Abstract
Introduction: Gait impairment occurs across the spectrum of traumatic brain injury (TBI); from mild (mTBI) to moderate (modTBI), to severe (sevTBI). Recent evidence suggests that objective gait assessment may be a surrogate marker for neurological impairment such as TBI. However, the most optimal method of objective gait assessment is still not well understood due to previous reliance on subjective assessment approaches. The purpose of this review was to examine objective assessment of gait impairments across the spectrum of TBI. Methods: PubMed, AMED, OVID and CINAHL databases were searched with a search strategy containing key search terms for TBI and gait. Original research articles reporting gait outcomes in adults with TBI (mTBI, modTBI, sevTBI) were included. Results: 156 citations were identified from the search, of these, 13 studies met the initial criteria and were included into the review. The findings from the reviewed studies suggest that gait is impaired in mTBI, modTBI and sevTBI (in acute and chronic stages), but methodological limitations were evident within all studies. Inertial measurement units were most used to assess gait, with single-task, dual-task and obstacle crossing conditions used. No studies examined gait across the full spectrum of TBI and all studies differed in their gait assessment protocols. Recommendations for future studies are provided. Conclusion: Gait was found to be impaired in TBI within the reviewed studies regardless of severity level (mTBI, modTBI, sevTBI), but methodological limitations of studies (transparency and reproducibility) limit clinical application. Further research is required to establish a standardised gait assessment procedure to fully determine gait impairment across the spectrum of TBI with comprehensive outcomes and consistent protocols.
1. Introduction
Traumatic brain injury (TBI) is defined as mild, moderate (modTBI), or severe (sevTBI) injury that results in symptoms that can persist across an acute (days to weeks) or chronic (months to years) time-period []. Mild TBI (mTBI), commonly known as concussion, has had predominant focus as it is the most common type of TBI (i.e., mTBI accounts for up to 84% of TBI) [,]. TBI can cause deficits in motor and non-motor functions, such as impaired cognitive function, headaches, fatigue, depression, anxiety, and irritability []. American Congress of Rehabilitation Medicine [] describes mTBI as a “mild insult to the head that results in a brief period of unconsciousness followed by impaired cognitive function”. Alternatively, moderate and severe TBI are described as traumatic brain injuries of increased severity lasting a longer period of time []. Individuals who present with modTBI express a great variability of injury severity and acute phase injury course, potentially leading into chronic difficulties at a later stage []. ModTBI sufferers may exhibit more aggressive symptoms of intra and extracranial injuries with the possibility of inducing secondary brain injury []. Furthermore, sevTBI patients demonstrate secondary implications of brain injury such as deviations in physiological variables, namely, systolic blood pressure, oxygen saturation, partial arterial pressure of oxygen, body temperature, serum sodium and glucose []. These symptoms can present within both the acute and chronic phases of injury and therefore represent a spectrum of injury. Motor impairments are prevalent, for example, 80% of people who suffer mTBI report balance impairments within days of injury [], and 30% report chronic (longer term; >12 weeks) symptoms of balance and/or gait impairment []. As such clinical assessment of physical and symptom deficits remain an important component of TBI assessment. TBI assessment has traditionally been based on subjective self-reporting or clinical rating of such symptoms, or neuropsychological ‘pen and paper’ testing, and standing balance, tandem gait performance []. However, specificity and accuracy of such tests can vary greatly due to the subjective nature in visual assessment and error tracking greatly limiting the replicability and validity of results [,,]. Therefore, recently significant attention has been dedicated to more objective assessment (force plates, and inertial measurement units, IMU) of TBI. Recent evidence has suggested that objective measures of gait may be useful in TBI assessment, as gait has been shown to be a useful biomarker for neurological impairments (e.g., dementia, neurodegenerative diseases) [,,]. Significant barriers limit the objective measure of gait in TBI within clinical practice, as current TBI assessment guidelines recommend the use of subjective/clinical visual assessment [,]. Objective gait measurement may be useful for diagnosis and management post-TBI, as it provides sensitive outcome measures for clinical interpretation []. However, there are many challenges to the transition of objective gait assessment into clinical settings. Rehabilitation and prognosis is often non-specific to different severities of TBI from mild, to moderate, to severe, which is further complicated by the stage of recovery or since injury of acute or chronic []. The spectrum of TBI complicates the use of objective biomarkers, as there needs to be a clear differentiation between sub-groups in order to suggest an outcome is an effective marker of neurological injury. To date, no review has examined the gait impairments in TBI across the spectrum of the condition.
