Gait Analysis and Electrodynogram

Number: 0263

Policy

Aetna considers gait analysis (also known as motion analysis studies), dynamic electromyography or the use of an electrodynogram experimental and investigational for conditions that result in gait deviations and for all other indications because there is insufficient peer-reviewed medical literature demonstrating the clinical value of these technologies. 

See also CPB 0294 - Pedobarograpghy 

Background

Gait analysis, also known as motion analysis, is a systematic evaluation of the dynamics of gait (walking and walking patterns). The standard method of gait analysis is by observational assessment (without the use of any computers or computer programs); focuses on motion of the hips, knees, ankles and feet throughout the gait.

Several investigators have advocated the use of gait analysis for planning surgery and therapy treatments for children with cerebral palsy (CP).  Although their rationale appears sound, it has not been supported by clinical outcome studies demonstrating its efficacy beyond visual analysis of gait abnormalities routinely performed by clinicians.

An assessment conducted by the BlueCross BlueShield Association Technology Evaluation Center (2002) concluded that “[t]he evidence does not permit conclusions on whether the use of gait analysis for evaluation of children with cerebral palsy improves health outcomes or is as beneficial as established alternatives.”  The assessment noted that several studies have reported that treatment decisions were affected by the results of gait analysis; these studies, however, do not demonstrate whether clinical outcomes were improved by basing treatment decisions on gait analysis.  The assessment identified only 1 study that directly addressed the question of whether gait analysis improves patient outcomes, compared with standard clinical assessment.  This retrospective study (Lee et al, 1992) reported on the outcomes of 23 children with CP, 15 of whom were treated according to the recommendation of gait analysis data plus clinical assessment, and 8 of whom were treated according to clinical assessment alone.  Of the 7 children who were classified as not improved, 5 had been treated according to clinical assessment alone.  The TEC assessment noted several problems with this study, including small numbers of study subjects, lack of consideration of confounding factors, and vague definition of outcomes.  Most important, however, it was not reported whether children analyzed as treated according to gait analysis data underwent treatments discordant with the recommendations of clinical assessment alone or in agreement with clinical assessment alone (BCBSA, 2002).  The TEC assessment notes that, if it is the latter, then the study is severely flawed and can not be used to compare clinical assessment and gait analysis.  The TEC assessment concluded that, “[i]n the absence of any well-designed observational or randomized controlled trials, no conclusion can be drawn about whether gait analysis in the clinical evaluation and treatment of cerebral palsy has an effect upon health outcomes.”

Gait analysis studies that have been published since the TEC assessment was released suffer from similar limitations as previous studies, in that they have not included outcomes of internal comparison groups of children with CP who were managed based on clinical assessment alone.  Comparisons of outcomes between studies (i.e., comparisons of outcomes of studies where gait analysis was used with studies where gait analysis was not used) is problematic due to confounding factors that may account for differences in outcomes (such as differences in surgical technique and experience, characteristics of study subjects, methods of assessing outcomes, etc.).  

Several studies have reported that treatment decisions are affected by the results of gait analysis (Lofterod et al, 2007; Kay et al, 2000; Molenaers et al, 2006; Wren et al, 2005); these studies, however, do not demonstrate whether clinical outcomes are improved by basing treatment decisions on gait analysis.  Similarly, studies examining correlations (or the lack thereof) between clinical measurements and quantitative gait analysis (Desloovere et al, 2006; Kawamura et al, 2006) do not prove that the quantitative measurements provided by gait analysis alter the management of patients such that clinical outcomes are improved. 

One small retrospective study evaluated the impact of computerized gait analysis on clinical outcomes (Chang et al, 2006).  The study included 10 patients with CP and 10 age- and sex-matched controls.  Children in the control group chose not to follow the gait analysis recommendation and chose a non-surgical treatment approach, whereas children in the gait analysis group followed a physician's recommendation for gait analysis.  The results documented that 74 % of patients in the control group had no change or a negative outcome, 26 % in the control group had a positive outcome.  Of the gait analysis group 61 % had no change or a negative outcome, while 44 % of this group had a positive outcome.  While the results of this study suggest that gait analysis recommendations may improve clinical outcomes, the control group did not undergo surgery, whereas those in the gait analysis group did.  Therefore, it is not possible to determine the relative contributions of gait analysis and surgery.  

A study by Wren et al (2009) is a retrospective analysis of numbers of procedures and costs in children whose surgery was planned by gait analysis and those whose surgery was performed without gait analysis.  The investigators found no significant differences overall in total numbers of procedures and costs between the 2 groups.  Limitations of the study include its retrospective nature and lack of randomized assignment.  Since the study was retrospective, the authors were unable to assess other outcomes such as function, participation, and quality of life, which are important outcomes that need to be examined in future prospective studies.

Dobson and colleagues (2007) evaluated the validity of existing classifications of gait deviations in children with CP.  The authors noted that numerous efforts have been made to develop classification systems for gait in CP to assist in diagnosis, clinical decision-making and communication.  The authors examined the internal and external validity of gait classifications in 18 studies, including their sampling methods, content validity, construct validity, reliability and clinical utility.  The authors found that half of the studies used qualitative pattern recognition to construct the gait classification and the remainder used statistical techniques such as cluster analysis.  Few adequately defined their samples or sampling methods.  The authors found that most classifications were constructed using only sagittal plane gait data, and that many did not provide adequate guidelines or evidence of reliability and validity of the classification system.  No single classification addressed the full magnitude or range of gait deviations in children with CP.  The authors concluded that, although gait classification in CP can be useful in clinical and research settings, the methodological limitations of many classifications restrict their clinical and research applicability.

Lofterod and Terjesen (2008) evaluated the outcome of orthopedic surgery in ambulant children with CP, when the orthopedic surgeons followed the recommendations from pre-operative 3-dimensional gait analysis.  A total of 55 children (mean age of 10 years and 11 months) were clinically evaluated by orthopedic surgeons who proposed a surgical treatment plan.  After gait analysis and subsequent surgery, 3 groups were defined.  In group A, there was agreement between clinical proposals, gait analysis recommendations, and subsequent surgery in 128 specific surgical procedures.  In group B, 54 procedures were performed based on gait analysis, although these procedures had not been proposed at the clinical examination.  In group C, 55 surgical procedures that had been proposed after clinical evaluation were not performed because of the gait analysis recommendations.  The children underwent follow-up gait analysis 1 to 2 years after the initial analysis.  The kinematic results were satisfactory, with improvement in most of the gait parameters in children who had undergone surgery and no significant deterioration in those who were not operated.  In group A, there were significant improvements in maximum hip extension in stance, minimum knee flexion in stance, timing of maximum knee flexion in swing and knee range of motion (ROM), maximum ankle dorsiflexion in stance, and mean femur rotation in stance.  In group B, there were significant improvements in maximum hip extension in stance, minimum knee flexion in stance, and knee ROM.  The authors concluded that gait analysis was useful in confirming clinical indications for surgery, in defining indications for surgery that had not been clinically proposed, and for excluding or delaying surgery that was clinically proposed.  The findings of this study need to be validated by well-designed studies.

An additional important factor that limits the ability to interpret the evidence on gait analysis is the fact that the technical parameters, diagnostic variables, and outcome measures vary among studies.  Some of the differences included the number and placement of video cameras, reflective markers, and force plates; number and type of gait parameters that are measured; and variations in the use of electromyography (EMG) data.  Study participants are heterogenous with regard to the type of gait disorder and clinical history.  Studies of the impact of computerized gait analysis on patient management evaluated different types of surgical procedures, and varied in the number and type of muscles that were operated on.  In addition, few studies include follow-up data.

Narayanan (2007) reviewed the scientific literature to describe the role of gait analysis in the orthopedic management of ambulatory children with CP and examined the current best evidence to support these roles.  The author stated that although gait analysis has been shown to alter decision making, there is little evidence that the decisions based on gait analysis lead to better outcomes.  Consequently, clinical gait analysis remains controversial, with wide variation in the rates of utilization of gait analysis in the management of children with ambulatory CP.  The author stated that the time is ripe for clinical trials and cohort studies to provide the evidence to establish the appropriate utilization of this technology.

