Introduction

Concussion, also referred to as mild traumatic brain injury (mTBI), is a physiological disruption in the normal functioning of the brain occurring as a result of a traumatic impact (American Congress of Rehabilitation Medicine [ACRM], 1993; see also Wood et al., 2019). Most often, concussion results from the head, face, neck, or other body part being struck or striking an object, causing rapid acceleration/deceleration of the brain (ACRM, 1993). Sports-related concussions (SRCs) are prevalent among athletes, especially those participating in contact sports (ACRM, 1993; Zuckerman et al., 2015).

SRCs can affect physical, emotional, and cognitive functioning, especially within the first few weeks following the injury (Iverson et al., 2017). Commonly reported symptoms include nausea, blurred vision, headaches, slurred speech, unsteady gait, dizziness, incoordination, and imbalance (ACRM, 1993; Catena et al., 2009; Gagnon et al., 2004). Clinical recovery from concussion refers to returning to normal activities following injury, including a return to normal neuromotor and neurocognitive functioning (Iverson et al., 2017). Although the literature previously suggested that concussion symptoms often resolve within 14 days of injury (Catena et al., 2009; Iverson et al., 2017), it is now recognized that sequelae can persist well beyond 14 days, and critically, even after a patient is no longer reporting symptoms (Catena et al., 2009; Chou et al., 2004; Parker et al., 2006). In fact, in a recent large-scale study (NCAA-DoD CARE Consortium) examining 34,709 athletes from 30 academic institutions, it was determined that a more realistic timeline for recovery is closer to 1-month post-injury (Broglio et al., 2022). Notably, the consequences of returning athletes before they have fully recovered, which would be the case if one relies solely or primarily on self-reported symptoms, are considerable and primarily involve increased risk for re-injury. Specifically, after one TBI, the risk of sustaining another injury is three times greater; after the second injury, the risk increases by a factor of eight (CDC, 1999). Even among elite athletes, the data suggest that sustaining a concussion and prematurely returning to activity increases the rate of subsequent concussions and other orthopedic injuries (e.g., Jildeh et al., 2021; Nyberg et al., 2015). Such findings highlight the importance of accurately assessing concussion and the need for a comprehensive, accurate, quantitative assessment of the sequelae of SRCs.

A recently formulated framework and Consensus Conference statement was introduced based on a meta-analysis that defined concussion subgroups and concussion-associated conditions (Lumba-Brown et al., 2020). In that analysis, one of the most prevalent concussion subtypes was labeled cognitive to reflect the most pronounced symptoms that manifest (Lumba-Brown et al., 2020). Pediatric neuropsychologists have many options (e.g., executive and/or working memory measures) to thoroughly quantify potential neurocognitive deficits. Within that same framework, Lumba-Brown and colleagues (2020) also identified two concussion subtypes that implicate gait and balance: labelled ocular-motor and vestibular. Researchers have also forwarded a rationale for identifying a gait-specific concussion subtype (see Williams et al., 2021). Moreover, in a 2017 consensus statement of the Concussion in Sport Group (CISG), gait and balance were both included as common sequelae of a concussion that merit attention in any concussion evaluation (McCrory et al., 2017). However, presently pediatric neuropsychologists have few validated instruments available to quantify gait.

This paper first provides an overview of the literature highlighting the neuromotor (specifically, gait) consequences of concussion. We also present a rationale for using quantitative assessments of neuromotor functioning that allow for both norm-referenced and self-referenced comparisons as part of a neuropsychological screen following a suspected concussion. Finally, we introduce and illustrate a method of gait analysis using accelerometers, including a presentation of test–retest reliability and recovery data.

Postural Control and Stability in Concussion Recovery

Long-term deficits in dynamic motor function, such as postural control and balance, following a concussion are well-documented (Chou et al., 2004; Gagnon et al., 2004; Parker et al., 2006) and are thought to contribute to cases of recurrent concussions (Catena et al., 2009). For example, children aged 7–16 years old who sustained mTBI showed post-injury balance deficits for up to 12 weeks and performed significantly worse 12 weeks post-injury compared to non-injured children (medium effect size, d = 0.51) on the Bruininks-Oseretsky Test of Motor Proficiency (BOTMP), which measures static and dynamic balance without external perturbations (Gagnon et al., 2004). This study also demonstrated an apparent recovery effect in motor proficiency, as the mTBI group performed better at week 12 than they had when measured 1-week post-injury, with this change represented a medium to large effect size (d = 0.77). Furthermore, college-aged adults who sustained a concussion demonstrated decreased dynamic balance compared to controls during a divided attention walking task administered at 1-month post-injury (Parker et al., 2006). The effect was large for gait stability, especially when attention was divided from the primary motor task (i.e., walking). The motoric consequences of concussion on gait were also shown at 28 days post-concussion, as those with mTBI tended to walk slower (d = 0.18) and had less separation between their whole-body center of mass (COM) and center of pressure (COP) than controls (d = 0.28), reflecting differences in gait speed and stability between those with mTBI and controls (Parker et al., 2006). Adults with mild-to-severe TBI showed significantly slower walking speed (gait velocity), stride length, and increased mediolateral motion during walking compared to controls (Chou et al., 2004). An increased mediolateral motion was also found in a study of high school students who had sustained a concussion and were tested at five-time intervals ranging from 72 h to 2 months post-injury (Howell et al., 2014). The observed effect for increased mediolateral motion was largest immediately after the injury, but the effects persisted even after 2 months (Howell et al., 2014).

A meta-analysis (Fino et al., 2018) examining gait consequences across the lifespan (children, adolescents, and adults) found acute effects for stride width, but less consistent findings at other post-incident timeframes. In general, long-term effects were more commonly found with complex gait tasks (e.g., dual attention, obstacle). However, these findings may depend on extraneous factors such as small sample sizes that limit statistical power.

