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Collaborating to advance brain and motion performance and recovery assessmnets

Research and Development 

At SportGait, we focus on accurate measurements

SportGait Consortium

The mission of our consortium is to develop a reliable, scalable, and effective collaboration, enabling our partners to:

  • Evaluate best practices and assessment tools to measure brain wellness and sports performance
  • Evaluate assessment tools that screen for injury, risk factors and baseline functioning
  • Investigate and identify injury and recovery patterns
Our BKG™ Platform In Action
Scientific Criteria for selecting the assessments in the SportGait platform

Why Use Peer Review Process For Research Process

Science is defined as the accumulation of knowledge, and a critical part of that knowledge is the peer review process. For the scientific community, peer review is essential as it allows scholarly work to be reviewed by journal editors and reviewers who are experts in the same field (Kelly et al., 2014), and that level of scrutiny to vet the quality research has a long history (Spier, 2002). Peer-reviewed publications are one of the most trusted and relied upon forms of scientific communication, and the process helps ensure that the quality of the research and that the validity of the findings are at a high standard.

Specific Criteria Used In Our Selection Process
High Test-Retest reliability (the typical standard in assessment is r-values of approximately 0.7 or higher)
  • Reliability is the instrument’s capacity to produce consistent results over time.
  • Without a reliable measure, it is impossible to determine if the change in score is due to the patient or due to the inconsistency of the instrument
Established validity capturing the multi-dimensional sequalae of concussion.
  • Validity is the scientific verification that the instrument measures what it claims to measure. For concussion that means properly measuring multiple domains.
  • Failure to properly assess each domain will result in an inaccurate assessment.
Large Stratified normative data for both age and gender.
  • Normative data is a representative comparison group that allows one to interpret the meaning of a score relative to others.
  • Good norms allow test scores to be interpreted even in the absence of baseline data and they help us understand functioning relative to others.
Selecting tests that minimize practice effects.
  • Practice effects are when test scores improve simply from repeatedly taking a test.
  • Good assessments should not show practice effects, as this otherwise creates a confound that interferes with knowing when a patient is actually recovering versus improving due to practice effects.
Selecting tests that minimize ceiling effects.
  • Ceiling effects are when it’s too easy for people to score at or near the highest score on a test and this is a common occurrence for tests that show practice effects.
  • Ceiling effects can mask actual impairments and recovery because the scores would not be sensitive to changes when the test is too easy.
Including tests that assess multiple domains to capture the multi-dimensional nature of concussion.
  • Concussion sequelae are multifactorial and include cognitive consequences (most typically executive functioning when dealing with mTBI), behavioral functioning (e.g., gait, balance), and symptom reporting. Other relevant domains include affective functioning and ocular-motor functioning.
  • Failure to assess multiple domains will necessarily result in an inaccurate (incomplete) assessment.
View References

Assessments Selected for the SportGait Platform

The SportGait Battery is defined by each of the component parts (reviewed separately).

Neurocognitive Assessment
What did we select?

The Conners Continuous Performance Test (Conners CPT) is an interactive cognitive test measuring processing speed, variability in attention, impulsivity, distractibility, and rapid decision making, all of which are important measures when evaluating concussion, cognitive decline and other indicators of neurologic impairment. The earliest CPT was validated over a half century ago to identify brain-injured patients (Beck et al., 1956). Since then, the Conners CPT 3 (and its predecessors) has been established as a well-validated tool that has been used by neuropsychologists and medical professionals for decades to assist in diagnosing neurocognitive (most typically attention) deficits (e.g., Conners, 2014; Frazier et al., 2004).

There have been three ostensibly similar versions of the Conners CPT developed over the years, and the previous and current version of the test (i.e., Conners CPT, Conners CPT 2, and Conners CPT 3) provide information regarding such cognitive factors as the speed of mental processing, attention–inattention, response inhibition, variability in responses, and errors of omission and commission. Moreover, continuous performance tasks produce internal reliability coefficients ranging from .74 to .92 for each of its subscales (Riccio et al., 2002) and speed of mental processing appears to be an especially robust component of the Conners CPT 3 (e.g., Shaked et al., 2019).

