The Utilization of Biomechanics to Understand and Manage the Acute ...

Kinesiology Review, 2015, 4, 39-51 http://dx.doi.org/10.1123/kr.2014-0080 © 2015 by The National Academy of Kinesiology

Official Publication of NAK and AKA www.KR-Journal.com THE ACADEMY PAPERS

The Utilization of Biomechanics to Understand and Manage the Acute and Long-term Effects of Concussion Jay L. Alberts and Susan M. Linder The acute and long-term effects of concussive and subconcussive head impacts on brain health have gained tremendous attention over the past five years. The treatment and management of concussion involves multiple providers from multiple disciplines and backgrounds. Varied backgrounds and approaches to assessing cognitive and motor function before and post-concussion are limiting factors in the efficient and effective management of concussion as discipline-specific rating scales and assessments serve as a barrier to effective patient hand-offs between providers. Combining principles of motor behavior with biomechanical approaches to data analysis has the potential to improve the continuity of care across the multiple providers managing athletes with concussion. Biomechanical measures have been developed and validated using mobile devices to provide objective and quantitative assessments of information processing, working memory, set switching, and postural stability. These biomechanical outcomes are integral to a clinical management algorithm, the Concussion Care Path, currently used across the Cleveland Clinic Health System. The objective outcomes provide a common data set that all providers in the spectrum of care can access which facilitates communication and the practice of medicine and in understanding the acute and long-term effects of concussion and subconcussive exposure on neurological function. Keywords: concussion, biomechanics, motor control, mobile device, care path Concussion and mild traumatic brain injury (mTBI) have recently been termed the “silent epidemic”. However, this is not the first “concussion crisis”; had it not been for the work and advocacy of Walter Camp, college football likely would have been banned in the early 1900s as a result of head and bodily injuries that resulted in multiple fatalities (Harrison, 2014). Nevertheless, due to the attention concussion, both sport and nonsport, has received in the media and scientific literature it can no longer be considered a silent epidemic. It is estimated that 1.6–3.8 million sport-related concussions or mTBIs occur in the United States each year (Langlois, RutlandBrown, & Wald, 2006), resulting in direct and indirect costs estimated at $60 billion (Faul, Wald, Xu, & Coronado, 2010). Emergency department visits for sport- and recreation-related traumatic brain injury (TBI) increased by 62% from 2001 to 2009 for individuals under the age of 19 (Centers for Disease Control and Prevention, 2011). It is speculated that this increased incidence in mTBI may be due to greater participation in high-risk Alberts is with the Department of Biomedical Engineering, Center for Neurological Restoration, and Cleveland Clinic Concussion Center, Cleveland Clinic, Cleveland, OH. Linder is with the Department of Biomedical Engineering and Cleveland Clinic Concussion Center, Cleveland Clinic, Cleveland, OH.

activities including contact sports or bigger and faster participants, resulting in greater impact forces (Centers for Disease Control and Prevention, 2011), or a more acute social awareness and recognition of concussion. Regardless of the reason for the increase, the Centers for Disease Control and Prevention (CDC) has concluded that mTBI is a public health problem, and recommended the development and testing of strategies to reduce mTBI incidence and to improve outcomes for individuals sustaining concussion and mTBI (National Center for Injury Preventions and Control, 2003). For the most part, the remainder of this manuscript will focus on concussion in sport, the unique role kinesiology is playing in the detection and treatment of concussion, and the potential of our discipline to take a central role in characterizing and understanding the acute and long-term effects of concussive and subconcussive blows to the head. The focus on sport is in no way meant to minimize military mTBI as a result of blunt force trauma or blast injury. Rather, the potential lessons learned and best practices developed in the management of sport concussion may be translated to military populations. At its core, concussion is a relatively simple concept or equation: the left side of the equation, impact, has clear biomechanical characteristics that can be quantified by measuring the rotational, angular, or linear forces thought to cause a sheering or rotational effect to the cerebral

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hemispheres about the upper brain stem, resulting in axonal shearing (Conidi, 2012), while the right side of the equation consists of the behavioral and physiological effects of these impact forces. Formally, concussion is considered a subset of TBI resulting in a functional rather than structural disturbance of the brain, due to direct or indirect forces transmitted to the head (Scorza, Raleigh, & O’Connor, 2012; McCrory et al., 2013; Harmon et al., 2013; Menon, Schwab, Wright, & Maas, 2010). It is important to note that although the general principles of mechanical strain and stress on the brain can be modeled, every concussion occurs under unique loading conditions. Thus, a significant challenge is to understand how various types, durations, and magnitudes of impact can be coalesced into a single universal approach to characterizing the precise dose of impact that results in a concussion. Following these consistently irregular impact forces of sufficient magnitude, a neurochemical and neurometabolic cascade ensues, resulting in the temporary disruption of neuronal cell function rather than cell death. It is during these acute phases of neurophysiological recovery that the brain is thought to be especially vulnerable to repeated injury including second impact syndrome and long-term disability (Barkhoudarian, Hovda, & Giza, 2011). The sheering or rotational forces imparted upon the brain results in axonal stretching and a resultant disruption to the neural membrane. An ionic and metabolic imbalance ensues in the presence of decreased cerebral blood flow, characterized by a release of primarily excitatory neurotransmitters resulting in an efflux of potassium and an influx of calcium causing mitochondrial dysfunction, impaired oxidative metabolism, and decreased ATP production (Barkhoudarian et al., 2011). In the absence of adequate levels of ATP, glycolysis is used in attempts to restore ionic homeostasis, resulting in extracellular lactate accumulation and metabolic acidosis. Oxidative metabolism recovers initially two days postinjury, diminishes through day 5, and typically recovers fully by day 10, at approximately the same time cerebral glucose metabolism recovers. In the absence of complete neurochemical and neurometabolic recovery, persistent alterations in neurotransmitter function can lead to symptoms commonly seen in post-concussive syndrome including diminished memory, cognition, attention, learning, disinhibition, and distractibility (Conidi, 2012).

