The Effectiveness of Biofeedback for Individuals With Long-term Post-concussive Symptoms

The Effectiveness of Neurofeedback and Heart Rate Variability Biofeedback for Individuals With Long-term Post-concussive Symptoms

Most concussions resolve within 7-10 days, but approximately 40% of individuals do not fully recover and suffer from persistent post-concussive symptoms. This 8-week intervention study will evaluate the efficacy of heart rate variability (HRV) biofeedback and neurofeedback on reducing the number and severity of concussion symptoms.

40% of minor head injuries are diagnosed with post-concussion syndrome 3 months after injury (Ingebrigtsen, Waterloo, Marup-Jensen, Attner, & Romner, 1998). These individuals have persistent symptoms after completing conventional rehabilitation programs. Persistent post-concussion symptoms not only decrease quality of life (Ingebrigtsen et al, 1998), but also impair cognitive and motor performance and increase the likelihood of impaired driving performance (Preece, Horswill, & Geffen, 2010) and motor vehicle accidents (Bivona et al, 2012). While case reports indicate that biofeedback can reduce the number and severity of post-concussive symptoms (Lagos, Thompson, & Vaschillo, 2013; Thompson, Thompson, Reid-Chung, & Thompson, 2013), no studies have systematically evaluated these biofeedback treatment programs. HRV biofeedback works by displaying beat-to-beat heart rate data to the participant, and through operant conditioning with breathing techniques, the participant learns to control their HRV (Lehrer & Gevirtz, 2014). This results in an increase in parasympathetic (PNS) activity and decrease in sympathetic (SNS) activity, which leads to reduced anxiety, and increased focus and concentration (Lagos, Bottiglieri, Vaschillo, & Vaschillo, 2012). Neurofeedback works in a similar fashion, except it monitors brain wave power, frequency, and connectivity using quantitative electroencephalogram (EEG). Brain functioning is displayed while playing an electronic game, and the participant learns through operant conditioning to increase the amplitude of desired EEG frequencies, such as low beta waves that are associated with active problem solving, usually while simultaneously decreasing the amplitudes of undesired EEG frequencies (Conder & Conder, 2014). This will be an eight-week intervention where participants suffering from long-term post-concussion symptoms will be recruited using email from the cohort of individuals that have been discharged after completing a concussion rehabilitation protocol (BrainEx90) at Parkwood Institute in London, Ontario. Non-concussed control participants will be recruited using posters. Participants will complete pre, mid, and post-intervention driving simulation tasks, electrocardiogram and HRV measures, and subjective questionnaires. These will be utilized to evaluate the effectiveness of HRV biofeedback and neurofeedback in this difficult to treat population.

This study will include two intervention arms and two control arms. The intervention arms will include 1) heart rate variability biofeedback, 2) a combination of heart rate variability biofeedback and neurofeedback. The control arms will be 1) age-matched post-concussive individuals and 2) age-matched individuals who have not been diagnosed with a concussion in the last two years.

Participants in this arm of the study will receive HRV biofeedback and neurofeedback. HRV biofeedback will occur twice daily, using an android device and application. Additionally, three times per week they will have one-hour long neurofeedback sessions.

Age-matched, previously concussed individuals that have completed the same concussion rehabilitation program (Brain Ex 90) will be recruited for this arm.

HRV biofeedback constitutes initial training with the android device and application, and HRV training performed at home. This training will occur twice daily, and each session will take five minutes.

LORETA Z-Score neurofeedback training will occur three times per week with a trained study investigator.

The interval between heartbeats, specifically the artifact-free intervals between R waves in the QRS complex, will be measured. This is known as the standard deviation of the norm (SDNN), and is a universal method of quantifying HRV (Camm et al., 1996). This information is collected using the Mindja application for android devices, created by Evoke Neuroscience.The physiologically relevant norms are a mean of 50 (SD 16) and a range from 32-93 ms (Shaffer F, Ginsberg JP. An Overview of Heart Rate variability Metrics and Norms. Frontiers in Public Health. 2017 Sep;5(258):1.)

Participants will perform a driving simulation task using the DriveSafety CDS-250 driving simulator. It will record the performance, and afterwards a trained rater will review and evaluate the number of driving errors using a standardized assessment form. The number of individuals that made a driving simulator mistake are reported. The minimum is zero and the maximum is the number of participants in the Arm/Group. We are not aware of any physiologically relevant ranges for this measure.

The amplitude and power of alpha, beta, theta, and delta frequencies will be evaluated relative to reference norms (Gevensleben et al., 2010) and expressed as Z-scores (deviation from the mean divided by the standard deviation). In terms of physiologically relevant norms, 99% of the population will have scores between -3 and +3. This information is collected and stored in a secured cloud between Evoke Neuroscience and Western University.

These are assessed using the Rivermead Post Concussion Questionnaire (RPQ). It evaluates the severity of 16 common post-concussion symptoms over the past 24 hours (with the option to add 2 additional symptoms not already listed). Some examples include headache, sleep disturbance, noise sensitivity and blurred vision. It asks the evaluator to compare each symptom to how they would "normally" have felt prior to the concussion. It is a 5-point scale, which goes from 0-4. When the symptom is not experienced at all, the evaluator is to put a 0 (better outcome), whereas 4 indicates the symptom is a severe problem (worse outcome). Scores range from 0-72, where 72 represents experiencing all symptoms, and they are all a severe problem (worse outcome).

