Evidence map›Paper›PMID 33051745›Full record

ArticleAnnals of biomedical engineering2020

A Two-Phased Approach to Quantifying Head Impact Sensor Accuracy: In-Laboratory and On-Field Assessments.

Emily E Kieffer, Mark T Begonia, Abigail M Tyson, Steve Rowson

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Article in Annals of biomedical engineering, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 66 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
66citing papers in PubMed, 3 pooled it
–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

66 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
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  8. Head Acceleration Events in Collegiate Rodeo Athletes.Annals of biomedical engineering · 2026
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6 more citing papers are in PubMed but not listed here.

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

4 authors.

Emily E KiefferBiomedical Engineering and Mechanics, Virginia Tech, Blacksburg, VA, USA. kieffere@vt.edu.ORCID http://orcid.org/0000-0002-8865-8290
Mark T BegoniaBiomedical Engineering and Mechanics, Virginia Tech, Blacksburg, VA, USA.
Abigail M TysonBiomedical Engineering and Mechanics, Virginia Tech, Blacksburg, VA, USA.
Steve RowsonBiomedical Engineering and Mechanics, Virginia Tech, Blacksburg, VA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Measuring head impacts in sports can further our understanding of brain injury biomechanics and, hopefully, advance concussion diagnostics and prevention. Although there are many head impact sensors available, skepticism on their utility exists over concerns related to measurement error. Previous studies report mixed reliability in head impact sensor measurements, but there is no uniform approach to assessing accuracy, making comparisons between sensors and studies difficult. The objective of this paper is to introduce a two-phased approach to evaluating head impact sensor accuracy. The first phase consists of in-lab impact testing on a dummy headform at varying impact severities under loading conditions representative of each sensor's intended use. We quantify in-lab accuracy by calculating the concordance correlation coefficient (CCC) between a sensor's kinematic measurements and headform reference measurements. For sensors that performed reasonably well in the lab (CCC ≥ 0.80), we completed a second phase of evaluation on-field. Through video validation of impacts measured by sensors on athletes, we classified each sensor measurement as either true-positive and false-positive impact events and computed positive predictive value (PPV) to summarize real-world accuracy. Eight sensors were tested in phase one, but only four sensors were assessed in phase two. Sensor accuracy varied greatly. CCC from phase one ranged from 0.13 to 0.97, with an average value of 0.72. Overall, the four devices that were implemented on-field had PPV that ranged from 16.3 to 91.2%, with an average value of 60.8%. Performance in-lab was not always indicative of the device's performance on-field. The methods proposed in this paper aim to establish a comprehensive approach to the evaluation of sensors so that users can better interpret data collected from athletes.

Indexed as

Brain ConcussionHead Protective DevicesWearable Electronic DevicesAccelerationAdolescentAdultBiomechanical PhenomenaFootballHeadHumansMaleAccelerometerBiomechanicsConcussionDataHelmetLinearRotationalWearable devices

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.