Acute Effects of Concussion The complex pathophysiological process associated with concussion results in a wide array of symptoms affecting the cognitive, physical, behavioral, and sleep domains of neurological function (Scorza et al., 2012; McCrory et al., 2013). The diagnosis of concussion remains a clinical one, determined largely by an appropriate mechanism of injury combined with symptom onset (Scorza et al., 2012). Currently, there is no known threshold for concussive injury, and no biomarker of sufficient sensitivity has been identified to provide a definitive diagnosis of

concussion. Furthermore, routine brain imaging is typically normal and typically not assistive in the diagnostic process (Scorza et al., 2012). However, the reliance on the self-report of symptoms is of concern for clinicians as it has been estimated that 50% of high school athletes and 70% of collegiate athletes may have symptoms of concussion, yet they are unreported (Broglio et al., 2014). Therefore, the objective and quantitative evaluation of neurologic function has been recommended for the accurate and timely diagnosis of concussion (Guskiewicz, 2011). Previously, concussion was classified according to grades of severity based largely on loss of consciousness (LOC) and posttraumatic amnesia (Cantu, 2001). It has since been determined that neither LOC nor amnesia are indicative of injury severity nor provide prognostic value in predicting injury recovery profiles, leading field experts and their affiliated professional organizations to abandon previously endorsed classification systems (McCrory et al., 2013; Harmon et al., 2013; Giza et al., 2013). Epidemiological studies have since identified modifiers or clinical factors and comorbidities that more accurately predict injury severity and those at risk for protracted recovery. The most common modifiers that predict prolonged recovery include any of the following comorbidities: history of prior concussion, migraine, depression, learning disability, attention deficit disorder, or sleep disorders (McCrory et al., 2013; Harmon et al., 2013). A delayed recovery can also be predicted based on the number of symptoms reported, their duration, and severity. Finally, females and individuals < 18 years of age have been shown to be at risk for delayed recovery. Systematically identifying and documenting these modifiers in the acute phase of injury may warrant more aggressive clinical management and potential early intervention from rehabilitative therapies.

Long-term Effects of Head Trauma While the acute effects of mTBI typically resolve in 7–10 days in an estimated 80–90% of cases (McCrory et al., 2013), recent events in the media have highlighted the potential long-term effects of concussion or the cumulative effects of repeated subthreshold impact. The acknowledgment of chronic or cumulative effects of repeated head impacts is not new, as Dr. Harrison Martland, a pathologist, reported about an acute condition in prizefighters in 1928, which he termed as “punch drunk” (Martland, 1928). In 1934, Dr. Harry Parker of the Mayo Clinic first described the cumulative effects of repetitive trauma in professional fighters, coining the diagnosis “traumatic encephalopathy” (Parker, 1934). Dr. Parker described, in detail, three cases of boxers developing progressive neurological decline. The clinical presentation he described included cognitive, behavioral, and motor abnormalities, and by 1937, was termed dementia pugilistica, due to its association with boxing (Millspaugh, 1937). Chronic traumatic encephalopathy, or CTE as it

Kinesiology and Management of Concussion 41

is known today, is a neurodegenerative disease associated with repetitive head trauma due to contact sport, military exposure, domestic abuse, and even self-inflicted head banging (Baugh, Robbins, Stern, & McKee, 2014; McKee et al., 2009). From a neuropathological perspective, CTE is characterized by neurofibrillary tangles of hyperphosphorylated tau (p-tau) protein with perivascular involvement initially in the cortical sulci (Baugh et al., 2014). In the later stages of the disease, p-tau deposition becomes more widespread, especially in the medial temporal lobes and in the white matter, leading to neuronal loss and gliosis. Varying degrees of axonal injury are commonly found, ranging from multifocal varicosities to severe axonal loss. Moderate to severe enlargement of the lateral and third ventricles is found in advanced CTE, along with septal perforations and pallor of the substantia nigra and locus coeruleus. The severity of neuropathology has been shown to correlate with the duration of athletic careers in the cases of former professional football players. The clinical signs of CTE typically present years after initial exposure and are characterized by declines in mood, behavior, cognition, or motor function (Baugh et al., 2014). Specifically, changes in mood include depression, irritability, and hopelessness, while impulsivity and aggression are observed in those with behavioral dysfunction. Cognitive symptoms include memory impairment, difficulty with executive function, diminished attention, and dementia. Motor dysfunction manifests as Parkinsonism, ataxia, and dysarthria (Faul et al., 2010). The definitive diagnosis of CTE is based on neuropathologic examination, as no reliable in vivo biomarker has been identified to differentiate between cases of CTE and other tauopathies.

Clinical Management of Concussion: An Opportunity for Kinesiology A significant challenge in the diagnosis and management of athletes with concussion is reconciling the diverse backgrounds, training, clinical management approaches, and outcomes used across the multidisciplinary team of clinicians. Further challenging the continuity of care are the resources and access to technology and electronic health records (EHR) available in the various settings in which concussion care and management occur, ranging from on the field to the emergency department environment, and finally, to the clinical setting. The lack of a unified and systematic documentation platform across disciplines and settings prevents effective interdisciplinary communication regarding injury details, immediate clinical presentation, action taken, and patient education provided. In response to this challenge, the Cleveland Clinic developed a Concussion Care Path, with the primary aim of creating an evidence-informed standardized approach to the diagnosis and comprehensive

management of individuals with concussion along the entire continuum of care. Integral to the development of this Concussion Care Path were the 56 certified athletic trainers (ATCs) within the Cleveland Clinic Health System, as they provided medical coverage to 50 high schools and colleges across Northeast Ohio. Furthermore, they are typically the providers that make the initial evaluation and diagnosis and are responsible for managing the return-to-play process in cooperation with a physician. Collectively, the stages of concussion management were divided into three phases identified in Figure 1: acute (0–7 days postinjury), subacute (7–21 days postinjury), and post-concussive (> 21 days postinjury). Figure 1 contains a workflow diagram of the Concussion Care Path with these various phases and the potential providers identified at each phase based on a clinical algorithm. The shaded components of the diagram reflect where standardized and objective biomechanical measures of motor and cognitive function have been included using a unified, electronic platform. Effective care of concussion requires the coordination of clinical resources and effective handoffs between providers. A fundamental challenge in the coordination of resources and handoffs is the use of discipline-specific measures of motor and cognitive function. We sought to use motor control principles in the design of motor and cognitive assessments and quantify these tests using objective biomechanical measures. The goal of this kinesiology-centric approach was to provide meaningful assessments that actually measured the function of interest (information processing, postural stability, motor learning, and others) and use quantitative measures that would result in a common dataset gathered across all healthy and injured athletes under the care of Cleveland Clinic ATCs or physicians. The lack of availability and accessibility to technology in the field, especially in rural or inner city high schools, prevents the integration and adoption of an EHR-based Concussion Care Path. Further, utilizing the EHR system would not provide an opportunity for objective assessment of cognitive or motor function. Our approach to overcome this barrier was to look to mobile technology as a solution. Considering many mobile devices come equipped with a suite of inertial sensors (linear accelerometer and gyroscope) as well as a capacitive touch screen, they were thought to be ideally suited to transition from expensive electronic notebooks to data collection systems. Therefore, we developed the Cleveland Clinic Concussion (C3) app. The C3 app serves as a method of documenting the injury in the field, a tool to facilitate the objective evaluation of the multiple domains of neurologic function affected by concussion, and as a manner of documenting the return-to-play rehabilitation process. The C3 app contains an incident report (IR) module, five assessment modules evaluating various aspects of neurologic function, and a return-toplay (RTP) module. Collectively, these modules allow for the systematic documentation of the injury and an objective measurement of the signs and symptoms associated