Overall, gait changes associated with TBI appear to extend well beyond the typical 2-week return-to-play window. This suggests that gait data may have a different (potentially longer) recovery trajectory, or gait variables may be more sensitive to ongoing concussion-related problems as compared to other (e.g., cognitive) domains during the latter stages of recovery (Parker et al., 2006). Moreover, the presence of gait alterations is consistent with patients’ subjective reports of “instability,” and subtle but neurologically relevant gait instabilities may not be observable with routine clinical (qualitative) gait examinations (Chou et al., 2004).

Accelerometers to Assess Gait

Advanced instrumentation to record gait functioning significantly improves the understanding of gait (Tao et al., 2012). Gait speed assessed using accelerometers has superior sensitivity to dysfunction relative to manually collected gait speed data, such as timing a walk over a fixed distance (Maggio et al., 2016). Moreover, accelerometers can produce reliable and objective outputs across a range of clinical populations (e.g., Byun et al., 2016; Fujiwara et al., 2020; Henriksen et al., 2004; Kluge et al., 2017; Kobsar et al., 2016; Moore et al., 2017; Werner et al., 2020).

The literature also includes several studies using accelerometers/sensors to assess various aspects of gait within the context of SRCs/mTBIs. For example, the gait features of those with mTBI were compared to matched controls using inertial sensors (accelerometers), with the mTBI group having a slower gait pace (d = 0.91) and slower turning (d = 0.82), despite the assessment occurring at an average of 1-year post-incident for the mTBI group (Martini et al., 2021). This research also found sensor-derived gait variability to relate significantly to scores on the Neurobehavioral Symptom Inventory. Parrington and colleagues (2019) used inertial sensors to show that a concussed group of collegiate athletes initially demonstrated more significant gait sway than a control group, as well as a pattern of increasing gait speed as recovery progressed that leveled off after the athlete returned to play. Finally, a 2019 meta-analysis of 22 studies with adult participants with at least one concussion found that sensor-assessed gait velocity significantly relates to neurocognitive status, with the concussed participants walking 0.12 m/sec slower than controls (Wood et al., 2019). Even after 28 days, previously concussed participants walked significantly slower than controls, with this effect declining over time (Wood et al., 2019).

Overall, the literature suggests that accelerometers can capture essential aspects of the gait cycle, and quantifying the data facilitates its relation to concussion outcomes. However, it is unknown whether such data can be collected with minimal or no equipment/instrumentation while maintaining reliability and validity with respect to SRCs, as this would make such assessments useful for broad adoption in clinical practice.

Introduction of a BioKinetoGraph (BKG) for the Assessment of Gait

We here describe the use of noninvasive, continuous tri-axial accelerometers to visually depict gait as a waveform, referred to here as a BioKinetoGraph (BKG). Analogous 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 the range of motion, amplitudes, and timing intervals for the various components of the waveform that relate directly to the gait cycle. The adopted method for collecting and representing gait data is similar to other published studies (e.g., Godfrey et al., 2015).

The BKG and the algorithms used to extract the various gait features were initially validated using a recorded video at 240 frames per second, which is twice the sampling rate of the accelerometers. This recording was completed in a test session with four accelerometers (affixed to both ankles, one wrist, and the sacrum) that matched the recorded data and key markers identified by the algorithm (heel strike and toe-off) to what was observed in the video. The camera was an iPhone X configured to record at 240fps while attached to a rolling dolly system that followed the participant as they walked in order to keep them in the center frame. The high-speed video was used to identify the start (heel strike) and end (toe-off) features of a gait cycle and match them to the accelerometer data. Subsequent gait events were identified on the waveform within these boundary events.

Although gait data can be collected from the sensors affixed to different parts of the body, the bulk of the published research and our own work is based on the data derived from a dedicated sensor affixed with a belt at the sacrum. This sensor records inertial measurement units (IMU; a general term for accelerometers, gyroscopes, and related technology) via transmitted signals at a rate of 200 Hz to a local device, which then transmits the data to remote servers for BKG motion analysis following each trial. Raw data are saved to a persistent data store as a collection of timestamps and accelerometer readings of each axis. Raw inertial data are then processed using algorithms that detect critical markers of the gait cycle and extract the spatial, temporal, kinetic, and spectral features reported below.

The above-described BKG output can generate hundreds of variables, but only a subset of those variables will be described here. Specifically, 18 variables organized into four conceptual domains: balance (stability), stride (timing intervals), power (amplitudes), and symmetry (regularity and consistency) are discussed, in part because these variables correspond to similar variables in the gait literature that were recently validated (Lecci et al., 2023).

Balance captures stability (and was previously labelled stability) during the walk, and is defined as the range of motion (ROM) at the center of mass (sacrum) and gait cycle variability, with data coming from both the straight and turnaround portions of a walking trial. This domain includes the stability during the straight portion of the gait cycle (side balance), the stability during the turnaround (turnaround balance), the timing of side-to-side movement (sway time), the force generated with lateral movement (side power), the rhythmicity of gait patterns (gait smoothness), and the variability of time in double stance phase (support consistency). All balance values are inverted such that low scores indicate problematic functioning.

Stride captures the time it takes to complete different parts of the gait cycle and is therefore a proxy for speed. Specifically, the stride domain refers to maintaining velocity during walking and includes the average timing interval between contralateral heel strikes (stride time), the average time spent in the double stance phase when both feet are in contact with the ground (double stance), the average time spent in right and left side stances (stance phase), and the average duration of the swing phase (swing phase). (Note: Stance phase and swing phase were added to the stride domain in the current mobile assessment, after the BKG’s initial validation using the sacrum sensor.) All stride values are inverted such that low scores denote problematic functioning.

