Evidence map›Paper›PMID 37310507›Full record

ArticleSports medicine - open2023

XR Programmers Give Their Perspective on How XR Technology can be Effectively Utilised in High-Performance Sport.

Peter J Le Noury, Remco C Polman, Michael A Maloney, Adam D Gorman

Abstract read
In one paragraph

Article in Sports medicine - open, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

4 authors.

Peter J Le NourySchool of Exercise and Nutrition Sciences, Faculty of Health, Queensland University of Technology, Brisbane, QLD, Australia. peter.lenoury@qut.edu.au.ORCID http://orcid.org/0000-0001-9825-4233
Remco C PolmanInstitute of Health and Wellbeing, Federation University, Berwick, VIC, Australia.
Michael A MaloneyAustralian Institute of Sport, Canberra, Australia.
Adam D GormanSchool of Exercise and Nutrition Sciences, Faculty of Health, Queensland University of Technology, Brisbane, QLD, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe successful use of extended reality (XR) in sport is highly dependent on the extent to which it can represent the perception-action couplings that exist in the performance setting. However, there are many unknowns regarding the effectiveness of XR technology which is limiting its adoption in sport. Therefore, providing high-performance sporting organisations with more information about the efficacy and utility of XR, specifically its strengths and limitations, is warranted.

resultsThe results provide insight into the limitations of XR and how those limitations are likely to reduce the effectiveness of XR for training motor skills. The participants described opportunities provided by XR for measuring athlete performance and highlighted several practical applications for enhancing athlete and coaching performance. Using artificial intelligence (AI) for training tactical decision-making and creating new movement solutions was also a key finding.

conclusionsThe use of XR in sport is in its infancy, and more research is required to establish a deeper understanding of its utility and efficacy. This research provides sporting organisations, coaches, athletes, and XR technology companies with insights into where XR technology can have the greatest positive impact on performance in sport.

Indexed as

Motor skillPerceptual–cognitive skillRepresentative designVirtual reality

Identifiers

PMID37310507
PMCPMC10264317

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.