Brain Injury Professional: Technology and Brain Injury edition

BRAIN INJURY professional vol. 20 issue 1

Technology and Brain Injury

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BRAIN INJURY professional

vol. 20 issue 1

departments

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Editor in Chief Message Guest Editor’s Message

features

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Tech in the Clinic: A Patient’s Perspective Sarah Renberg

Rehabilitation Technology: A Case for Improved End-user Engagement in the Design, Development, and Selection Processes Kristian Nitsch, PhD

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ApplTree: Understanding How Assistive Technology Can Support Memory After Brain Injury Dr. Matthew Jamieson • Dr. Jonathan Evans

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Neumind, a Company Setting out to Transform ABI Rehabilitation Ellis Parry, PhD

Eye Tracking Technology: A Primer Jacqueline Theis, OD, FAAO

Artificial Intelligence Language Models for Brain Injury Rehabilitation: A ChatGPT Experience Stephen Trapp, PhD • ChatGPT

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Robotic Interventions for Acquired Brain Injury in Spain Melina Longoni, MD • Elena Orcajo Oteo, MD • Natacha León, MD

Concussion Diagnosis Versus Impairment Identification: Where Should the Focus Be and How Can Technology Play a Role?

Ryan Pelo, PT, DPT, NCS

Brain Injury Professional is a membership benefit of the North American Brain Injury Society and the International Brain Injury Association

NORTH AMERICAN BRAIN INJURY SOCIETY CHAIRMAN Mariusz Ziejewski, PhD VICE CHAIR Debra Braunling-McMorrow, PhD IMMEDIATE PAST CHAIR Ronald C. Savage, EdD TREASURER Bruce H. Stern, Esq. SECRETARY Brian Greenwald, MD FAMILY LIAISON Skye MacQueen EXECUTIVE DIRECTOR/ADMINISTRATION Margaret J. Roberts EXECUTIVE DIRECTOR/OPERATIONS J. Charles Haynes, JD MARKETING MANAGER Megan Bell-Johnston BRAIN INJURY PROFESSIONAL PUBLISHER J. Charles Haynes, JD CO-EDITOR IN CHIEF Beth Slomine, PhD - USA CO-EDITOR IN CHIEF Nathan Zasler, MD - USA ASSOCIATE EDITOR Juan Arango-Lasprilla, PhD – Spain TECHNOLOGY EDITOR Stephen K. Trapp, PhD - USA EDITOR EMERITUS Debra Braunling-McMorrow, PhD - USA EDITOR EMERITUS Ronald C. Savage, EdD - USA DESIGN AND LAYOUT Kristin Odom ADVERTISING SALES Megan Bell-Johnston EDITORIAL ADVISORY BOARD Nada Andelic, MD - Norway Philippe Azouvi, MD, PhD - France Mark Bayley, MD - Canada Lucia Braga, PhD - Brazil Ross Bullock, MD, PhD - USA Fofi Constantinidou, PhD, CCC-SLP, CBIS - USA Gordana Devecerski, MD, PhD - Serbia Sung Ho Jang, MD - Republic of Korea Cindy Ivanhoe, MD - USA Inga Koerte, MD, PhD - USA Brad Kurowski, MD, MS - USA Jianan Li, MD, PhD - China Christine MacDonell, FACRM - USA Calixto Machado, MD, PhD - Cuba Barbara O’Connell, OTR, MBA - Ireland Lisandro Olmos, MD - Argentina Caroline Schnakers, PhD - USA Lynne Turner-Stokes, MD - England Olli Tenovuo, MD, PhD - Finland Asha Vas, PhD, OTR - USA Walter Videtta, MD – Argentina Thomas Watanabe, MD – USA Alan Weintraub, MD - USA Sabahat Wasti, MD - Abu Dhabi, UAE Gavin Williams, PhD, FACP - Australia Hal Wortzel, MD - USA Mariusz Ziejewski, PhD - USA EDITORIAL INQUIRIES Managing Editor Brain Injury Professional PO Box 131401, Houston, TX 77219-1401 Tel 713.526.6900 Email: mbell@hdipub.com Website: www.nabis.org ADVERTISING INQUIRIES Megan Bell-Johnston Brain Injury Professional HDI Publishers PO Box 131401, Houston, TX 77219-1401 Tel 713.526.6900 Email: mbell@internationalbrain.org NATIONAL OFFICE North American Brain Injury Society PO Box 1804, Alexandria, VA 22313 Tel 703.960.6500 / Fax 703.960.6603 Website: www.nabis.org ISSN 2375-5210 Brain Injury Professional is a quarterly publication published jointly by the North American Brain Injury Society and HDI Publishers. © 2023 NABIS/HDI Publishers. All rights reserved. No part of this publication may be reproduced in whole or in part in any way without the written permission from the publisher. For reprint requests, please contact, Managing Editor, Brain Injury Professional, PO Box 131401, Houston, TX 77219-1400, Tel 713.526.6900, Fax 713.526.7787, e-mail mbell@hdipub.com.

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from the editor in chief Traumatic brain injury (TBI) is one of the leading causes of disability and death worldwide. Every year millions of people around the world suffer from TBI and often incur injuries that lead to short- and long-term physical, cognitive, and emotional difficulties. In addition to the pathophysiological ramifications of TBI, some of the most devastating concers post-TBI include personal, social, family and work reintegration problems.

Dr. Juan Carlos Arango-Lasprilla

Evidence suggests that individuals with moderate to severe TBI may benefit from rehabilitation programs that extend well beyond the average length of hospital stay.

However, community-based specialized rehabilitation options are limited. Geographic distance, lack of transportation, loss of mobility and functional independence, as well as physical and cognitive impairments associated with TBI, have also been identified as barriers to outpatient rehabilitation attendance.

For this reason, it is essential to offer an innovative alternative to traditional treatment programs. In the last 20 years, the development of technologies for use in the field of human rehabilitation has been increasing. Since then, these technologies have changed many aspects of clinical practice, especially diagnostic and rehabilitation methods. Today technological innovation is a hallmark of most advanced rehabilitation services around the world.

The goal of this issue is 1. to provide an overview of the use of technology in the neurorehabilitation of people with acquired brain damage, 2. to present the advantages and disadvantages of the use of technology in rehabilitation and 3. describe some practical implications of the use of technology and some future considerations.

For this issue, we have put together a collection of articles on relevant topics including technology from the patient perspective, design and development considerations, and practice implications. We recognize that our readers tend to be at the cutting edge in the field of neurorehabilitation and our hope that this offering of articles will span common interests in the diverse arena of brain injury technology.

Editor Bio Dr. Juan Carlos Arango-Lasprilla is currently an Ikerbasque Research Professor at BioCruces Vizcaya Health Research Institute affiliated with Cruces University Hospital in Bilbao, Spain. A neuropsychologist by training, his areas of expertise are neuropsychology, traumatic brain injury (TBI), and rehabilitation. Dr. Arango has received over 20 awards for his accomplishments in the area of brain injury and rehabilitation, including early career/emerging professional awards from the National Academy of Neuropsychology, the American Psychological Association’s Division 22 Rehabilitation Psychology and Division 45 (Society for the Psychological Study of Ethnic Minority Issues), the International Brain Injury Association Young Investigator Award and Mid-Career awards such the Arthur Benton Mid-Career award from the International Neuropsychological Society and The Mitchell Rosenthal Mid-Career award From the American Congress of Rehabilitation Medicine. He has published more than 300 articles and book chapters and edited 10 books.

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from the guest editor Recent years have witnessed remarkable progress in the development of brain injury technology. These advances have offered newfound hope for rehabilitation, forwarded patient-centered care, and improved quality of life for the millions of people affected by acquired brain injury. This special issue examines a range of topics pertinent to those involved in brain injury rehabilitation, including the patient perspective, design and development, and implications for clinical practice.

Stephen K. Trapp, PhD

The issue begins with a survivor story written by Sarah Renberg, in which a student athlete describes how innovative technologies accelerated her rehabilitation journey.

Our design and development section includes a commentary by Dr. Kristian Nitsch on the fundamental importance of including end-users throughout the complete course of technology development. This is followed by a description of two technologies fundamentally linked by a shared history. Drs. Jonathan Evans and Matthew Jamieson describe early mobile interventions, NeuroPage and ApplTree, that set the stage for the many mobile neurorehabilitation technologies we encounter today. The following piece describes Dr. Ellis Parry’s next-generation mobile technology, neumind, that was inspired by a personal brain injury story and springboarded from the foundation of Dr. Evan’s work.

The issue moves on to practice considerations with a primer on eye tracking technology

by Dr. Jacqueline Theis. This is followed by commentary – or caution – by Dr. Stephen Trapp on the use of readily available artificial intelligence applications, like ChatGPT. Physiatrist, Dr. Melina Longoni, follows with an article on the use of robotics for neurorehabilitation in Spain. Physical therapist and rehabilitation scientist, Dr. Ryan Pelo, concludes the practice section with a commentary on the role of technology in the tension between diagnostic testing and impairment characterization for post-concussion care.

Our goal with this special issue was to facilitate next-step questions on how our community of brain injury professionals can lead the way in designing, developing, and using brain injury technology for neurorehabilitation. The most innovative technological solutions are generated by those most familiar with the problems. We hope this encourages each of you to reconsider how to use off-the-shelf technology, build upon what exists, and trigger novel solutions for the betterment of our field.

Editor Bio Stephen K. Trapp, PhD, is a rehabilitation psychologist and Director of the Center of Health Creation at the Metrodora Institute. His clinical and research efforts focus on a range of neurorehabilitative conditions, rehabilitation technology, and cross-cultural topics. Among other roles, he is the Technology Editor for Brain Injury Professional and affiliated with the Department of Psychiatry at the University of Utah.

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Tech in the Clinic: A Patient’s Perspective Sarah Renberg

I sit readying myself, my unpatched eye focused on a rocket ship at the center of a computer monitor.

Huge asteroids begin to fly in from all directions.