Symmetry is a measure of consistency in one's gait and refers to the uniformity and regularity of movement across the anterior–posterior and vertical dimensions. This domain includes a comparison of the forward and backward displacement from the center of mass (forward movement symmetry), a comparison of the distance of upward and downward displacement from the center of mass (vertical movement symmetry), and a comparison of the velocity of upward and downward movement from the center of mass (vertical sway symmetry). Low scores denote problematic functioning.

Power is reflected in the amplitude of one’s gait and is defined in terms of the amount and efficiency of energy generated and expended in the gait cycle. This domain includes the total net force generated at the center of mass (total power), the force generated with vertical movement (vertical power), the force produced with forward movement (forward power), the force generated by the foot strike (striking force), and force generated by the toe pushing off (pushing force). Low scores denote problematic functioning.

These four domains and their corresponding BKG variables are automatically generated by the BKG mobile assessment and produce output as T-scores (mean 50, SD = 10) based on normative data. Research has also established that the BKG can produce consistent scores over time. Specifically, a study of 60 participants aged 18 to 35 (61.7% female) with no apparent health problems established the test–retest reliability of the BKG variables based on an accelerometer attached to the sacrum. The resulting correlations between test scores taken on two separate days (with a mean retest interval of 4 days) ranged from 0.72 to 0.91, with a mean of 0.80 (Lecci et al., 2023). This suggests that BKG variables can produce consistent (reliable) output using a dedicated sacrum sensor.

This same sacrum sensor was used in a sample of 1,008 individuals (53.4% female) aged 8 to 50 years (M = 16.98, SD = 4.43) to validate the gait assessment. Specifically, 950 ostensibly healthy individuals completed the BKG as part of a standard baseline evaluation, and 58 individuals completed the BKG while undergoing post-concussion evaluations. The findings indicated that the BKG variables grouped within the above-noted conceptual domains are highly correlated with NIH 4-m gait scores (multiple R = 0.51, p < 0.001), with robust associations for power and stride (i.e., less power and lower stride scores are associated with slower walking speed) (Lecci et al., 2023). This illustrates convergent validity between the BKG data and a well-validated measure of gait speed (i.e., the time it takes to traverse 4 m is essentially a measure of speed). Although not reported in the original study, this same data set shows that the BKG variables can likewise significantly predict Balance Error Scoring System (BESS) total scores (multiple R = 0.28, p < 0.001), though in this instance the balance and symmetry variables take on a more prominent predictive role.

Most relevant to the current research, BKG data have been shown to predict concussion-related outcomes. For example, in a sample of 111 cases, BKG scores (when combined with other data from the SportGait platform) predict the remove-from-play decisions of a pediatric neurologist, achieving a classification accuracy of 91% and AUC of 1.0 when using a machine learning general linear model (Keith et al., 2019). In addition, the BKG variables predict the endorsement of CDC concussion symptoms significantly, and out-predicted by upwards of four-fold the separate and combined effects of two frequently used and well-validated measures of gait and balance (NIH 4-m gait and BESS scores) when predicting concussion symptoms (Lecci et al., 2023). Moreover, BKG variables significantly predict CDC concussion symptom endorsement over and above the NIH 4-m gait and BESS measures (Lecci et al., 2023). Thus, although gait speed and balance measures are significant predictors of concussion outcomes, sensor-based assessments of motion that quantify many gait variables in addition to speed and balance, provide a more robust assessment of gait and concussion-related sequelae, which is in keeping with the literature on mTBI (Dever et al., 2022). This initial research validated the BKG methodology and technology using a sacrum sensor.

Next, we present data on the test–retest reliability of BKG mobile (smartphone) assessment, as establishing psychometric properties in the mobile environment is critical to broadening the accessibility and use of accelerometers in quantifying gait. Moreover, test–retest reliability is crucial for interpreting data collected from repeat testing, as repeat testing and comparisons to previous data points typically occur during recovery.

Mobile BKG Test–Retest Data in a Large Baseline Sample

A sample of 4150 individuals (35% female) ranging in age from 5 to 78 (M = 16.91, SD = 6.12) was selected from baseline testing completed between December 14, 2020 to January 9, 2022 using the SportGait Mobile App. The SportGait App includes a cognitive assessment, symptom rating, and a measure of affect, along with the BKG. For the purpose of this paper, only the BKG gait assessment data are presented. The sample was drawn from an original data set containing 4797 consecutive evaluations not previously published. (Note: Date are available from the first author.) This research was approved by the University of North Carolina Wilmington IRB; Protocol #21–0047.

Evaluations were removed if they were not baseline assessments (a total of 129 or 3%), if they were duplicates (456 or 11%), or if they failed to follow the directions (a total of 62 people or 1.5%). Some of the problems following directions included having a difference of 5 or more steps in the number of steps before and after the turnaround, or the mobile sensors on the phone detecting an improper positioning of the phone. Of the included data, extreme outliers were winsorized (i.e., capped at ± 3 SDs) for two variables, with the total number of affected data points accounting for less than 1% of the data for those variables.

Participants were instructed to download and open the SportGait App to their phones. Both iPhones (running iOS) and Androids of various generations were used. However, having different operating systems for iPhones and Androids is not inherently problematic, as the App works the same regardless of the operating system and the same algorithms are applied for all users. Although there were some cosmetic changes to the App during the data collection period, the changes primarily focus on appearance and not functionality. The oldest operating systems (6.0.1) were detected on Android devices, with a very small number dating back to 2013. The oldest Android phone was Nexus 5 which launched in 2013 with an OS version 6.0.1. The oldest operating system on an iPhone was 11.3, and the oldest iPhone was an iPhone 6 (released Sept. 2014), using iOS 12.4.1. The majority of our data come from iOS users (N = 3733, 90% of the sample), with the remaining 10% (N = 417) using Android OS. Although we do normalize the incoming data, which are subtly different from these different operating systems, past research indicates no substantive differences based on the version of the phones or operating systems (see Freund, 2021 for data illustrating comparability of different phones with respect to the cognitive task within the same App). Of course, the phone sensors can be damaged, and under those circumstances, the data can be less optimal.