Randomized controlled clinical trials to compare outcomes of surgery planned with and without gait analysis are currently ongoing.  One of these studies, sponsored by the Federal Agency for Healthcare Research and Quality (AHRQ) and conducted at 2 children's hospitals in Los Angeles, has been completed, and its results (Wren, et al., 2012) are reported below.  The AHRQ explained why this study is needed: "Gait analysis testing has been used to assist orthopedic surgeons in developing treatment plans for children with gait abnormalities, particularly children with cerebral palsy.  Previous studies have shown that gait analysis testing significantly impacts surgical decision-making for these patients.  However, no controlled studies have been done to determine whether gait analysis and the subsequent changes in surgical decision-making affect clinical outcomes.  Consequently, the use of gait analysis in clinical practice remains controversial.  The purpose of this study is to conduct a randomized controlled trial to assess the effects of preoperative gait analysis on surgical outcomes in ambulatory children with cerebral palsy" (AHRQ, 2005).

Another randomized controlled clinical study of gait analysis, funded by the Canadian government, is being conducted at 2 children's hospitals in Toronto.  The study description explains the need for this trial: "Pre-operative planning is based on the physical examination and visual (observational) analysis of the child's gait.  In some centres, patients undergo additional gait analysis in a motion laboratory.  While gait laboratory analysis is accepted as an important research tool, there is controversy about its clinical utility in decision making for the surgical management of this population.  To date, no clinical trials have been undertaken to answer this question, and the appropriate clinical utilization of this technology is yet to be established.  The consequence of this uncertainty is that ambulatory children with cerebral palsy are either being deprived of a useful assessment tool in some centres, or alternatively they are being subjected to an unnecessary evaluation that is both expensive and time consuming in other centres" (Hospital for Sick Children, 2007).  Both of these randomized controlled clinical trials will examine the impact of gait analysis on a number of parameters, the most important of which relate to clinical outcomes (i.e., improvements in function and quality of life), as opposed to intermediate outcomes.

Maquet and colleagues (2010) evaluated gait characteristics during simple and dual task in patients with mild cognitive impairment (MCI) and compared them with those of healthy elderly subjects and mild Alzheimer's disease (AD) patients.  These researchers proposed a gait analysis to appreciate walking (simple task and dual task) in 14 MCI, 14 controls and 6 AD subjects who walked at their preferred speed.  A 20-second period of stabilized walking was used to calculated stride frequency, stride length, symmetry and regularity.  Speed walking was measured by electrical photocells.  Variables measured during simple and dual tasks showed an alteration of motor function as well in mild AD patients as in MCI patients.  The authors concluded that at the end of this preliminary study, they defined a specific gait pattern for each cognitive profile.  They stated that further researches appear necessary to enlarge the study cohort.  Furthermore, in a review on clinical gait analysis, Chapin (2010) stated that additional research documenting the value of incorporating clinical gait analysis in the treatment planning process may ultimately change the payment patterns of insurers.

Ornetti and colleagues (2010) stated that kinematic gait analysis consisting of measuring gait parameters (e.g., dynamic joint angles, gait speed, and stride length) is a potential outcome measure in osteoarthritis (OA).  These investigators evaluated the psychometric properties of gait analysis.  A systematic literature search was performed in PubMed and the Cochrane database until January 2008 by selecting manuscripts assessing any psychometric property of gait analysis in knee or hip OA.  These researchers assessed feasibility (access, cost, and time); reliability; discriminant capacity by differences between OA and non-OA patients; construct validity by correlation between gait analysis and OA symptoms: pain or functional disability (Lequesne/WOMAC); and responsiveness by improvement of gait analysis after treatment of OA using effect size.  Among the 252 articles identified, the final analysis included 30 reports (i.e., 781 knee OA patients and 343 hip OA patients).  Gait analysis presents various feasibility issues and there was limited evidence regarding reliability (3 studies; 67 patients).  Discriminant capacity showed significant reduction of gait speed, stride length and knee flexion in OA patients compared to healthy subjects.  Few data were available concerning construct validity (3 studies; 79 patients).  Responsiveness of gait speed was moderate to large with effect size ranging respectively from 0.33 to 0.89 for total knee replacement, and from 0.50 to 1.41 for total hip replacement.  The authors concluded that available data concerning validity and reliability of kinematic gait analysis are insufficient to date to consider kinematic parameters as valuable outcome measures in OA.  They stated that further studies evaluating a large number of patients are needed.

Calhoun et al (2011) compared kinematic and kinetic gait patterns in children with autism versus age-matched controls.  A total of 12 children with autism and 22 age-matched controls participated in the study.  An 8-camera motion capture system and 4 force plates were used to compute joint angles and joint kinetics during walking.  Parametric analyses and principal component analyses were applied to kinematic and kinetic waveform variables from the autism and control groups.  Group differences in parameterization values and principal component scores were tested using 1-way ANOVAs and Kruskal-Wallis tests.  Significant differences between the autism and control group were found for cadence, and peak hip and ankle kinematics and kinetics.  Significant differences were found for 3 of the principal component scores:

  1. sagittal ankle moment principal component one,
  2. sagittal ankle angle principal component one, and
  3. sagittal hip moment principal component two.
Results suggest that children with autism demonstrate reduced plantar-flexor moments and increased dorsiflexion angles, which may be associated with hypotonia.  Decreased hip extensor moments were found for the autism group compared to the control group, however, the clinical significance of this result is unclear.  This study has identified several gait variables that were significantly different between autism and control group walkers.  This is the first study to provide a comprehensive analysis of gait patterns in children with autism.  The role of gait analysis, if any, in the managment of children with autism has yet to be established.

The American Association of Electrodiagnostic Medicine/American Academy of Physical Medicine and Rehabilitation's technology review on "Dynamic electromyography in gait and motion analysis" (1999) concluded that "its utility in pre-operative planning has not been proven in well-designed, large, multi-center studies.  There remains many legitimate differences of opinion as to the relative benefits of surface versus fine-wire techniques that future studies will need to resolve.  Also in doubt is the best way of determining onset of electrical activity and other technical variables.  Dynamic EMG, as part of comprehensive motion analysis, has found applications in the optimization of athletic performance.  The subjects in these studies are not patients in the classic sense and did not necessarily carry any type of medical diagnosis".

A randomized controlled clinical trial (Wren et al, 2013) found no significant difference in primary outcome measures and most secondary outcome measures with use of gait analysis in cerebral palsy surgery.  This study examined the impact of gait analysis on surgical outcomes in 156 ambulatory children with CP through a randomized controlled trial.  Patients underwent gait analysis and were randomized to 2 groups:

  1. Gait Report group (n = 83), where the referring surgeon received the patient’s gait analysis report, and
  2. Control group (n = 73), where the surgeon did not receive the gait report.
Outcomes were assessed pre- and 1.3 + 0.5 years post-operatively.  An intent-to-treat analysis compared outcomes between the 2 groups.  The primary outcome measures were the walking scale of the Gillette Functional Assessment Questionnaire (FAQ), the Gait Deviation Index (GDI), and the oxygen cost of walking (O2 cost).  Secondary outcome measures included the gross motor function measure (GMFM-66) and health-related quality of life questionnaires (Child Health Questionnaire (CHQ), Pediatric Outcomes Data Collection Instrument (PODCI), and Pediatric Evaluation and Disability Inventory (PEDI).  The outcomes that differed significantly between groups were change in health component of the CHQ, which was rated as much better for 56 % (46/82) of children in the Gait Report group compared with 38 % (28/73) in the Control group (p = 0.04), and the upper extremity physical function component of the PODCI.  There were no significant differences in outcomes between the Gait Report group and the Control group in the primary outcome measures: the FAQ, the GDI, and O2 cost.  There were also no significant differences in most secondary outcomes, including the GMFM-66, all four components of the PEDI, and components of the CHQ other than change in health (i.e., global health, physical functioning, role/social limitations - physical, pain/discomfort, self esteem, general health perception, parental impact - emotional, and parental impact - time), and components of the PODCI other than upper extremity physical function (i.e., sports/physical function, transfer/basic mobility, pain/comfort, and global functioning).  The authors posited that one potential reason for the lack of difference between the Gait Report group and the Control group for most measures was because surgeons who received gait reports followed the report recommendations less than half (42 %) of the time.

