方法和算法使用进行了编码。审查了每项研究的主要目标，以确定暴露的主要衡量指标、主要结果和任何其他协变量。共有168篇文章符合纳入标准，近年来发表量有所增加。大多数使用过滤算法 (74%)。大多数人没有对所有传感器记录的事件使用观察者和/或视频确认 (64%)，这表明这些研究对头部撞击暴露的估计可能不准确。