The BKG gait assessment portion of the App involves three unobstructed walks of approximately 20 feet each, with participants instructed to walk at their usual pace (speed). Before beginning the BKG, participants are asked to find a location with approximately 20 continuous feet of unobstructed walking space (e.g., open room, hallway) with a firm surface (concrete, tile, hardwood floor, low carpet). Participants are also instructed to walk either barefoot or in socks. These instructions were included because previous research has shown that footwear and the walking surface can influence gait variables, at least to some degree (Lecci et al., 2023).

At the beginning of each walk, the App instructs participants to hit the start button on the screen, then hold the phone to their chest, with the phone oriented horizontally and the screen facing their body, with both hands over it. They then walk ten paces, turn around and return to the start point before hitting the stop button. After the walk, the SportGait App notifies the participant if the data were successfully collected. The same procedure was used for the second and third walks. Data extracted from the second and third walks were compared to evaluate the test–retest reliability figures, and this corresponds to the method adopted in previous research using an affixed sensor (Lecci et al., 2023). The App provides a visual illustration of the procedure, depicted in Fig. 1.

Fig. 1
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Depiction of the walk

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In a previous data collection, participants were asked to walk six times to identify the optimal number of walks needed to adequately sample gait biomechanics with the BKG. The first and second walks resulted in the largest differences in the obtained values, with differences between subsequent walks decreasing markedly. Thus, the BKG task instructions now require three walks, with the mean of the second and third walks serving as the primary data of interest. (Note: Future research will explore the first walk as a potential “separate” assessment involving gait under the condition of a somewhat novel task with dual attention components, due to the participant counting out the target distance.)

The rationale for focusing on two walks in such close temporal proximity is that although these represent two separate walks, variables that can impact gait (e.g., footwear, walking surface, minor idiosyncrasies in how the phone is held, the type of phone used), but are irrelevant to assessing the underlying biomechanics of gait, would be identical. Moreover, factors relevant to gait biomechanics and captured by the BKG, such as the presence of orthopedic and head injuries, would likewise be equivalent in that short time interval. Thus, an assessment of the consistency in scores across the two trials would make for an optimal evaluation of test–retest reliability for the BKG.

Before analyzing the initial (heel strike) and final (toe-off) foot–ground contact events of each gait cycle from the Mobile BKG, the signal is processed to remove extraneous data recorded before and after the gait trial, adjust for tilt and orientation of the sensor relative to gravity, and filtered to reduce noise (see Lecci et al., 2023 for a more detailed description). The raw signal data is then fourth-order-zero-phase-shift filtered (Winter, 2009), with upper and lower cutoffs of 3 Hz and 0.111 Hz, respectively. Initial and final contact events were detected algorithmically by first identifying local maxima representative of the mid-swing with toe-off and heel strike events indicated by minima occurring immediately before and after, respectively. Displacement and velocity were obtained for each axis by trapezoidal integration and subtraction of the zero-phase rolling average (Oppenheim and Schafer, 1989) equal to one gait cycle.

BKG variables from the second and third walks were correlated and are presented in Table 1. Pearson correlations for the 18 Mobile BKG variables ranged from r = 0.51 to 0.92, with an overall mean of 0.79. The four domains resulted in average correlations of 0.79 for the five power variables, 0.88 for the four stride variables, 0.76 for the six balance variables, and 0.73 for the three symmetry variables. Thus, each domain and the overall mean reflect strong test–retest reliability, and the 18 BKG variables are at least adequate when considered individually. Moreover, the obtained values are commensurate with the test–retest figures obtained in monitored, controlled environments using a static sensor attached at the sacrum (Lecci et al., 2023). Establishing good test–retest reliability is critical in supporting the use of the BKG with mobile devices, with data coming from a wide range of mobile phones (i.e., iPhones and Androids of different generations) and collected from a variety of settings.

Table 1 Pearson correlations illustrating test–retest reliabilities for BKG variables by domain
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These findings allow for the broad utilization of the BKG to, for example, engage in baseline testing where athletes could use their phones from home or even while at practice, provided they do so on a firm surface without shoes. This would also allow for the BKG to be administered following a visit with a health professional for an individual who has experienced an SRC and is in a return-to-play (RTP) protocol. Assessments could occur during and in between office visits to provide more detailed information for the RTP decision by providing a clearer picture of the recovery trajectory. The advantage of collecting additional data to augment the data from an in-office visit is that it reduces the reliance on a single or small number of data points to define the trajectory of change for the patient, thereby improving assessment accuracy.

In the next section, we present a case study to illustrate the use of the mobile BKG in a recovery protocol.

BKG Recovery Data: A Closer Look

We here present recovery data for an athlete to illustrate how BKG quantitative data can be used to make both norm-referenced and self-referenced comparisons and to provide informational support for clinical judgments. The depicted data were specifically selected because they reflect some complexities in post-injury recovery, such that scores do not universally worsen post-injury and then return to normal. This will allow for a more nuanced discussion of the factors to consider when interpreting recovery data.

We will also provide a separate discussion of qualitative data for another case depicting various gait features. Depicted individuals are identified by their subject ID number and are drawn from a large, anonymous database of thousands of individuals. Some of the specific details that are less relevant to the case have been altered to help maintain anonymity.