As I hold my gaze on each of them, they explode one by one. There’s a satisfaction in being able to cause an on-screen explosion with just my stare, like I’m a Jedi.

“NEW HIGH SCORE!” flashes across the screen.

I take a deep breath, coming back to reality. I am halfway through a neuro-optometric vision rehab session practicing gaze stability and saccadic eye movements with the use of a computer game and eye tracking system.

It’s a setting I’m quite familiar with.

Neurologic rehabilitation has been an ongoing process since a 2014 mild traumatic brain injury resulted in significant autonomic dysregulation, physical, vestibular, visual, and cognitive changes.

My most recent rehabilitation experience came after a successful endoscopic brain surgery to address a problematic pineal cyst. These rounds of neuro-rehab have permitted me to resume rock climbing, begin writing a book, and live the normal life of a twenty-something.

Key to my rehabilitation successes have been technological interventions. From these experiences, I believe neuro-rehab technology has added value in three main ways: 1) technology assists with patient education, 2) it refines patient self-report, and 3) can make rehab more gameful.

As the patient, clinician education has cultivated a sense of trust, acceptance, and stability. Technology has served as a critical teaching tool by offering easy-to-comprehend targets for improvement. For example, Video-Oculography (VOG), a videobased eye tracking system that utilizes infrared goggles to assess eye movements, revolutionized the way I understood my own struggles with visual tasks. It allowed me to witness how my eyes drifted during a simple gaze task and captured my nystagmus. Being able to see my own eyes’ dysfunction with seemingly simple demands was a light bulb moment for me. It explained my current challenges with school and driving, which not only helped facilitate self-compassion, but made both my deficits and our plan to tackle them clear and approachable.

Technology also reduces the clinician’s need to rely exclusively on patient self-report. As a patient, I know I was criminally unreliable when ranking my pain, dizziness, nausea, and fatigue. Layering technology in a neurorehabilitation setting provided objective metrics and opportunities for specificity. This gave me clear boundaries to push my limits, while minimizing my chance of overdoing it in the clinic and at home.

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The use of biometric data to capture fatigability was especially important in modifying my behaviors and treatment to maximize progress. In the clinic, this was as simple as utilizing a pulseoximeter, heart rate monitor, or even a smart watch throughout the session. In its most simplified form, changes in heart rate, respiration rate, or oxygen saturation signified - in real time - the body/brain working, and to what degree. Like a workout, I was able to assess my ability to recover and the relative strain I was experiencing from each exercise.

Lastly, and perhaps most importantly, technology can help instill a gameful approach to rehab. Not only are games fun, but they have been shown to help us maintain motivation and improve self-efficacy when incorporated into healthcare settings. The vision rehabilitation video game I mentioned above is a good example of making rehab gameful. Without playful technology, the clinical goal could have been attained by shifting my gaze among visual stimuli on a wall.

While this can be effective, I was significantly more engaged by the game which reduced the physical discomfort and frustrations that can arise in neuro-rehab.

Recent innovations hold an abundance of potential for more efficient and engaging rehabilitation. Technology can easily bring the real world demands that patients face into the clinic in a fun, safe and supportive manner. Beyond these opportunities, integrating thoughtful technology would allow clinicians to run an increasingly data driven and gameful practice, improving outcomes for their patients.

Author Bio Sarah Renberg is a student, patient advocate, freelance writer, and aspiring clinician. She graduated Phi Beta Kappa from the University of Michigan in 2022 where she studied Biopsychology, Cognition, and Neuroscience.

She works as a clinical assistant in functional neurological rehabilitation in Chelsea, MI and previously was an undergraduate research assistant in the University of Michigan’s Neurotrauma Lab.

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Rehabilitation Technology: A Case for Improved End-user Engagement in the Design, Development, and Selection Processes Kristian Nitsch, PhD

Technological advancement is a hallmark of human ingenuity, with evidence of the first tool-use tracing back 2.6 million years. The industrial revolution and information ages ushered in exceptionally rapid development of technology, with examples seen across a range of industries, including telecommunications (e.g., telephones, cellular networks, internet, satellites), medicine and biological sciences (e.g., gene therapy, advanced surgical techniques, pharmaceuticals), and consumer goods (e.g., computers, automobiles, smart homes). It is not surprising then, that with general and societal advances in technology, the development and use of technology in physical medicine and rehabilitation contexts, including brain injury rehabilitation, has also progressed substantially.

Assistive technology (or rehabilitation technology) is broadly defined as any product, instrument, piece of equipment, or technology, whether adapted or specifically designed, for improving functioning and independence in an individual living with disability. Examples of advanced technological application in brain injury rehabilitation include the use of cellphones, tablets and computers as memory aides and communication devices; complex navigation systems for routing; computer access (e.g., adaptive cameras, microphones,

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keyboards and mice, external switches); video gaming (e.g., adaptive controllers, eye tracking); and home modification and smart home integration (e.g., lighting, thermostats, locks, passive occupant monitoring).

Rehabilitation engineers, and other professionals who design rehabilitation technologies, do so with the intent of creating technology that benefits the end-user. However, the matters of individual choice and consumer empowerment are essential elements of the independent living philosophy within the Disability community, which emphasizes rehabilitation medicine and services as a consumer-centered practice (Deegan, 1992). This approach encourages individuals with disabilities to adopt active roles in managing their own rehabilitation and lives, as they become experts on their own bodies and functioning through their lived experience of disability. This gained expertise allows people with disabilities to advocate on their own behalf and makes them uniquely qualified to provide feedback and recommendations throughout the rehabilitation technology design, development, and selection processes, based on personal insights that are not available to individuals who do not share a similar disability experience.

Thus, an important step in the process of designing and developing any new rehabilitation technology, and when selecting rehabilitation technologies for implementation, should be to establish an understanding of the context in which the technology will be used, and by whom. This can often be achieved by simply engaging end users in discussions throughout these processes, which also emphasizes the person-centered approach that is a cornerstone of rehabilitation medicine.

The considerable resistance observed among individuals in Deaf culture at the advent of the cochlear implant serves as a salient cautionary illustration of how developing rehabilitation technologies without first consulting intended end users can go awry. Historically, constituents of Deaf culture view deafness as a natural variation in human experience and contend that any technology that is intended to “cure” deafness implies that the deaf experience is inferior to the experience of hearing individuals (Nunes, 2001). While not all rehabilitation technologies in development and use are as “hightech” as the cochlear implant (or similar neural prosthesis such as spinal cord stimulators, deep brain stimulators, advanced limb prosthetics), the basic principle of including end users remains relevant when designing, producing, and selecting more “low-tech” devices and technologies.

Research examining the adoption of assistive technology has repeatedly shown that devices are far more likely to be abandoned, or result in decreased user satisfaction, when the technology does not fulfill the need of a consumer – or does so at the detriment of other areas of functioning. Accordingly, involving people with disability in the development process is likely to serve as a mechanism for understanding how an individual may use a rehabilitation technology, and therefore reduce the likelihood of user dissatisfaction or device abandonment.

Involving prospective consumers in the development process is also consistent with an independent living approach to disability through creating opportunities for direct engagement in meaningful decision-making. Finally, by incorporating end user feedback, technology developers can ensure that the most relevant concerns and preferences are considered, and this process may also protect against erroneous assumptions made by technology developers regarding the desired functioning of a technology and what characteristics are likely to result in abandonment or poor consumer satisfaction.

References Deegan, P. E. (1992). The Independent Living Movement and people with psychiatric disabilities: Taking back control over our own lives. Psychosocial Rehabilitation Journal, 15(3), 3.

Nunes, R. (2001). Ethical dimension of paediatric cochlear implantation. Theoretical Medicine and Bioethics, 22, 337-349.

Author Bio Kristian Nitsch, PhD, is a clinical neuropsychologist who specializes in the assessment and treatment of medical and rehabilitation populations, with an emphasis on acquired brain injury. Dr. Nitsch earned his doctorate in clinical psychology, with an emphasis in rehabilitation psychology, at the Illinois Institute of Technology in 2018. He completed his internship in clinical neuropsychology at the Charleston Consortium followed by a two-year postdoctoral fellowship in rehabilitation neuropsychology at the Johns Hopkins University School of Medicine. Dr. Nitsch joined Shepherd Center’s Psychology Department and Shepherd Pathways, Shepherd’s post-acute acquired brain injury program, in 2020. He collaborates with an interdisciplinary team of health care providers to support patients’ cognitive and emotional well-being as they navigate the rehabilitation and recovery processes. Dr. Nitsch also serves as the primary research advisor for Shepherd Center’s clinical neuropsychology fellowship program.

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ApplTree: Understanding How Assistive Technology Can Support Memory After Brain Injury Dr. Matthew Jamieson • Dr. Jonathan Evans Technologies have shaped and enhanced human cognitive abilities throughout history. The written word, printed press and audio recording have all had profound historical impacts because they allowed people to retain and access information in detail for much longer than the brain can remember it. Today, our phones contain an array of cognitive tools to support and enhance our natural abilities in many domains from memory and planning abilities (e.g. calendars, phonebook), to attention (notification and alarms), mathematic abilities (calculator) and communication (text messages). These tools are in widespread use amongst the general population; every phone has a calendar, a calculator, and a messenger app.

This recent advancement of personal mobile technology has brought with it the potential to support people with cognitive impairments. However, our research has highlighted that people with impaired cognitive abilities, who may be able to gain the most benefit from digital cognitive aids, use them much less than the general population. It is also clear that most technologies that aim to support memory, planning and attention (e.g. calendars, reminding apps and alarms) are developed for the general population to use, and there are few technologies specifically designed for neuropsychological rehabilitation.

