Abstract

The return of concussed students and student-athletes to the classroom is commonly referred to as return-to-learn (RTL). RTL, however, is often overshadowed by returning a student-athlete back to athletic competition (return-to-play), with few recommendations and studies evaluating the effect of improper management of recovery from a concussion in an academic setting. Therefore, the research proposed here aims to track how symptom severity, student behaviors, and oculomotor performance formulate our ability to prognosticate how a student will respond to academic stimuli post-injury. This will be achieved by longitudinally tracking student-athletes as they recover from concussion, using a repeated measures design to sample data. The data was analyzed using an analysis of variance mixed effects model to understand the relationship between daily behaviors and symptom prevalence. The study identified overall time, caffeine intake, alcohol consumption, screen time, music listened to, physical activity, sleep duration, step count, and gender as significant factors associated with concussion symptom recovery and classroom management. Linear regression was utilized to correlate RTL recovery time to oculomotor scores, to preliminarily show how these scores can inform medical personnel when a student can return, unrestricted, to the classroom, and the types of accommodations to suggest for use in the classroom during recovery. Additionally, the Rochester Institute of Technology was used as a case analysis of current RTL procedures (athletic and academic management) to find areas of inefficiencies in providing timely and sufficient support to concussed students. The data collected and presented in this study was utilized to develop preliminary, evidence-based RTL guidelines to provide clinicians, athletic training staff, and university stakeholders with policies and practices to better ensure proper care is taken among students recovering from a concussion.

Publication Date

12-2022

Document Type

Thesis

Student Type

Graduate

Degree Name

Science, Technology and Public Policy (MS)

Department, Program, or Center

Public Policy (CLA)

Advisor

Zachary Bevilacqua

Advisor/Committee Member

Eric Hittinger

Advisor/Committee Member

Jennifer Bailey

Campus

RIT – Main Campus

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