There is considerable published research on the use of neurocognitive measures like the Conners CPT 3 to assess attention, processing speed, and executive control that demonstrate sensitivity to the effects of concussion (e.g., Moore et al., 2017). For example, the Conners CPT 3 (and go-no-go or continuous performance tasks more broadly) has been shown to predict cognitive impairment in a pediatric sample with mTBI (Allen et al., 2010); it captures remediation in adolescents with TBI (Galbiati et al., 2009) and is associated with improved scores as a function of time following mild head trauma (Naunheim et al., 2008). Thus, the data generated from the Conners CPT 3 are not only reliable but appear to predict important outcomes related to the experience of concussions.

Why was the CPT selected for our platform?

The broader research literature has demonstrated the Conners CPT 3’s strong psychometric characteristics:

Test-Retest Reliability:

  • 0.70 to 0.90 (Hui-Chen et al., 2009)
  • 0.92 to 0.94 internal reliability for clinical and nonclinical samples (Conners et al., 2018; Multi-Health Systems Inc., n.d.)

Validity:

  • Measures the sequalae of concussion (e.g., Allen et al., 2010; Galbiati et al., 2009; Moore et al., 2017; Naunheim et al., 2008) with large effect sizes even when assessed an average of four weeks after the injury (Lecci et al., 2019; Lecci et al., 2021)

Normative Data: stratified by age and gender (Conners et al., 2018; Multi-Health Systems Inc., n.d.)

  • Age Range:
    • Desktop: 5 years to 85 years (Conners et al., 2018; Multi-Health Systems Inc., n.d.)
    • Mobile: 8 years up to 82 years
  • Gender: Male, female, and nonbinary

Practice and Ceiling Effects are not observed with the Conners CPT 3 (Conners et al., 2018; Multi-Health Systems Inc., n.d.). The built-in practice trial minimizes practice effects.

View Neurocognitive Peer-Reviewed Papers

Neurobehavioral Assessments
What did we select?

The National Institutes of Health (NIH) 4-Meter Gait Test  is a gait test commonly used to assess walking speed over a short distance. Gait involves dynamic postural control, where the individual changes their base of support throughout the movement. Concussed individuals appear to adopt a conservative gait strategy to maintain stability. Specifically, individuals with concussions have shorter stride length, whereas walking, as well as slower gait velocity relative to healthy controls, and gait slowing has been reported to persist for 28 days post-concussion (Cantena et al., 2007a, b, 2009; Parker et al., 2008). Gait slowing effects have been shown to be remarkably reliable and durable over time (Parker et al., 2006). Additionally, slower walking speeds and shorter stride lengths are evident in concussed participants evaluated 1 year after injury (Chou et al., 2004)

The 4-meter gait test has proven to be remarkably reliable, consistently yields some of the highest reliability coefficients, with inter- class correlation coefficients (ICC) values of .96 to .98 for adults (Peters et al., 2013) and ICCs of .81 for adolescents (Alsalaheen, 2014). While it only measures one domain, gait speed, it is readily and demonstrably indicative of the impairment associated with concussion and mTBI. As a result, when paired with other reliable neurocognitive measures, the 4-meter gait test could prove to be an invaluable part of an updated, more accurate concussion protocol.

Why we selected it

The research has demonstrated the NIH 4-meter test’s strong characteristics:

Test-Retest Reliability: 0.96 to 0.98 for adults (Peters et al., 2013); 0.81 for adolescents (Alsalaheen, 2014)

Validity: Gait slowing is predictive of concussion outcomes (e.g., Chou et al., 2004; Lecci et al., 2020; Parker et al., 2008)

Normative Data: stratified by age and gender (National Institutes of Health)

  • Age range: 5-85 years from NIH Toolbox (Kallen et al., 2012)
  • Gender: Male and female

No documented ceiling or practice effects with repeat testing.

View Peer-Reviewed Gait Papers
What did we select?