Case 1

Here is a 16-year-old cisgender Caucasian female athlete. She is a member of the swim team who injured herself during training when she was struck and fell, hitting her head on a solid surface. The injury occurred two days prior to her reporting her symptoms, and this was during winter break. She completed ratings of self-report symptoms on the days she was unable to see the athletic trainer. The athlete was officially “removed from play” and the injury was logged on 12/16/21 though the actual injury occurred two days earlier. On 12/16/21, the athletic trainer also administered the SCAT 5 22-symptom scale, with severity ratings ranging from 0–6. The athlete then completed the SportGait Mobile assessment (identified as “Injury Screener 1”). Two additional Injury Screener assessments were completed on 1/13/22 and 1/14/22. The athlete was returned to play on 2/7/22. An athletic trainer followed the athlete throughout the return-to-play protocol. The BKG was also administered during a preseason baseline, with the post-incident assessments occurring approximately 150 days after the baseline data were collected. The last assessment occurred almost 400 days after the initial baseline when the athlete completed a baseline for the following season.

Examining Self-referenced Recovery Data

The first data to be presented involves recovery trajectories plotted as T-score points (mean = 50, SD = 10) over time, with the x-axis depicting days since the first baseline assessment (BL1) (see Figs. 2, 3, 4, and 5). As noted, the data include a baseline and three post-concussion evaluations ranging from approximately 5 to 6 months post-baseline. A final evaluation occurred approximately eight months after the injury and thirteen months after the first baseline (i.e., after the athlete had completed the RTP protocol and was taking part in their baseline testing for the next season).

Fig. 2
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The effects on power (amplitude) variables following injury

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Fig. 3
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The effect of stride (a timing variable that is a proxy for speed) following injury, whereby scores on all five variables are universally slower (lower T-scores denote slower speed) for the three post-injury assessments relative to the baseline score

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Fig. 4
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The trajectory of the balance (stability) variables following injury

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Fig. 5
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The effects on symmetry following injury

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Figure 2 shows that all five power (amplitude) variables drop from baseline T-scores near 50 (i.e., approximately average) to T-scores in the low 30 s to low 40 s during the three post-injury evaluations. This change represents declines of approximately one standard deviation or more, broadly showing that one of the gait consequences of this athlete’s concussion is that the amplitude of her gait (i.e., power) declined. The final evaluation, which serves as the following season’s baseline, indicates that all the power variables have returned to normal and are now consistent with the initial baseline scores. It is noted that declines of one standard deviation or more would be considered large effects, as injuries are often defined by less pronounced declines (e.g., 3–4 T-score points).

Figure 3 illustrates the effect of stride (a timing variable that is a proxy for speed), whereby scores on all five variables are universally slower (lower T-scores denote slower speed/more time spent within the different portions of the gait cycle) for the three post-injury assessments relative to the baseline score. The gait speed variables then improve (denoting faster tempo/speed) in the final assessment occurring over 1 year later.

Figure 4 illustrates the trajectory of the balance (stability) variables. This variable shows a mixed clinical picture, with gait smoothness showing the typical pattern of worsening scores post-injury and a return to normal functioning by the second baseline. However, other variables show no marked changes (e.g., sway and sway time) or more unusual trajectories. As an example of the latter, the support consistency scores result in more problematic scores at both the second post-injury assessment and second baseline assessment relative to the other scores. The other variables either show no consistent directional trend or even show some improvement post-injury relative to baseline. Although these findings seem counterintuitive on the surface, they can in fact be meaningful when considered in conjunction with the other scores. The nuances of interpreting the balance data will be discussed in the next session, as we interrelate data from all four domains in the Mobile SportGait App.

The symmetry scores compare movements around the torso in different directions. Figure 5 illustrates that overall, the obtained values during the recovery phase are not markedly different relative to baseline data, aside from the third post-injury assessment, suggesting poorer symmetry at that time. Thus, the athlete would not be judged to have changed markedly on gait symmetry throughout the year following the injury.

As noted in this illustration, not all gait variables show a consistent decline post-concussion or similar rates of recovery. A mixed clinical picture can emerge for a variety of reasons. For example, not all aspects of gait functioning should experience similar functional changes from a given injury, as injuries will vary in the breadth and extent of any gait-specific sequalae. This is especially true when dealing with SRCs that are mild in nature. Although much rarer in their occurrence, profound SRCs (i.e., moderate-to-severe TBIs) would likely result in more significant dysfunction across all gait domains, and would likely attenuate neurocognitive functioning as well. Patients will also differ in recovery based on such factors as the extent of the injury, co-occurring injuries, history of previous injuries, engagement in active rehabilitation, and the athlete’s premorbid strengths and weaknesses. With respect to the latter, those with better balance and coordination prior to the injury may be more proficient in compensating for post-concussive difficulties in those domains.

When the clinical data are mixed, medical professionals must first consider the possibility of lingering effects of the concussion. Additional considerations would include whether the concussed athlete also sustained orthopedic injuries that may likewise influence gait. As an example, if at the time of the concussion a lower extremity was injured, such as a sprained right knee, then this injury would likely have a significant effect on BKG symmetry data (especially mediolateral symmetry). Under such circumstances in an RTP decision, less recovery in the symmetry domain could be de-emphasized, at least with respect to its relevance for the decision regarding concussion recovery.

Another consideration would be how the BKG scores relate to each other. With respect to the above case, it was noted that the athlete experienced slower gait speed and less power post-injury, but the balance and symmetry domains were less (or not at all) affected. Such variability in the BKG scores could illustrate how individuals alter some aspect(s) of their gait in order to improve or stabilize their walk. As an illustration, for this athlete, the gait speed measures (stride) were all slower relative to baseline. This more intentional or deliberate walking pace may have been adopted in order to achieve better balance and symmetry. This would be analogous to an individual with an orthopedic injury who decreases limping by walking at a slow, methodical pace. Health providers working with this patient could also specifically instruct them to do the gait test during the RTP protocol while trying to maintain normal gait speed. Presumably in that context, the stride domain would look more normal, but this would likely result in poorer balance and symmetry scores. Thus, the interpretation of recovery data requires a consideration of all of the BKG output to best characterize the gait dynamics at play.