The purpose of this review was to systematically examine the literature on gait impairment amongst adults with TBI across the spectrum of the injury (mTBI, modTBI, and sevTBI). Specifically, this study aimed to examine; (1) how gait was measured; (2) gait outcome measures and equipment used; (3) how does TBI severity impact upon gait metrics. This will help to inform the extent of gait impairment in TBI, as well as whether gait is a useful biomarker and inform clinical assessment/management. Methods of gait assessment will be discussed to determine clinical application.
2. Materials and Methods
2.1. Search Strategy
This review follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The key search terms were “traumatic brain injury” and “gait”. A catalogue of synonyms was formulated for each key term (Figure 1). Relevant Boolean and medical subject subheadings (MeSH) were applied as seen in Figure 1. The search strategy compromised of four electronic databases: AMED, CINAHL, PubMed and OVID, from 1960 to February 2021. Studies were considered relevant if they incorporated terminology which focussed on gait assessment in TBI and healthy control subjects in the title, abstract or keywords. An initial title screen for relevant articles was performed by the reviewer (AD) once the searched database results had been combined. After initial title screen, both the titles and abstracts of the selected articles were reviewed by two independent reviewers (AD, DP). A review of full text was required if it was not clear from the title or abstract whether the study met the review criteria.

Figure 1.
Key Search Terms. Reference to Title, Abstract and Key Terms.
2.2. Inclusion and Exclusion Criteria
Articles were included if they reported use of a digital device to measure gait in people with TBI. Studies were included only if they included a control group for comparison to TBI cohorts, so that TBI specific differences could be identified. Articles were excluded if they involved children (<18 years old), participants who had sustained a previous TBI, or a TBI group that did not have any information on the diagnosis (i.e., self-reported history of TBI with no current symptoms), did not provide specific objective gait outcomes from a digital device (i.e., only reported subjective outcomes) and involved a rehabilitation or intervention of some form. Only articles written in English were considered for review and any abstracts, case studies, conference proceedings, reviews, commentaries, discussion papers, or editorials were excluded.
2.3. Data Extraction
Data were extracted by the reviewer (AD) then synthesised into table format, with a second reviewer (DP) confirming the data. Data included demographic, instrumentation, study protocol, outcome measures and key findings.
3. Results
3.1. The Evidence Base
The search strategy yielded 156 articles, we excluded 48 duplicates (Figure 2). An initial screen identified 108 articles of interest, but 75 articles were excluded at title screen for not meeting the inclusion criteria and a further 19 were excluded during the full-text screen, with a further five removed at the final review stage. In total, 13 articles were included by consensus from the screening reviewers (AD, DP, and SS). Most of the removed articles were excluded because they included adolescents (Under 18′s), participants who suffered a previous TBI or did not include a healthy control group (full list of excluded articles and reasons located in Supplementary Material S1).

Figure 2.
PRISMA flow chart of study search.
3.2. Particpants
The reviewed articles (n = 13) investigated individuals who suffered a TBI over acute and chronic time periods across a range of severity from mTBI to sevTBI (Table 1). Most studies (n = 6) examined participants with mTBI, with modTBI (n = 1) [] and sevTBI (n = 1) [] less studied. Several studies examined across a range of different TBI severities, specifically; one study investigated modTBI to sevTBI [], another examined sevTBI and very sevTBI [], and three studies examined gait in general TBI (combining mTBI, modTBI, sevTBI into one group) [,,]. Five studies examined participants within an acute stage (<7 days), one study was conducted at a sub-acute stage (>7 days) [] and seven studies examined participants at a chronic stage (>12 weeks). Only one study examined participants across a range of TBI stages from sub-acute to chronic (time since injury ranging from 2 months to 28 months post injury) [].
In terms of demographic characteristics, the majority of the studies included both males and females, with ages that ranged from 18 to 53 years. One article did not provide specific demographic information for age []. There were various inclusion and exclusion criteria for TBI participants (Table 1).