In a cohort study, Chow and colleagues (2012) examined the velocity-dependent change in medial gastrocnemius (MG) activity during the stance phase of gait in patients with moderate-to-severe resting hypertonia after stroke or traumatic brain injury (TBI).  Convenience sample of patients with chronic TBI and stroke (n = 11 each), and age- and sex-matched healthy controls (n = 22).  Main outcome measures included frequency and gain (steepness) of positive (greater than 0) and significant positive (greater than 0 and goodness of fit p ≤ 0.05) electromyogram-lengthening velocity (EMG-LV) linear regression slope in MG during the stance phase of gait.  Positive and significant positive slopes were found significantly more often on the more affected (MA) than less affected (LA) side in patients with TBI but not stroke.  Both the frequencies of positive and significant positive slopes on the MA side in patients with TBI were also significantly higher than in controls.  However, neither the gain of positive nor significant positive EMG-LV slope was different between the MA and LA sides or in comparison with controls.  Positive slope parameters were not related to Ashworth score on the MA side.  The authors concluded that the frequency and gain of positive EMG-lengthening slope did not effectively differentiate patients from controls, nor were they related to the resting muscle hypertonia.  Motor output during MG lengthening in the stance phase of gait is apparently not exaggerated or related to resting hypertonia in patients with chronic TBI and stroke.  Thus, changes in gait during stance cannot be ascribed to increased stretch reflex activity in MG muscle after acquired brain injury.

An evidence review of management of children with cerebral palsy (Narayanan, 2012) stated that there is good evidence that gait analysis does alter surgical decision-making at least some of the time. However, "there remain concerns about the reliability (reproducibility) of these decisions or whether implementing these recommendations would result in different, let alone better outcomes" (Narayanan, 2012). The review reported on one study of gait analysis that found that, when the same gait analysis data were examined by gait analysis experts from 6 different institutions, there was only slight to moderate agreement in the list of problems generated by the experts (citing Skaggs, et al., 2000). Agreement about specific surgical recommendations was similarly poor. The review explained that, although gait analysis data are themselves objective, there is subjectivity in interpretation even among experts, with diagnoses and treatment recommendations varying significantly by surgeon or institution. The evidence review stated that, in another study (citing Noonan, et al., 2003), there was variability in the kinematic data generated in 4 different motion laboratories that tested the same 11 patients. Although the clinical significance of some of this variability has been challenged, the treatment recommendations generated from these data were different across the 4 centers for 9 of the 11 patients. The author of the review stated: "Variability in the interpretation of gait data reflects the prevailing uncertainty (or controversies) about the causes and/or significance of specific findings and will only be resolved with ongoing clinical research and experience using gait analysis. Similarly, variability in treatment recommendations based on the same gait data also reflects differences of opinion about best strategies to deal with specific problems, which in turn can only be definitively resolved with comparative clinical trials or observational studies" (Narayanan, 2012). The review author concluded that "as long as such significant variability exists, the recommendation that gait analysis is essential for all preoperative decision-making before multilevel orthopaedic surgery in clinical (as opposed to research) practice is currently not supported by the literature" (Narayanan, 2012).

An UpToDate review on “Gait disorders of elderly patients” (Ronthal, 2013) states that “It becomes evident that the control of walking is a highly complex and integrated activity.  With multiple control points, multiple areas of vulnerability to disruption of normal gait are present.  Knowledge of the basic physiology is a good starting point in gait disorder analysis.  More sophisticated analysis of gait by posturography and studies of gait variability are largely research tools, but can help with rehabilitation”.

Lamberts et al (2016) noted that three-dimensional gait analysis (3DGA) is commonly used to assess the effect of orthopedic single-event multi-level surgery (SEMLS) in children with spastic cerebral palsy (CP).  These investigators provided an overview of different orthopedic SEMLS interventions and their effects on 3DGA parameters in children with spastic CP.  A comprehensive literature search within 6 databases revealed 648 records, from which 89 articles were selected for the full-text review and 24 articles (50 studies) included for systematic review.  The Oxford Centre for Evidence-Based Medicine Scale and the Methodological Index for Non-Randomized Studies (MINORS) were used to appraise and determine the quality of the studies.  Except for 1 level II study, all studies were graded as level III according to the Oxford Centre for Evidence-Based Medicine Scale.  The MINORS score for comparative studies (n = 6) was on average 15.7/24, while non-comparative studies (n = 18) scored on average 9.8/16; 19 kinematic and temporal-distance gait parameters were selected, and a majority of studies reported improvements after SEMLS interventions.  The largest improvements were seen in knee range of motion (ROM), knee flexion at initial contact and minimal knee flexion in stance phase, ankle dorsiflexion at initial contact, maximum dorsiflexion in stance and in swing phase, hip rotation and foot progression angles.  However, changes in 3DGA parameters varied based on the focus of the SEMLS intervention.  The authors concluded that the current article provided a novel overview of a variety of SEMLS interventions within different SEMLS focus areas and the post-operative changes in 3DGA parameters.  This overview would assist clinicians and researchers as a potential theoretical framework to further improve SEMLS techniques within different SEMLS focus groups.  In addition, it could also be used as a tool to enhance communication with parents, although the results of the studies couldn’t be generalized and a holistic approach is needed when considering SEMLS in a child with spastic CP.

Moreover, the authors stated that “With regards to the gait analyses itself, 3DGA is seen as the gold standard, however, the gait data should be interpreted within the reliability of the gait measurement itself, and the subjective interpretation of the data might slightly vary between the different experts.  There is also a lack of description of 3DGA data collection protocols, as well as variability within the studies, which might influence the results of the studies.  Future research should aim to reach a consensus on a general 3DGA model.  The use of an overall gait pattern score, such as the Gait Deviation Index (GDI), and normalization of temporal-distance parameters should be encouraged.  In addition, alternative clinical statistics, such as Cohen effect sizes and magnitude based inferences, can potentially add additional values to these studies next to the traditional statistical methods”.

Jeans et al (2017) stated that a non-operative approach in the treatment of idiopathic clubfoot has been taken in an attempt to reduce the incidence of surgical outcomes.  Although both the Ponseti casting (Ponseti) and the French physiotherapy (PT) methods have shown gait and pedobarograph differences at age 2 years, improved gait results have been reported by age 5 years.  These researchers evaluated plantar pressures in feet treated with the Ponseti versus the PT methods at this intermediate stage.  Clubfoot patients treated non-operatively (Ponseti or PT) underwent pedobarograph data collection at age 5 years.  The foot was subdivided into the medial/lateral hind-foot, mid-foot, and fore-foot regions.  Variables included peak pressure, maximum force, contact area%, contact time%, pressure time integral, the hind-foot-fore-foot angle, and displacement of the center of pressure (COP) line; 20 controls were used for comparison.  Pedobarograph data from 164 patients (238 feet; 122 Ponseti and 116 PT) showed no significant differences between the Ponseti and the PT feet, except the PT feet had a significantly less medial movement of the COP than the Ponseti feet (p = 0.0379).  Compared with controls, both groups had decreased plantar pressures in the hind-foot and 1st metatarsal regions, whereas the mid-foot and lateral fore-foot experienced significant increases compared with controls.  This lateralization was also reflected in the hind-foot-forefoot angle and the COP.  The authors concluded feet that remained non-operative and avoided surgical intervention were considered a good clinical result.  However, pedobarograph results indicated mild residual deformity in these feet despite clinically successful outcomes.

Guinet and Desailly (2017) noted that CP describes a group of permanent disorders of the development of movement and posture, causing activity limitation, that are attributed to non-progressive disturbances that occurred in the developing fetal or infant brain.  The overall prevalence of CP is 1.77 per 1,000 live births.  Cerebral palsy is the largest cause of motor disability in childhood.  The benefits of regular physical activity on health are widely recognized, especially by World Health Organization.  Children with CP practice less daily physical activity (PA) than typically developing (TD) children.  This may affect their quality of life and may increase their impairments.  These researchers determined the correlations between clinical gait analysis (3-DGA) and clinical examination with PA.  The hypothesis was that correlations are not strong enough to estimate PA from 3-DGA or physical examination parameters.  The authors noted that if some kinematics, spatio-temporal parameters and clinical examination parameters were correlated with intensity and quantity of PA, the links found in this study were not enough to predict PA from 3-DGA and physical examination.  Gait analysis and clinical examination could very partially reflect the overall level and quantity of daily PA; however, none of the tested models from 3-DGA and clinical examination strongly predicted PA.  Regarding the weakness of the observed correlations, these researchers recommended to associate 3-DGA and actimetry in the longitudinal follow-up of patients with CP.

