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dc.contributor.authorWatson, PJ
dc.contributor.authorFieldsend, JE
dc.contributor.authorStiles, VH
dc.date.accessioned2021-12-09T10:46:11Z
dc.date.issued2021-11-28
dc.date.updated2021-12-08T16:28:04Z
dc.description.abstractTo aid the implementation of athlete surveillance systems relative to logistical circumstances, easy-to-access information that summarises the extent to which methods of acquiring data are used in practice to monitor athletes is required. In this scoping review, Social Network Analysis and Mining (SNAM) techniques were used to summarise and identify the most prevalent combinations of methods used to monitor athletes in research studying team, individual, field- and court-based sports (357 articles; SPORTDiscus, MEDLINE, CINHAL, and WebOfScience; 2014-2018 inc.) . The most prevalent combination in team and field-based sports were HR and/or sRPE (internal) and GPS, whereas in individual and court-based sports, internal methods (e.g., HR and sRPE) were most prevalent. In court-based sports, where external methods were occasionally collected in combination with internal methods of acquiring data, the use of accelerometers or inertial measuring units (ACC/IMU) were most prevalent. Whilst individual and court-based sports are less researched, this SNAM-based summary reveals that court-based sports may lead the way in using ACC/IMU to monitor athletes. Questionnaires and self-reported methods of acquiring data are common in all categories of sport. This scoping review provides coaches, sport-scientists and researchers with a data-driven visual resource to aid the selection of methods of acquiring data from athletes in all categories of sport relative to logistical circumstances. A guide on how to practically implement a surveillance system based on the visual summaries provided herein, is also presented.en_GB
dc.format.extent175-197
dc.identifier.citationVol. 20 (2), pp. 175-197en_GB
dc.identifier.doihttps://doi.org/10.2478/ijcss-2021-0011
dc.identifier.urihttp://hdl.handle.net/10871/128067
dc.identifierORCID: 0000-0003-1107-6484 (Stiles, VH)
dc.language.isoenen_GB
dc.publisherSciendo / International Association of Computer Science in Sport (IACSS).en_GB
dc.rights© 2021 P. J. Watson et al., published by Sciendo. Open acess. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.en_GB
dc.subjecttraining loaden_GB
dc.subjectathlete monitoringen_GB
dc.subjectaccelerometryen_GB
dc.subjectcentralityen_GB
dc.subjectFiedler vectoren_GB
dc.titleA scoping review using social network analysis techniques to summarise the prevalance of methods used to acquire data for athlete survelliance in sporten_GB
dc.typeArticleen_GB
dc.date.available2021-12-09T10:46:11Z
dc.identifier.issn1684-4769
dc.descriptionThis is the final version. Available on open access from Sciendo via the DOI in this recorden_GB
dc.identifier.eissn1684-4769
dc.identifier.journalInternational Journal of Computer Science in Sporten_GB
dc.relation.ispartofInternational Journal of Computer Science in Sport, 20(2)
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/en_GB
dcterms.dateAccepted2021
rioxxterms.versionVoRen_GB
rioxxterms.licenseref.startdate2021-11-28
rioxxterms.typeJournal Article/Reviewen_GB
refterms.dateFCD2021-12-09T10:43:28Z
refterms.versionFCDVoR
refterms.dateFOA2021-12-09T10:49:39Z
refterms.panelCen_GB
refterms.dateFirstOnline2021-11-28


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© 2021 P. J. Watson et al., published by Sciendo. Open acess. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Except where otherwise noted, this item's licence is described as © 2021 P. J. Watson et al., published by Sciendo. Open acess. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.