3.3. Equipment
Table 2 shows that there was a lack of standardisation in instruments used to assess the characteristics of gait that were assessed in the reviewed studies, with inertial measuring units (IMUs), instrumented gait mats, force plates or motion capture systems all used. Majority of the articles used IMU devices (n = 5) to monitor spatiotemporal gait features, which were placed at various locations (i.e., feet, lumbar region, sternum, forehead etc.). The sampling frequencies used to quantify gait performance using IMU’s appears consistent (128 Hz), while motion capture varied between 60 Hz [] and 120 Hz [,,]. Three studies used force plates at a sampling frequency ranging from 960 to 1080 Hz [,,]. One study used a smartphone to quantify gait speed [].
3.4. Procedures
Table 3 shows that there was a lack of consistency in the specific study protocols, but the majority of the studies included in this review investigated both single and dual-task gait conditions (n = 6), while some studies investigated single task (n = 4), dual-task (n = 1) and complex task (n = 2) parameters alone. In terms of dual-task paradigm, eleven articles used a question-and-answer task, including serial subtraction in sevens (n = 5), spelling a 5-letter word backwards (n = 2), reciting months of the year in reverse order (n = 3). Additionally, the audio Stroop test (n = 1) and modified Stroop test (n = 1) and reading aloud a piece from a newspaper article were used (n = 1). Complex gait tasks used obstacle crossing (n = 2) with obstacles individualised according to the participants height.
3.5. Outcome Measure
There was a lack of standardisation of outcomes reported with reviewed articles providing various outcome measures on spatiotemporal, kinetic, and kinematic markers of gait. The majority of the articles included examined spatiotemporal parameters of gait with the most consistent measures being gait speed (n = 9) and measurements surrounding stride (i.e., stride length or stride time) (n = 6). Similarly, centre of mass displacement (n = 4) was the most common outcome measure used when considering kinematic assessment of gait. Furthermore, regarding kinetic parameters, ground reaction forces (n = 3) were reported.
3.6. Key Findings
This review identified a variety of methods associated to measuring gait following a TBI (Table 2). For example, when measuring gait using a single task paradigm, this review identified gait speed as a distinguishing factor between TBI participants and controls [,,,,]. However, Fino et al. (2016) reported that single-task gait was not different between TBI and controls and suggested that dual-task paradigms are needed to elicit gait deficits []. This was seen in studies that examined dual-tasks, as gait impairments were found during dual-task compared to single-task walking across the spectrum of TBI and different acute and chronic stages of the injury. Furthermore, complex gait tasks were examined in several studies and showed that deficits can be found using these protocols. For example, Vallée et al. (2006) determined that TBI participants were slower while performing the Stroop task when avoiding the wide obstacle and walked more slowly for narrow and wide obstacle conditions []. Furthermore, McFadyen et al., (2003) also showed an increased lead-limb clearance margins for TBI group throughout all conditions and TBI spectrum []. Overall, despite the differences in methodologies between studies, participants with TBI had impairment in gait with single-task, dual-task, and complex task performance in the reviewed studies, which was regardless of severity or stage, but the deficits were selective to particular outcomes within studies and lacked consistency across studies.

Table 1.
Study populations, time since injury, inclusion/exclusion criteria, and TBI diagnosis.
Table 1.
Study populations, time since injury, inclusion/exclusion criteria, and TBI diagnosis.
Author | TBI Population | Controls | Time Since Injury | Inclusion | Exclusion | TBI Diagnosis | |
---|---|---|---|---|---|---|---|
Basford et al. [] | TBI Group: n = 10. (F:4, M:6). Age: 40.9 ± 11.3. TBI Severity: Mild: 4/10 Moderate: 2/10. Severe: 4/10. | Control Group. n = 10 (F:4, M:6). Age: 41.2 ± 11.4. Matched according to height, age (± 5 years), gender and weight (± 7.5 cm). | Time since Injury:
|
|
|
| |
Belluscio et al. [] | TBI Group: (19 RTA’s, 1 Fall) Severe TBI Group: >19 GCS score. n = 10 (F:2, M:8) Age: 33.2 ± 9.6. Very Severe TBI Group: ≤ 19 GCS score. n = 10. F:3, M:7) Age: 36.1 ± 13.1. | Control Group: n = 20. (F:5, M:15) Age: 33.9 ± 9.5. |
|
|
|
| |
Fino et al. [] | Mild TBI Group: 4 injured. (M:1, F:3) Age: 19 ± 0.8. | Control group. 4 matched control participants. (M:1, F:3) Age: 19.5 ± 1.2. |
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| |
Fino, [] | Mild TBI Group: (M:2, F:3) Age: 18.8 ± 0.8. | Control group 4 matched control participants. (M:1, F:3) * No eligible control consent gained for 5th participant. Age: 19.5 ± 1.2. |
|
|
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| |
Martini et al. [] | Chronic Mild TBI Group n = 65 Age: 39.6 ± 11.7. Time since TBI—1.1 years. | Control Group: n = 57. Age: 36.9 ± 12.2 Time since injury: 1.1 years. |
| Mild TBI Group.