Zugner and associates (2017) simultaneously examined 14 patients with optical tracking system (OTS) and dynamic radio-stereometric analysis (RSA) to evaluate the accuracy of both skin- and a cluster-marker models.  The mean differences between the OTS and RSA system in hip flexion, abduction, and rotation varied up to 9.5° for the skin-marker and up to 11.3° for the cluster-marker models, respectively.  Both models tended to under-estimate the amount of flexion and abduction, but a significant systematic difference between the marker and RSA evaluations could only be established for recordings of hip abduction using cluster markers (p = 0.04).  The intra-class correlation coefficient (ICC) was 0.7 or higher during flexion for both models and during abduction using skin markers, but decreased to 0.5 to 0.6 when abduction motion was studied with cluster markers.  During active hip rotation, the 2 marker models tended to deviate from the RSA recordings in different ways with poor correlations at the end of the motion (ICC less than or equal to 0.4).  During active hip motions soft tissue displacements occasionally induced considerable differences when compared to skeletal motions.  The best correlation between RSA recordings and the skin- and cluster-marker model was found for studies of hip flexion and abduction with the skin-marker model.  The authors concluded that studies of hip abduction with use of cluster markers were associated with a constant under-estimation of the motion; and recordings of skeletal motions with use of skin or cluster markers during hip rotation were associated with high mean errors amounting up to about 10° at certain positions.

Rogan and colleagues (2017) evaluated the concurrent validity of the RehaWatch system using the GAITRite system as a criterion reference for gait assessment in the long-term care (LTC) elderly.  In this study, a total of 23 elderly participants (mean age of 90.9 ± 8.4 years) performed 4 walking trials at normal and fast walking speed during single-task and dual-task walking.  Data for both systems were collected simultaneously for each trial.  Concurrent validity was assessed through limits of agreement (LoA) methodology using Bland-Altman plots.  No systematic bias could be determined.  Mean biases for step duration, velocity and cadence were above the pre-specified ±7 % value from zero lines for normal walking during single-task and dual-task walking.  The LoA had a wide range between -21 % and 25 %.  Only cadence showed small LoA for normal walking speed during single- (-8.4 % to 7.7 %) and dual-tasking (-4.1 % to 3 %).  Heterogeneous bias was determined for step duration during fast walking during dual-task and for velocity during fast walking during single-task and dual-task.  Heteroscedasticity was shown for step length during normal walking under the dual-task condition and fast walking during single-task and dual task activities.  The authors concluded that no gait parameters were interchangeably usable between the 2 systems for normal walking during single-task and dual-task activities.

Mutoh and colleagues (2018) obtained data of gait parameters on predicting long-term outcome of hippotherapy.  In 20 participants (4 to 19 years; GMFCS levels I to III) with cerebral palsy (CP), gait and balance abilities were examined after 10-m walking test using a portable motion recorder.  Hippotherapy was associated with increased Gross Motor Function Measure (GMFM)-66 at 1 year from the baseline (p < 0.001).  Hippotherapy increased stride length, walking speed, and mean acceleration and decreased horizontal/vertical displacement ratio over time (p < 0.05).  Stride length and mean acceleration at 6 weeks predicted the elevation of GMFM-66 score.  The authors concluded that these data suggested that 1-year outcome of hippotherapy on motor and balance functions can be assessed from the early phase by serial monitoring of the gait parameters  The drawbacks of this study included its single-arm design with relatively small number of subjects (n = 20), which meant that the results may not be extrapolated to all types and severity levels of CP.  In this study, no matched-control or other training modalities (e.g., horseback riding simulator and whole-body vibration) was provided because parents of all potential participants were adamant that they wanted their sons/daughters to undergo the actual intervention as a result of the likely benefit of hippotherapy.  These issues need to be overcome in future studies.

Petis and associates (2018) examined the impact of surgical approach for total hip arthroplasty (THA) on quantitative gait analysis.  Patients undergoing THA for primary osteoarthritis of the hip were assigned to 1 of 3 surgical approaches: anterior, posterior and lateral.  Standardized implants were used at the time of surgery.  Three-dimensional gait analysis was performed pre-operatively and at 6 and 12 weeks post-operatively.  At each time-point, these researchers compared temporal parameters, kinematics and kinetics.  They included 30 patients in their analysis (10 anterior, 10 posterior, and 10 lateral).  The groups were similar with respect to age (p = 0.27), body mass index (BMI; p = 0.16), and Charlson Comorbidity Index score (p = 0.66).  Temporal parameters were similar among the groups at all time-points.  The lateral cohort had higher pelvic tilt during stance on the affected leg than the anterior cohort at 6 weeks (p = 0.041).  Affected leg ipsilateral trunk lean during stance was higher in the lateral group than in the other cohorts at 6 weeks (p = 0.008) and 12 weeks (p = 0.040).  The anterior and posterior groups showed increased external rotation at 6 weeks (p = 0.003) and 12 weeks (p = 0.012) compared with the lateral group.  The authors concluded that temporal gait parameters were similar following THA for all approaches.  Moreover, they stated that the impact of gait anomalies on the long-term mechanical durability of implant fixation remains unknown; future studies, such as corroborating biomechanical changes with soft tissue changes seen on cross-sectional imaging with long-term follow-up, would provide insight into how healed or unhealed tissue may explain gait aberrancies.

This study had several drawbacks.  The lack of true randomization may have introduced selection bias on behalf of the surgeon and expectation bias on behalf of the patient.  Studies have shown that patients believe minimizing muscle damage is important after major reconstructive surgery, such as THA.  Thus, knowing that an approach potentially is “muscle-sparing” may psychologically prime an individual to be more motivated to achieve earlier mobilization and hasten progress with rehabilitation.  It is important to consider this confounding factor across all comparative studies that examine minimally invasive or muscle-sparing, techniques.  The addition of an age-, sex- and BMI-matched control group would provide useful information to understanding how well each surgical approach restores gait mechanics.  In addition, randomization may have reduced pre-operative kinematic and kinetic differences between the cohorts, although these variables were likely more a function of individual differences than sample selection.  These investigators did not report changes in leg length and femoral offset following THA, which could affect gait mechanics by changing the length of the muscles around the hip joint.  These findings were limited to a short-term follow-up of 12 weeks, which may be too short to observe optimal restoration of gait mechanics in all groups.  Finally, the single-center study design limited the generalizability of the data, as only 3 surgeons performed the procedures.

Scheidt and colleagues (2018) noted that the rise in the number of patients with lumbar back pain has led to an increase in the number of spinal surgeries.  To avoid unfavorable outcomes, high accuracy and reliability of indication for surgery are essential.  This requires critical evaluation of post-operative outcomes with its 2 key dimensions pain and function.  While imaging findings give details about the technical dimension of the intervention, they are prone to high inter-/intra-observer variability, with limited relation to functional outcomes.  Pain improvement can be directly asked from patients or documented by questionnaires.  There is abundant literature on post-operative function based on questionnaires, but quantifiable data such as gait or posture analysis are scarce.  High precision measurement tools are available and easy to implement in a clinician's work routine.  These researchers examined if lumbar fusion surgery changes gait and postural variables and how these changes are related to patients' descriptions of alterations in their levels of pain.  Back profiles and gait analyses were measured by treadmill gait analysis and video rasterstereography.  Measurements were recorded before surgery, at discharge, after 3 months in a longitudinal (n = 30), and after 12 months in a cross-sectional group (n = 29).  A reference group was formed (n = 28).  The improvement on the Numeric Pain Rating Scale was documented and compared with changes in gait and posture.  A significant reduction in kyphotic (52 to 43°, p = 0.014) and lordotic (28 to 11°, p < 0.001) angles was observed.  The values again increased after 3 months, with a significant reduction in cadence (98 to 91 steps/min, p = 0.006).  While improvements in pain were also obtained by surgery (p < 0.001), no clear correlation could be detected between 3-month alleviation in pain and changes in kyphotic/lordotic angle or cadence.  The authors concluded that although both methods offered high-precision measurement, changes in gait and posture were not related with the patients' reported pain relief after lumbar fusion surgery.