The Balance Error Scoring System (BESS)  is an assessment to measure static balance and postural control. Postural control is an individual’s amount of sway, with greater neuromotor control associated with less postural sway. Balance and gait stability that supports postural control is an especially useful two-pronged approach for capturing the neurobehavioral consequences of a concussion, as well as providing the needed sensitivity to inform return to play decisions (Howell et al., 2016).

The full BESS has been shown to have reliability ranging from moderate to good (Chang et al., 2014), with reliability coefficients of .7 achieved in a sample of children and adolescents, when using separate norms for men and women (Mcleod et al., 2006; note: test–retest values of more than .90 can be obtained when taking the average of multiple BESS administrations in one day; Broglio et al., 2009.) BESS scores typically return to baseline 3–5 days post-concussion (McCrea et al., 2004), thereby providing some information within the initial days post-concussion. Additionally, best practice guidelines recommend balance testing as a critical component in the clinical examination of a concussion (Harmon et al., 2013; McCrory et al., 2013).

The computerized BESS, which automates and facilitates the delivery and timing of the full BESS, was more recently developed to provide a more objective and quantitative method of assessing balance errors. Comparisons of the computerized BESS to standard scoring procedures have shown that computerized BESS scoring is more sensitive in its measurements of performance and postural stability than scores calculated from traditional motion capture data alone (Alberts et al., 2014). Additionally, inter-rater reliability between computerized and standard human-calculated BESS scores have been found to range from fair to excellent (0.44-0.99) across the six main stances (Caccese & Kaminski, 2014; cf. Houston et al., 2019). With respect to validity, the computerized scores are generally equivalent to human-rated scores across balance conditions (Glass et al., 2019).

Why we selected it

The following research has demonstrated strong characteristics of the BESS:

Test-Retest Reliability: 0.70-.90 in children and adults (e.g., Mcleod et al., 2006)

Validity: Critical in measuring the sequalae of concussion (e.g., Harmon et al., 2013; McCrory et al., 2013) and has predictive validity concussion symptoms (Lecci et all., 2020).

Normative Data: stratified by age and gender (e.g., Iverson & Koehle, 2013).

  • Age range: 5 to 23 stratified by age and gender, age 24+ stratified by age (e.g., Iverson & Koehle, 2013).
  • Gender: Male and female

No documented practice or ceiling effects.

View Peer-Reviewed BESS Papers

The BioKinetoGraph
What we selected

The BioKinetoGraph

The broad research literature clearly indicates that gait speed assessed using accelerometers has superior sensitivity to dysfunction relative to manually collected gait speed data (Maggio et al., 2016). Moreover, accelerometers can produce reliable outputs across a range of clinical populations (Byun et al., 2016; Fujiwara et al., 2020; Henrikson et al., 2004; Kluge et al., Kobsar et al., 2016; Moore et al., 2017; Werner et al., 2020). Recent research introduced a noninvasive, continuous triaxial accelerometer data collection to create a visual depiction of gait, referred to as a BioKinetoGraph (BKG). Similar to the 12-lead electrocardiogram, BKG waveforms are related to specific neuromotor gait cycle events, including cadence, foot-strike, push-off, double stance time, and swing phase. Raw data are combined to generate BKG waveforms as gravitational accelerations over time. Tracings are used to obtain range of motion, amplitudes, and timing intervals for the various components of the waveform that relate directly to the gait cycle, and the adopted method is similar to that employed in other published studies (Godfrey et al., 2015). Sixteen variables organized into four conceptual domains: power (amplitudes), stride (timing intervals), balance (stability), and symmetry (regularity) are discussed, in part because these variables correspond to similar variables in the gait literature and were recently validated (Lecci et al., 2023).