| Control Group
|
|
|
McFadyen et al. [] | TBI Group: n = 8 (M:8). Post Traumatic Amnesia (Weeks): 3.9 ± 4.4.
| Control Group: n = 4 (M:4). Age: Range 22.75 to 44.3. Median: 25.9. No standard deviation or average reported. |
|
|
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| |
Oldham et al. [] | Mild TBI Group: n = 50 (F:32, M: 18). Age: 20.2 ± 1.27. | Control Group: n = 25. (F: 13, M:12) 21.1 ± 2.2. | Time since injury: 72 h. |
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| |
Parker et al. [] | University/college athletes, club sport athletes. Moderate TBI Group: n = 29 (Suffered grade 2 TBI according to the Academy of Neurology Practice). Age: 21.6 ± 3.26. (F:14, M:15) | Control Group: n = 29 Age: 21.38 ± 3.38. (F:14, M:15). |
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Parrington et al. [] | 53 Participants (Collegiate Athletes across 6 sporting departments in various universities). Mild TBI Group: n = 23 n = 2 did not return to play during specified 8-week period. (F:5, M:18) Age: 20.1 ± 1.3. Contact: Non-Contact Sport. 18:5. | Control Group: n = 25 (F:6, M:19) Age: 39.3 ± 13.0. Contact: Non-Contact Sport. 12:13. |
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| |
Pitt et al. [] | Mild TBI Group: n = 11 (F:7, M:4). Age: 20.1 ± 1.3 | Control Group: Healthy matched—n = 11 (F:7, M:4). Age: 20.6 ± 1.9. |
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| |
Shan Chou et al. [] | TBI Group: n = 10. (F:4, M:6). Age: 40.9 ± 11.3.
| Control Group: n = 10 (F:4, M:6). Age: 41.2 ± 11.4. Matched with age, gender, height, and weight. | Time since Injury:
|
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| |
Vallée et al. [] | 18 Participants: Moderate to Severe TBI Group: n= 9(F:1, M:8) Age: 39.3 ± 13.0. | Control Group: n = 9 (F:1, M:8) Age: 39.7 ± 12.3. |
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| |
Williams et al. [] | TBI Group: n = 41 (F:10, M:31) Age: 29.1 ± 9.4 Time since injury (days): 2609.4 ± 2327.3. Posttraumatic Amnesia (days): 84.9 ± 57.5. HiMAT Score: 22.7 ± 11.5. | Control Group: n = 25. (F:9, M:16) Age: 27.8 ± 7.4. |
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Table 2.
Study aims, procedures, equipment, outcomes, and findings.
Table 2.
Study aims, procedures, equipment, outcomes, and findings.
Author | Aims | Procedures | Equipment | Outcome Measures | Key Findings |
---|---|---|---|---|---|
Basford et al. [] | Assess the gait and dynamic balance of individuals with instability or imbalance after TBI. Examine the relationship between symptoms. |
Motion Analysis/Single Task Gait.
13 body segments. 4 upper extremities. 6 lower extremities. 1 pelvic, trunk, and head. |
|
|
|
Belluscio et al. [] | Quantify gait patterns in severe traumatic brain injury through wearable inertial sensors. Investigate the association of sensor-based quality of gait indices with the scores of administered scales. | Clinical Assessment:
|
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|
Fino et al. [] | To determine the local dynamic stability of athletes who recently suffered a TBI during single and dual-task gait. |
|
|
|
|
Fino, [] | To determine single and dual-task turning kinematics in TBI and healthy athletes. |
|
| Stride characteristics
| Path Trajectory:
|
Martini et al. [] | Determine if gait domains are different without and with chronic Mild TBI. Determine if adding dual-task exacerbates differences in gait across the domains. Determine if self-reported severity scores are related to gait performance. | Single and dual-task conditions:
Audio Stroop Test:
| Inertial Sensors.
| Single Task and Dual-task.
