Rehabilitation Oculomotor Screening Evaluation (ROSE)-A Proof-of-Principle Study for Acquired Brain Injuries.
Tina Yu-Zhou LiKelsey MadgeFrancesca RichardPreeti SarpalElizabeth DannenbaumJoyce FungPublished in: Journal of clinical medicine (2024)
Background/Objectives: Acquired brain injury (ABI) is a major cause of global disability. Many ABI patients exhibit oculomotor dysfunctions that impact their daily life and rehabilitation outcomes. Current clinical tools for oculomotor function (OMF) assessment are limited in their usability. In this proof-of-principle study, we aimed to develop an efficient tool for OMF screening and to assess the feasibility, acceptability, and relevance in a small sample of ABI and control participants. Methods: We created the Rehabilitation Oculomotor Screening Evaluation (ROSE) by reviewing existing OMF assessments. ROSE was pilot-tested on ABI patients ( n = 10) and age-matched controls ( n = 10). Data regarding the characteristics of the assessment, such as the duration, level of participant comprehension, and participant experience were also collected. Results: ROSE takes <20 min (x¯ = 12.5), is easy to complete (agreement x¯ = 4.6/5), and is well-accepted (x¯ = 4.8/5). Patients scored higher in all subtests and total score (x¯ = 34.8 for ABI vs. 8.9 for controls). Most subtests did not provoke any symptoms, especially for controls. There were no significant between-group differences in symptom provocation. This proof-of-principle study shows that ROSE is feasible, acceptable, and relevant for adult ABI patients. Conclusions: ROSE needs further evaluation for reliability testing and validation in larger samples and diverse neurological conditions. Establishing norms for various ages, sexes, and populations should be considered for the deployment of ROSE as an OMF clinical tool.
Keyphrases
- end stage renal disease
- ejection fraction
- brain injury
- newly diagnosed
- peritoneal dialysis
- prognostic factors
- healthcare
- subarachnoid hemorrhage
- adipose tissue
- machine learning
- electronic health record
- multiple sclerosis
- depressive symptoms
- insulin resistance
- skeletal muscle
- big data
- white matter
- functional connectivity