The quantitative assessment of the sequalae of concussion is of increasing importance given the prevalence of concussions, also referred to as mild traumatic brain injures (mTBIs), and our evolving understanding of the adverse consequences associated with their clinical mismanagement (e.g., Daneshvar et al., 2011). For example, when individuals return to activities such as sports when they have not fully recovered, their risk of subsequent concussive and orthopedic injury is markedly elevated (e.g., Jildeh, Meta, Young, Page, Nwachukwu, Westermann, & Okoroha, 2021; Nyberg, Mossberg, Lysholm, & Tegner, 2015). This highlights the need to properly assess concussions and their sequelae.
Over the last two decades, a large number of measures have been introduced into the concussion assessment market, many of which have little or no empirical data to support their use. Obviously, in the absence of scientifically vetted research, measures should not be adopted. However, even among those measures that have been widely adopted (e.g., ImPACT), a number of psychometric shortcomings have been consistently articulated in the literature (e.g., Brollio et al., 2007; Farnsworth et al., 2017). In fact, the nature and extent of the psychometric problems with the most widely used measures is such that researchers have concluded that they should not be used (e.g., Houston et al., 2020). This means that health providers who are involved with concussion assessments have limited options for properly quantifying the nature of a concussion and return-to-play decisions.
A recently suggested alternative approach is to use the scientific literature to: 1) Identify the common sequalae of concussion that should be targeted for assessment, 2) Identify existing measures that already assess those domains and have established norms, reliability, and validity, and 3) Incorporate those measures into a battery in order to assess their combined predictive utility (Lecci et al., 2018). This three-step, science-first, approach was adopted when developing the SportGait platform.
Building on decades of research, a 2017 consensus statement of the Concussion in Sport Group (CISG) recommended that both neurocognitive and neuromotor (gait and balance) measures be included to capture the common sequelae of concussion (McCrory et al., 2017). With respect to the neurocognitive domain, the extant literature suggests that the neurocognitive symptoms of mTBI can be captured by measures of executive control and processing speed (Dehaan et al, 2007; Halterman et al, 2005; Paré et al, 2009). Among the more longstanding measures of executive control and processing speed are continuous performance tests (CPTs) that follow the non-X paradigm. For example, the Conners CPT requires individuals to respond when a letter other than an “x” appears on the screen, and to withhold responding when the letter “x” appears on the screen. Although the Conners CPT has been widely used to assist in the diagnosis of attention deficit disorder, this type of task was first validated over a half century ago as a means of identifying brain injured patients (Beck et al., 1956). Subsequent research showed that CPT scores are sensitive to the effects of recently concussed individuals (Keightley et al., 2010), predict post-concussion remediation (Galbiati et al., 2009) and grey matter concentrations in those with concussion histories (Gale et al., 2005), and scores show improvement over several hours post-concussion, even when brain imaging fails to show any effects (Naunheim et al., 2008). More recently, data suggest that both the full length Conners CPT, as well as a 7.5-minute version, can significantly predict concussion outcomes; achieving effect sizes characterized as large to very large (Lecci et al., 2020; 2021). Importantly, the Conners CPT3 results in effect sizes that are 5-6-fold larger than those of other neurocognitive measures, and it achieves these effects sizes despite the assessments occurring an average of 3 to 4 weeks after the concussion has occurred (Lecci et al., 2020; 2021). Thus, the Conners CPT3 was selected as the neurocognitive component in the platform.
The platform also includes measures of balance (the Balance Error Scoring System or BESS) and gait (the National Institutes of Health 4-meter gait), both of which have been widely used and have excellent norms and established reliability (e.g., Peters et al., 2013). Moreover, these measures have been shown to be psychometrically sound and predict concussion outcomes (e.g., McCrea et al., 2004; Parker et al., 2006). More recently, when assessed along with the Conners CPT3 within the same platform, the neurobehavioral measures were found to predict substantial and unique variance in concussion outcomes (Lecci et al., 2020).
The platform also includes an automated gait assessment using accelerometers, that create a gait waveform known as the BioKinetoGraph (BKG). The BKG quantifies gait variables organized under the broad domains of power (amplitudes), stability (balance), stride (speed within segments of the gait cycle), and symmetry. BKG variables can be reliably assessed and they converge with other validated measures of gait (Lecci, Dugan, Zeiger, et al., 2022). The BKG also predicts concussion outcomes such as the return-to-play decisions of a pediatric neurologist (Keith et al., 2019), and the endorsement of concussion symptoms; with BKG accelerometer data out predicting the NIH 4-meter gait test and the BESS combined (Lecci, Dugan, Zeiger, et al., 2022).