One possibility is that assistive technologies are not used by those with cognitive impairments because people do not find them useful or do not like using them. There is a history of research in neuropsychological rehabilitation that has shown that digital technologies can be highly effective and are acceptable for people with cognitive impairments. Research from the early 2000s showed the benefit of a pager system, NeuroPage, which sent individual reminders to each user’s pager, in supporting memory for people with acquired brain injury. When combining efficacy evidence from technologies aimed to prompt at a set time to support memory, our 2014 systematic review and meta-analysis showed a large positive effect. Qualitative feedback from participants in these and other studies has consistently showed that people like using these technologies. The fact that personal smartphones are now in widespread use has helped reduce a barrier of people feeling conspicuous when using a device to support memory.

So, if they are useful and people like them, why is the uptake still not as high as it is in the general population? How can we create assistive technologies that encourage uptake and long-term use amongst people with cognitive difficulties? And how should these technologies be designed to best meet their needs?

Our research considers technology use from a social, cognitive and psychological perspective; the technology needs to fit into people’s lives and the lives of the people around them (social), they need to like and trust it (psychological), and it needs to meet the needs they have (in this case, cognitive support needs).

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The need to understand assistive technology in these terms rings true in our research findings. We started by trying to understand the barriers and facilitators to using assistive technology. We found cognitive barriers such as people ‘forgetting they forget’ and being overwhelmed by information when presented with new software. There were social barriers like people around them not feeling confident to support them with the technology. There were psychological barriers such as not wanting to become reliant on technology and not feeling like they needed the support. This was also linked to the issue of insight into difficulties which is a crucial consideration with any type of neuropsychological rehabilitation.

Some of these considerations and barriers can be addressed in the design of assistive technology. For example, less information on each screen might help people feel less overwhelmed when using a new app. Pro-active prompting can help encourage people to set reminders, especially if they tend to forget to set reminders.

We designed a smartphone reminder app, ApplTree, with these considerations in mind. This app was then developed collaboratively with caregivers and people with ABI to ensure the flow of setting a reminder, and the language used, was optimal to support people who may have memory, attention or communication difficulties to use the app.

Industry standards that inform mobile and app development are developed with general populations in mind and may not lead to user interface and feature design that is ideal for people receiving neuropsychological rehabilitation. ApplTree has allowed us to test research hypotheses and contribute to the knowledge around cognitively accessible user interfaces.

For example, we found that a user interface that had a small amount of information on many screens led to people missing less information when setting a reminder than they did when using a scroll navigation (these are used in most off-the-shelf reminding apps). While pro-active prompts may be perceived as annoying or unnecessary by many in the general population, there is evidence that prompts that say ‘do you need to set a reminder?’ or simply ‘STOP, THINK’ are appreciated and helpful to people with memory difficulties after a brain injury. These prompts are not contingent on an actual task, but they do help people think about their tasks, goals and rehabilitation strategies.

We’re now working to translate this research knowledge into practice in our collaboration with neumind plc. They are developing software with personalised strategies, guidance and support for people with neurological conditions and their carers. Initially, ‘onboarding’ questions, asked during the first use of the app give a profile based on self-reported difficulties and preferences. This allows the app to be personalised and recommend features most likely to benefit each user.

The core features of the app include reminders, push prompts or ‘nudges’ that can be set to help remind or notify the user of their goals, lists or routines, memorisation of information, and help with tracking fatigue, memory or mood over time.

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The ApplTree App App The ApplTree

Additionally, a set of in-app courses have been developed that educate users about different aspects of brain injury and demonstrate how to develop neuro-rehabilitative strategies.

The aim is to provide current best practice in neuropsychological education, supporting people to develop and maintain strategies recommended by clinicians and be personalised according to clinical need.

The best care for people who experience cognitive impairment is in-person neuropsychological rehabilitation based on a detailed, expert, assessment of their needs and goals. However, this care is not available to every person, and is often not available for the necessary amount of time. Assistive technology has the potential to help with the massive divide between neuropsychological rehabilitation that is needed, and what is currently provided. There is huge potential to combine neuropsychological rehabilitation principles with accessible technology to improve interventions.

With the gap between need and access so wide across neurorehabilitative services, this technology has the potential to impact many people.

Author Bio Dr. Matthew Jamieson is Research Associate, and Professor Jonathan Evans is Professor of Clinical Neuropsychology, in the School of Health & Wellbeing at the University of Glasgow, Scotland. Jon has worked in the field of neuropsychological rehabilitation for over 30 years, and has been involved in development/evaluation of assistive technology for people with cognitive impairments after brain injury for more than 25 years. Matt developed ApplTree as part of this PhD studies and has continued to develop and evaluate key features of ApplTree during his post-doctoral work.

Dr. Jonathan Evans is Professor of Clinical Neuropsychology at the University of Glasgow and honorary Consultant Clinical Psychologist with NHS Greater Glasgow and Clyde. Jon was the first Clinical Director of the Oliver Zangwill Centre for Neuropsychological Rehabilitation in Ely, Cambridgeshire. He has received several awards from the British Psychological Society including the May Davidson Award, the Barbara Wilson Lifetime Achievement Award and the M.B Shapiro award.

Jon has published more than 200 papers, books and book chapters in the field of neuropsychology and has a particular interest in the assessment and rehabilitation of disorders of attention and executive functioning after brain injury. Among other roles, he currently serves as the President of the International Neuropsychological Society.

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Neumind, a Company Setting out to Transform ABI Rehabilitation Ellis Parry, PhD

It was a rare sunny February morning when I arrived at the Oliver Zangwill Center - one of the UK’s top neurorehabilitation facilities.

It was 2015, and knowing I was studying engineering, the then manager Dr. Andrew Bateman had sent me an email to say he wanted to “run some interesting ideas by me”.

Two years previously, whilst both studying together at Oxford, my identical twin brother Luke suffered a serious traumatic brain injury.

In that instant, our worlds changed forever. Luke spent 18-months bouncing between different centers, hospitals, and home whilst my family tried desperately to get him neurorehabilitation. In the UK, this is in theory provided by the NHS; however, whether you even get rehab, let alone the quality and amount, is largely down to persistence and luck.

At the time, Luke needed round the clock support. We knew he could still make huge progress, but there was no way our family could afford the cost of private rehabilitation. My mum had read about the Oliver Zangwill Center - founded by Barbara Wilson - and fought tooth and nail to get Luke a place. We were lucky.

Andrew invited me into his office. The topic of discussion was tech. The center had pioneered NeuroPage, a paging device used to deliver practical and therapeutic prompts. Neuropage was developed in the 90s by an engineer whose son had suffered a head injury. Neuropage served as a practical crutch for Luke’s memory, but also reinforced concepts and strategies Luke was learning throughout the day and when he was at home.

The benefit was obvious. Practical, easy-to-use, and surprisingly powerful when combined with a traditional neurorehab program.

But Andrew knew modern tech had a lot more to offer.

The field of ‘Cognitive Assistive Technology’ (CAT) had blossomed in the late 90s with the emergence of Personal Digital Assistants.

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Yet, despite the rapid advancement of technologies such as smartphones and availability of data, CAT research and development had largely stalled.

It was clear that there was an unmet need and immense potential.

Together with my family’s experience and my training in engineering, I knew after that conversation I would do something in this field.

Five years later, after earning my PhD, I could resist no longer. I reconnected with Andrew and Neumind began.

I was fortunate enough to be joined on this journey by an experienced developer, Rufus Russell, and fellow engineering graduate Claudia Hill. Our first experiment was taking the concept of NeuroPage and putting it into a smartphone. We focused on supporting memory and executive functioning, two of the common factors affecting independence following brain injury.

We codenamed the first app, Alfred, after Batman’s butler. A smartphone was discrete and convenient, but also allowed us to do a lot more. Prompts were delivered to the phone as notifications and we made it simple to connect with healthcare professionals, family members and caregivers, who then could collectively help manage an individual’s prompting schedule.

We gained fascinating behavioral and family system insights. For example, personal reminders created by a family member were nearly twice as likely to be completed than reminders set by the primary user. Most often, the family members themselves were best situated to assist with neurorehabilitation due to the insights developed from spending much of their day caring for their lovedone. Yet despite best intentions, they often lacked the clinical knowledge to assist effectively.

Accordingly, we identified the importance of connecting clinicians to the app. Without guidance, users tended to create simple reminders for day-to-day activities. However, those connected with clinicians used Alfred’s prompts more robustly to deliver and track compensatory strategies, therapy tasks, and psychoeducation. We found that people thrived when the practical and cognitive support of prompting was combined with structured guidance, effective motivation strategies, and goal setting.

At the start of 2022, we were fortunate to receive a governmental grant. This funding allowed us to step back and further our evidence-based development. In doing so, we collaborated with leading academics and clinicians. We teamed up with Prof. Jonathan Evans’ research group in Glasgow, leaders in the field of cognitive assistive technology. We interviewed hundreds of brain injury survivors, family members, and clinicians. Through this design and development process, we explored many paths, prototyped countless ideas, and continuously tested new hypotheses.

The result was neumind - a personalized digital support system for life after brain injury. Underpinned by a set of powerful cognitive tools combining prompting, tracking, and strategies all in one place; versatile enough to support a range of cognitive areas from memory to fatigue, whilst remaining simple and practical to use. At the core of the platform was connectedness between the individual, family, and caregivers. In neumind, family members and caregivers can join the user’s circle, where they can be updated on their loved-one’s progress and help manage their account. We also use information from the individual and connected family members, caregivers or clinicians to build a clearer picture of the individual’s needs.

This is used to recommend personalized content and strategies aligned with the person’s challenges and goals. Caregivers also receive complementary information that helps them reinforce and encourage their loved-one with rehabilitation tasks.

In neumind, family members and caregivers can join the user’s circle, where they can be updated on their loved-one’s progress and help manage their account.

A common challenge healthcare professionals face is maintaining therapeutic interventions outside of contact hours and integrating them into their clients day-to-day lives. To solve for this, we designed the app to help healthcare professionals connect with their clients to deliver personalized strategies, help manage prompts, and track client progress. A psychologist or occupational therapist can add tasks such as a fatigue tracker or time management strategy with just a few clicks.