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Figure 1 — A schematic of the Cleveland Clinic Concussion Care Path across the three phases of concussion: acute, subacute, and post-concussive. The shaded blocks indicate a time point within the Concussion Care Path in which biomechanical measures are completed using the Cleveland Clinic Concussion (C3) iPad application. ED = emergency department; RTP = return-to-play; PM & R = physical medicine and rehabilitation.


with the injury as recommended by the National Athletic Trainers’ Association (NATA), in addition to providing clinical guidance according to evidence-informed best practice standards (Broglio et al., 2014).

Documenting the Injury In the recent NATA recommendations for the management of concussion, it was stressed that the documentation of the specifics of the injury are critical for patient care and potential protection against litigation (Broglio et al., 2014). We developed three versions of the IR to align with best practice standards in the three most common points of intake for concussion patients: (1) on-field, (2) emergency department, and (3) in-clinic. The on-field version is most applicable in the management of sport-related concussion and includes a checklist of “red flags” which would potentially warrant immediate transfer to a hospital setting, detailed information regarding the incident (location of impact, mechanism of injury, protective equipment used, immediate symptoms, presence/absence of LOC, presence/absence of amnesia), neurologic evaluation via the Glasgow Coma Scale, and actions taken by medical personnel onsite. Importantly, this module addresses the most common allegations of negligence against ATCs, which include the improper evaluation and testing of the patient, improper documentation, misunderstood communications with the patient, and a lack of education of the patient or patient’s family (Faul et al., 2010). Since deployment of the IR module in August 2014 through December 2014, more than 500 IRs have been completed by our ATCs. This information has proved valuable to the treating physician, as they are able to review the demographics and symptoms of the injury before meeting with the patient and their family, and spend more time practicing medicine and less time obtaining an often murky or conflicting recollection of the injury. Further, results indicate that those athletes in which an ATC was not present at the event were more likely to return to play on the same day of the incident. Most often these athletes were between K-8th grades, and were participating in club or recreational leagues or tournaments that may not have an ATC on site. Not returning an athlete to play on the same day of a suspected head injury is universally agreed upon across disciplines (Harmon et al., 2013; Broglio et al., 2014; Giza et al., 2013) and is legally mandated by legislation in 49 states and the District of Columbia.

Quantifying the Injury The assessment modules of the C3 app were designed to provide an objective quantification of cognitive function, postural stability, and visuo-vestibular function, in addition to a self-reported graded symptom checklist (GSC). The GSC is an expanded version of the Sideline Concussion Assessment Tool 3 (SCAT3) and utilizes a 7-point Likert scale to quantify self-reported symptom severity (McCrory et al., 2013). The Standardized Assessment of

Concussion (SAC), an outcome measure included in the SCAT3 and recommended by the Zurich Consensus Statement on Concussion in Sport, is also included in the C3 app and entails a brief assessment of orientation, immediate memory, delayed recall, and concentration (Scorza et al., 2012). Since the SAC has been found to be valid only within the first 48 hr after an acute concussion, the following tests evaluating neurocognitive function have also been included: Trail Making Test (TMT), Reaction Time (RT), and Processing Speed Test. The TMT, RT, and balance modules were all developed using motor behavior principles and objective biomechanical measures to quantify motor and cognitive function. The rationale and validation of these modules and their use in understanding the acute and long-term effects of head trauma will be the focus of the remainder of this manuscript. The TMT is a neuropsychological measure of mental attention and agility requiring the individual to connect 25 numeric targets in order (TMT-A) and to switch between alphabetic and numeric sets (TMT-B) as quickly as possible. It has been thoroughly reviewed, is a standard test of neurocognition, and is one of the neuropsychological tests recommended as part of the National Institutes of Health Common Data Elements in the assessment of executive function (National Institute of Neurological Disorders and Stroke, 2013). Studies have used the TMT to show altered cognition in numerous diseases from Parkinson’s and Huntington’s disease to schizophrenia and traumatic brain injury (O’Rourke et al., 2011; Mahurin et al., 2006; Collins et al., 1999; Hargrave, Nupp, & Erickson, 2012; Cicerone & Azulay, 2002; Demery, Larson, Dixit, Bauer, & Perlstein, 2010). It is simple to administer and interpret, and has been performed in essentially the same manner as when it was developed in the 1940s for the U.S. Army. Successful completion of the TMT is thought to require a subject’s attention and the ability to set-switch (numbers and letters), sequence the targets, visually scan for them, and apply the appropriate dexterity to move between targets (Lange, Iverson, Zakrzewski, Ethel-King, & Franzen, 2005). Currently, measurable outcomes of this test are restricted to the total time to complete the test in a standardized test-taking atmosphere, and rarely, the number of errors committed by the subject (Ruffolo, Guilmette, & Willis, 2000). This does not discriminate between cognitive processes required to complete the TMT, some of which may be of special interest in acute or long-term neurocognitive pathology. Subsequently, researchers have looked to relationships between performance on the TMT-A and the TMT-B as an indicator of specific cognitive functions (Mahurin et al., 2006; Arbuthnott & Frank, 2000). Common metrics, TMT B-A and TMT B/A for example, attempt to compare the difference or ratio of the TMT-B to the TMT-A to isolate cognitive processes, and create a metric in which the patient is their own control. It is believed that the differences in TMT-B and TMT-A may relate more to cognitive processing and set-switching and less with scanning and movement (measured in TMT-A) (Arbuthnott & Frank, 2000; Corrigan & Hinkeldey, 1987). However, the utility