| Individuals with chronic Mild TBI exhibit deficits across a multitude of gait characteristics.
|
McFadyen et al. [] | To definitively understand residual locomotor effects following a TBI on obstructed and unobstructed walking. | Locomotor Capacity and Gait:
|
|
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|
Oldham et al. [] | Examine whether changes between baseline and acute post-TBI single task and dual-task tandem gait performance differed between male and female athletes. | Tandem gait measures recorded consistently with SCAT-3. Single Task:
| Time recorded using smartphone. NR of inertial sensors. |
|
|
Parker et al. [] | Examine the relationship between measures of dynamic motor performance (single and dual-task walking) and neuropsychological function following concussion over the course of 28 days. | Gait Stability Testing.
|
| Neuropsychological testing
Average speed of responding to
|
|
Parrington et al. [] | Evaluate the recovery of gait and balance in concussed athletes to account for changes in trends following return to play. |
|
|
| BESS:
|
Pitt et al. [] | Provide an objective description of angular velocity and acceleration profiles along orthogonal axes from one IMU situated on L5 vertebrae. Demonstrate that detectable differences could be identified in IMU metrics and be utilised to distinguish individuals with a TBI during dual-task walking. | TBI participants:
| Superlab 5 software:
| Peak velocities
| Healthy and TBI participants were distinguished across the two-month post-TBI period through.
|
Shan Chou et al. [] | Determine the possibility of quantitatively assessing dynamic stability that did not have an obvious neuromuscular origin in individuals who suffered a TBI. |
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|
Vallée et al. [] | Establish the effects of increasingly demanding environments related to simultaneous visual tasks and physical obstructions to locomotor ability of people who have suffered TBI. | Visual Acuity:
|
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|
|
Williams et al. [] | Identify the most common gait abnormalities following a TBI and determine their rate of incidence. | 25 reflective
| Kinematic: Motion Analysis: 25 small reflective markers 3DGA.
| Spatiotemporal.
|
|

Table 3.
Objective gait task paradigm.
Table 3.
Objective gait task paradigm.
Article | Single Task | Dual-Task | Complex Task |
---|---|---|---|
Basford et al. [] | ✓ | ||
Belluscio et al. [] | ✓ | ||
Fino et al. [] | ✓ | ✓ | |
Fino, [] | ✓ | ✓ | |
Martini et al. [] | ✓ | ✓ | |
McFadyen et al. [] | ✓ | ||
Oldham et al. [] | ✓ | ✓ | |
Parker et al. [] | ✓ | ✓ | |
Parrington et al. [] | ✓ | ✓ | |
Pitt et al. [] | ✓ | ||
Shan Chou et al. [] | ✓—obstacle crossing | ||
Vallée et al. [] | ✓—obstacle crossing | ||
Williams et al. [] | ✓ |
A notable methodological limitation was found when considering gait impairment across the spectrum of TBI. Specifically, none of the reviewed studies examined gait deficits in TBI across the full spectrum of the injury (mTBI to sevTBI), with several studies combining TBI severities into a single TBI category rather than defining and assessing specific sub-groups. Therefore, there was no evidence on how gait differs between different severity levels of TBI, or if there are consistent deficits that become worse with increased TBI severity.
4. Discussion
To the authors knowledge, this review represents the first systematic synthesis of the literature examining gait impairment across the spectrum of TBI. Here we examined 13 studies that reported gait assessments in healthy controls and TBI participants specifically (i) how gait was measured; (ii) gait outcome measures and equipment used; (iii) how does TBI severity impact upon gait metrics.
4.1. Instrumentation
There was a lack of standardisation of instruments used to examine gait, therefore gait performance was quantified using several different technologies, but largely motion capture systems or IMUs were favoured over instrumented gait mats or force plates. Motion capture systems are a traditional approach to gait assessment, which are expensive, time consuming to set up, require specialist training and are often limited to specialist research centres or supervised laboratory surroundings, which may not be scalable to low resource settings []. Therefore, findings and conclusions drawn cannot be applied, relate or be replicated in other real-life contexts. Alternatively, IMUs have been suggested to overcome this challenge as they are easily implemented, low cost and portable [], with excellent validity and reliability for gait assessment []. Progression to use of IMUs was seen in the majority of reviewed articles, as most used IMU’s to measure gait in TBI [,,,,,,] which were reported to be a viable and reliable method of gait assessment. IMU’s (and 3D motion capture) were shown to detect abnormalities in gait and provide an overall account as to an individual’s gait cycle following a TBI, as all of the reviewed studies showed gait differences between those with TBI and healthy controls.