More recently, research has shown that the CPT3 and BKG can be reliably collected using an App on mobile devices (e.g., Lecci, Dugan & Woodley, 2022; Lecci, Williams, Dugan et al., 2022). The SportGait Mobile app is relatively brief, with a duration of under 10 minutes to assess symptoms, neurocognitive and neurobehavioral functioning, and mood. Because mobile devices are widely available, this allows for remote data collection without the use of cumbersome or expensive equipment. The potential to collect additional data either in between medical visits or before an individual can be scheduled for a more comprehensive evaluation, can be critical to better elucidating the nature and course of a concussive injury.
Importantly, the SportGait platform was developed to be extensible, as opposed to fixed. The rationale for this approach is that as science and technology evolve, the platform can likewise change to accommodate new findings. As an example, recent research suggests that neuro-ophthalmic abnormalities, including the pupillary light reflex, accommodation, convergence, stereoacuity, and saccades, can occur secondary to experiencing a concussion (e.g., Pillai & Gittinger, 2017). This resulted in the addition of ocular functioning to platform. Similarly, research suggests that mood symptoms are common sequelae of concussion (Sandel, Reynolds, Cohen, et al., 2017), and may even define a specific concussion subtype (Lumba-Brown et al., 2020). As a result, an assessment of mood is also included in the platform. The flexibility of the platform to include the assessment of other domains is critical to keeping the tool at the forefront of science, and research continues to investigate whether additional domains can add significantly to our understanding of concussion.
The platform has been used to help inform diagnostic and return-to-play decisions by a wide range of health practitioners including primary care and ED doctors, athletic trainers, and the like. It has also been used for preseason and baseline testing, and as a screener to identify individuals who might be better served with a more comprehensive neuropsychological evaluation following a suspected injury. Each test in the platform allows for normative comparisons (i.e., how one’s scores compare to those of a similar age and gender) as well as self-referenced comparisons, whereby scores are compared to previously collected data points (e.g., baseline data or previous test scores collected during a return-to-play protocol). The platform is also fully compliant with HIPAA and cybersecurity standards (i.e., passing SOC-2 Type audits for use in hospital systems), thereby making it functional in large medical settings, as well as for individual practitioners.
Research continues to define, refine, and challenge our best methods for assessing the consequences of concussion. Having a flexible platform that can accommodate the emerging research literature is critical to meeting that challenge.
References
Beck, L.H., Bransome, E.D. Jr., Mirsky, A.F., Rosvold, H.E., Sarason, I. (1956). A continuous performance test of brain damage. Journal of Consulting and Clinical Psychology, 20(5), 343‐350. doi:10.1037/h0043220
Broglio, S. P., Ferrara, M. S., Macciocchi, S. N., Baumgartner, T. A., & Elliott, R. (2007). Test–retest reliability of computerized concussion assessment programs. Journal of Athletic Training, 42(4), 509–514.
Daneshvar, D. H., Nowinski, C. J., McKee, A. C., & Cantu, R. C. (2011). The epidemiology of sport-related concussion. Clinics in Sports Medicine, 30(1), 1–vii. doi.org/10.1016/j.csm.2010.08.006
Dehaan, A., Halterman, C., Langan, J., Drew, A. S., Osternig, L. R., Chou, L.S., & Donkelaar,
P. V. (2007). Cancelling planned actions following mild traumatic brain injury. Neuropsychologia, 45(2), 406–411. doi:10.1016/j.neuropsychologia.2006.06.008
Farnsworth, J. L., Dargo, L., Ragan, B. G., & Kang, M. (2017). Reliability of computerized neurocognitive tests for concussion assessment: A meta-analysis. Journal of Athletic Training, 52(9), 826–833. https://doi.org/10.4085/1062-6050-52.6.03.
Galbiati, S., Recla, M., Pastore, V., Liscio, M., Bardoni, A., Castelli, E., & Strazzer, S. (2009). Attention remediation following traumatic brain injury in childhood and adolescence. Neuropsychology, 23(1), 40–49. https://doi.org/10.1037/a0013409.
Gale, S. D., Baxter, L., Roundy, N., & Johnson, S. C. (2005). Traumatic brain injury and grey matter concentration: A preliminary voxel based morphometry study. Journal of Neurology, Neurosurgery and Psychiatry, 76(7), 984–988. doi.org/10.1136/jnnp.2004.036210.
Halterman, C. I., Langan, J., Drew, A., Rodriguez, E., Osternig, L. R., Chou, L.S., & Donkelaar, P. V. (2005). Tracking the recovery of visuospatial attention deficits in mild traumatic brain injury. Brain, 129(3), 747–753. doi:10.1093/brain/awh705
Houston, M. N., Van Pelt, K. L., D’Lauro, C., Brodeur, R. M., Campbell, D. E., McGinty, G. T., et al. (2020). Test-Retest reliability of concussion baseline assessments in United States Service Academy Cadets: A report from the National Collegiate Athletic Association (NCAA) - Department of Defense (DoD) CARE Consortium. Journal of the International Neuropsychological Society, 16, 1–12. https://doi.org/10.1017/S1355617720000594.