Overall, the above findings illustrate how gait variables can vary over the course of a year and reflect the neurobehavioral sequelae of concussion. The obtained BKG data provide detailed information for quantifying gait functioning that could be used to help support clinical decisions, including the timing of a return to activity, and/or whether an individual requires additional testing or remediation (e.g., balance, strength training) to facilitate their return.

Although the focus of the preceding section was on self-referenced comparisons (e.g., comparing post-injury data to baseline data), the fact that the plotted values are in the form of T-scores based on a normative sample indicates that normative evaluations can also be made. Specifically, if the scores are markedly lower than the mean of 50, one can conclude that norm-referenced comparisons are likewise indicative of problematic functioning. In the next section, we further elaborate on this point by using the norm- and self-referenced data drawn directly from the Mobile SportGait App for another case example.

Examining Norm-referenced and Self-referenced Recovery Data

Figures 6, 7, 8 and 9 are taken directly from the Mobile SportGait App to illustrate how norm-referenced and self-referenced data are displayed and how the information can assist in evaluating a patient’s progress following a suspected injury. The norm-referenced data are adjusted for age and gender and are presented as standard T-scores. To facilitate interpretation, the color in the App is included to denote normative information, such that when the individual’s score is depicted in green, it means the values are normatively average or better. The color changes from green to yellow (at one standard deviation) and then from yellow to amber to red (at two standard deviations) to denote increasingly non-normative scores in the problematic direction. In all cases, lower (outside the green range) scores denote more problematic functioning.

Self-referenced information is conveyed by illustrating scores relative to (1) baseline data, denoted as a dashed line in the Mobile SportGait App, and/or (2) previously collected data from the RTP process. This allows for both informational sources (i.e., trends for normative and self-referenced changes) to better inform RTP decisions.

Case 2

Here is a 17-year-old, cisgender Caucasian female athlete, who was kneed to the head while falling during a soccer game. She had immediate swelling to the orbital socket, and reported symptoms of headache and dizziness. At the time of injury, her SCAT 5 symptom score was 11 out of 41. She also evidenced some trouble with eye tracking, as she was unable to move her eyes independent of her head. She was removed from play. The athlete completed a baseline assessment on 11–10-22 and the initial post-injury assessment on 11–24-22. She was subsequently assessed on 11–25-22 and 11–26-22, and was cleared to return to play after this final assessment. An athletic trainer followed her throughout the recovery. Figures 6, 7, 8, and 9 depict screenshots from the Mobile BKG from the 11–24-22 assessment (the bottom of the figure shows the data across all four assessments).

Fig. 6
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Depicts screenshots for Stride from the Mobile BKG from the 11–24-22 assessment

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Fig. 7
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Depicts screenshots for Power from the Mobile BKG from the 11–24-22 assessment

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Fig. 8
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Depicts screenshots for Balance from the Mobile BKG from the 11–24-22 assessment

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Fig. 9
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Depicts screenshots for Symmetry from the Mobile BKG from the 11–24-22 assessment

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For the stride data (Fig. 6), which essentially quantifies gait speed broken down into separate components of the gait cycle, lower values denote slower speed (i.e., more time spent in different portions of the gait cycle). Baseline data, denoted by the dashed line, are consistently in the normal (green) range. However, each of the four stride variables are lower with respect to both normative standards (outside the green range) and self-referenced standards (lower than the baseline values). The scores at the time of the second assessment are generally in the yellow range (T-scores of 35 to 41), suggesting that although they are consistently lower/slower, the extent of the decline post-injury is still modest from a normative and relative standpoint.

Normatively speaking, the five power metrics (Fig. 7) are essentially in the normal range (all green) and are not significantly different from the preseason baseline scores. This suggests that the athlete’s power has remained consistent over time and appears to have been unaffected by the injury.

Small but consistent effects appear to emerge for the balance domain (Fig. 8). Normatively speaking, only one score is fully in the normal (green) range, and that’s for support consistency. This score is also slightly higher/better than the baseline value. The five remaining variables are all trending in the non-normative/problematic direction and are also marginally but consistently lower than the baseline value. Thus, normative and self-referenced data provide a similar picture, even though the effects are subtle. The one exception is sway time, which shows a larger effect in the problematic direction with a T-score of 32.

Finally, none of the gait symmetry variables (Fig. 9) indicate problematic scores when making either norm-referenced or self-referenced comparisons. In fact, for all three symmetry measures, the athlete is scoring better relative to baseline scores and they are consistently in the normative (green) range. This indicates that their symmetry scores are average or better and have not declined following the injury. Thus, the athlete demonstrated symmetrical gait patterns for strength (power), stride (speed), and stability (balance), indicating regularity, consistency, and evenness in gait functioning. This finding is interesting and again illustrates that not all gait variables will necessarily be in the problematic range for every athlete and/or every injury. In fact, SRCs are often quite idiosyncratic in their presentation, and it would not be expected that all values would fall in the problematic range, especially when injuries are less severe, as is the case for many SRCs (i.e., mTBIs). With pronounced head trauma (i.e., moderate to severe TBI), a more pervasive decline in all scores would likely emerge.