One of the reasons technology has been slow to emerge for ABIs is the uniqueness of each individual and the difficulity merging rehabilitation with every day activities. When designing neumind, we sought to correct this course. We hope the journey we have taken in creating neumind will help thousands of others on their own recovery journeys’.

Author Bio Ellis Perry, PhD, has a decade of personal experience as a family member and caregiver. He is obsessed with using technology to support cognition and daily-living, and has experimented with countless ideas and concepts to try and help his brother’s recovery.

Ellis has a MEng and PhD in Engineering from Oxford University and was awarded Young Innovator and Next Steps Awards from Innovate UK for his work with neumind (www.neumind.co.uk).

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Eye Tracking Technology: A Primer Jacqueline Theis, OD, FAAO

Though seemingly simple, eye movements, also known as oculomotor function, require an expansive, multifaceted neural network to seamlessly provide visual information to the brain and allow for cognitive and physical interaction with our environment.

These neural networks involve every lobe of the brain, brainstem, cerebellum, thalamus, basal ganglia, cranial nerves, and visual tracts. The oculomotor system also interacts with the vestibular and cervical systems to ensure proper reflexive movement and proprioception when the head and body is in motion. Due to its extensive neuroanatomy, the oculomotor system is highly vulnerable to neurologic injury.

Oculomotor impairment is common in acute,1 subacute,2 and chronic3,4 time periods after traumatic brain injury (TBI), and the presence of new oculomotor dysfunction post-TBI may be associated with worse outcomes and protracted recovery.5,6 Early detection allows for improved management of patient expectations and impacts the treatment timeline by expediting the referral process.

Thus, oculomotor assessment is imperative to the diagnosis and management of TBI.7 The emergence of eye tracking technology has revolutionized oculomotor assessment and has the potential to be an accurate, sensitive, and objective biomarker for neurologic function and brain injury.8,9 There are numerous benefits of utilizing eye tracking technology in clinical practice. First, eye trackers are often compact, portable devices that can provide testing in a variety of clinical and nonclinical spaces. Theoretically, they document more objective and accurate evaluations than a clinician, providing a more sensitive baseline, and can quantify the dynamic components of eye motion, allowing for more accurate monitoring of recovery. While eye movements can be subjectively manipulated based on cognitive effort and attention (i.e., the patient decides if they want to look at a target or not), overall, oculomotor metrics like velocity, amplitude, and gaze path are reflexive, providing an objective diagnostic measure.

16 BRAIN INJURY professional

Eye trackers are non-invasive, and can be performed quickly, in stark contrast to neuroimaging. While offering the potential to generate a revenue stream for providers as a reimbursable procedure, eye tracking is still cost efficient for both the patient and insurance provider. Finally, there is immediate patient satisfaction. TBI, particularly concussion, is an invisible injury which can cause added psychological distress to the patient. Oculomotor dysfunction detected by eye tracking can provide symptom validation for the patient and confirmation of TBI diagnosis when combined with clinical examination.

Eye tracking innovates TBI assessment and management, but buyer beware; accurate oculomotor metrics can be elusive, and current technology is still in the experimental phase and is not as reliable and insightful as a standalone assessment as clinicians may hope.

The ultimate potential for value of an eye tracker is to see eye motion that cannot be physically seen by a clinician, more precisely with quantitative data, and analyze pattern differences for diagnosis and management. To identify the best eye tracker for your practice, you need to understand the system specifications that differentiate the products on the market, as the “perfect” eye tracker does not yet exist.

Eye Movement All types of eye movements are susceptible to brain injury, and every type and direction (vertical vs horizontal) of eye movement has a different neuropathophysiology. Thus, if you want to use oculomotor assessment as a biomarker you should evaluate every eye movement in each direction. Unfortunately, there is yet to be a device that can reliably assess all eye movements in one sitting, so you may need multiple devices to provide a comprehensive evaluation.

Data Validity and Reliability The smallest and fastest eye movement that can be detected is dependent upon the spatial and temporal resolution, accuracy of the eye tracker, and the reliability of its data. This is dependent upon multiple factors such as imaging quality, signal to noise ratio, and the algorithms used for detecting and tracking the eye.

In eye tracking, validity implies that the eye motion data measured accurately corresponds to the actual position/movement pattern of the eye. Devices list gaze accuracy in degrees of visual angle, as a measure of the spatial distance between the true and measured gaze direction. Eye movements vary in size and speed and depending on which eye movement you want to track the accuracy of the machine can be crucial. Keep in mind, that accuracy is assessed on artificial eyes, but can vary clinically because it is dependent on the individual’s eye characteristics and the testing environment.10,11,12 This is why many devices require calibration before every patient use.

Precision, on the other hand, measures the consistency and repeatability of gaze point estimation if the eye movement being tracked was constant. The lower the precision of a device, the less reliable the data. It is suggested that precision should be below 0.05o to accurately measure fixations and saccades and lower than 0.03o to accurately measure microsaccades.12

Head Movement Head movement impacts eye movement. Eye trackers can be a wearable head-mounted device like a helmet or spectacle frame, a free space device like a cellphone, or a more stable desktop device.

While head-mounted and head-unsupported free space devices can provide real-world eye tracking outside of the clinic setting – on the sideline perhaps – the precise fit of the device and movement of the head, device or face due to facial expressions or speaking, can impair data quality.13 Wearable and head-unsupported eye trackers use software algorithms to compensate for head movement, but these algorithms still have a limit of how much the head can tolerably move for accurate data acquisition. Thus, the advantage of a desktop device with head stabilization with a chin/forehead rest has less head movement and offers increased accuracy, repeatability, and spatial resolution.14

Sampling Rates Eye trackers use cameras to take multiple images of the eye position over time, and then software determines the eye movement pattern based upon algorithmically predicted eye position and sampling rate.

Sampling rate (frequency) indicates the number of times per second (Hz) the position of the eyes is registered by the tracker. The higher the sampling rate, the more likely the eye tracker can estimate the true path of the eye as it moves, and the higher the precision. The lower the sampling rate, the greater the time interval between eye position detection, allowing for increased under-representation of eye motion abnormalities and possible misdiagnosis of “normal.”

A higher sampling frequency will more accurately detect the gaze path but is more expensive as it requires more advanced cameras, illumination, and data storage. Every eye movement has a different frequency, and according to the Nyquist-Shannon sampling theory, the sampling rate should be at least 2x the speed of the eye movement you want to capture.15

So to measure a saccade you need a sampling frequency higher than 200-300Hz to accurately calculate velocity, latency and duration.16,17

Ocular Structure The ocular structure being tracked, and the type of light source used, will impact data acquisition. Current eye trackers used in clinical practice can track the corneal reflection of light, the pupil, the iris, the corneal limbus (sclera/iris junction), the reflections of light from the lens and cornea (dual purkinje images) or the retina.11 When considering tracking, it is important to consider nuances across tracker type. For example, eye tracking based on corneal reflections is error prone due to ocular surface disease, like dry eye or corneal scars, which alter the reflection,18 and ambient room or variability in outdoor lighting can cause uneven illumination which can cause additional light spots impacting signal detection.19 Pupil/iris tracking has a range of nuances to consider because they are dynamic ocular structures and in constant flux. Pupil size impacts accuracy and precision20 and can vary based on sympathetic/parasympathetic tone, cognitive attention, drugs, emotional state, ambient room lighting, gaze position, and head position/head movement – all of which are factors in patients with TBI. Furthermore, pupil detection can vary due to iris color21 and ethnicity22, making data difficult to collect across diverse patient populations, particularly when data are collected with mobile phone cameras which use light in the visible spectrum – as opposed to infrared light sources.

Patients with TBI and migraine have abnormal pupillary dynamics26,27 and dry eye28 compared to normative control groups. It is unknown how accurate pupil/corneal reflection trackers are in a brain injury population as most device testing is done on an artificial eye or in a normal, healthy control population. Therefore, please note that confounding conditions such as dysautonomia, cranial nerve palsy, and nystagmus are not well normed in these devices.

Retinal trackers are the most precise and accurate of existing eye trackers.11 Benefits of the retina include uniformity in photoreceptor size across different demographics including age and ethnicity, and independence from influence by adrenergic/cholinergic medications, environmental testing conditions, mood, attention, and TBI. The tradeoff for precision in these devices is cost for the technology, reduced field of gaze, and necessity of a chin/forehead rest.

Visual Clarity Finally, one factor that all eye trackers depend upon is the necessity of the patient to be able to see. Visual clarity is required for precise eye movements and reduced visual acuity can impact eye movements.32 This can be a problem in patients with reduced vision due to uncorrected refractive error, ocular disease, or accommodative dysfunction, the latter of which is present in up to 51% of patients post-concussion.30,33

Clinical Interpretation Eye tracking as a biomarker is in its scientific infancy. At present, eye trackers are incapable of diagnosing the etiology of abnormal eye movements, and it still takes clinical judgment and experience to decipher if the oculomotor abnormality detected was secondary to the TBI, pre-existing or due to another neurologic disorder.32 While there is a great future potential for a role in artificial intelligence in pattern differentiation of eye movements and neurologic diagnosis, current software still relies on clinical interpretation.

BRAIN INJURY professional 17

Conclusion •

•

•

Oculomotor assessment is a critical component to a comprehensive evaluation of patients with TBI and impacts patient quality of life and recovery.

The oculomotor system is an objective biomarker in all stages of brain injury and can be used for diagnosis, as well as recovery and monitoring of treatment interventions.

Current technology has its disadvantages that clinicians should be aware of, but the benefits of eye tracking technology and the future of artificial intelligence will rapidly increase the utilization of this technology in clinical practice.

22.

Blignaut, P., & Wium, D. (2014). Eye-tracking data quality as affected by ethnicity and experimental design. Behavior research methods, 46(1), 67–80. https://doi.org/10.3758/s13428-013-0343-0

23.

Hooge, I., Holmqvist, K., & Nyström, M. (2016). The pupil is faster than the corneal reflection (CR): Are video based pupil-CR eye trackers suitable for studying detailed dynamics of eye movements?.