of these measures and their sensitivity in detecting disease has been contested (Lange et al., 2005; Corrigan & Hinkeldey, 1987; Martin, Hoffman, & Donders, 2003). These metrics are limited in that they still only measure completion time and compare two separate performances. Using the well-developed experimental paradigms from the Stelmach laboratory, in which handwriting and arm movements are recorded using a digitizer (Phillips, Stelmach, & Teasdale, 1989; Teulings & Stelmach, 1991), the C3 app was developed to allow the athlete to complete the Trails A and B test on the screen of the iPad (Apple Inc., Cupertino, CA). We are able to sample X-Y position and time at a rate of 60 Hz, which is sufficient for subsequent kinematic analysis of performance (Seidler, Alberts, & Stelmach, 2001). Using biomechanical methods to analyze the kinematic patterns of movement provide insight into the cognitive and motor processes that are used during task completion. Thus, we are able to estimate a “cognitive time” (i.e., time when the athlete is not moving based on the algorithm developed by Teasdale, Phillips, and Stelmach [1990]) and a “motor time” (i.e., time when the athlete is moving between targets). Results indicate that some injured athletes exhibit slowing in the motor and the cognitive domains. The ability to accurately dissociate cognitive from motor function allows clinicians to recommend more specific treatment protocols and use a more precise approach to decision-making in determining when the athlete is ready to return to full academic status and unrestricted return to play. Finally, it is important to note that some athletes show prolonged cognitive times while others demonstrate prolonged motor time, thus underscoring that concussion results in a diversity of symptoms even within a given area of executive function. The diversity of symptoms and their manifestation across athletes necessitates a more objective and detailed analysis of standard neuropsychological tasks in an effort to enhance detection and improve concussion management. One of the oldest and most frequently used measures in motor behavior is reaction time, which is universally

thought to reflect information processing capabilities (Schmidt, 1991). In fact, one of the few laws in motor control, Hick’s Law, comes from the utilization of a choice reaction time paradigm (Hick, 1952; Hyman, 1953). The classic simple and choice reaction time paradigms required a participant to depress a button and make the appropriate response based on the stimulus presented. This paradigm has been repeated thousands of times in undergraduate motor behavior laboratory courses across the world. Reaction time (RT), simple reaction time (SRT), and choice reaction time (CRT) are all measured as the time interval between the presentation of the stimulus and the initiation of the appropriate response, often the release of a button (Schmidt, 1991). Movement time (MT) is defined as the duration between response initiation and target acquisition (Schmidt, 1991). As every undergraduate in kinesiology who completed the RT laboratory was taught, MT is distinct from RT and they are not to be combined to provide a measure of information processing. One of the unfortunate unintended consequences associated with the advent of computerized neurocognitive testing over the past 15–20 years is that many computerized tests purporting to assess information processing via a RT measure also include MT, as the participant is required to depress a key or the space bar in response to a stimulus without specifically controlling for initial hand position. In the development of the C3 app, we stayed true to the original experimental designs in which SRT and CRT did not include a movement component. As shown in Figure 2, the participant is required to hold a single finger (SRT) or both fingers (CRT) on the home button(s) and, when the appropriate target appears, respond as fast as possible and touch the target. This paradigm provides a clear RT, MT, and a measure of movement consistency. Healthy baseline and 48 hr post-concussion data for the two-choice RT paradigm for an 18 year-old football player is shown in Figure 2. As illustrated, his CRT at baseline (healthy) was, as expected, 456 ms while his MT

Figure 2 — Screen representation of the two-choice (compatible) reaction time (CRT) paradigm. The participant places their index finger on the “touch and hold” rectangle and touches the target that turns green. Reaction time is measured as the duration between stimulus presentation and initiation of response while movement time is the interval between lifting the finger and touching the appropriate light. The circles within the target reflect the location the target was touched for each trial. The left panel reflects the performance of 25 trials under baseline testing while the right panel is 48 hr after a diagnosed concussion. Not only does CRT increase as a result of the concussion, but movement time also increased and movement consistency declined.


was 139 ms. Approximately 48 hr post-concussion, CRT increased to 696 ms while MT also increased to 378 ms, and he showed greater variability in where the target was contacted. His information processing slowed by 52% while his movement execution slowed by 171% following concussion. The utilization of basic motor behavior principles and paradigms provides a more detailed view of the specific effects of concussion on brain function; hence, concussion may not just impact information processing, but also movement execution and speed. By accurately measuring RT and MT, our ATCs and clinicians are better able to determine specific motor and cognitive declines, track the process of returning to baseline function, and make symptom-specific treatment recommendations. The NATA position statement regarding the management of sport concussion recommended that objective assessment of balance be included as a fundamental component in the management of concussion, as postural stability provides a window into motor control processes and how concussion may impact these processes (Broglio et al., 2014). While clinical balance assessments have become the standard of care due to convenience and feasibility in the field, their ability to provide an objective and reliable measure of postural control and stability has been questioned (Finnoff, Peterson, Hollman, & Smith, 2009; Brown et al., 2014). Clearly, biomechanical measures of postural stability, including computerized dynamic posturography (CDP), force platforms, and 3D motion capture systems, are more sensitive in detecting subtle deficits in postural stability not captured with subjective clinical assessments of postural stability (Cavanaugh et al., 2005; Cavanaugh et al., 2006; Powers, Kalmar, & Cinelli, 2014). While traditional, laboratory-based biomechanical approaches are accurate and objective, these approaches have not been broadly implemented in clinical settings due to equipment cost, space requirements, and time to collect and analyze data, all with the potential to disrupt clinical workflow and productivity. The development and integration of an accurate method of quantifying postural stability that is portable while simultaneously being affordable and conducive to integration in the field and clinical environments addresses a fundamental need in the management of concussion: the objective quantification of postural stability across the concussion spectrum (from injury to return to play) that can be shared across providers. Recently, we demonstrated that a prevalent consumer electronics tablet, the Apple iPad, provided kinematic data of sufficient precision and quality to accurately characterize postural stability during performance of the NeuroCom Sensory Organization Test (SOT; Natus Medical, Inc., Clackamas, OR) (Alberts et al., in press). Figure 3 depicts the agreement between the SOT and iPad across the six conditions; in fact, the equilibrium scores (ES) calculated using accelerometer and gyroscope data from the iPad were nearly identical to the NeuroCom ES (Alberts et al., in press). Further, we showed that