Davids and colleagues (2018) stated that abnormal hip rotation is a common deviation in children with CP.  Clinicians typically assess hip rotation during gait by observing the direction that the patella points relative to the path of walking, which is referred to as the knee progression angle (KPA).  Two kinematic methods for calculating the KPA are compared with each other.  Video-based qualitative assessment of KPA is compared with the quantitative methods to determine reliability and validity.  The KPA was calculated by both direct and indirect methods for 32 typically developing (TD) children and a convenience cohort of 43 children with hemiplegic type CP.  An additional convenience cohort of 26 children with hemiplegic type CP was selected for qualitative assessment of KPA, performed by 3 experienced clinicians, using 3 categories (internal, greater than 10 degrees; neutral, -10 to 10 degrees; and external, greater than -10 degrees).  Root mean square (RMS) analysis comparing the direct and indirect KPAs was 1.14 + 0.43 degrees for TD children, and 1.75 + 1.54 degrees for the affected side of children with CP.  The difference in RMS among the 2 groups was statistically, but not clinically, significant (p = 0.019).  Intra-class correlation coefficient revealed excellent agreement between the direct and indirect methods of KPA for TD and CP children (0.996 and 0.992, respectively; p < 0.001).  For the qualitative assessment of KPA there was complete agreement among all examiners for 17 of 26 cases (65 %).  Direct KPA matched for 49 of 78 observations (63 %) and indirect KPA matched for 52 of 78 observations (67 %).  The authors concluded that RMS analysis of direct and indirect methods for KPA was statistically but not clinically significant, which supported the use of either method based upon availability.  They stated that video-based qualitative assessment of KPA showed moderate reliability and validity.  The differences between observed and calculated KPA indicated the need for caution when relying on visual assessments for clinical interpretation; and demonstrated the value of adding KPA calculation to standard kinematic analysis.

Rasmussen et al (2019) reported on a prospective, single-blind, parallel-group, randomized controlled trial investigating the effectiveness of interventions based on the use of gait analysis in children with cerebral palsy. Primary outcome was gait (Gait Deviation Index) and secondary outcomes were walking and patient-reported outcome measures of function, disability, and health-related quality of life. Follow-ups were done at 26 weeks (questionnaires) and at the primary end point of 52 weeks (all outcomes). Sixty participants with CP (39 males, 21 females, mean age 6y 10mo, standard deviation 1y 3mo, range 5y-9y 1mo) in Gross Motor Function Classification System levels I or II, were randomized to interventions with or without gait analysis. No significant or clinically relevant between-group differences in change scores of the primary or secondary outcomes were found. The recommended categories of interventions were dominated by non-surgical interventions and were applied in 36% to 86% of the participants. The investigators concluded that interventions using gait analysis were not superior to 'usual care' on gait, walking, or patient-reported outcomes in a sample of relatively young and independently walking children with CP not expected to need surgery. The investigators stated that gait analysis in children with cerebral palsy in Gross Motor Function Classification System levels I or II recommends interdisciplinary interventions. Compliance to interventions recommended after gait analysis was low. No statistically significant advantages were identified for the intervention group versus the control group.

Caldas and co-workers (2020) noted that due to the high susceptibility of the walking pattern to be affected by several disorders, accurate analysis methods are necessary. Given the complexity and relevance of such assessment, the utilization of methods to facilitate it plays a significant role, provided that they do not compromise the outcomes. These investigators identifyied the standards for the application of adaptive predictive systems to gait analysis, given the extensive research on this field. Furthermore, they also examined if such methods could effectively support clinicians in determining the number of physiotherapy sessions needed to recover gait-related dysfunctions. Through a screening process of scientific databases, these researchers considered studies from 1968 to April 2019. Within these 50 years, these investigators found 24 papers that met the inclusion criteria. They were analyzed according to their data acquisition and processing methods via ad-hoc questionnaires. Furthermore, these researchers examined quantitatively the adaptive approaches. Concerning data acquisition, the included papers presented a mean score of 6.1 SD 1.0, most of them applying opto-electronic systems, and the ground reaction force (GRF) was the most used parameter. The AI quality assessment showed an above-average rate of 7.8 SD 1.0, and artificial neural networks (ANN) being the paradigm most frequently utilized. This systematic review identified only 1 study that addressed therapeutics including a predictive method. The authors concluded that while much progress has been identified to predict assessment aspects, there is little effort to assist healthcare professionals in establishing the rehabilitation duration and prognostics. Thus, future studies should focus on accomplishing the production of applications of predictive methods to therapeutics and prognosis, not lingering extremely on the analysis of gait features.

Mueske et al (2019) examined the effects of gait analysis data on pathology identification and surgical recommendations in children with spina bifida.  Two pediatric orthopedic surgeons and 2 therapists with more than 10 years of experience in gait analysis reviewed clinical, video, and gait analysis data from 43 ambulatory children with spina bifida (25 males; mean age of 11.7 years, SD = 3.8; 25 sacral, 18 lumbar).  Primary gait pathologies were identified by each assessor both before and after consideration of the gait analysis data.  Surgical recommendations were also recorded by the surgeons before and after consideration of the gait analysis data.  Frequencies of pathology and surgery identification with and without gait analysis were compared using Fisher's exact test, and percent (%) change in pathology and surgery identification was calculated.  Pathology identification often changed for common gait problems including crouch (28 % of cases), tibial rotation (35 %), pes valgus (18 %), excessive hip flexion (70 %), and abnormal femur rotation (75 %).  Recognition of excessive hip flexion and abnormal femur rotation increased significantly after consideration of gait analysis data (p < 0.05).  Surgical recommendations also frequently changed for the most common surgeries including tibial de-rotation osteotomy (30 %), antero-lateral release (22 %), plantar fascia release (33 %), knee capsulotomy (25 %), 1st metatarsal osteotomy (60 %), and femoral de-rotation osteotomy (89 %).  At the patient level, consideration of gait analysis data altered surgical recommendations for 44 % of patients.  The authors concluded that since gait analysis data often changed pathology identification and surgical recommendations, treatment decision-making may be improved by including gait analysis in the patient care process.  These researchers stated that the drawbacks of this study included the retrospective convenience sample of patients and limited sample size (n = 43).  Furthermore, patients of varying levels of lesion from sacral to lumbar were analyzed together.  It was possible that those patients with a higher level of lesion who would have greater gait complexity would show even more changes in identification of gait pathology and surgical recommendations following review of the gait analysis data.  It should be noted that this study reported on changes in management from gait analysis, but did not provide reliable data on whether the changes resulted in improvements in clinical outcomes.

Flux et al (2020) stated that with the rise of biofeedback (BFB) in gait training in cerebral palsy (CP) there is a need for real-time measurements of gait kinematics.  The Human Body Model (HBM) is a recently developed model, optimized for the real-time computing of kinematics.  These researchers examined differences between HBM and 2 commonly used models for clinical gait analysis: the Newington Model, also known as Plug-in-Gait (PiG), and the calibrated anatomical system technique (CAST).  A total of 25 children with CP participated; 3D instrumented gait analyses were performed in 3 laboratories across Europe, using a comprehensive retro-reflective marker set comprising 3 models: HBM, PiG and CAST.  Gait kinematics from the 3 models were compared using statistical parametric mapping, and RMSE values were used to quantify differences.  The minimal clinically significant difference was set at 5°.  Sagittal plane differences were mostly less than 5°.  For frontal and transverse planes, differences between all 3 models for almost all segment and joint angles exceeded the value of minimal clinical significance.  Which model holds the most accurate information remains undecided since none of the 3 models represents a ground truth.  Meanwhile, it can be concluded that all 3 models are equivalent in representing sagittal plane gait kinematics in clinical gait analysis.  The authors concluded that overall, the differences in gait kinematics found between marker models were not pointing to a specific outlier, i.e., based on agreement there was no preferred use of either PiG, CAST or HBM.  For the sagittal plane angles the models were found to be equivalent, i.e., any non-systematic difference was below the minimal clinically significant difference of 5°.  However, differences of up to 25° were found in frontal and transversal planes for hip, knee and ankle joints, between all models.  For these planes, the 3 models could not be used inter-changeably.  A ground truth would be needed to decide on which of the 3 models is the most accurate.

The authors stated that a limitation of the study was that subjects walked on a treadmill in the VUmc and over ground in OPBG and KUL.  The effect of treadmill walking versus over ground walking on the different models was not addressed in this study, although the effect was thought to be small based on previous studies.  Furthermore, children wore gymnastic shoes on the treadmill and not over ground, which may have affected the foot kinematics and the comparison between models.  However, differences between ankle kinematics showed similar results for all 3 laboratories, and thus treadmill walking and shoe movement artefacts were not thought to influence the findings.  Overall, these findings suggested that sagittal plane kinematics were generally similar between models.