Vision research, 128, 6–18. https://doi.org/10.1016/j.visres.2016.09.002

24.

Nyström, M., Hooge, I., & Holmqvist, K. (2013). Post-saccadic oscillations in eye movement data recorded with pupil-based eye trackers reflect motion of the pupil inside the iris. Vision research, 92, 59–66. https://doi.org/10.1016/j.visres.2013.09.009

25.

Nyström, M., Hooge, I., & Andersson, R. (2016). Pupil size influences the eye-tracker signal during saccades. Vision research, 121, 95–103. https://doi.org/10.1016/j.visres.2016.01.009

26.

Truong, J. Q., & Ciuffreda, K. J. (2016). Comparison of pupillary dynamics to light in the mild traumatic brain injury (mTBI) and normal populations. Brain injury, 30(11), 1378–1389. https://doi.org/10.108 0/02699052.2016.1195922

27.

Harle, D. E., Wolffsohn, J. S., & Evans, B. J. (2005). The pupillary light reflex in migraine. Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists), 25(3), 240–245. https://doi.org/10.1111/j.1475-1313.2005.00291.x

28.

Lee C, Felix E, Levitt R, et al. Traumatic brain injury, dry eye and comorbid pain diagnoses in US veterans. Br J Ophthalmol 2018;102:667–673.

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Irving, E. L., González, E. G., Lillakas, L., Wareham, J., & McCarthy, T. (2011). Effect of stimulus type on the eye movements of children. Investigative ophthalmology & visual science, 52(2), 658–664. https://doi.org/10.1167/iovs.10-5480

30.

Merezhinskaya, N., Mallia, R. K., Park, D., Bryden, D. W., Mathur, K., & Barker, F. M., 2nd (2019). Visual Deficits and Dysfunctions Associated with Traumatic Brain Injury: A Systematic Review and Metaanalysis. Optometry and vision science : official publication of the American Academy of Optometry, 96(8), 542–555. https://doi.org/10.1097/OPX.0000000000001407

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Johnson, B., Hallett, M., & Slobounov, S. (2015). Follow-up evaluation of oculomotor performance with fMRI in the subacute phase of concussion. Neurology, 85(13), 1163–1166. https://doi. org/10.1212/WNL.0000000000001968

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Ghasia, F. F., Otero-Millan, J., & Shaikh, A. G. (2018). Abnormal fixational eye movements in strabismus. The British journal of ophthalmology, 102(2), 253–259. https://doi.org/10.1136/ bjophthalmol-2017-310346

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Matuseviciene, G., Johansson, J., Möller, M., Godbolt, A. K., Pansell, T., & Deboussard, C. N. (2018).

Longitudinal changes in oculomotor function in young adults with mild traumatic brain injury in Sweden: an exploratory prospective observational study. BMJ open, 8(2), e018734. https://doi. org/10.1136/bmjopen-2017-018734

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Alexander, R. G., Macknik, S. L., & Martinez-Conde, S. (2018). Microsaccade Characteristics in Neurological and Ophthalmic Disease. Frontiers in neurology, 9, 144. https://doi.org/10.3389/ fneur.2018.00144

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Capó-Aponte, J. E., Jorgensen-Wagers, K. L., Sosa, J. A., Walsh, D. V., Goodrich, G. L., Temme, L. A., & Riggs, D. W. (2017). Visual Dysfunctions at Different Stages after Blast and Non-blast Mild Traumatic Brain Injury. Optometry and vision science : official publication of the American Academy of Optometry, 94(1), 7–15. https://doi.org/10.1097/OPX.0000000000000825

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Master, C. L., Scheiman, M., Gallaway, M., Goodman, A., Robinson, R. L., Master, S. R., & Grady, M. F. (2016). Vision Diagnoses Are Common After Concussion in Adolescents. Clinical pediatrics, 55(3), 260–267. https://doi.org/10.1177/0009922815594367

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DuPrey, K. M., Webner, D., Lyons, A., Kucuk, C. H., Ellis, J. T., & Cronholm, P. F. (2017).

Convergence Insufficiency Identifies Athletes at Risk of Prolonged Recovery From SportRelated Concussion. The American journal of sports medicine, 45(10), 2388–2393. https://doi. org/10.1177/0363546517705640

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Cheever, K. M., McDevitt, J., Tierney, R., & Wright, W. G. (2018). Concussion Recovery Phase Affects Vestibular and Oculomotor Symptom Provocation. International journal of sports medicine, 39(2), 141–147. https://doi.org/10.1055/s-0043-118339

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Leonard, B. T., Kontos, A. P., Marchetti, G. F., Zhang, M., Eagle, S. R., Reecher, H. M., Bensinger, E. S., Snyder, V. C., Holland, C. L., Sheehy, C. K., & Rossi, E. A. (2021). Fixational eye movements following concussion. Journal of vision, 21(13), 11. https://doi.org/10.1167/jov.21.13.11

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Bin Zahid, A., Hubbard, M. E., Lockyer, J., Podolak, O., Dammavalam, V. M., Grady, M., Nance, M., Scheiman, M., Samadani, U., & Master, C. L. (2020). Eye Tracking as a Biomarker for Concussion in Children. Clinical journal of sport medicine : official journal of the Canadian Academy of Sport Medicine, 30(5), 433–443. https://doi.org/10.1097/JSM.0000000000000639

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Hunfalvay, M., Roberts, C. M., Murray, N., Tyagi, A., Kelly, H., & Bolte, T. (2019). Horizontal and vertical self-paced saccades as a diagnostic marker of traumatic brain injury. Concussion (London, England), 4(1), CNC60. https://doi.org/10.2217/cnc-2019-0001

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Nyström, M., Andersson, R., Holmqvist, K., & van de Weijer, J. (2013). The influence of calibration method and eye physiology on eyetracking data quality. Behavior research methods, 45(1), 272–288. https://doi.org/10.3758/s13428-012-0247-4

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Holmqvist, K., Örbom, S. L., Hooge, I., Niehorster, D. C., Alexander, R. G., Andersson, R., Benjamins, J. S., Blignaut, P., Brouwer, A. M., Chuang, L. L., Dalrymple, K. A., Drieghe, D., Dunn, M. J., Ettinger, U., Fiedler, S., Foulsham, T., van der Geest, J. N., Hansen, D. W., Hutton, S. B., Kasneci, E., … Hessels, R. S. (2022). Eye tracking: empirical foundations for a minimal reporting guideline. Behavior research methods, 10.3758/s13428-021-01762-8. Advance online publication. https://doi.org/10.3758/ s13428-021-01762-8

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Cognolato, M., Atzori, M., & Müller, H. (2018). Head-mounted eye gaze tracking devices: An overview of modern devices and recent advances. Journal of rehabilitation and assistive technologies engineering, 5, 2055668318773991. https://doi.org/10.1177/2055668318773991

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Niehorster, D. C., Santini, T., Hessels, R. S., Hooge, I., Kasneci, E., & Nyström, M. (2020). The impact of slippage on the data quality of head-worn eye trackers. Behavior research methods, 52(3), 1140–1160. https://doi.org/10.3758/s13428-019-01307-0

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Carter, B. T., & Luke, S. G. (2020). Best practices in eye tracking research. International journal of psychophysiology : official journal of the International Organization of Psychophysiology, 155, 49–62. https://doi.org/10.1016/j.ijpsycho.2020.05.010

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Andersson, R., Nystrom, M., Holmqvist K, (2010). Sampling frequency and eye-tracking measures: how speed affects durations, latencies, and more. Journal of Eye Movement Research, 3(3), 6, 1-12.

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Inchingolo, P., & Spanio, M. (1985). On the identification and analysis of saccadic eye movements--a quantitative study of the processing procedures. IEEE transactions on bio-medical engineering, 32(9), 683–695. https://doi.org/10.1109/TBME.1985.325586

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Juhola, M., J¨antti, V., & Pyykk¨ o, I. (1985). Effect of sampling frequencies on computation of the maximum velocity of saccadic eye movements. Biological Cybernetics, 53(2), 67– 72.

18.

Goto, E., Dogru, M., Sato, E. A., Matsumoto, Y., Takano, Y., & Tsubota, K. (2011). The sparkle of the eye: the impact of ocular surface wetness on corneal light reflection. American journal of ophthalmology, 151(4), 691–696.e1. https://doi.org/10.1016/j.ajo.2010.09.027

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Wan Z., Xiong C., Chen W., & Zhang H, (2021). Robust and accurate pupil detection for head-mounted eye tracking. Computers and Electrical Engineering, 9, 1-14.

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Choe, K. W., Blake, R., & Lee, S. H. (2016). Pupil size dynamics during fixation impact the accuracy and precision of video-based gaze estimation. Vision research, 118, 48–59. https://doi.org/10.1016/j. visres.2014.12.018

21.

Hessels, R.S., Andersson, R., Hooge, I., Nystrom, M., Kemner., C. (2015). Consequences of eye color, positioning, and head movement for eye-tracking data quality in infant research. Infancy. 20(6), 601-633

18 BRAIN INJURY professional

Author Bio Jacqueline Theis, OD, FAAO is an optometrist with residency training in neuro-optometry and binocular vision (oculomotor) disorders from UC Berkeley. She is the former founder and chief of the UC Berkeley Sports Vision and Concussion Clinic. Currently, she practices in Richmond, VA at Virginia Neuro-Optometry, a private practice located within the transdisciplinary brain injury clinic, Concussion Care Centre of Virginia. Dr. Theis also performs clinical research, is an Assistant Professor at the Uniformed Services University, School of Medicine, Department of Physical Medicine and Rehabilitation, and is the Chief Medical Officer for C. Light Technologies.