4.2. Outcome Measures
Gait can be characterised into spatiotemporal, kinematic, or kinetic outcome measures that are underpinned by selective neurological mechanisms []. There was a lack of consensus on the approaches used in assessing and reporting gait impairment in TBI, but studies generally reported spatiotemporal and kinematic outcomes. There were a wide range of gait outcomes reported between studies, but most studies reported on a limited amount of selected gait characteristics. The lack of standard assessment and reporting limits the generalisability of the findings, and does not support the use of quantitative methods of review reporting (i.e., meta-analysis) due to risk of bias. The most consistently reported outcome was gait speed (or velocity/pace). Gait speed is a measure of global walking performance [,], and is essentially an accumulation of multiple gait features that cannot be accurately quantified with a single outcome measure (e.g., speed []). As a result, gait speed in isolation is not a disease specific outcome and it does not reflect the subtle and precise underlying neural mechanisms involved in gait, which requires a more comprehensive examination of multiple gait outcomes. Despite gait speed being used in several studies we are unable to definitively report that it is useful at differentiating TBI groups, as the differences in methodologies (i.e., instruments, protocols, outcomes etc.) mean that we cannot directly compare outcomes across studies, and future work is needed to standardise procedures.
Different underlying brain regions control different aspects of gait [,] and therefore with TBI of different regions and severity there is a need for comprehensive gait outcome measure assessment and reporting. Gait is underpinned by a complex system of neural cortical and sub-cortical networks [] and impairment of any of the specific elements of the networks involved can result in impairment. Comprehensive reporting of gait in TBI literature is limited by the cohort sizes that have been examined, as there are many outcome measures that can be assessed and reported, but small samples sizes limit reporting capabilities and may lead to statistical errors. Many of the reviewed articles in this review had small TBI cohorts (n < 20) and as a result the number of outcomes reported may have led to inappropriate statistical analysis or reporting, due to the number of statistical comparisons []. There have been attempts to control for the number of comparisons made by using gait models within TBI cohorts (i.e., statistical analysis to reduce data in order to avoid statistical error issues []). However, only one of the reviewed articles [] used a data reduction technique to assess gait outcomes, which highlights the emerging nature of gait assessment and reporting in this field. The development of an outcome measure framework would enable a hypothesis-driven research plan aiming to explain gait disturbance and examine the effect TBI on gait performance across the spectrum. Thus, leading to a greater consensus on most sensitive and accurate gait measure within TBI.
4.3. Protocols
There was a lack of consistency when reporting basic methodological procedures in classifying TBI severity, time scale (acute, sub-acute and chronic) and inclusion and exclusion criteria, which limits the generalisability and understanding of results. However, findings suggest that gait is impaired in TBI across the spectrum from mild (concussion) to severe injury status. Despite gait impairments being found, there was a lack of standardisation of procedures that limit the future implementation of gait assessment protocols.
All articles included in this review (n = 13) were undertaken in a gait laboratory setting. While laboratory assessments allow for complete experimental control that may uncover gait deficits, the environment may lack functional validity as it may not reflect ‘real-world’ gait []. Specifically, assessment of gait in a laboratory setting may fail to capture subtle deficits due to TBI that may ensue within usual environments (e.g., home, community, clinic, work, sports pitch/field, etc.), where there are multiple distractions and a vast array of environmental information to process to complete tasks effectively and safely [,]. None of the reviewed studies made the progression to examine gait outside of the laboratory within free-living environments, which has been conducted with physical activity and turning characteristics in previous TBI studies [], which limits the understanding of the functional impact of potential gait impairments following a TBI.
There is no ‘gold-standard’ protocol for assessment of gait in TBI, as studies used a variety of tasks in an attempt to uncover deficits (e.g., single-task, dual-task, complex tasks etc.). The variety of experimental protocols employed across the included tasks of various complexity that sought to uncover specific TBI-related deficits. For example, single-task gait along a straight path was used in the majority of studies as this is thought to be a ‘baseline’ task that is controlled subcortical processing with minimal executive control [], which can then be used to compare with more complex gait tasks that may elicit subtle deficits following a TBI. Dual-task gait was commonly used as a more difficult gait task that requires simultaneous cognitive and motor processing involving the executive function [], which was compared to single task gait and healthy control gait to uncover TBI deficits. The least common gait assessment protocol was complex gait tasks, such as obstacle crossing, which require higher order cortical planning in order to plan and execute obstacle avoidance during walking []. The lack of a standardised protocol limits the generalisability of results across studies (i.e., even single-task walking was conducted for different times and distances), which means that quantitative analysis of the outcomes across studies is inappropriate until a common protocol is developed.