Considering the Potential Divergence of BKG Data and Symptom Reporting

The SportGait App also captures self-reported concussion symptoms, thereby allowing healthcare providers to compare symptom endorsements to the obtained BKG scores when interpreting the data. Importantly, the association between self-reported concussion symptoms and the BKG data is expected to vary, with higher convergence occurring when individuals are in an acute, post-concussive state and/or when symptoms are more severe (e.g., severe TBI). However, as symptoms lessen in their number and intensity, which typically occurs as time following the injury increases, the convergence between concussion symptom endorsement and BKG data should decrease. As noted in the literature, abnormalities in gait can persist long after individuals stop reporting/endorsing concussion symptoms. For this reason, before using the BKG to determine readiness for return to activity, it is recommended that concussion symptom reporting be at or near zero. The rationale for this approach is that concussion symptoms would typically be sufficient to delay a return to play (though there presence would not necessarily prevent rehabilitative activity). More pronounced symptoms would be present either at the earliest stages of recovery and/or following a more severe injury. After concussion symptoms abate, the use of more sensitive measures would be more helpful. Thus, the BKG data should more strongly influence medical and return-to-play decisions after concussion symptoms have begun to subside.

Of course, differences between self-reported symptoms and quantitative assessments of gait functioning may be due to other issues, such as problems with symptom self-reporting during return-to-play protocols, especially when the athlete’s motivation to return to play is high (e.g., Meier et al., 2015). In this situation, athletes may under-report symptoms to facilitate an early return, and this too would need to be considered when interpreting the BKG data.

Concussion symptom reporting and BKG data may also diverge due to the presence of other non-concussive injuries. For example, an athlete may no longer be reporting concussion symptoms but their BKG data may still be problematic when there is a persisting orthopedic injury. This is likely to be a common situation, as the literature indicates that orthopedic injuries are much more likely to occur following a SRC (e.g., Jildeh et al., 2021; Nyberg et al., 2015). This would also result in a complex clinical decision. Obviously, the orthopedic injury would not prevent an athlete from returning to their sport (or initiating a return-to-play protocol) with respect to concussion, but it may nevertheless indicate that a return to activity should be delayed or closely monitored, and that orthopedic interventions (e.g., additional strength rehabilitation to a knee) may still be needed. In short, BKG data would not necessarily be confounded by the presence of other injuries, but rather, they can provide a comprehensive assessment of functional sequalae regardless of the source/etiology of the injury or injuries at play. Importantly, the assessment of other functional abilities that would be well within the scope of the typical neuropsychological practice (i.e., neurocognitive functioning) could inform the relevant etiology and course. For example, scores on cognitive tests should not be affected by an orthopedic injury, but would likely show some consequences of a SRC, as the cognitive subtype is considered to be the most prevalent form of concussion.

Qualitative Assessment of the BKG: Stabilogram and Displacement Graphs

BKG parameters can also be used to create a qualitative output, one example of which is the stabilogram (see Fig. 10). Overall, this graph follows the same convention as a typical stabilogram, although it is more commonly used during a stable stance than a walking task. The 2D version of the tracing interface represents a view from above. The 2D stabilogram represents the anterior–posterior (AP) and mediolateral (ML) displacement intuitively, such that the Y-axis is oriented with forward displacement at the top and rearward displacement at the bottom. The X-axis represents the ML displacement from left to right. The 3D version incorporates the vertical axis with negative values representing up and positive being down. The depicted units of measurement are in centimeters.

Fig. 10
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Qualitative assessment of the BKG: Stabilogram and displacement graphs

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Because the stabilogram measures the displacement of the trunk (not distance), the forward velocity relative to the ground is removed, leaving the cyclic movement of the center of mass (i.e., as though one were walking on a treadmill). Those cyclic movements are what form the bowtie shape, explained by the inverted pendulum model (Tesio & Rota, 2019). During a single gait cycle, there would be a left-side and right-side (mediolateral) swing and a forward and back (anterior–posterior) motion on both the right and left side. The formation of the bowtie shape provides insight into how the center of mass (CoM) is controlled during gait by looking at one axis relative to the other in terms of scale and symmetry, and by how well-defined the bowtie appears (see Tesio & Rota, 2019). Faster walking will naturally result in a more pronounced U-shape in the 3D version, as ML motion is narrowed and vertical (VT) movement increases. On the 2D version, a U-shape on the AP axis means they are leaning forward or backward asymmetrically (usually at the heel strike or toe-off). The literature suggests that across a range of populations, most stability issues manifest on the ML axis (e.g., Conradsson et al., 2018; Osada et al., 2022), and this does appear to converge with the BKG data collected to date. The AP axis also emerges as an indicator of the participant’s balance, such that less AP movement translates into a more conservative gait, possibly to compensate for balance issues. A chaotic pattern can indicate an issue with the CoM following a consistent trajectory.

Thus, the stabilogram provides an overall qualitative summary of gait that is most related to the balance and symmetry domains. The other graphs in the SportGait Mobile BKG interface showing displacement and velocity over time are more useful to explore what might be occurring during each gait cycle, where one can isolate issues related to a particular phase of the cycle or to a particular limb. Different parts of the body are engaged during each gait phase, which can help reveal physiological issues (especially asymmetrical ones), while neurological function is shown in the ability to coordinate movement and maintain balance overall, with specific segments of the cycle being more taxing than others.

The baseline stabilogram depicted in Fig. 10 is taken from the second case study, and illustrates a clear and tight bow-shape, indicating a compact and regular gait, with the AP displacement within ± 2 cm, and the ML axis within ± 3 cm. The initial post-injury assessment taken on 11–24-22 shows a more chaotic pattern with greater displacement on the AP (± 4 cm) and ML (± 4 cm) axes. In essence, the increased displacement is directly related to stability (balance). With each subsequent post-injury assessment, the pattern becomes more regular and compact, as the individual is moving towards recovery.