4.875 x 4.875.qxp_front 11/1/17 12:02 Page 1

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BRAIN INJURY professional 19

Artificial Intelligence Language Models for Brain Injury Rehabilitation: A ChatGPT Experience Stephen Trapp, PhD • ChatGPT

ChatGPT, a large language model developed by OpenAI, is emerging as a promising tool for brain injury rehabilitation. This artificial intelligence chatbot is a natural language processing algorithm that can generate human-like responses to text-based input. These models use deep learning algorithms and neural networks to analyze massive amounts of textual data and learn patterns and relationships within the language content. According to ChatGPT, this technology can be harnessed to assist a number of domains pertinent to brain injury rehabilitation, including:

•

Cognitive Rehabilitation

•

• • •

Personalized cognitive training programs that target specific areas of difficulty, such as memory or attention Automated reminders and memory aids to help individuals remember important tasks or appointments Personalized exercises and activities that focus on the specific areas of cognitive difficulty for the individual

Communication Assistance • • •

Personalized language therapy programs that target the specific areas of difficulty Creation of chatbot interfaces that simulate conversations with humans, providing a safe and controlled environment to practice language and social skills Analysis of an individual’s written language to identify areas where they may be struggling, such as word generation and syntax

Information and Education •

Access information and education about brain injuries, including their causes, symptoms, treatments, and management

20 BRAIN INJURY professional

Educational materials, rehabilitation programs, and other resources that can help individuals understand their condition and the steps they can take towards recovery

Personalized Assistance •

Automated feedback and guidance to individuals as they progress through their rehabilitation program, providing ongoing support and assistance Integration into telemedicine platforms to enable healthcare providers to better monitor and treat individuals with brain injuries

When requesting specific cognitive rehabilitation examples, ChatGPT offered mostly rudimentary strategies that variably aligned with current clinical practices. These suggestions ranged from valid compensatory strategies to simplistic attention training tasks, though the chatbot erred when proposing a memory task that was more akin to executive functioning. Fortunately, ChatGPT would commonly end each suggestion by cautioning the user to the complexities of brain injury rehabilitation and suggesting the inclusion of a healthcare professional in the process of rehabilitation.

While mostly plausible and surprisingly believable, the application of ChatGPT for medical use – let alone brain injury rehabilitation – is not yet consumer ready. The evidence base is in its early stages and caution is noted in its accuracy and reliability. A quote from Johnson and colleagues (2023), describes the state of this technology well: Thus, any use of the current version of ChatGPT for medical knowledge dissemination must consider its capacity to come to a totally mistaken conclusion, which is delivered in an authoritative and convincing manner.

This was typified when ChatGPT was requested to generate references for its proposed suggestions for brain injury rehabilitation.

To my surprise, all references provided by the language model were completely fictional. Yet, the content was all quite plausible and written in a credible presentation. Below is an excerpt from my interaction with ChatGPT when I questioned the sourcing of the content it delivered: Stephen: Please provide links to the articles.

ChatGPT: Here are the links to the articles referenced: Yoo, H. J., Park, E., Kim, J. H., & Kim, K. J. (2021). A cognitive rehabilitation training program using artificial intelligence for patients with brain injury: A pilot study. Journal of Clinical Neurology, 17(1), 63-72. https://www.ncbi.nlm.nih.gov/pmc/ articles/PMC7824292/ Turton, A. J., Cunningham, S. P., & Herrod, P. J. (2021). Use of an artificial intelligence communication aid by patients with traumatic brain injury. Disability and Rehabilitation: Assistive Technology, 16(5), 494-499. https://www.ncbi.nlm.nih.gov/ pmc/articles/PMC8059729/ …

Stephen: I cannot find evidence that these articles exist.

ChatGPT: I apologize for the confusion. As an AI language model, I do not have the ability to provide links to articles or verify the accuracy of the information I provide. I strive to provide accurate and helpful information based on my training and knowledge, but it is always important to fact-check and verify any information you receive, particularly if it will be used for academic or professional purposes. I recommend consulting with a healthcare professional or conducting your own research using reliable sources to ensure that the information you receive is accurate and up-to-date.

In an era where digitally generated information is often accepted with limited scrutiny, the use of artificial intelligence language models for healthcare purposes requires particular caution.

Assistance with trained neurorehabilitation professionals is critical to ensure the validity of information provided by the AI language model and is essential in guiding its use. Unlike human clinicians trained in research methods and established bedside acumen, many current AI language models are unable to verify the accuracy of the information provided, methodological rigor, and may lack the nuance and contextual understanding required to verify and apply the data appropriately.

Another concern is the potential for technology to exacerbate existing disparities in access to healthcare and rehabilitation services. This includes unfairness for those with limited access to technology or technology not designed for their use, as well as issues with the AI language models not adequately trained on a range of language and cultural data. Current FDA policy is notably flawed in its review of medical devices reliant on AI and there continue to be appalling examples of training models based on biased data.

As with any emerging technology, there is much research to be done to fully understand the potential benefits and limitations of using AI in brain injury rehabilitation. ChatGPT should not be used as a substitute for clinical judgment at this time, yet it deserves serious attention for next step innovation. Current inadequacies in design and development can be overcome with transdisciplinary teams of end-user stakeholders, clinicians, clinical and computer scientists, and software developers. This collaboration will ready AI language models to assist with clinical demand.

Note To muddle the moral crumple zone of authorship attribution, this article was co-authored by Stephen Trapp and ChatGPT. If interested in discussing the co-construction of textual content by a human author and a large language model author, please contact the human author at stephen.trapp@metrodora.co or dig into the exciting work of far brighter individuals, like Madeleine Clare Elish, and others who examine human-AI teamwork.

References Johnson, D., Goodman, R., Patrinely, J., Stone, C., Zimmerman, E., Donald, R., ... & Wheless, L. (2023).

Assessing the Accuracy and Reliability of AI-Generated Medical Responses: An Evaluation of the Chat-GPT Model.

This artificial intelligence chatbot is a natural language processing algorithm that can generate human-like responses to text-based input. These models use deep learning algorithms and neural networks to analyze massive amounts of textual data and learn patterns and relationships within the language content.

Author Bios Chat GPT: I am ChatGPT, an AI language model trained by OpenAI to provide informative and helpful responses to a wide range of questions. My training data includes a diverse range of text sources, allowing me to offer comprehensive and accurate answers. I am constantly learning and updating my knowledge base to stay current with the latest research and developments in various fields, and my goal is to provide helpful and accurate responses to your questions.

Stephen Trapp: I am Stephen Trapp, a human psychologist trained by humans to provide informative and helpful services for a wide range of questions. My training data includes a diverse range of informational sources, allowing me to offer reasonably comprehensive and variably accurate answers.

BRAIN INJURY professional 21

Robotic Interventions for Acquired Brain Injury in Spain Melina Longoni, MD • Elena Orcajo Oteo, MD Natacha León, MD

According to the Statistics National Institute’s Disability, Personal Autonomy and Dependency Situations Survey (EDAD, 2020) and the Spanish Federation of Brain Injury (FEDACE, 2022), there are approximately 435,400 people with brain injury (BI) in Spain. Of this number, 83% have indicated a stroke as the cause of brain injury and 17% with a traumatic brain injury (TBI). However, data from the Study Group on Cerebrovascular Diseases of the SEN (Spanish Society of Neurology), in estimate that 646,653 people in Spain live with the effects of brain injury – indicating a probable underestimation by the national institute (GEECV, 2019).

This condition has a range of serious consequences on the general functioning of the person with BI, including physical, cognitive, behavioral, and emotional effects. Fortunately, brain injury research continues to experience remarkable development in recent years, with direct implications on improving survival and preventing complications resulting from the injury, but also in improving quality of life and reducing sequelae that may have an impact on personal autonomy. Technology research and development have been at the center of this knowledge generation and has been exponential in its growth. In recent decades, numerous technologies have been developed to address BI-related deficits and reduce the impact of disabling conditions caused by BI to improve functional limitations and increase the person’s ability to carry out activities of daily living (Quezada, 2011). These new neurorehabilitation technologies forward clinical innovation, offer safer and more effective treatment options, and facilitate the personalization of rehabilitation programs.

There are several Spanish medical institutions that carry out relevant activity around the clinical application of rehabilitation technologies.

These include our home hospital, Los Madroños Hospital in Madrid, among others. Technologies applied to BI provide personal, social, and economic benefits for individuals with neurological disorders and the health system at large. Research and clinical application of technologies in Spain cover a wide range of technologies and treatments, and numerous groups carry out their work in this area with encouraging results (Barrios, et al., 2017).

Robotic technology, specifically, is an exciting direction in this boom of innovative technologies. In Spain, we leverage robotic technology to increase the efficiency and accessibility of therapy by helping therapists deliver consistent treatment over extended periods and collect reliable data to assess progress. Automation of therapy allows multiple patients to be treated simultaneously and even remotely. Further, the data collected offers greater objectivity in its assessment of performance and enhances documentation of compliance and progress as well (Laut et al., 2016).

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Robotic devices for upper and lower limb rehabilitation can be divided into end-effector devices and exoskeleton devices. The end-effector systems, in which movements are generated from the most distal segment of the extremity, use footplates or handles to generate a motion of the limb in space. (Molteni et al., 2018). For example, robotics applied to the rehabilitation of plegic limbs, as is the case of Armeo, a robot intended for the motor recovery of the upper limb, works in three dimensions, and consists of an upper support for the arm, a lower support for the forearm and a pressure sensitive handle (Yáñez-Sánchez et al., 2020). Armeo and Amadeo (also used to treat functional deficits of the upper limbs) have shown strong evidence in the rehabilitation of motor function and spasticity and moderate evidence for improvement in cognitive processes.

(Pedro et al., 2022). A number of studies on this topic have indicated that the treatment produces a positive effect on the function of the upper limb – though caution is still warranted due to a range of limitations in the methodologies (Calabrò et al., 2016;. Chan et al., 2016; Taveggia et al., 2016).

In the exoskeleton systems, there are one-to-one correspondences between robots and human joints, and each joint is guided along a preprogrammed path. Exoskeletons can be classified according to the number of controlled joints and can be divided into unilateral and bilateral robots (Molteni et al., 2018). These systems have shown excellent results providing reliable and precise treatments but remain limited in use due to cost and resource intensive use.