Gait provides a simple marker for an individual’s overall health and is a widely accepted predictor of quality of life, decline of cognitive proficiency and falls []. Due to neurological decline and difficulties with age, gait becomes a more difficult task to perform efficiently and economically causing a transfer from an automatic to a cognitive level of control in order to execute and perform within a complex environment [,]. Increased task complexity for gait assessment was thought to increase the sensitivity of gait analysis for discriminating participants with TBI from healthy controls. While dual-task and complex task gait were not assessed within the same study protocol, there were similar outcomes when walking with these additional tasks (i.e., slower gait in TBI groups), which may indicate that adding any additional task could highlight impairments. However, despite the reviewed studies finding gait differences in TBI with the increased cognitive (or cortical) demand of dual-tasks and complex tasks, the benefit over using single-task gait (a simpler and quicker task) remains unknown, as gait deficits were detected across the TBI spectrum (mTBI, modTBI, SevTBI) using single-task. This is further complicated by the lack of consistency in the type of dual-task, and the set-up of the complex task (obstacle crossing) makes it difficult to directly compare outcomes across studies, and therefore difficult to make any clinical assessment recommendations. Future studies should consider whether their protocols require increased task complexity in order to detect gait deficits, as performance of a single-task walk may be sufficient to detect deficits when comprehensively investigating gait with data-driven digital technologies.
4.4. Outcome Interpretation
While the reviewed studies found differences in gait in those with TBI compared to controls, or within TBI when examined using tasks of increasing complexity, there were substantive methodological limitations that impact the interpretation of the reported outcomes. Specifically, none of the reviewed studies examined gait impairment differences between the various severity levels of the injury (mTBI, modTBI, sevTBI), with few studies examining modTBI. This is likely a result of the difficulties in defining the various levels of TBI, as there were variations in the reported diagnostic criteria (i.e., some acute diagnosis was 7 days, others only hours, and chronic ranged from months to years post-injury) and the specific individuals involved in the diagnostics within the reviewed studies (i.e., athletic trainer or a team physician, or merely medical recorded screen). Without being able to clearly define the severity and stage of TBI using a standardised criteria and then examine gait across these sub-groups, it is difficult to determine whether gait could be an effective biomarker for determining diagnosis, severity level, prognosis or monitoring of this neurological condition. Additionally, none of the reviewed studies included area under the curve or receiver operating characteristic curve analysis for specificity or sensitivity of gait characteristics in determining TBI gait differences with controls, which limits the interpretation of results (i.e., there may be differences but they may have low diagnostic value []. Therefore, future studies are needed to develop standard procedures for examining gait impairment in TBI, which will aid in the determination of gait as a marker of TBI.
5. Conclusions
Gait was shown to be impaired in TBI within the reviewed studies regardless of the severity or stage of the injury, but the specific impairments and the outcomes of clinical relevance are yet to be fully established across the spectrum of the condition. Further research is required to establish standardized methods for gait assessment in TBI, which will help to determine the gait deficits at each severity level of injury (mTBI, modTBI, sevTBI) in larger well-defined cohorts to establish findings.
Supplementary Materials
The following are available online at https://www.mdpi.com/article/10.3390/s22041480/s1, Table S1: List of articles that did not meet inclusion criteria and reason for exclusion.
Author Contributions
Conceptualization, methodology, software, formal analysis, investigation, supervision, writing, funding acquisition, project administration; A.D., D.P., S.S. Reviewing; L.G., R.M., J.D., S.J.M., R.V. and A.G. All authors have read and agreed to the published version of the manuscript.
Funding
This work was supported by Northumbria University as part of a doctoral research development fund programme (D Powell) and by the Private Physiotherapy Education Foundation (PPEF – 368; PI: Stuart). Dr Stuart is supported by grants from the Parkinson’s Foundation (PDF-FBS-1898, PDF-CRA-2073).
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
Not applicable.
Conflicts of Interest
The authors declare no conflict of interest.
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