This investigation will assess the utility of a novel wearable electroencephalography (EEG) headband linked to a mobile application to monitor cognitive activity post-concussion, and alert a patient when it is time to take a mental break. Personalized cloud-based machine learning algorithms will analyze EEG data in real-time for neural features indicative of mental workload and mental fatigue, and will notify a patient when it is time to rest based on these measures. It is hypothesized that this technology may provide a much needed data-driven management tool to better inform the cognitive pacing process for both patients with concussion, as well as their clinicians.
Despite advancements in the field of concussion care, the individualized nature and nuances of concussion make it a difficult condition to manage. It has been shown that both complete rest or too much activity can prolong recovery from concussion, indicating there is an ideal zone of activity that can aid in concussion recovery. Heart-rate guided sub-symptom aerobic physical activity has been shown to speed concussion recovery and provide an objective measure for patients with concussion to inform their rehabilitative physical activities. However, no such equal exists for guiding cognitive pacing. This study will utilize a wearable EEG headband linked to a mobile application to monitor cognitive activity post-concussion, and alert a patient when it is time to take a mental break. Personalized cloud-based machine learning algorithms will analyze EEG data in real-time for neural features of mental workload and mental fatigue, and will notify a patient when it is time to rest based on these measures. These algorithms have been developed and validated on healthy participants, and refined in concussion patients in an ongoing observational investigation yet to be published. The proposed investigation is a randomized, prospective pilot study to test the early efficacy of this technology in concussion recovery compared to standard of care alone. The results of this pilot investigation will be used to inform a future large-scale clinical trial to confirm the efficacy of this technology on concussion recovery.
Study Type
INTERVENTIONAL
Allocation
RANDOMIZED
Purpose
TREATMENT
Masking
NONE
Enrollment
50
EEG headband to record brain signals. The Sensorband uses Bluetooth to link to the mobile application on a user's device. This raw EEG data is processed on a HIPPA compliant cloud based server and displays mental workload and brain energy data on the application. This can help the user with cognitive pacing to avoid overexertion. The control version will not display mental workload and brain energy data on the application.
Immediate Post-Concussion Assessment and Cognitive Testing score
Change in neurocognitive health and changes in concussion recovery. Higher score indicates improvement.
Time frame: Baseline, post-intervention (2 weeks)
Study inclusion rate and completion rate to indicate feasibility
Rates of inclusion, deviations and completion
Time frame: Baseline, post-intervention (2 weeks)
Rivermead Post-Concussion Symptoms Questionnaire Outcomes
Change in symptoms and burden of concussion. Lower score indicates improvement.
Time frame: 12 weeks
Change in Sports Concussion Assessment Tool score
Change in concussion symptoms. Lower score indicates improvement in symptoms.
Time frame: Baseline, post-intervention (2 weeks)
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