The displacement graph (also taken from the second case study) depicts the magnitude of the displacement changes over time (see Fig. 11). In terms of the patterns, the ML displacement (orange) starts with a prominent “shoulder” at baseline, then becomes more rounded on the initial post-injury test, with the shoulder slowly returning and becoming more prominent through recovery. The vertical displacement (green) is relatively consistent over time, but the anterior–posterior displacement (blue) becomes more erratic at the second and third post-injury assessments.

Fig. 11
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The displacement graph depicts the magnitude of the displacement changes over time

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The displacement and stabilogram graphs provide a visual, qualitative counterpart to the quantitative assessment from the 18 Mobile BKG variables reported earlier. Together, they provide a wealth of information regarding gait dynamics and how they can inform recovery decisions. It is also important to consider how the qualitative and quantitative data interrelate. As noted, an individual’s walking speed (i.e., the BKG variables captured primarily by stride and power) should be considered when interpreting a stabilogram, because an individual could normalize their gait (i.e., create a tighter and more symmetrical stabilogram) by adopting a more conservative walk (i.e., walking deliberately and more slowly). In such instances, a post-injury stabilogram could look more normal, and this effect could be enhanced if compared to baseline BKG data that was completed in a rushed manner (i.e., the uninjured person who is more harried when walking). Thus, any normalization over time in the stabilogram should be tempered by a consideration of gait speed, whereby markedly lower power and stride scores would suggest slower walking speed. Practitioners could also instruct patients to walk at a quicker pace to better evaluate balance and stability under such conditions. It is also noted that the stabilogram itself provides some gait speed information, as the dashed lines would indicate slower movement relative to the solid lines, though this information is less exact than the quantitative BKG output.

Implications and Conclusions

The research literature and best practice guidelines for concussion recovery converge with respect to emphasizing the need to assess neuromotor functioning. However, the SRC literature, and especially the contributions from neuropsychology, have more typically emphasized the quantitative assessment of cognitive functioning. While such assessments are valuable, they can paint a more limited understanding of concussion and its recovery. The relative neglect of functional neurobehavioral sequelae of concussion is likely due in part to the relative scarcity of standardized measures to quantify neuromotor functioning in the form of gait. Indeed, practicing neuropsychologists will typically comment on the presence of any unusual gait presentations in the behavioral observations (e.g., limping, shuffling, ataxia, problems with balance), but formal quantification is less common.

We here introduced a method for quickly quantifying a variety of gait outcomes using an App that can be downloaded on most mobile devices. The BKG uses the technology available in most mobile devices with sensors and/or gyroscopes to capture gait metrics such as stride (speed), power (energy/amplitude), balance (stability), and symmetry (evenness/smoothness) based on previously validated variables. Data presented here illustrate that the mobile BKG achieves good test–retest reliability, and previously published work has established predictive validity with respect to concussion, as the BKG out-predicts widely used measures of gait and balance, such as the NIH 4-m gait test and the balance error scoring system (BESS) (Lecci et al., 2023). The mobile version of the BKG also has considerable normative data to help interpret an individual’s performance relative to their age- and gender-matched peers. Because normative data begins at 6 years of age and the gait task is simple (walking ten steps there and back), this allows for use in pediatric neuropsychological settings. In addition, the quantitative analysis allows for both normative comparisons as well as self-referenced comparisons over time, including comparisons to premorbid functioning (e.g., baseline data) or relative to previously collected post-injury data. This allows users to track the general trajectory of recovery in multiple ways. Moreover, because the BKG can be completed quickly, with the three walks taking approximately 1 to 2 min total, it is easy to integrate the assessment in a standard pediatric neuropsychological battery.

Although we here focus on the 18 BKG variables categorized into four domains, the accelerometers and algorithms generate hundreds of data points that go well beyond those presented here, but are still reliably defined, and have the potential to predict important outcomes. As an illustration, the BKG mobile output includes additional measures of symmetry, formulations of variability, as well as information regarding how an individual manages the deceleration and acceleration that occurs when going into and coming out of the turnaround, respectively. Similarly, the visual depiction and qualitative characterization of the stabilogram could also include a quantification of the total area, the extent to which it corresponds to an infinity shape, and a comparative evaluations of the shape’s consistency over time. It is also reasonable to assume that the output most relevant to predicting concussion outcomes likely diverges from the output that predicts the onset or worsening of a movement disorder (e.g., Parkinson’s disease), fall risk (e.g., foot drags), various developmental delays, cerebral palsy, seizures, autism spectrum disorder, and any number of other neuropsychologically relevant outcomes. Future research will explore these possibilities and define new parameters that can aid in diagnosis, defining course, and characterizing the efficacy of treatments.

One of the primary limitations of any clinical decision is the availability of data. Data acquisition is typically limited to instances when the patient is able to come into the office and have direct contact with a provider or technician. In the case of pediatric neuropsychologists, this typically unfolds in a single lengthy assessment or a small number of briefer visits. Pediatric neuropsychologists also have limited options when it comes to the systematic quantification of gait. Thus, within the context of a traditional neuropsychological assessment, multiple quantitative assessments of gait are rarely achieved. However, the mobile BKG provides a platform for achieving that end. Assessments could occur during visits and also in between visits in order to provide more detailed information regarding the trajectory of recovery. As an illustration, if a patient is only tested twice in the office, then the determination of recovery and the observed recovery trend is necessarily limited by the accuracy of the two obtained data points. In contrast, when there are multiple recovery data points, this will reveal variability in performance while also providing an indication of any recovery trends that may be occurring over and above the naturally occurring variability/error in the scores unrelated to injury. Thus, the mobile BKG provides clinicians with norm-referenced and self-referent comparisons along a variety of dimensions that can assist pediatric neuropsychologists in making better informed, data-driven return-to-play decisions.