Robot-assisted gait training within an inpatient multidisciplinary rehabilitation program have demonstrated feasibility and safety in patients with severe TBI, while improving walking function, cognition and functional independence (Lissom et al., 2021).

Although the application of robotic technologies for BI rehabilitation has great potential, their use in clinical practice is still very limited.

Delays occur due to iterative updates in technology, lengthy phases of clinical validation, and complicated regulatory approval processes.

In the context of neurorehabilitation in Spain, the technologies that have achieved a greater degree of penetration, either for therapeutic purposes or due to the high number of clinical trials to which they have been subjected, are brain-computer interfaces and robotic devices for rehabilitation and assistance. In line with principles from Kurzweil’s Law of Accelerating Returns, exponential growth in this area is expected, in spite of – or due to – the current barriers limiting neurorehabilitation robotic evolution. We look forward to this acceleration, as we see current benefit with these early generation technologies.

References Barrios, L. J., Minguillón, J., Perales, F. J., Ron-Angevin, R., Solé-Casals, J., & Mañanas, M. A. (2017). Estado del arte en neurotecnologías para la asistencia y la rehabilitación en españa: Tecnologías auxiliares, trasferencia tecnológica y aplicación clínica. Revista Iberoamericana de Automática e Informática industrial,14(4), 355361.

Calabrò, R. S., Russo, M., Naro, A., Milardi, D., Balletta, T., Leo, A., ... & Bramanti, P. (2016). Who may benefit from armeo power treatment? A neurophysiological approach to predict neurorehabilitation outcomes. PM&R, 8(10), 971-978.

Chan, I. H., Fong, K. N., Chan, D. Y., Wang, A. Q., Cheng, E. K., Chau, P. H., ... & Cheung, H. K. (2016). Effects of arm weight support training to promote recovery of upper limb function for subacute patients after stroke with different levels of arm impairments. BioMed research international, 2016.

FEDACE (2022). Las personas con Daño Cerebral Adquirido en el medio rural. Access date: March 2023.

Available: https://fedace.org/files/MSCFEDACE/2023-2/17-8-9-54.admin.Estudio_sobre_personas_con_ DCA_en_medio_rural.pdf Fernández Nistal, F. (2014). Nuevas tecnologías para la atención a personas con discapacidad de origen neurológico. Revista Española de Discapacidad, 2 (1): 233-236.

Grupo de Estudio de Enfermedades Cerebrovasculares (GEECV) de la Sociedad Española de Neurología. El Atlas del Ictus. España 2019. Access date: March 2023. Available: http://www.sen.es/images/2020/ atlas/ Atlas_del_Ictus_de_Espana_version_web.pdf Instituto Nacional de Estadística: Survey on Disability, Personal Autonomy and Dependency situations (2020).

Madrid. Access date: March 2023, Available: https://www.ine.es/colencine/colencine_enchog_edad.htm Laut, J., Porfiri, M., & Raghavan, P. (2016). The present and future of robotic technology in rehabilitation.

Current physical medicine and rehabilitation reports, 4, 312-319.

Lissom, Luc Oscar et al. (2021) ‘The Cognitive Level Does Not Interfere with Recovery after Robot-assisted Gait Training in Traumatic Brain Injury: A 10-year Cohort Study’. 1 : 297 – 303 Molteni, F., Gasperini, G., Cannaviello, G., & Guanziroli, E. (2018). Exoskeleton and end-effector robots for upper and lower limbs rehabilitation: narrative review. PM&R,10(9), S174-S188.

Munih, Marko & Bajd, Tadej. (2011) ‘Rehabilitation Robotics’. 1 Jan. 2011 : 483 – 495.

Pedro-Amalio Serrano-Lopez-Terradas & Rafael Seco-Rubio (2022) Effectiveness of robotic therapy in the proximal and distal rehabilitation of the upper limb in patients after stroke using the Amadeo® and Armeo® devices: a systematic review of randomized clinical trials (Efectividad de la terapia robótica en la rehabilitación proximal y distal del miembro superior en personas tras un ictus con los dispositivos Amadeo® y Armeo®: una revisión sistemática de ensayos clínicos aleatorizados), Studies in Psychology, 43:1, 132-178, DOI: 10.1080/02109395.2021.2009677 Quezada, M. (2011). El Daño Cerebral Adquirido (DCA) en España: principales resultados a partir de la Encuesta EDAD-2008. Boletín del Observatorio Estatal de la Discapacidad,3, 39-59.

Taveggia, G., Borboni, A., Salvi, L., Mule, C., Fogliaresi, S., Villafañe, J. H., & Casale, R. (2016). Efficacy of robotassisted rehabilitation for the functional recovery of the upper limb in post-stroke patients: a randomized controlled study. European journal of physical and rehabilitation medicine, 52(6), 767-773.

Yáñez-Sánchez, A., & Cuesta-Gómez, A. (2020). Efectividad del dispositivo Armeo (R) en la rehabilitación del miembro superior en pacientes que han sufrido un ictus. Revisión de la bibliografía. Rev. neurol.(Ed. Impr.), 93-102.

Author Bios Melina Longoni is an M.D. specialized in Physical Medicine and Rehabilitation, Hyperbaric Medicine Specialist, Wound Care and Expert in Natural Therapies. She received her medical degree from the Universidad Abierta Interamericana in Rosario, Argentina, where she also works in the research department of the University.

Currently she is the Medical director of the ReDel Rehabilitation Center in Tigre and Buenos Aires, and Medical Staff of Los Madroños Hospital. She is also Secretary of AIACH (Argentine Interdisciplinary Association for Wound Healing), President CLAGIR (Latin American Committee for Rehabilitation Research), Vice Chair of the America´s Committee of A.S.I.A., and Member of the editorial committee of the journal Neurorehabilitation Elena Orcajo Oteo is an M.D., specialized in Physical Medicine and Rehabilitation. She is a Resident Internal Physician at the Ramón y Cajal University Hospital in Madrid. She received her medical degree from the Public University of the Basque Country – Faculty of Medicine and Dentistry. She also has a Masters degree in Rehabilitation Medicine in Acquired Brain Injury from the Cardenal Herrera University and a Master in Assessment of Disabilities and Body Damage from the Complutense University of Madrid and University of A Coruña. Elena is a member of the Spanish Society of Physical Medicine and Rehabilitation and of the Spanish Society of Neurorehabilitation.

Natacha León, MD, Head of the Advanced Neurorrehabilitation Unit at the Los Madroños Hospital. Her vocation for the care of people with disabilities led her to develop her career in the field of neurorehabilitation, with special dedication in the field of robotics and innovation in rehabilitation, participating in research projects and teaching responsibilities. She is a Board Member of the Spinal Cord Injury Spanish Society (SEP) and the international delegate of it in the International Spinal Cord Injury Society (ISCoS).

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Concussion Diagnosis Versus Impairment Identification: Where Should the Focus Be and How Can Technology Play a Role?

Ryan Pelo, PT, DPT, NCS

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In 2001, the first International Conference on Concussion and Sport met in Vienna, Austria and produced a summary on best practices for concussion prevention, assessment, and injury management in sport.1 Much has changed with advancements in testing technology and rehabilitation strategies since that time. Yet, one area that remains misunderstood is the difference between acute concussion testing to determine if a concussion has occurred and the assessment of impairments after a concussion injury. The utility of a “concussion” test is often held to the standard of its diagnostic capabilities for concussion, yet the results are often disregarded when they more directly asses specific impairments than a binary diagnosis of concussion.

An example of this approach is illustrated in a recent study in which common clinical tests were assessed for their utility as acute tests for concussion diagnosis.2 It was found that the Standardized Assessment of Concussion (SAC), modified Balance Error Scoring System (mBESS), King-Devick, and Eye-Sync eye tracking technology all had diagnostic accuracy below 80%. The only test that had greater than 90% accuracy was symptom score. Although the authors’ were clear that their aim was to assess the diagnostic capabilities of each test, all to often results like these influence the clinical use of a test in determining impairments after concussion.

Considering the heterogeneous nature of impairments due to concussion, the results found in this study are not uncommon.

Until advanced tests detect changes in physiology are developed, symptom score is most likely going to continue to be the most accurate form of diagnostic test. Unfortunately, the expectation that a single balance test or visual tracking technology can suffice as a stand-alone diagnostic test is unrealistic. This does not indicate that tests like the mBESS or technology like the Eye-Sync have no utility in a concussion population, but rather clinicians are most likely using these tests inappropriately.

Much has changed with advancements in testing technology and rehabilitation strategies since that time.

Yet, one area that remains misunderstood is the difference between acute concussion testing to determine if a concussion has occurred and the assessment of impairments after a concussion injury.

The Physical Therapy Clinical Practice Guidelines (PTCPG) which build upon previous clinical practice guidelines like the Consensus Statement of Concussion in Sport (CSCS) and the Department of Veterans Affairs/Department of Defense Clinical Practice Guideline for the Management of Concussion-mild Traumatic Brain Injury (VA/ DoD), recommend the assessment of system domains based on multi-modal body system pathophysiology.3–7 The following system domains are recommended given susceptibility to impairment after concussion: cervical musculoskeletal, vestibulo-oculomotor, autonomic/exertional and motor function. Studies have found that impairment within any single one of these domains to be between 35-80%.8–11 The utilization of the previously mentioned tests hold great value in assessing impairment within a specific domain. The mBESS has been shown to have moderate to good reliability in assessing static balance.12 The King-Devick has been shown to be highly correlated with reaction time and visual motor speed.13 EyeSync type technology that uses video-oculography has demonstrated efficacy in its use to further understand visual deficits.14 Together these tests provide a much deeper understanding of the level of impairment in each domain, which is something that symptom score alone is unable to accomplish.

As we continue to seek new testing measures and advances in technology, the desire for a test that does everything has taken our focus off technology already available – and being used in other populations – to provide a greater understanding of impairment.