Sees et al (2020) noted that limb deformities in ambulatory children with CP are common.  The natural history of lower extremity (LE) deformities is variable and the impact on gait is managed with many treatment modalities.  Effective interventions must consider the underlying pathophysiology, patient-specific goals, and incorporate objective outcome assessment.  Evaluation and treatment include observation, tone management multi-level orthopedic surgery to address muscle contractures and bony deformities, and the use of gait analysis for pre-operative and post-operative assessment.  These researchers carried out a PubMed search of the orthopedic literature for studies published between January 2016 and February 2019.  Eligible abstracts included the use of 3-dimensional (3-D) instrumented gait analysis in the evaluation and treatment of the LE in ambulatory children with CP.  A total of 720 abstracts were reviewed, with 84 papers identified as eligible, of which 45 full manuscripts were included for detailed review.  The review summarized recent advances regarding the treatment of torsional alignment, knee deformities and clinical gait evaluation with visual assessment tools compared with instrumented gait analysis.  The authors concluded that gait analysis of ambulatory children with CP remains essential to evaluation and surgical decision-making; promising results have been reported with the goal of maintaining or reaching a higher level of function and increased endurance.  Level of evidence = IV.

In a pilot study, Carcreff et al (2020) compared walking speed, in the laboratory and daily life, in young individuals with CP and with typical development (TD), and quantified to what extent gait observed in clinical settings compared to gait in real life.  A total of 15 children, adolescents and young adults with CP (6 GMFCS I, 2 GMFCS II, and 7 GMFCS III) and 14 with TD were included.  They wore 4 synchronized inertial sensors on their shanks and thighs while walking at their spontaneous self-selected speed in the laboratory, and then during 2 weekdays and 1 weekend day in their daily environment.  Walking speed was computed from shank angular velocity signals using a validated algorithm.  The median of the speed distributions in the laboratory and daily life were compared at the group and individual levels using Wilcoxon tests and Spearman's correlation coefficients.  The corresponding percentile of daily life speed equivalent to the speed in the laboratory was computed and observed at the group level.  Daily-life walking speed was significantly lower compared to the laboratory for the CP group (0.91 [0.58 to 1.23] m/s versus 1.07 [0.73 to 1.28] m/s, p = 0.015), but not for TD (1.29 [1.24 to 1.40] m/s versus 1.29 [1.20 to 1.40] m/s, p = 0.715).  Median speeds correlated highly in CP (p < 0.001, rho = 0.89), but not in TD.  In children with CP, 60 % of the daily life walking activity was at a slower speed than in-laboratory (corresponding percentile = 60).  On the contrary, almost 60 % of the daily life activity of TD was at a faster speed than in-laboratory (corresponding percentile = 42.5).  Nevertheless, highly heterogeneous behaviors were observed within both populations and within subgroups of GMFCS level.  At the group level, children with CP tended to under-perform during natural walking as compared to walking in a clinical environment.  The heterogeneous behaviors at the individual level indicated that real-life gait performance could not be directly inferred from in-laboratory capacity.  The authors concluded that these findings emphasized the importance of completing clinical gait analysis with data from daily life, to better understand the overall function of children with CP.

The authors stated that this study had several drawbacks.  First, the sample size was low, only 29 subjects, which was why this pilot study aimed at giving a first methodological framework to assess daily life performance rather than drawing definitive clinical conclusions for the CP population.  However, the effect found in the CP group for the comparison between in-laboratory and daily life median speeds showed a medium substantial difference (effect size = 0.543) which was satisfactory for confidence in these preliminary results.  Besides, limits related to the use of inertial sensors should be mentioned.  Firstly, the calibration method based on principal component analysis (PCA) was not the most accurate approach from a biomechanical point of view.  This method was adopted as an optimal solution since an approach based on functional calibration using a pre-defined set of movements was difficult to be envisaged for children with functional disabilities, especially in the home environment without the supervision of the investigator.  The PCA method was based on the assumption that the pitch angular velocity was maximal in the sagittal plane during forward walking.  This assumption may have induced potential errors, with an impact on the computation of shank and thigh angles, hence on the walking speed estimation, especially for the children with a high level of impairment, with higher frontal and transverse components at shank and thigh levels during walking.  Further analyses should be performed to find a method for sensor-to-segment alignment that is accurate and feasible in this challenging population and at home.  This would enable the computation of lower limbs kinematics, which are highly relevant gait features.  Second, precautionary measures were applied to avoid the inclusion of non-walking activity into the analysis.  However, these researchers could not exclude erroneous inclusion of false positives, which could be responsible for the outliers in the speed distribution.  Furthermore, the double pendulum model proposed by Aminian et al (2002) relied on precise leg dimension (thigh and shank segment lengths) measurements that can be challenging with patients with bone deformities and joint contractures; this was also a potential source of errors.  Last but not least, while walking speed in laboratory was estimated under same controlled conditions, walking speed in real-life condition was affected by the context changing the locomotion, e.g., due to crowd, weather, or path properties.  Information regarding this context of walking in daily life was not available as this was difficult to obtain in real-world condition unless the use of an additional system such as GPS (for location, indoor, outdoor) or an embedded camera.  However, the use of such additional devices is problematic in this population due to their age and privacy issue.  As an attempt to limit the effect of the context, only walking bouts longer than 10 steps were included.  However, the power law distribution of walking bouts involves much more short walking bouts in daily life corresponding mostly to indoor walking or walking in a room, and 10 steps may represent more than the maximum number of steps taken in a row in the laboratory.  Some solutions could be to include only frequently repeated walking bouts to eliminate unique behaviors or events from the analysis; to use technological developments such as multi-modal sensing (e.g., GPS, barometric pressure, microphone, weather records, etc.) to be more precise regarding the contexts, e.g., discriminate between indoor and outdoor, even and irregular surface, or straight and curved path, detect load carriage, a surrounding crowd or weather conditions.

Fonvig et al (2020) noted that children with CP often exhibit an altered gait pattern; however, it is unclear if the use of an instrumented gait analysis in inter-disciplinary interventions affects the perceived experience of family-centered service (FCS) and/or gross motor function.  In a secondary analysis of tertiary data from a randomized controlled trial (RCT), these investigators examined if individually tailored inter-disciplinary interventions, based on an instrumented gait analysis report, has a superior effectiveness on perceived FCS and gross motor function in children with CP, compared to “care as usual” without the use of instrumented gait analysis.  These researchers also examined potential associations between perceived FCS and gross motor function improvement with the goal of improving future therapy on gross motor function.  This study was a sequel analysis on tertiary outcome measures from a prospective, single-blind, randomized, parallel group study including 2 groups of 30 children aged 5 to 8 years with spastic CP at Gross Motor Function Classification System levels I to II (n = 60).  The intervention group underwent a 3-D gait analysis, from which a clinical report was written with recommendations on inter-disciplinary interventions, such as physical therapy (PT), orthopedic surgery, orthotics or spasticity management.  To examine the effectiveness on perceived FCS and gross motor function, at baseline, 26 weeks and 52 weeks, the 5 domains in the Measure of Processes of Care (MPOC-20) (Enabling and partnership, Providing general information, Providing specific information about the child, Respectful and supportive service, and Coordinated and comprehensive care) and the Gross Motor Function Measurement (GMFM-66) were used as outcome measures.  No significant differences in between-group change scores in any of the 5 MPOC-20 domains were observed (p = 0.40-0.97).  In favor of the intervention group a significantly higher between-group change score in GMFM-66 (mean difference [MD]: 3.05 [95 % confidence interval [CI]: 1.12 to 4.98]; p = 0.003) after 52 weeks was observed.  The authors concluded that the addition of an instrumented gait analysis report to “care as usual” did not improve the parents' perceptions of FCS in treatment of children with CP.  However, superior improvement in the GMFM-66 was observed in the intervention group, suggesting meaningful gross motor function improvement.  These researchers stated that the findings of this study could not support an implementation of instrumented gait (IGA) analysis as part of standard care with the sole intention of improving the parents’ perception of FCS.  However, the addition of IGA to “care as usual” may benefit the child’s gross motor function.  The authors stated that these findings need to be confirmed in a priori designed studies with the specific purpose of detecting change in gross motor function.  The study could not detect an association between perception of FCS and gross motor function.