anterior-posterior (AP) center of gravity (COG) sway based on sensor data were similar to the NeuroCom’s COG measurement for all trials, with a mean difference of –0.010 degree of COG sway. In addition to characterizing postural stability using AP movement, the accelerometer and gyroscope within the tablet also collect medial-lateral (ML) and trunk rotation (TR) movement data. We have recently shown significant correlation between inertial sensors within the iPad and 3D motion analysis in a cohort of healthy older adults (Ozinga & Alberts, 2014). While participants completed six balance tasks of varying difficulty, four summative metrics were computed using data from the iPad and motion capture: (1) Peak-to-peak (P2P), which measures displacement amplitude; (2) normalized path length (NPL); (3) root mean square distance (RMS), which measures magnitude of center of mass (CoM) displacement; and (4) ellipsoid volume, a metric containing the center of points of sway in 3D with 95% probability. The summative metrics computed using data from the iPad correlated significantly with motion capture computations for all six balance conditions (Ozinga & Alberts, 2014). Overall, our validation studies (Alberts et al., in press; Ozinga & Alberts, 2014) indicate that the iPad provides data of sufficient quantity and quality to accurately quantify postural stability. It is important to note we are not advocating that mobile technology can replace force platforms or motion capture systems; rather, these devices provide a reasonable approximation of balance in a package that is affordable, readily available, and portable. For concussion, the Balance Error Scoring System (BESS) is most often used to assess balance in the field and clinical environments (Guskiewicz, Ross, & Marshall, 2001; Riemann & Guskiewicz, 2000; Broglio, Zhu, Sopiarz, & Park, 2009; Riemann, Guskiewicz, & Shields, 1999). The BESS is an inexpensive and portable balance test designed to estimate postural stability (Guskiewicz et al., 2001; Riemann & Guskiewicz, 2000; Riemann et al., 1999) and has been adopted as the clinical standard of care for balance assessment in concussion (McCrory et al., 2013). Its three stances on firm ground have been incorporated into the SCAT3 to provide an assessment of the motor domain of neurologic function (McCrory et al., 2013). The BESS manipulates somatosensory and visual afferent inputs by requiring participants to maintain postural stability with feet together, on one leg, and with feet in tandem for 20-s trials with eyes closed on both firm and foam surfaces. Postural stability is estimated by a test administrator who observes the subject perform each condition and counts errors committed. Any of the following are considered an error: (1) lifting hands off iliac crests; (2) opening eyes; (3) a step, stumble, or fall; (4) moving the hip into greater than 30 degrees of flexion or abduction; (5) lifting the forefoot or heel; or (6) remaining out of correct testing position for more than 5 s (Guskiewicz et al., 2001; Riemann, Guskiewicz, & Shields, 1999). Each type of error is scored equally and error scores for each of the six conditions, with a


Figure 3 — (A) Mean and standard deviation values of equilibrium scores from NeuroCom Sensory Organization Test (SOT) (filled bars) and calculated from iPad sensor data (open bars) from 49 participants are shown for the six SOT conditions and composite score. Illustrations of SOT conditions 1 through 6 are shown below the x-axis. Image courtesy of Natus Medical Incorporated. (B) Scatter plots of raw data for each SOT condition. NeuroCom and iPad ES showed significant positive correlation (r = .45–.83). **p < .01. The diagonal line corresponds to y = x.

maximum of 10 per condition, are summed to provide a total BESS score (range 0–60) where higher error scores suggest greater postural instability (Guskiewicz et al., 2001; Riemann, Guskiewicz, & Shields, 1999). While the conditions of the BESS tax the physiologic domains associated with postural stability, it lacks a truly objective scoring system which compromises its use across providers (Finnoff et al., 2009). Recently we sought to characterize BESS performance using the balance module of the C3 app. Using AP, ML, and TR movement data from the iPad, the iBESS

volume metric was created to serve as a biomechanical measure of balance. Figure 4 illustrates the iBESS volumes, combining ML, AP, and TR acceleration from the iPad for one representative participant across the six conditions. Figure 4 indicates an increase in movement acceleration, resulting in an increase in iBESS volumes as the task becomes more challenging by altering the stance from double support to single and tandem stances (condition 1 vs. 2 & 3 and 4 vs. 5 & 6) and changing the support surface from firm to foam (conditions 1–3 vs. 4–6).


Figure 4 — Representative plots from a single subject to show the three-dimensional traces combining linear and angular acceleration using the tablet’s built-in accelerometer and gyroscope during all six Balance Error Scoring System conditions. 95% ellipsoid volumes, iBESS volumes, are shown as an ellipse, illustrating the amount of movement increasing and thus increasing postural instability as the balance task becomes more difficult. AP = anterior-posterior; ML = medial-lateral; TR = trunk-rotation.

The value of tracking changes in postural stability in each of these directions was highlighted in a recent study in 29 collegiate athletes who, after sustaining a head injury, performed at baseline on the SOT 3–4 days following concussion. However, approximate entropy values measured in AP and ML time series remained abnormal relative to baseline values well after SOT scores returned to baseline (Cavanaugh et al., 2005; Cavanaugh et al., 2006). Thus, the ability for the iBESS volume to quantify postural sway in the AP, ML, and TR planes of movement may provide diagnostic value, and may improve the sensitivity of detecting residual deficits in postural control in injured athletes. While the BESS was not initially conceived to distinguish among the three domains of postural stability (visual, somatosensory, and vestibular) or to capture movement in specific planes of movement, it demonstrates clinical utility by minimizing or eliminating one or more of these afferent influences during performance of the six balance tasks. In quantifying postural sway in each of the three planes of movement during a given task, the iBESS volume metric may provide diagnostic value for clinicians in the detection of injury and certainly in the tracking and rehabilitation

of balance impairments postinjury. For example, a symmetrical increase in sway may demonstrate increased latency in the afferent system’s recognition of postural sway changes or a delay in the efferent response to perturbations. Conversely, asymmetrical changes, in which there is significant bias toward one direction of movement over the other, may indicate a unilateral vestibular hypofunction or unilateral impairment in the motor control mechanisms (either afferent input or efferent response) responsible for balance maintenance. Importantly, this biomechanical assessment of postural stability provides an objective metric to evaluate motor control as recommended by the NATA position statement on the management of concussion in sport (Broglio et al., 2014). This objective metric can serve as a method to evaluate injured athletes’ motor control, helping to inform the clinical team in return-to-play decisions and facilitating clinical hand-offs between the interdisciplinary team of providers. In addition, these detailed metrics may inform rehabilitation strategies used by clinicians. Studies are currently underway in injured and diseased populations that may provide a greater understanding of the clinical interpretation and implications of iBESS volume metric.


