Evidence map›Paper›PMID 39625630›Full record

ArticleAnnals of biomedical engineering2025

Quality Issues in Kinematic Traces from Three Head Impact Sensors in Boxing: Prevalence, Effects, and Implications for Exposure Assessment.

Enora Le Flao, Gunter P Siegmund, Seth Lenetsky, Robert Borotkanics

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Article in Annals of biomedical engineering, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

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3citing papers in PubMed
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1 · What the graph read from it

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Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Quantitative video analysis of head acceleration events: a review.Frontiers in bioengineering and biotechnology · 2025
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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Enora Le FlaoThe Ohio State University, Columbus, OH, USA. enora.leflao@osumc.edu.ORCID http://orcid.org/0000-0002-0006-0244
Gunter P SiegmundMEA Forensic Engineers & Scientists, Laguna Hills, CA, USA.ORCID http://orcid.org/0000-0003-2387-941X
Seth LenetskyAuckland University of Technology, Auckland, New Zealand.
Robert BorotkanicsAuckland University of Technology, Auckland, New Zealand.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeWhen used in-vivo or in biofidelic environments, many head impact sensors have shown limitations related to the quality and validity of the kinematics measured. The objectives were to assess the quality of kinematic traces from three head impact sensors, determine the effects of signal quality on peak accelerations, and compare measurements across sensors.

methodsHead impacts were collected with instrumented mouthguards, skin patches, and headgear patches during boxing sparring. The quality of the raw kinematic traces for 442 events for each sensor was categorized using pre-defined objective criteria into high, questionable, and low-quality classes. The proportion of high-quality recordings was analyzed by participant, type of impact, and impact location. Associations between signal quality and peak kinematics were assessed within each sensor, and peak kinematics (resolved to the head center of gravity) were compared between sensors.

resultsHigh-quality criteria were met in 53%, 20%, and 26% of events for the mouthguard, skin patch, and headgear patch, respectively. High-quality recordings were less frequent for impacts occurring close to the sensor (e.g., 30% vs. 61% for the mouthguard) and showed lower peak kinematics than low-quality recordings (p < 0.001). Despite careful selection of high-quality simultaneous recordings, there was little to no association between the sensors' measurements (Spearman's p ≥ 0.043).

conclusionsThe kinematic data often reflected the motion of the sensor itself rather than the motion of the head, overestimating head impact magnitude. Researchers should evaluate data quality prior to analyzing kinematics or injury severity metrics. Comparison of data across studies or in relation to injury risk functions needs to be done with caution when data were acquired from different sensors.

Indexed as

HeadHead Protective DevicesAdultBiomechanical PhenomenaFemaleHumansMaleBiomechanicsBrain injuryCombat sportsConcussionWearable sensors

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.