The authors stated that this study had several drawbacks.  These findings may not be generalizable to older children with CP or to children with more profound disabilities.  Furthermore, the risk of selection bias was unknown, as data on non-participants (n = 84) was unavailable.  The present study design did not allow the parents to attend a consultation where they were individually informed about the content of the IGA report by the researchers who evaluated and recommended the interventions.  Doing so could potentially have helped the parents understand the contents of the report, thereby providing them with specific information about their child and increasing their ability to be critically involved in the planning of their child’s treatment.  No data of the parents’ psychological health was collected, and thus it could not be evaluated.  Another drawback was that an anchor-based minimum clinically important difference (MCID) for the measure of processes of care (MPOC-20) was not available, and, consequently, the cut-off point of 1 MPOC-20 point may be considered arbitrary despite being based upon reference values in the literature.  Furthermore, 3 out of 5 post-hoc power analyses for MPOC-20 domains were under-powered for effects in between-group FCS change.  Additionally, statistical results were not corrected for multiple testing, as the study was based on tertiary outcomes.  However, no statistical differences in between-group change scores were observed, and the observed wide 95 % confidence intervals [CIs] demonstrated with a clear statistical certainty that the intervention did not provide superior effectiveness on parents’ perception of FCS.  A primary analysis on an anchor-based MCID for the MPOC-20 is needed to evaluate clinically relevant changes in perception of FCS over time.  Finally, as the present study was not powered to detect a between-group difference in GMFM-66, it is necessary to confirm the afore-mentioned results in an a priori designed study with the primary purpose to examine the use of an IGA report in inter-disciplinary intervention on gross motor function improvement.

Michelini et al (2020) stated that motion capture systems are widely used to quantify human gait.  Two-dimensional (2D) video systems are simple to use, easily accessible, and affordable; however, their performance as compared to other systems (i.e., 3D gait analysis) is not well-established.  These investigators provided a comprehensive review of design specifications and performance characteristics (validity and reliability) of 2D motion capture systems.  They carried out a systematic literature search in 3 databases from 1990 to 2019 and identified 30 research articles that met the inclusion/exclusion criteria.  Reliability of measurements of 2D video motion capture was found to vary greatly from poor-to-excellent.  Results relating to validity were also highly variable.  Comparisons between the studies were challenging due to differences in protocols, instrumentation, parameters assessed, and analyses performed.  The authors concluded that variability in performance could be attributed to study design, gait parameters being measured, and technical aspects.  The latter included camera specifications (i.e., resolution and frame rate), setup (i.e., camera position), and analysis software.  Given the variability in performance, additional validation testing may be needed for specific applications involving clinical or research-based assessments, including specific patient populations, gait parameters, mobility tasks, and data collection protocols.  These researchers stated that this review provided guidance on the application of 2D video gait analysis in a clinical or research setting.  While not suitable in all instances, 2D gait analysis has promise in specific applications.

Trivedi et al (2021) noted that high-grade spondylolisthesis (HGS) (Myerding grade III to V) in adolescents can lead to a marked alteration of gait pattern and maybe the presenting symptom in these patients.  This characteristic gait pattern in patients with HGS has been referred to as the "pelvic waddle".  Modern 3-dimensional (3D) gait analysis serves an important tool to objectively analyze the different components of this characteristic gait pre-operatively and post-operatively and is an objective measure of post-operative improvement.  In a case-series study, these researchers examined the use of 3D gait analysis pre-operatively and post-operatively in a cohort of 4 consecutive patients with HGS treated surgically at a single tertiary referral center and examined outcome of surgical treatment in these patients.  This has not been reported previously in a cohort of patients.  This was a prospective analysis of patients with HGS who underwent surgical intervention for spondylolisthesis at a single institution.  Patient demographics, clinical, and radiologic assessment were recorded, and all patients underwent 3D gait analysis before and after surgical intervention . Kinetic, kinematic, and spatial parameters were recorded pre-operatively and post-operatively for all patients.  This allowed the outcome of change in gait deviation index, before and after surgical treatment, to be evaluated.  These researchers were able to review complete records of 4 adolescent patients who underwent surgical treatment for HGS.  Mean age at surgery was 13.5 years with a minimum follow-up of 2.5 years post-operatively (average of 40 months).  Pre-operative gait analysis revealed marked posterior pelvic tilt in 2 patients, reduced hip and knee extension in all 4 patients and external foot progression in 3 of the 4 patients.  Along with an observed improvement in gait, there was an objective improvement in gait parameters post-operatively in all 4 patients.  Gait deviation index score improved significantly from 78.9 to 101.3 (mean).  The authors concluded that pre-operative gait abnormalities exist in HGS and can be objectively analyzed with gait analysis.  Surgical intervention may successfully resolve these gait abnormalities and gait analysis is a useful tool to assess the outcome of surgery and quantify an otherwise intangible benefit of surgical intervention.  Level of Evidence = IV.

The authors were aware of the small sample size (n = 4) and medium-term follow-up (average of 40 months) in this study with the absence of a comparative cohort treated alternatively, however, this has served as a suitable pilot cohort for future studies.  All patients were followed-up until they completed their skeletal growth with no patient showing progression of their slip after surgery.  Another drawback of the study was the absence of EMG analysis of the individual muscle groups in the gait analysis, which may have allowed us to focus treatment on specific muscles; however the observed improvement in gait clinically and on analysis post-operatively, emphasized that the pathology lies within the spine in these patients with the muscle response especially within the hamstrings being secondary to the spinal pathology.

An UpToDate review on “Cerebral palsy: Overview of management and prognosis” (Barkoudah and Glader, 2021a) does not mention gait analysis as a management tool. An UpToDate reviews on “Cerebral palsy: Treatment of spasticity, dystonia, and associated orthopedic issues” (Barkoudah and Glader, 2021b) states that “Gait analysis, using physical examination, videotaping, force plates, electromyography, and computerized analysis of limb motion, can be used as an assessment tool to identify abnormalities in muscle function and limb alignment and to evaluate the effects of surgery.  This should be performed when the gait is mature, usually between 6 and 10 years of age”.  Moreover, gait analysis is not mentioned in the “Summary and Recommendations” section of the review.

Differential Diagnosis of Progressive Supranuclear Palsy and Idiopathic Normal-Pressure Hydrocephalus

In a cross-sectional study, Selge and colleagues (2018) examined if quantitative gait analysis of gait under single- and dual-task conditions can be used for a differential diagnosis of progressive supranuclear palsy (PSP) and idiopathic normal-pressure hydrocephalus (iNPH).  Temporal and spatial gait parameters were analyzed in 38 patients with PSP (Neurological Disorders and Stroke and Society for Progressive Supranuclear Palsy diagnostic criteria), 27 patients with iNPH (International iNPH guidelines), and 38 healthy controls.  A pressure-sensitive carpet was used to examine gait under 5 conditions: single task (preferred, slow, and maximal speed), cognitive dual task (walking with serial 7 subtractions), and motor dual task (walking while carrying a tray).  The main results were as follows.  First, both patients with PSP and those with iNPH exhibited significant gait dysfunction, which was worse in patients with iNPH with a more broad-based gait (p < 0.001).  Second, stride time variability was increased in both patient groups, more pronounced in PSP (p = 0.009).  Third, cognitive dual task led to a greater reduction of gait velocity in PSP (PSP 34.4 % versus iNPH 16.9 %, p = 0.002).  Motor dual task revealed a dissociation of gait performance: patients with PSP considerably worsened, but patients with iNPH tended to improve.  The authors concluded that patients with PSP appeared to be more sensitive to dual-task perturbations than patients with iNPH.  They stated that an increased step width and anisotropy of the effect of dual-task conditions (cognitive versus motor) appeared to be good diagnostic tools for iNPH.

The authors stated that this study had several drawbacks.  The most important one was the absence of a histopathologic diagnosis.  This was a drawback of every study that relied on a clinical diagnosis.  Because these researchers were aware of this problem, they included only typical clinical cases that were examined and characterized in detail.  A second unavoidable drawback of studies that compared different diseases was the definition of symptom load.  These investigators tried to solve this problem by determining the Functional Gait Assessment.  They did not address whether the participants prioritized one task (calculation task versus walking task) over the other under the dual-task conditions because the authors did not examine the single-task calculation while the patient was seated.  Furthermore, this study exclusively examined patients with typical cases; thus limiting its generalizability to atypical cases.