Understanding the Long-term Effects of Head Trauma Cumulative head trauma has been linked to changes in neurological function and development of degenerative neurological disorders affecting cognition, behavior, and motor skills (Goldstein et al., 2012; McKee et al., 2013). Yet, there are fundamental gaps in our ability to identify who is at risk and understand the progression of the long-term neural effects of repetitive subconcussive and concussive head trauma (McCrory, 2011; Smith, Johnson, & Stewart, 2013). To make individual decisions on continuing exposure and in anticipation of trials of potential disease modifying interventions, there is a pressing need for reliable biomarkers capable of objectively quantifying and tracking ongoing damage to the brain or predicting incipient clinical impairment in those exposed to repeated traumatic brain injury. We believe biomechanical measures will be critical in this process as changes in kinematic patterns of movement during the performance of a cognitive-motor task or balance task may serve as an inexpensive behavioral biomarker that could be used in conjunction with imaging measures or serum based biomarkers. Our preliminary data with over 400 fighters and matched controls have revealed specific MRI measures that correlate with total exposure (Bernick et al., 2013). While a link between total exposure to trauma and brain morphology has been described previously, our findings are unique in that we have demonstrated that these structural changes continue to decline over time with ongoing exposure (Bernick & Banks, 2013). While behavioral and cognitive symptoms are often cited as the initial clinical manifestations of CTE, there is evidence to suggest that midline brain structures are affected relatively early in the disease process. These effects may be reflected in changes in motor control, particularly balance which is often affected in retired boxers (Davie et al., 1995). There have not been any previous prospective studies of postural stability in those actively exposed to head trauma and there have not been any easy ways to measure balance in a portable and cost efficient approach. To bridge the gap between subjective clinical ratings and expensive and sophisticated biomechanical assessment methods related to measuring postural stability, we used the balance module of the C3 app. Figure 5 depicts balance testing (eyes closed, feet together while standing on a firm surface) for an age-matched control participant who had no known head trauma over the course of 12 months and an active boxer in the professional fighters brain health study over the course of one year. As depicted in Figure 5, the control participant had a relatively consistent level of postural stability over time as their volume score was 0.02 compared with 0.035 from baseline to year 1, respectively. Interestingly, at baseline the active boxer actually had a substantially higher level of variability in postural stability compared with the control participant. These preliminary data suggest that those individuals with previous exposure to head

trauma may already have difficulties in the integration of somatosensory, visual, and vestibular input to maintain a stable posture. When comparing baseline to year 1, the active boxer demonstrated more than a threefold increase in variability of postural stability. In terms of relating these changes to dose, this boxer reported active and regular training during the course of the year and had two sanctioned fights. During that time he did not report having been diagnosed with a concussion. However, results from MRI data for this participant indicate reduction in caudate and thalamus volumes bilaterally.

Opportunity for Kinesiology The concept of taking a multifactorial approach to concussion detection, prevention, and management is not novel (Guskiewicz, 2011). The composition of the typical kinesiology department in North America includes exercise physiologists, motor behavior specialists (including motor control, learning, and development), biomechanists, sport psychologists, and in some cases sport management and sport history. Collectively and individually, all of these specialties have the opportunity to advance our basic understanding of concussion and address specific questions that remain unanswered. For example, there is currently significant debate regarding when children should be allowed to participate in contact sports. We would contend that motor development specialists are uniquely qualified to address and study this issue with rigorous and objective outcomes that capture the potential effects of head impacts on the young athlete. There continues to be a fundamental gap in the actual measurement of impact and blast forces as a result of head impacts; those with specific training in biomechanics and kinetic modeling could advance the precision and reliability of measurement and the processing of the data. Currently, it is thought that there is no effective treatment that may counteract the long-term effects of CTE; yet exercise physiologists know that exercise impacts more than cardiovascular function and we have shown it to change brain function in other neurological populations such as Parkinson’s disease (Alberts, Linder, Penko, Lowe, & Phillips, 2011; Ridgel, Vitek, & Alberts, 2009) and those with stroke (Linder, Rosenfeldt, Rasanow, & Alberts, 2015). If there is to be an effective exercise-based intervention for CTE, we will need the expertise of exercise physiologists to design the intervention and gather the correct outcomes. It has been estimated that more than half of concussions in high school sports go unreported (Broglio et al., 2014), despite an unprecedented amount of education and media exposure regarding concussion. Those in exercise and sport psychology are uniquely positioned with their training and understanding of how education impacts food choice or exercise behaviors to drive effective educational campaigns. Finally, the majority of athletic training programs reside within kinesiology or exercise science departments. We have an obligation to include a level of breadth and depth into the cores of


Figure 5 — Representative iBESS volumes for a healthy control participant (upper plots) and active professional fighter (lower plots) with their eyes closed and feet together on a firm surface during year 1 and year 2 of the professional fighters brain health study. An increase in postural instability is noted in the fighter while the control participant did not have a change in balance over the same time period. AP = anterior-posterior; ML = medial-lateral; TR = trunk-rotation.

kinesiology in the education of athletic trainers. The field of kinesiology is not the panacea to preventing or understanding concussion. However, we are poised to provide leadership in solving this no longer silent epidemic. Acknowledgments Funding for this research supported by NIH R01NS073717-01; Department of Defense W81XWH-14-1-0532; Lincy Foundation; and Edward and Barbara Bell Family.

References Alberts, J.L., Koop, M.M., Schindler, D.D., Kana, D.E., Linder, S.M., Campbell, S., & Thota, A.K. (in press).Quantification of postural stability using accelerometer and gyroscopic measures. Journal of Athletic Training. Alberts, J.L., Linder, S.M., Penko, A.L., Lowe, M.J., & Phillips, M. (2011). It is not about the bike, it is about the pedaling: forced exercise and Parkinson’s disease. Exercise and Sport Sciences Reviews, 39(4), 177–186.

Arbuthnott, K., & Frank, J. (2000). Trail making test, part B as a measure of executive control: validation using a setswitching paradigm. Journal of Clinical and Experimental Neuropsychology, 22(4), 518–528. doi:10.1076/13803395(200008)22:4;1-0;FT518 Barkhoudarian, G., Hovda, D.A., & Giza, C.C. (2011). The molecular pathophysiology of concussive brain injury. Clinics in Sports Medicine, 30(1), 33–48. doi:10.1016/j. csm.2010.09.001 Baugh, C.M., Robbins, C.A., Stern, R.A., & McKee, A.C. (2014). Current understanding of chronic traumatic encephalopathy. Current Treatment Options in Neurology, 16(9), 306. doi:10.1007/s11940-014-0306-5 Bernick, C., & Banks, S. (2013). What boxing tells us about repetitive head trauma and the brain. Alzheimer’s Research & Therapy, 5(3), 23. doi:10.1186/alzrt177 Bernick, C., Banks, S., Phillips, M., Lowe, M., Shin, W., Obuchowski, N., . . .Modic, M. (2013). Professional fighters brain health study: rationale and methods. American Journal of Epidemiology, 178(2), 280–286. doi:10.1093/ aje/kws456