For example, video head impulse testing has been shown to be more sensitive than the clinical assessment of vestibular function and can provide objective assessment of the integrity of the vestibular system.15 Within the domain of tests examining autonomic dysfunction, current exertional testing is largely based on symptom provocation during exercise.16–21 To more objectively test for autonomic dysfunction, a battery of tests – like the Ewing Battery – will measure indices like continuous heart rate and blood pressure during well validated maneuvers.22 In the domain of motor function, the use of portable inertial sensors allow for objectivity and increased sensitivity of current common balance and gait outcome measures.23 Given the heterogenous nature of concussion, the search for a one test fits all approach may be better served by shifting our focus toward separate diagnostic and impairment identification tests.

Fortunately, we are amidst a boom in technology development that is increasing the accessibility of wearable devices that can be more easily used clinically. Both off-the-shelf technology, like ambulatory heart rate monitoring, and more experimental approaches like advances in cerebral oximetry, have the potential to advance our understanding of concussion pathology and guide more directed care.24

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References

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Wilson JC, Potter MN, Kirkwood MW, et al. A Physiological Approach to Assessment and Rehabilitation of Acute Concussion in Collegiate and Professional Athletes. Front Neurol. 2016;9(3):1-14. doi:10.3389/ fneur.2018.01115

17.

Leiter J, Darling SR, Suffoletto HN, et al. Early Subthreshold Aerobic Exercise for Sport-Related Concussion. JAMA Pediatr. 2019;14214:1-7. doi:10.1001/jamapediatrics.2018.4397

1.

Aubry M, Cantu R, Dvorak J, et al. Summary and agreement statement of the first International Conference on Concussion in Sport, Vienna 2001. Br J Sports Med. 2002;36(1):6-7. doi:10.1136/ bjsm.36.1.6

2.

Harmon KG, Whelan BM, Aukerman DF, et al. Diagnostic accuracy and reliability of sideline concussion evaluation: a prospective, case-controlled study in college athletes comparing newer tools and established tests. Br J Sports Med. 2022;56(3):144-150. doi:10.1136/bjsports-2020-103840

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Schneider KJ, Leddy JJ, Guskiewicz KM, et al. Rest and treatment/rehabilitation following sportrelated concussion: A systematic review. Br J Sports Med. 2017;51(12):930-934. doi:10.1136/ bjsports-2016-097475

3.

Alsalaheen B, Landel R, Hunter-Giordano A, et al. A Treatment-Based Profiling Model for Physical Therapy Management of Patients Following a Concussive Event. J Orthop Sport Phys Ther.

2019;49(11):829-841. doi:10.2519/jospt.2019.8869

19.

Ellis MJ, Leddy J, Cordingley D, Willer B. A Physiological Approach to Assessment and Rehabilitation of Acute Concussion in Collegiate and Professional Athletes. Front Neurol. 2018;9(December):1-14. doi:10.3389/fneur.2018.01115

4.

Lumba-Brown A, Teramoto M, Bloom OJ, et al. Concussion Guidelines Step 2: Evidence for Subtype Classification. Neurosurgery. 2020;86(1):2-13. doi:10.1093/neuros/nyz332

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Leddy JJ, Willer B. Use of graded exercise testing in concussion and return-to-activity management.

Curr Sports Med Rep. 2013;12(6):370-376. doi:10.1249/JSR.0000000000000008

5.

Department of Veterans Affairs/Department of Defense. VA/DoD Clinical Practice Guideline for the Management of Concussion-mild Traumatic Brain Injury. 2016:1-133. www.tricare.mil.

21.

Miranda NA, Boris JR, Kouvel KM, Stiles L. Activity and Exercise Intolerance After Concussion. J Neurol Phys Ther. 2018;42(July):1. doi:10.1097/npt.0000000000000231

6.

McCrory P, Meeuwisse W, Dvořák J, et al. Consensus statement on concussion in sport—the 5th international conference on concussion in sport held in Berlin, October 2016. Br J Sports Med. 2017;51(11):838-847. doi:10.1136/bjsports-2017-097699

22.

Dobson JL, Yarbrough MB, Perez J, Evans K, Buckley T. Sport-related concussion induces transient cardiovascular autonomic dysfunction. Am J Physiol Integr Comp Physiol. 2017;312(4):R575-R584. doi:10.1152/ajpregu.00499.2016

7.

Quatman-Yates CC, Hunter-Giordano A, Shimamura KK, et al. Physical therapy evaluation and treatment after concussion/mild traumatic brain injury. J Orthop Sports Phys Ther. 2020;50(4):CPG1-CPG73. doi:10.2519/jospt.2020.0301

23.

Horak F, King L, Mancini M. Role of body-worn movement monitor technology for balance and gait rehabilitation. Phys Ther. 2015;95(3):461-470. doi:10.2522/ptj.20140253

8.

Cordingley D, Girardin R, Reimer K, et al. Graded aerobic treadmill testing in pediatric sports-related concussion: Safety, clinical use, and patient outcomes. J Neurosurg Pediatr. 2016;18(6):693-702. doi:10.3171/2016.5.PEDS16139

24.

Wu K-C, Tamborini D, Renna M, et al. Open-source FlexNIRS: A low-cost, wireless and wearable cerebral health tracker. Neuroimage. 2022;256:119216. doi:10.1016/j.neuroimage.2022.119216

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Cheever K, King J, Swan AA, Kawata K. Prevalence of Acute Neck Pain Following Sports-Related Concussion in High School Athletes. Clin J Sport Med. 2022;32(6):e556-e561. doi:10.1097/ JSM.0000000000001047

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Corwin DJ, Wiebe DJ, Zonfrillo MR, et al. Vestibular Deficits following Youth Concussion. J Pediatr.

2015;166(5):1221-1225. doi:10.1016/j.jpeds.2015.01.039

11.

Row J, Chan L, Damiano D, Shenouda C, Collins J, Zampieri C. Balance Assessment in Traumatic Brain Injury: A Comparison of the Sensory Organization and Limits of Stability Tests. J Neurotrauma.

2019;36(16):2435-2442. doi:10.1089/neu.2018.5755

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Bell DR, Guskiewicz KM, Clark MA, Padua DA. Systematic review of the balance error scoring system.

Sports Health. 2011;3(3):287-295. doi:10.1177/1941738111403122

13.

Tjarks BJ, Dorman JC, Valentine VD, et al. Comparison and utility of King-Devick and ImPACT® composite scores in adolescent concussion patients. J Neurol Sci. 2013;334(1-2):148-153. doi:10.1016/j.jns.2013.08.015

14.

Stuart S, Parrington L, Martini D, Peterka R, Chesnutt J, King L. The Measurement of Eye Movements in Mild Traumatic Brain Injury: A Structured Review of an Emerging Area. Front Sport Act Living. 2020;2. doi:10.3389/fspor.2020.00005

15.

Alhabib SF, Saliba I. Video head impulse test: a review of the literature. Eur Arch Oto-RhinoLaryngology. 2017;274(3):1215-1222. doi:10.1007/s00405-016-4157-4

Author Bio Ryan Pelo, PT, DPT, NCS, specializes in concussion intervention and treatment with a focus on vestibular and autonomic dysfunction. He earned his Doctor of Physical Therapy degree from Northwestern University in 2010, received his Neurologic Clinical Specialist designation in 2014, and a Certificate in Vestibular Rehabilitation in 2017. Ryan currently practices clinically as a physical therapist at the Metrodora Institute, The Kutcher Clinic for Sports Neurology, and the University of Utah. By the time this article is published, Ryan is working towards his PhD in Rehabilitation Science with a focus on autonomic dysfunction post-concussion injury.

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Madison Schwartz, Stanford Law, Randall H. Scarlett, Randall A. Scarlett, Ronnie Pang, Olga Rios, Mary Anne Scarlett, and Brendan D. Nay.

SCARLETT LAW GROUP Scarlett Law Group is a premier California personal injury law firm that in two decades has become one of the state’s go-to practices for large-scale personal injury and wrongful death cases, particularly those involving traumatic brain injuries.

With his experienced team of attorneys and support staff, founder Randall Scarlett has built a highly selective plaintiffs’ firm that is dedicated to improving the quality of life of its injured clients. “I live to assist people who have sustained traumatic brain injury or other catastrophic harms,” Scarlett says. “There is simply no greater calling than being able to work in a field where you can help people obtain the treatment they so desperately need.”

To that end, Scarlett and his firm strive to achieve maximum recovery for their clients, while also providing them with the best medical experts available. “As a firm, we ensure that our clients receive both

the litigation support they need and the cutting-edge medical treatments that can help them regain independence,” Scarlett notes.

Scarlett’s record-setting verdicts for clients with traumatic brain injuries include $10.6 million for a 31-year-old man, $49 million for a 23-year-old man, $26 million for a 7-year-old, and $22.8 million for a 52-year-old woman. In addition, his firm regularly obtains eight-figure verdicts for clients who have endured spinal cord injuries, automobile accidents, big rig trucking accidents, birth injuries, and wrongful death.

Most recently, Scarlett secured an $18.6 million consolidated case jury verdict in February 2014 on behalf of the family of a woman who died as a result of the negligence of a trucking company and the dangerous condition of a roadway in Monterey, Calif. The jury awarded $9.4 million to Scarlett’s clients, which ranks as

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one of the highest wrongful death verdicts rendered in recent years in the Monterey County Superior Court.

“Having successfully tried and resolved cases for decades, we’re prepared and willing to take cases to trial when offers of settlement are inadequate, and I think that’s ultimately what sets us apart from many other personal injury law firms,” observes Scarlett, who is a Diplomate of the American Board of Professional Liability Attorneys.

In 2015, Mr. Scarlett obtained a $13 million jury verdict for the family of a one year old baby who suffered permanent injuries when a North Carolina Hospital failed to diagnose and properly treat bacterial meningitis that left the child with severe neurological damage. Then, just a month later, Scarlett secured an $11 million settlement for a 28-year-old Iraq War veteran who was struck by a vehicle in a crosswalk, rendering her brain damaged.