This recent study (Lecci et al., 2023) introduced a method for analyzing raw accelerometer data (inertial measurement units) to reconstruct the gait cycle using BioKinetoGraph (BKG). They examined the association between BKG and NIH 4-meter gait and compare BKG to other neurobehavioral measures for predicting concussion symptoms. Additionally, they examined whether footwear and walking surface influence gait (BKG) and evaluate test-retest reliability. In Study 1, a within-subjects design with 60 participants illustrated the effects of footwear (shoes/no-shoes) and walking surface (tile floor/grass) on BKG data, indicating the need to standardize both variables when measuring gait. Study 1 also established the BKG test-retest reliability (Pearson rs) for the no shoes/ tile surface condition ranging from .72-.91 (mean = .80).

In Study 2, a cross-sectional, cohort design of 1,008 participants established the BKG’s correlation with NIH 4-m gait, shows the BKG to significantly predict CDC concussion symptom endorsement, and the BKG outperforms (2-3 fold) BESS and NIH 4-meter gait when predicting CDC concussion symptoms. A similar study also introduced a clinical analysis of recovery using quantitative data from the four domains (power, stride, balance, and symmetry). In addition, it introduced a clinical analysis of qualitative data in the form of stabilogram and displacement graphs (Lecci et al., 2023).

Gait assessments should be standardized for footwear and especially walking surface. When standardized (no shoes/hard surface) BKG results in strong test-retest reliability. BKG variables are strongly related to NIH 4-m gait and are superior to standard measures of gait speed and balance when predicting concussion symptoms and offering additional information when predicting the sequalae of concussion.

Why we selected it

Summary of research demonstrating strong characteristics of the BKG:

Test-Retest Reliability: Gait accelerometers produce reliable outputs across a range of clinical populations (e.g., Byun et al., 2016; Fujiwara et al., 2020; Henrikson et al., 2004; Kluge et al., Kobsar et al., 2016; Moore et al., 2017; Werner et al., 2020).

  • 0.72 to 0.91 (mean = 0.80) for no shoes (Lecci, Dugan, et al., 2023)
  • mean = 0.79 for BKG mobile across four domains (power, stride, balance, and symmetry) in sample of over 4,000 subjects (Lecci, Williams et al., 2023).

Validity: Gait speed using accelerometers predicts mTBI (Dever et al., 2022) and the BKG gait specifically has strong predictive validity in measuring concussion outcomes (Lecci, Dugan et al., 2023)

Normative Data: stratified by age and gender

  • BKG mobile norms established by Lecci et al. (2023)
  • Age range: 5 to 78 years old
  • Gender: Male and female

No documented ceiling or practice effects.

View Peer-Reviewed BKG Gait Papers

How The SportGait Battery Works Together

Published research shows that SportGait’s battery (including neurocognitive and neurobehavioral measures) combine to achieve accuracies of 91 to 84.4% (AUCs of 1.0 and .947, respectively) when predicting the return to play decision making of a pediatric neurologist in a sample of 111 cases (Keith et al., 2019)

Published research shows the additive benefit of the different components of the SportGait Battery. The neurocognitive test alone (CCPT 3) accounts for 21.5% of the variance (d = 1.05) in symptoms, and the neurobehavioral measures (BESS and NIH 4-Meter Gait) account for an additional 11.5% variance (explaining 18.6% variance, d = .96, when entered first (Lecci et al., 2019; Lecci et al., 2021). These effect sizes are considered large to very large and reflect a marked increase (two to five fold higher) in predictive validity relative to existing measures commonly used in concussion assessments. Neurobehavioral and neurocognitive domains each provide unique and substantial information with respect to symptom endorsement.

References

  • 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. https://doi.org/10.1007/s40817-019-00075-1
  • Lecci, L., Freund, C.T., Ayearst, L.E., et al. (2021). Validating a short Conners’ CPT3 as a screener: Predicting self-reported CDC concussion symptoms in children, adolescents, and adults. Journal of Pediatric Neuropsychology, 7, 169–181. https://doi.org/10.1007/s40817-021-00107-9
  • Lecci, L., Williams, M., Taravath, S., et al. (2019). Validation of a concussion screening battery for use in medical settings: Predicting Centers for Disease Control concussion symptoms in children and adolescents. Archives of Clinical Neuropsychology, 35(3), 265-274. https://doi.org/10.1093/arclin/acz041
Sportgait Research Consortium

