INNOVATIVE APPROACH TO CONCUSSION ASSESSMENT

dc.contributor.advisor Tamura, Kaori
dc.contributor.author Hashida, Kumiko
dc.contributor.department Kinesiology and Rehabilitation Science
dc.date.accessioned 2023-07-11T00:20:44Z
dc.date.available 2023-07-11T00:20:44Z
dc.date.issued 2023
dc.description.degree Ph.D.
dc.identifier.uri https://hdl.handle.net/10125/105129
dc.subject Kinesiology
dc.subject concussion assessment
dc.subject concussion subtype
dc.subject divided attention
dc.subject dual-task
dc.subject neurocognitive test
dc.subject sports-related concussion
dc.title INNOVATIVE APPROACH TO CONCUSSION ASSESSMENT
dc.type Thesis
dcterms.abstract The current return to learn or play protocol following concussions dictates a gradual return to learn or play by increasing the difficulty of physical and cognitive activities in a controlled environment under the supervision of health care providers. While the return to learn or play protocol is well established and universally accepted, about half of the concussed individuals reported recurrent symptoms or worsening of symptoms after returning to school and sports activities following a gradual RTP protocol. A possible explanation for this return to learn or play failure is that the current concussion assessment tools are not able to identify the residual post-concussion deficits effectively. This indicates that there is a need for concussion assessment tools that are able to identify residual post-concussion deficits precisely in clinical settings. To better inform the ultimate understanding of post-concussion deficits and improve concussion assessment, this dissertation proposes three innovative approaches to concussion assessment using dual task, mobile application-based neurocognitive test, and subtype.
dcterms.language en
dcterms.publisher University of Hawai'i at Manoa
dcterms.rights All UHM dissertations and theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission from the copyright owner.
dcterms.type Text
local.identifier.alturi http://dissertations.umi.com/hawii:11740
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