Jildeh, T. R., Meta, F., Young, J., Page, B., Nwachukwu, B., Westermann, R. W., & Okoroha, K. R. (2021). Concussion is associated with increased odds of acute lower-extremity musculoskeletal injury among National Basketball Association players. Arthroscopy, Sports Medicine, and Rehabilitation, 3(1), e219–e225. doi.org/10.1016/j.asmr.2020.09.014
Keightley, M. L., Yule, A., Garland, K., Reed, N., McAuliffe, J., Garton, J., Green, S., & Taha, T. (2010). Sports-related mild traumatic brain injury in female youths. BMJ Case Reports, 2010. https://doi.org/10.1136/bcr.09.2009.2309.
Keith, J., Williams, M., Taravath, S., & Lecci, L. (2019). A clinician’s guide to Machine Learning in neuropsychological research and practice. Journal of Pediatric Neuropsychology, 5 (4), 177–187.
Lecci, L., Freund, T. C., Ayearst, L., Sitarenios, G., Pickett, B., Crews, F. S., Dugan, K., Lange, L., Clark, A., Linz, T., Taravath, S., Williams, M., & Keith, J. (2021). Validating a short Conners CPT 3 as a Screener: Predicting self-reported CDC concussion symptoms in children, adolescents, and adults. Journal of Pediatric Neuropsychology, 7, 169-181. doi.org/10.1007/s40817-021-00107-9
Lecci, L., Hollander, J., Key, D., & Keith, J. (2018). Concussion in urgent care: A primer for implementation. The Journal of Urgent Care Medicine, June.
Lecci, L., Dugan, K., & Woodley, M. M. (2022). Validation of a brief cognitive assessment for concussion delivered on a mobile device. Under Review.
Lecci, L., Dugan, K., Zeiger, K., Keith, J., Taravath, S., Tseh, W., Pickett, B., & Williams, M. (2022). Validation of an accelerometer-based gait assessment: Establishing test-retest reliability, convergent validity, and predictive validity for concussion symptom endorsement. Under Review.
Lecci, L., Williams, M., Dugan, K., Laney, S., Bruin, C., Cummings, P., Keith, J. (2022). Neuromotor functioning and concussion in adolescents: Introduction and analyses of a mobile gait assessment. Under Review.
Lecci, L., Williams, M., Taravath, S., Frank, H. G., Dugan, K., Page, G. R., & Keith, J. R. (2020). Validation of a concussion screening battery for use in medical settings. Archives of Clinical Neuropsychology, 35, 265-274. https://doi.org/10.1093/arclin/acz041
Lumba-Brown, A., Teramoto, M., Bloom, O. J., Brody, D., Chesnutt, J., Clugston, J. R., Collins, M., Gioia, G., Kontos, A., Lal, A., Sills, A., & Ghajar, J. (2020). Concussion Guidelines Step 2: Evidence for Subtype Classification. Neurosurgery, 86(1), 2–13. https://doi.org/10.1093/neuros/nyz332
McCrea, M., Guskiewicz, K. M., Marshall, S. W., Barr, W., Randolph, C., Cantu, R. C. et al. (2004). Acute effects and recovery time following concussion in collegiate football players. Sports Medicine Update, 38, 369–371.
Naunheim, R. S., Matero, D., & Fucetola, R. (2008). Assessment of patients with mild concussion in the emergency department. The Journal of Head Trauma Rehabilitation, 23(2), 116–122. doi.org/10.1097/01.htr.0000314530.30401.70.
Nyberg, G., Mossberg, K. H., Lysholm, J., & Tegner, Y. (2015). Subsequent traumatic injuries after a concussion in elite ice hockey: A study over 28 years. Current Research: Concussion, 2(3), 109-112.
Paré, N., Rabin, L. A., Fogel, J., & Pépin, M. (2009). Mild traumatic brain injury and its sequelae: Characterisation of divided attention deficits. Neuropsychological Rehabilitation, 19(1), 110–137. doi: 10.1080/09602010802106486
Parker TM, Osternig LR, van Donkelaar P, Chou LS. (2006). Gait stability after a concussion. Med Sci Sports Exerc.;38(6):1031-1040.
Peters DM, Fritz SL, Krotish DE. (2013). Assessing the reliability and validity of a shorter walk test compared with the 10-meter walk test for measurements of gait speed in healthy, older adults. J Geriatr Phys Ther;36(1):24-30.
Pillai, C., & Gittinger, J. W. (2017). Vision testing in the evaluation of concussion. Seminars in Ophthalmology, 32(1): 144–152. DOI: 10.1080/08820538.2016.1228412
Sandel, N., Reynolds, E., Cohen, P. E., Gillie, B. L., & Kontos, A. P. (2017). Anxiety and Mood Clinical Profile following Sport-related Concussion: From Risk Factors to Treatment. Sport, exercise, and performance psychology, 6(3), 304–323. https://doi.org/10.1037/spy0000098