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Can participants predict where ambulance-requiring cases occur at a half marathon?
Karlstads universitet.
Göteborgs universitet.
Göteborgs universitet.
Ersta Sköndal Bräcke University College, Department of Social Sciences, Institute for Civil Society Research.
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2018 (English)In: Scandinavian Journal of Medicine and Science in Sports, ISSN 0905-7188, E-ISSN 1600-0838, Vol. 28, no 12, p. 2760-2766Article in journal (Refereed) Published
Abstract [en]

OBJECTIVES: Despite endurance races leading to a substantial number of ambulance-requiring cases (ARC), little is known regarding where they occur, meaning that knowing where to place medical teams, ambulance pick-up points, etc, is difficult. This article investigates whether the location of ARCs can be identified by race participants.

METHODS: Using the world's largest half marathon (Gothenburg half marathon) as a case, 237 runners were asked, post-race, to mark on a map which geographical point of the race was most exhausting. Using the level of agreement tests, these geographical points were then compared with the GPS positions of ARCs.

RESULTS: According to the level of agreement tests, the most exhausting positions (MEP), as identified by participants, seem to be highly correlated to the location of ARCs. This study can also show that ambulance-requiring cases seem to be more prevalent towards the end of the race and in uphill sections.

CONCLUSIONS: By asking participants where they found the race most exhausting it seems possible to identify high-risk places for an ARC. From a practical perspective, using this method could considerably increase the safety of competitors as well as improving the cost-effectiveness of safety interventions at endurance races. Further studies are needed to understand the specific risk factors of the high-risk areas as well as characteristics of collapsed runners.

Place, publisher, year, edition, pages
2018. Vol. 28, no 12, p. 2760-2766
Keywords [en]
Ambulance, Collapse, Endurance race, Running
National Category
Sport and Fitness Sciences
Identifiers
URN: urn:nbn:se:esh:diva-7101DOI: 10.1111/sms.13299PubMedID: 30218614OAI: oai:DiVA.org:esh-7101DiVA, id: diva2:1262912
Available from: 2018-11-13 Created: 2018-11-13 Last updated: 2019-05-17Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
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