This is assessed using the Generalized Anxiety Disorder 7-Item Scale (GAD-7). Seven anxiety symptoms experienced over the past 2 weeks are evaluated on a 4-point scale, which goes from 0-3. Some examples include feeling nervous or anxious, inability to stop worrying, and trouble relaxing. When the symptom is not experienced at all, the evaluator is to put a 0 (better outcome), whereas 3 indicates the symptom is experienced nearly every day (worse outcome). Score totals range from 0 to 21, where 21 represents experiencing all symptoms, and they are all experienced nearly every day (worse outcome).

Inclusion Criteria: Participants in HRV and the HRV/Neurofeedback intervention arms, and the post-concussion control arm: Previously suffered a clinically diagnosed concussion Participated in, completed, and have been discharged from the BrainEx90 outpatient concussion rehabilitation program at Parkwood Institute Continued post-concussive symptoms 18 years of age or older Access to transportation Capable of utilizing hand-held technology (ie. cell phone, tablet, etc.) Holds a valid Driver's License English speaking Participants in the non-concussed control arm: 18 years of age or older Holds a valid driver's license English speaking Has not suffered a concussion in the last two years Exclusion Criteria: All participants: Any heart disease, pacemaker, abnormal heartbeat patterns, coronary artery disease, or bypass surgery Any mental health disorder that would interfere with participation in the study Under 18 years of age Unable to provide written informed consent or complete questionnaires due to language or cognitive difficulties Inability to operate a motor vehicle Inability to look at a digital screen for 30 minutes Participants in the non-concussed control arm: 1. Suffered a concussion in the last two years

Each participant will be identified with a code (eg. PCS001) that correlates to his or her addition to the study. The master sheet will be the only document that contains the decoding system, and will be stored in a locked filing cabinet. All other data will be labeled using the participant's identification code. Neurophysiological data sent to Evoke Neuroscience will not contain any personal identifiers. Data sent between Evoke Neuroscience and Western University is sent in a secure file transfer. All other de-identified data stored on a Western University hard drive is within a secure university network (J drive).

Bivona U, D'Ippolito M, Giustini M, Vignally P, Longo E, Taggi F, Formisano R. Return to driving after severe traumatic brain injury: increased risk of traffic accidents and personal responsibility. J Head Trauma Rehabil. 2012 May-Jun;27(3):210-5. doi: 10.1097/HTR.0b013e31822178a9.

Conder RL, Conder AA. Heart rate variability interventions for concussion and rehabilitation. Front Psychol. 2014 Aug 13;5:890. doi: 10.3389/fpsyg.2014.00890. eCollection 2014.

Heart rate variability: standards of measurement, physiological interpretation and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Circulation. 1996 Mar 1;93(5):1043-65. No abstract available.

Fisk GD, Schneider JJ, Novack TA. Driving following traumatic brain injury: prevalence, exposure, advice and evaluations. Brain Inj. 1998 Aug;12(8):683-95. doi: 10.1080/026990598122241.

Gevensleben H, Holl B, Albrecht B, Schlamp D, Kratz O, Studer P, Rothenberger A, Moll GH, Heinrich H. Neurofeedback training in children with ADHD: 6-month follow-up of a randomised controlled trial. Eur Child Adolesc Psychiatry. 2010 Sep;19(9):715-24. doi: 10.1007/s00787-010-0109-5. Epub 2010 May 25.

Ingebrigtsen T, Waterloo K, Marup-Jensen S, Attner E, Romner B. Quantification of post-concussion symptoms 3 months after minor head injury in 100 consecutive patients. J Neurol. 1998 Sep;245(9):609-12. doi: 10.1007/s004150050254.

Lagos, L., Bottiglieri, T., Vaschillo, B., & Vaschillo, E. (2012). Heart Rate Variability Biofeedback for Postconcussion Syndrome: Implications for Treatment. Biofeedback, 40(4), 150-153. doi:10.5298/1081-5937-40.4.05

Lagos, L., Thompson, J., & Vaschillo, E. (2013). A Preliminary Study: Heart Rate Variability Biofeedback for Treatment of Postconcussion Syndrome. Biofeedback, 41(3), 136-143. doi:10.5298/1081-5937-41.3.02

Lehrer PM, Gevirtz R. Heart rate variability biofeedback: how and why does it work? Front Psychol. 2014 Jul 21;5:756. doi: 10.3389/fpsyg.2014.00756. eCollection 2014.

Milleville-Pennel I, Pothier J, Hoc JM, Mathe JF. Consequences of cognitive impairments following traumatic brain injury: Pilot study on visual exploration while driving. Brain Inj. 2010;24(4):678-91. doi: 10.3109/02699051003692159.

Munivenkatappa A, Rajeswaran J, Indira Devi B, Bennet N, Upadhyay N. EEG Neurofeedback therapy: Can it attenuate brain changes in TBI? NeuroRehabilitation. 2014;35(3):481-4. doi: 10.3233/NRE-141140.

Preece MH, Horswill MS, Geffen GM. Driving after concussion: the acute effect of mild traumatic brain injury on drivers' hazard perception. Neuropsychology. 2010 Jul;24(4):493-503. doi: 10.1037/a0018903.

Thompson, M., Thompson, L., Reid-Chung, A., & Thompson, J. (2013). Managing Traumatic Brain Injury: Appropriate Assessment and a Rationale for Using Neurofeedback and Biofeedback to Enhance Recovery in Postconcussion Syndrome. Biofeedback, 41(4), 158-173. doi:10.5298/1081-5937-41.4.07