Document Type
Article
Publication Date
12-15-2022
Abstract
Neuromotor dysfunction after a concussion is common, but balance tests used to assess neuromotor dysfunction are typically subjective. Current objective balance tests are either cost- or space-prohibitive, or utilize a static balance protocol, which may mask neuromotor dysfunction due to the simplicity of the task. To address this gap, our team developed an Android-based smartphone app (portable and cost-effective) that uses the sensors in the device (objective) to record movement profiles during a stepping-in-place task (dynamic movement). The purpose of this study was to examine the extent to which our custom smartphone app and protocol could discriminate neuromotor behavior between concussed and non-concussed participants. Data were collected at two university laboratories and two military sites. Participants included civilians and Service Members (N = 216) with and without a clinically diagnosed concussion. Kinematic and variability metrics were derived from a thigh angle time series while the participants completed a series of stepping-in-place tasks in three conditions: eyes open, eyes closed, and head shake. We observed that the standard deviation of the mean maximum angular velocity of the thigh was higher in the participants with a concussion history in the eyes closed and head shake conditions of the stepping-in-place task. Consistent with the optimal movement variability hypothesis, we showed that increased movement variability occurs in participants with a concussion history, for which our smartphone app and protocol were sensitive enough to capture.
Recommended Citation
Rhea, Christopher K.; Yamada, Masahiro; Kuznetsov, Nikita A.; Jakiela, Jason T.; LoJacono, Chanel T.; Ross, Scott E.; Haran, F. J.; Bailie, Jason M.; and Wright, W. Geoffrey, "Neuromotor Changes in Participants With a Concussion History Can Be Detected With a Custom Smartphone App" (2022). Moss-Magee Rehabilitation Papers. Paper 2.
https://jdc.jefferson.edu/mossrehabfp/2
Creative Commons License
This work is licensed under a
Creative Commons Public Domain Dedication 1.0 License.
Language
English
Comments
This article is the author's final published version in PLoS ONE, Volume 17, Issue 12, December 2022, Article number e0278994.
The published version is available at https://doi.org/10.1371/journal.pone.0278994. This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.