Evidence map›Paper›PMID 35960418›Full record

ReviewAnnals of biomedical engineering2022

On-Field Deployment and Validation for Wearable Devices.

Calvin Kuo, Declan Patton, Tyler Rooks, Gregory Tierney, Andrew McIntosh, Robert Lynall, Amanda Esquivel, Ray Daniel, Thomas Kaminski, Jason Mihalik and 2 more

Abstract readReview
In one paragraph

Review 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 44 papers.

0numbers the graph read from it
0cells of the map it votes in
44citing 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

44 citing papers in PubMed.

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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

12 authors.

Calvin KuoThe University of British Columbia, Vancouver, Canada.ORCID http://orcid.org/0000-0001-8401-9136
Declan PattonChildren's Hospital of Philadelphia, Philadelphia, USA.
Tyler RooksUnited States Army Aeromedical Research Laboratory, Fort Rucker, USA.
Gregory TierneyUlster University, Belfast, UK.
Andrew McIntoshMcIntosh Consultancy and Research, Sydney, Australia.
Robert LynallUniversity of Georgia, Athens, USA.
Amanda EsquivelUniversity of Michigan, Dearborn, USA.
Ray DanielUnited States Army Aeromedical Research Laboratory, Fort Rucker, USA.
Thomas KaminskiUniversity of Delaware, Newark, USA.
Jason MihalikUniversity of North Carolina at Chapel Hill, Chapel Hill, USA.
Nate DauBiocore, LLC, Charlottesville, USA.
Jillian UrbanWake Forest University School of Medicine, 575 Patterson Ave, Suite 530, Winston-Salem, NC, 27101, USA. jurban@wakehealth.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Wearable sensors are an important tool in the study of head acceleration events and head impact injuries in sporting and military activities. Recent advances in sensor technology have improved our understanding of head kinematics during on-field activities; however, proper utilization and interpretation of data from wearable devices requires careful implementation of best practices. The objective of this paper is to summarize minimum requirements and best practices for on-field deployment of wearable devices for the measurement of head acceleration events in vivo to ensure data evaluated are representative of real events and limitations are accurately defined. Best practices covered in this document include the definition of a verified head acceleration event, data windowing, video verification, advanced post-processing techniques, and on-field logistics, as determined through review of the literature and expert opinion. Careful use of best practices, with accurate acknowledgement of limitations, will allow research teams to ensure data evaluated is representative of real events, will improve the robustness of head acceleration event exposure studies, and generally improve the quality and validity of research into head impact injuries.

Indexed as

Craniocerebral TraumaSportsWearable Electronic DevicesAccelerationBiomechanical PhenomenaHeadHead Protective DevicesHumansBest practicesConcussionHead acceleration eventsMachine learningOn field validationTime windowingVideo verificationWearable devices

Identifiers

PMID35960418
PMCPMC9652208

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.