Evidence map›Paper›PMID 36104642›Full record

ArticleAnnals of biomedical engineering2022

Consensus Head Acceleration Measurement Practices (CHAMP): Laboratory Validation of Wearable Head Kinematic Devices.

Lee Gabler, Declan Patton, Mark Begonia, Ray Daniel, Ahmad Rezaei, Colin Huber, Gunter Siegmund, Tyler Rooks, Lyndia Wu

Abstract read
In one paragraph

Article in Annals of biomedical engineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

18 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Quantitative video analysis of head acceleration events: a review.Frontiers in bioengineering and biotechnology · 2025
    Review
  9. Article
  10. Article
  11. Article
  12. Article
  13. Review
  14. Article
  15. Article
  16. Measuring Head Acceleration Like a CHAMP.Journal of athletic training · 2023
    Article
  17. On-Field Deployment and Validation for Wearable Devices.Annals of biomedical engineering · 2022
    Review
  18. Article
4 · The record

Corrections and comments

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

9 authors.

Lee GablerBiomechanics Consulting and Research, LLC, Charlottesville, VA, USA.
Declan PattonCenter for Injury Research and Prevention, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
Mark BegoniaDepartment of Biomedical Engineering and Mechanics, Virginia Tech, Blacksburg, VA, USA.
Ray DanielUnited States Army Aeromedical Research Laboratory, Fort Rucker, AL, USA.
Ahmad RezaeiDepartment of Mechanical Engineering, The University of British Columbia, Vancouver, BC, Canada.
Colin HuberCenter for Injury Research and Prevention, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
Gunter SiegmundMEA Forensic Engineers & Scientists, Richmond, BC, Canada.
Tyler RooksUnited States Army Aeromedical Research Laboratory, Fort Rucker, AL, USA.
Lyndia WuDepartment of Mechanical Engineering, The University of British Columbia, Vancouver, BC, Canada. lwu@mech.ubc.ca.ORCID http://orcid.org/0000-0002-8236-032X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Wearable devices are increasingly used to measure real-world head impacts and study brain injury mechanisms. These devices must undergo validation testing to ensure they provide reliable and accurate information for head impact sensing, and controlled laboratory testing should be the first step of validation. Past validation studies have applied varying methodologies, and some devices have been deployed for on-field use without validation. This paper presents best practices recommendations for validating wearable head kinematic devices in the laboratory, with the goal of standardizing validation test methods and data reporting. Key considerations, recommended approaches, and specific considerations were developed for four main aspects of laboratory validation, including surrogate selection, test conditions, data collection, and data analysis. Recommendations were generated by a group with expertise in head kinematic sensing and laboratory validation methods and reviewed by a larger group to achieve consensus on best practices. We recommend that these best practices are followed by manufacturers, users, and reviewers to conduct and/or review laboratory validation of wearable devices, which is a minimum initial step prior to on-field validation and deployment. We anticipate that the best practices recommendations will lead to more rigorous validation of wearable head kinematic devices and higher accuracy in head impact data, which can subsequently advance brain injury research and management.

Indexed as

Brain InjuriesWearable Electronic DevicesAccelerationBiomechanical PhenomenaConsensusHeadHumansPractice Guidelines as TopicAccuracyBest practicesHead impact kinematicsRecommendationsValidation

Identifiers

PMID36104642
PMCPMC9652295

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.