Peer-Reviewed Research Studies

Validation of a Brief Cognitive Assessment for Concussion Delivered on a Mobile Device

Publication:
Journal of Pediatric Neuropsychology
Screener:
Cognitive
Published:
March 14, 2024
View Publication
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Introduction and Clinical Analyses of an Accelerometer-Based Mobile Gait Assessment to Evaluate Neuromotor Sequelae of Concussion in Adolescents and Adults

Publication:
Journal of Pediatric Neuropsychology
Screener:
BKG Gait
Published:
May 04, 2023
View Publication
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Validation of an accelerometer-based gait assessment: Establishing test-retest reliability, convergent validity, and predictive validity for concussion symptom endorsement

Publication:
Journal of Concussion
Screener:
BKG Gait
Published:
Feb 20, 2023
View Publication
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A Science-Driven Model for Concussion Evaluation: Overview of the SportGait Platform

Publication:
The Score

Screener:
Clinic
Published:
Jan 6, 2023
View Publication
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Consideration of a Neuromotor Concussion Subtype

Publication:
International Journal on Neuropsychology and Behavioural Sciences
Screener:
Clinic
Published:
June 29, 2021
View Publication
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Validating a Short Conners CPT 3 as a Screener: Predicting Self-reported CDC Concussion Symptoms in Children, Adolescents, and Adults

Publication:
Journal of Pediatric Neuropsychology
Screener:
Cognitive
Published:
May 7, 2021
View Publication
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Baseline Concussion Testing Increases Agreement With Favorable Concussion Safety Decisions in Hypothetical Scenarios

Publication:
Health Education & Behavior
Screener:
Clinic
Published:
Apr 14, 2021
View Publication
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A Clinician’s Guide to Machine Learning in Neuropsychological Research and Practice

Publication:
Journal of Pediatric Neuropsychology
Screener:
Clinic
Published:
Nov 29, 2019
View Publication
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Validation of a Concussion Screening Battery for Use in Medical Settings

Publication:
Archives of Clinical Neuropsychology
Screener:
Clinic
Published:
Sep 25, 2019
View Publication
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Concussion Management in Urgent Care: A Primer for Implementation

Publication:
The Journal of Urgent Care Medicine
Screener:
Clinic
Published:
June 1, 2018
View Publication
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Discover

Why Choose SportGait Consortium?

Our consortium offers multiple benefits enabling you to receive acknowledgment for your published research papers. Your valuable contributions improve the science supporting concussion assessment and care.


We accomplish these benefits through the collaboration of Medical Providers, Academia, and Innovators of Concussion

  • Collaborations with Experts

    SportGait Consortium gives you an opportunity to collaborate with leading academia, innovators, neuroscientists, mathematicians, medical providers, and others in the field of concussion.

  • Access to Advanced Technology

    We give you access to advanced technology such as IoT, gyroscopes, accelerometers, and others to identify and detect information regarding measurements and develop innovative concussion assessment tools and technologies.

  • Purposeful Data Collection and Analysis

    SportGait facilitates purposeful collection and analysis of different data types through various means, including artificial intelligence (AI), big data, data science libraries, data cleansing, traditional statistical modeling, and machine learning.

  • Accomplish Process Standardization

    With SportGait, you can accomplish research process standardization. In addition, it enables you to boost data sharing through IRB approval, findings validation and dissemination, alpha and beta testing to conduct market studies.

Secure. Private. Compliant.

FDA Listed Medical Devices

CPT Cognitive is currently registered under 21 CFR Part 882. 1470- 510(K) Exempt. Class II. BKG™ Gait is currently registered under 21 CFR 890. 1600 - 510(k) exempt, Class I.

Additionally, SportGait completes regular external audits with AICPA SOC 2 Type II reports, also including legal requirements with HIPAA, HITECH, and applicable state laws.

Get in touch with us now to explore how SportGait can work for you!

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