Broglio, S.P., Cantu, R.C., Gioia, G.A., Guskiewicz, K.M., Kutcher, J., Palm, M., . . .Valovich McLeod, T.C. (2014). National Athletic Trainers’ Association position statement: management of sport concussion. Journal of Athletic Training, 49(2), 245–265. doi:10.4085/1062-6050-49.1.07 Broglio, S.P., Zhu, W., Sopiarz, K., & Park, Y. (2009). Generalizability theory analysis of balance error scoring system reliability in healthy young adults. Journal of Athletic Training, 44(5), 497–502. doi:10.4085/1062-6050-44.5.497 Brown, H.J., Siegmund, G.P., Guskiewicz, K.M., Van Den Doel, K., Cretu, E., & Blouin, J.S. (2014). Development and validation of an objective balance error scoring system. Medicine and Science in Sports and Exercise, 46(8), 1610–1616. doi:10.1249/MSS.0000000000000263 Cantu, R.C. (2001). Posttraumatic retrograde and anterograde amnesia: pathophysiology and implications in grading and safe return to play. Journal of Athletic Training, 36(3), 244–248. Cavanaugh, J.T., Guskiewicz, K.M., Giuliani, C., Marshall, S., Mercer, V., & Stergiou, N. (2005). Detecting altered postural control after cerebral concussion in athletes with normal postural stability. British Journal of Sports Medicine, 39(11), 805–811. doi:10.1136/bjsm.2004.015909 Cavanaugh, J.T., Guskiewicz, K.M., Giuliani, C., Marshall, S., Mercer, V.S., & Stergiou, N. (2006). Recovery of postural control after cerebral concussion: new insights using approximate entropy. Journal of Athletic Training, 41(3), 305–313. Centers for Disease Control and Prevention. (2011). Nonfatal traumatic brain injuries related to sports and recreation activities among persons aged ≤19 years–United States, 2001-2009. MMWR. Morbidity and Mortality Weekly Report, 60(39), 1337–1342. Cicerone, K.D., & Azulay, J. (2002). Diagnostic utility of attention measures in postconcussion syndrome. The Clinical Neuropsychologist, 16(3), 280–289. doi:10.1076/ clin.16.3.280.13849 Collins, M.W., Grindel, S.H., Lovell, M.R., Dede, D.E., Moser, D.J., Phalin, B.R., . . .McKeag, D.B. (1999). Relationship between concussion and neuropsychological performance in college football players. Journal of the American Medical Association, 282(10), 964–970. doi:10.1001/ jama.282.10.964 Conidi, F.X. (2012). Sports-related concussion: the role of the headache specialist. Headache, 52(Suppl. 1), 15–21. doi:10.1111/j.1526-4610.2012.02138.x Corrigan, J.D., & Hinkeldey, N.S. (1987). Relationships between parts A and B of the Trail Making Test. Journal of Clinical Psychology, 43(4), 402–409. doi:10.1002/1097-4679(198707)43:43.0.CO;2-E Davie, C.A., Pirtosek, Z., Barker, G.J., Kingsley, D.P., Miller, P.H., & Lees, A.J. (1995). Magnetic resonance spectroscopic study of parkinsonism related to boxing. Journal of Neurology, Neurosurgery, and Psychiatry, 58(6), 688–691. doi:10.1136/jnnp.58.6.688 Demery, J.A., Larson, M.J., Dixit, N.K., Bauer, R.M., & Perlstein, W.M. (2010). Operating characteristics of executive functioning tests following traumatic brain injury. The

Clinical Neuropsychologist, 24(8), 1292–1308. doi:10.1 080/13854046.2010.528452 Faul, M.D., Wald, M.M., Xu, L., & Coronado, V.G. (2010). Traumatic Brain Injury in the United States: Emergency Department Visits, Hospitalizations and Deaths, 2002-2006. Atlanta, GA: Center for Disease Control and Prevention. Finnoff, J.T., Peterson, V.J., Hollman, J.H., & Smith, J. (2009). Intrarater and interrater reliability of the Balance Error Scoring System (BESS). PM & R, 1(1), 50–54. doi:10.1016/j.pmrj.2008.06.002 Giza, C.C., Kutcher, J.S., Ashwal, S., Barth, J., Getchius, T.S., Gioia, G.A., . . .Zafonte, R. (2013). Summary of evidence-based guideline update: evaluation and management of concussion in sports: report of the Guideline Development Subcommittee of the American Academy of Neurology. Neurology, 80, 2250–2257. doi:10.1212/ WNL.0b013e31828d57dd Goldstein, L.E., Fisher, A.M., Tagge, C.A., Zhang, X.L., Velisek, L., Sullivan, J.A., …McKee, A.C. (2012). Chronic traumatic encephalopathy in blast-exposed military veterans and a blast neurotrauma mouse model. Science Translational Medicine, 4(134):134ra60. Guskiewicz, K.M. (2011). Balance assessment in the management of sport-related concussion. Clinics in Sports Medicine, 30(1), 89-102. Guskiewicz, K.M., Ross, S.E., & Marshall, S.W. (2001). Postural stability and neuropsychological deficits after concussion in collegiate athletes. Journal of Athletic Training, 36(3), 263–273. Hargrave, D.D., Nupp, J.M., & Erickson, R.J. (2012). Two brief measures of executive function in the prediction of driving ability after acquired brain injury. Neuropsychological Rehabilitation, 22(4), 489–500. doi:10.1080/09602011. 2012.662333 Harmon, K.G., Drezner, J.A., Gammons, M., Guskiewicz, K.M., Halstead, M., Herring, S.A., . . .Roberts, W.O. (2013). American Medical Society for Sports Medicine position statement: concussion in sport. British Journal of Sports Medicine, 47(1), 15–26. doi:10.1136/bjsports-2012-091941 Harrison, E.A. (2014). The first concussion crisis: head injury and evidence in early American football. American Journal of Public Health, 104(5), 822–833. doi:10.2105/ AJPH.2013.301840 Hick, W.E. (1952). On the rate of gain of information. The Quarterly Journal of Experimental Psychology, 4, 11–26. doi:10.1080/17470215208416600 Hyman, R. (1953). Stimulus information as a determinant of reaction time. Journal of Experimental Psychology, 45(3), 188–196. doi:10.1037/h0056940 Lange, R.T., Iverson, G.L., Zakrzewski, M.J., Ethel-King, P.E., & Franzen, M.D. (2005). Interpreting the trail making test following traumatic brain injury: comparison of traditional time scores and derived indices. Journal of Clinical and Experimental Neuropsychology, 27(7), 897–906. doi:10.1080/1380339049091290 Langlois, J.A., Rutland-Brown, W., & Wald, M.M. (2006). The epidemiology and impact of traumatic brain injury: a brief overview. The Journal of Head Trauma Rehabilitation, 21(5), 375–378. doi:10.1097/00001199-200609000-00001


Linder, S.M., Rosenfeldt, A.B., Rasanow, M., & Alberts, J.L. (2015). Forced aerobic exercise enhances motor recovery following stroke: a case report. The American Journal of Occupational Therapy, 69. Mahurin, R.K., Velligan, D.I., Hazleton, B., Davis, J.M., Eckert, S., & Miller, A.L. (2006). Trail making test errors and executive function in schizophrenia and depression. The Clinical Neuropsychologist, 20(2), 271–288. doi:10.1080/13854040590947498 Martin, T.A., Hoffman, N.M., & Donders, J. (2003). Clinical utility of the trail making test ratio score. Applied Neuropsychology, 10(3), 163–169. doi:10.1207/ S15324826AN1003_05 Martland, H.S. (1928). Punch drunk. Journal of the American Medical Association, 91(15), 1103–1107. doi:10.1001/ jama.1928.02700150029009 McCrory, P. (2011). Future advances and areas of future focus in the treatment of sport-related concussion. Clinics in Sports Medicine, 30(1), 201-8.. McCrory, P., Meeuwisse, W., Aubry, M., Cantu, B., Dvorak, J., Echemendia, R.J., . . .Turner, M. (2013). Consensus statement on concussion in sport: the 4th International Conference on Concussion in Sport held in Zurich, November 2012. British Journal of Sports Medicine, 47(5), 250–258. doi:10.1136/bjsports-2013-092313 McKee, A.C., Cantu, R.C., Nowinski, C.J., Hedley-Whyte, E.T., Gavett, B.E., Budson, A.E., . . .Stern, R.A. (2009). Chronic traumatic encephalopathy in athletes: progressive tauopathy after repetitive head injury. Journal of Neuropathology and Experimental Neurology, 68(7), 709–735. doi:10.1097/NEN.0b013e3181a9d503 McKee, A.C., Stern, R.A., Nowinski, C.J., Stein, T.D., Alvarez, V.E., Daneshvar, D.H., . . .Cantu, R.C. (2013). The spectrum of disease in chronic traumatic encephalopathy. Brain, 136(Pt 1), 43–64. doi:10.1093/brain/aws307 Menon, D.K., Schwab, K., Wright, D.W., & Maas, A.I. (2010). Position statement: definition of traumatic brain injury. Archives of Physical Medicine and Rehabilitation, 91(11), 1637–1640. doi:10.1016/j.apmr.2010.05.017 Millspaugh, J.A. (1937). Dementia pugilistica. United States Naval Medical Bulletin, 35, 297–303. National Center for Injury Preventions and Control. (2003). Report to Congress on Mild Traumatic Brain Injury in the United States: Steps to Prevent a Serious Public Health Problem. Atlanta, GA: Centers for Disease Control and Prevention. National Institute of Neurological Disorders and Stroke. (2013). F1141 Recommended Neuropsychology Instruments Adult.docx [cited 2013 April 30]. Available from: http://www.commondataelements.ninds.nih.gov/epilepsy. aspx#tab=Data_Standards. O’Rourke, J.J., Beglinger, L.J., Smith, M.M., Mills, J., Moser, D.J., Rowe, K.C., . . .PREDICT-HD Investigators of the Huntington Study Group. (2011). The Trail Making Test in prodromal Huntington disease: contributions of disease progression to test performance. Journal of Clinical and Experimental Neuropsychology, 33(5), 567–579. doi:10. 1080/13803395.2010.541228

Ozinga, S.J., & Alberts, J.L. (2014). Quantification of postural stability in older adults using mobile technology. Experimental Brain Research, 232(12), 3861–3872. doi:10.1007/ s00221-014-4069-8 Parker, H.L. (1934). Traumatic encephalopathy (‘punch drunk’) of professional pugilists. Journal of Neurology and Psychopathology, 15(57), 20–28. doi:10.1136/jnnp. s1-15.57.20 Phillips, J., Stelmach, G.E., & Teasdale, N. (1989). Preliminary assessment of spatio-temporal control of handwriting in Parkinsonians. In R. Plamondon, C.Y. Suen, & M.L. Simner (Eds.), Computer Recognition and Human Production of Handwriting (pp. 317–331). Singapore: World Scientific Publishing.,. Powers, K.C., Kalmar, J.M., & Cinelli, M.E. (2014). Recovery of static stability following a concussion. Gait & Posture, 39(1), 611–614. doi:10.1016/j.gaitpost.2013.05.026 Ridgel, A.L., Vitek, J.L., & Alberts, J.L. (2009). Forced, not voluntary, exercise improves motor function in Parkinson’s disease patients. Neurorehabilitation and Neural Repair, 23(6), 600–608. doi:10.1177/1545968308328726 Riemann, B.L., & Guskiewicz, K.M. (2000). Effects of mild head injury on postural stability as measured through clinical balance testing. Journal of Athletic Training, 35(1), 19–25. Riemann, B.L., Guskiewicz, K.M., & Shields, E.W. (1999). Relationship between clinical and forceplate measures of postural stability. Journal of Sport Rehabilitation, 8, 71–82. Ruffolo, L.F., Guilmette, T.J., & Willis, G.W. (2000). Comparison of time and error rates on the trail making test among patients with head injuries, experimental malingerers, patients with suspect effort on testing, and normal controls. The Clinical Neuropsychologist, 14(2), 223–230. doi:10.1076/1385-4046(200005)14:2;1-Z;FT223 Schmidt, R.A. (1991). Processing Information and Making Decisions. Motor Learning and Performance (pp. 15–44). Champaign, IL: Human Kinetics Publishers, Inc. Scorza, K.A., Raleigh, M.F., & O’Connor, F.G. (2012). Current concepts in concussion: evaluation and management. American Family Physician, 85(2), 123–132. Seidler, R.D., Alberts, J.L., & Stelmach, G.E. (2001). Multijoint movement control in Parkinson’s disease. Experimental Brain Research, 140(3), 335–344. doi:10.1007/ s002210100829 Smith, D.H., Johnson, V.E., & Stewart, W. (2013). Chronic neuropathologies of single and repetitive TBI: substrates of dementia? Nature Reviews. Neurology, 9(4), 211–221. doi:10.1038/nrneurol.2013.29 Teasdale, N., Phillips, J., & Stelmach, G.E. (1990). Temporal movement control in patients with Parkinson’s disease. Journal of Neurology, Neurosurgery, and Psychiatry, 53(10), 862–868. doi:10.1136/jnnp.53.10.862 Teulings, H.L., & Stelmach, G.E. (1991). Force amplitude and force duration in Parkinsonian handwriting. In J. Requin & G.E. Stelmach (Eds.), Tutorials in Motor Neuroscience (pp. 133–145). Dordrecht, The Netherlands: Kluwer Academic Publishers.