Evaluation of Children with Idiopathic Toe Walking

O'Sullivan and associates (2018) stated that toe-walking is a normal variant in children up to 3 years of age but beyond this a diagnosis of idiopathic toe-walking (ITW) must be considered.  Idiopathic toe-walking is an umbrella term that covers all cases of toe-walking without any diagnosed underlying medical condition and before assigning these diagnosis potential differential diagnoses such as CP, peripheral neuropathy, spinal dysraphism and myopathy must be ruled out.  Gait laboratory assessment (GLA) is thought to be useful in the evaluation of ITW, and kinematic, kinetic and EMG features associated with ITW have been described.  However, the longer term robustness of a diagnosis based on GLA has not been investigated.  These researchers examined if a diagnosis of ITW based on GLA features persisted.  All patients referred to a national gait laboratory service over a 10-year period with queried ITW were sent a postal survey to establish if a diagnosis of ITW that had been offered following GLA persisted over time.  The gait and clinical parameters differentiating those reported as typical ITW and not-typical-ITW following GLA were examined in the survey respondents.  Of 102 referrals to the laboratory with queried ITW, a response rate of 40.2 % (n = 41) was achieved.  Of the respondents, 78 % (n = 32) were found to be typical of ITW following GLA and this diagnosis persisted in the entire group at an average of 7 years post-GLA.  The remaining 9 subjects were reported as not typical of ITW following GLA and 44.4 % (n = 4) received a subsequent differential diagnosis.  The clinical examination and gait analysis features differentiating these groups were consistent with previous literature.  The authors concluded that GLA appeared to be an useful objective tool in the assessment of ITW and a diagnosis based on described features persisted in the long-term.  The main drawback of this study was the low response rate (40.2 %) of the survey.

Caserta and colleagues (2019) noted that ITW is a diagnosis of exclusion for children walking on their toes with no medical cause.  In a systematic review, these investigators evaluated the clinical utility, validity and reliability of the outcome measures and tools used to quantify lower limb changes within studies that included children with ITW.  The following databases were searched from inception until March 2018: Ovid Medline, Ebsco, Embase, CINAHL Plus, PubMed.  Inclusion criteria were studies including children with ITW diagnosis, reporting use of measurement tools or methods describing lower limb characteristics, published in peer-reviewed journals, and in English.  The relevant psychometric properties of measurement tools were extracted, and assessed for reported reliability and validity.  Included articles were assessed for risk of bias using McMaster quality assessment tool.  Results were descriptively synthesized and logistic regression used to determine associations between common assessments.  From 3,164 retrieved studies, 37 full texts were screened and 27 full texts included.  There were 27 different measurement tools described across joint ROM measurement, gait analysis, EMG, accelerometer, strength, neurological or radiology assessment.  Interventional studies were more likely to report ROM and gait analysis outcomes, than observational studies.  Alvarez classification tool in conjunction with Vicon motion system appeared the contemporary choice for describing ITW gait.  There was no significant association between the use of ROM and gait analysis outcomes and any other outcome tool or assessment in all studies (p > 0.05).  There was limited reliability and validity reporting for many outcome measures.  The authors concluded that this review highlighted that a consensus statement should be considered to guide clinicians and researchers in the choice of the most important outcome measures for this population.  They stated that having a standard set of measures would enable future treatment trials to collect similar measures; thus allowing future systematic reviews to compare results.

Spatio-Temporal Gait Analysis in Foot Dystonia

Datta and co-workers (2018) stated that foot dystonia (FD) is a disabling condition causing pain, spasm and difficulty in walking.  These investigators treated 14 adult patients experiencing FD with onabotulinum toxin A injection into the dystonic foot muscles.  They analyzed the spatio-temporal gait utilizing the GaitRite system pre- and 3 weeks post-botulinum toxin injection along with measuring dystonia by the Fahn-Marsden Dystonia Scale (FMDS), pain by the visual analog scale (VAS) and other lower limb functional outcomes such as gait velocity, the Berg Balance Scale (BBS), the Unified Parkinson's Disease Rating Scale-Lower Limb Score (UPDRS⁻LL), the Timed Up and Go (TUG) test and the Goal Attainment Scale (GAS).  These researchers found that stride length increased significantly in both the affected (p = 0.02) and unaffected leg (p = 0.01) after treatment, and the improvement in stride length was roughly the same in each leg.  Similar results were found for step length (p = 0.02) with improvement in the step length differential (p = 0.01).  The improvements in the lower limb functional outcomes were also significant -- FMDS, VAS, TUG, and UPDRS⁻LL decreased significantly after treatment (all p < 0.001), and BBS (p = 0.001), GAS (p < 0.001) except cadence (p = 0.37).  The authors concluded that botulinum toxin injection improved walking in FD as evidenced through gait analysis, pain and lower limb functional outcomes.  The main drawbacks of this study were small sample size (n = 14) and the lack of a control group.

Assessment and Monitoring of Patients with Lumbar Spinal Stenosis

In a systematic review, Chakravorty and associates (2019) examined the accuracy and reliability of wearable devices for objective gait measurement of patients with lumbar spinal stenosis (LSS), with a focus on relevant gait metrics.  Systematic searches were conducted of 5 electronic databases to identify studies that assessed gait metrics by wearable or portable technology.  Data was collected according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement guidelines.  A total of 4 articles were identified for inclusion in this review.  The objectives, methodology and quality of the studies varied.  No single gait metric was examined in all 4 studies, making comparison difficult.  The most relevant metrics reported included gait cycle, gait velocity, step length and cadence, which were reported in 2 studies; 2 studies examined gait symmetry.  Differences between LSS patients and normal healthy subjects were demonstrable using wearable technology.  The authors concluded that although the data on gait metrics from wearable devices for LSS patients were limited, the available evidence suggested that this group exhibits decreased step length and gait velocity compared to normal subjects.  Helpful gait metrics were gait cycle, step length, velocity and number of steps, which have the potential to be easily measured using new wearable devices for pre-operative and post-operative assessment of LSS patients.  Moreover, these researchers stated that data and analysis were limited, and further studies are needed to comment on reliability.

The authors stated that this systematic review had several drawbacks.  The 5 primary gait parameters examined in this study were essential to clinically evaluate patients’ gait.  Although more gait parameters, such as gait phase and joint angle may be analyzed for improved assessment of patients’ gait, these investigators believed that a simple (single-point fixation) wearable provided adequate assessment of decline and recovery for the LSS patient.  Further studies to assess the reliability of different wearable technologies for gait analysis are needed.  However preliminary evidence and pilot studies suggested that simple wearables were as effective and more convenient in providing standard gait metrics compared to traditional in-laboratory gait analysis.

After Total Hip Arthroplasty

In a systematic review and meta-analysis, Yoo and colleagues (2019) analyzed the differences in gait such as time-dependent parameters, kinetics, and kinematics after total hip arthroplasty (THA) using the direct anterior approach (DAA) compared with antero-lateral approach (ALA).  PubMed Central, OVID Medline, Cochrane Collaboration Library, Web of Science, Embase and AHRQ carried out a comprehensive search for all relevant randomized controlled trials (RCTs) and comparative studies, up to December 2018.  Based on the following criteria, studies were selected: study design: RCTs or non-randomized comparative studies; study population: patients with primary OA or avascular necrosis (AVN); intervention: THA by DAA or ALA; Kinetic and kinematic data after gait analysis in the plains during post-operative follow-up.  Of the 148 studies, 7 RCTs and 5 comparative studies were finally included in this systematic review.  The peak hip flexion within 3 months after surgery was described in 2 studies and was significantly higher in the DAA group. (odds ratio [OR] = 1.90; 95 % confidence interval [CI]: 1.67 to 2.13; p < 0.01, Z = 16.18).  The gait speed within 3 months after surgery was reported in 3 studies and was significantly higher in the DAA group than in the ALA group. (standard mean difference [SMD] = 0.17; 95 % CI: 0.12 to 0.22]; p < 0.01, Z = 6.62).  There was no difference between the 2 groups in stride length, step length, and hip ROM in sagittal plane.  The authors concluded that in this meta-analysis, gait speed and peak hip flexion within 3 months after surgery were significantly higher in the DAA group than in the ALA group.  Despite a few significant differences between 2 approaches, determining whether the reported differences in terms of post-operative gait values were clinically meaningful remained a substantial challenge.  These investigators stated that further studies are needed.

The authors stated that this study had several drawbacks.  First, this study lacked evaluation of pre-operative mechanism of compensation for each study, which may have influenced post-operative gait analysis.  Second, the recovery of strength and proprioception of injured muscle may vary with the different types of approach, and capsulotomy or capsulectomy affected the outcome.  Third, the data of the included studies had heterogeneity.  This is the limit of the meta-analysis, but these researchers used a random model instead of a fixed model.  Forth, because the timing of gait analysis was varied, it did not include all the studies in each meta-analysis.

The KneeKG System / Knee Kinesiography in the Management of Osteoarthritis

Lustig et al (2012) noted that accurately quantifying knee joint motion is not simple.  Skin movement over the medial and lateral femoral condyles is the greatest obstacle to obtaining accurate movement data non-invasively.  The KneeKG system was developed with the objective of providing high reliability movement analysis.  These investigators reviewed the technical details, clinical evidence, and potential applications of this system for evaluation of rotational knee laxity.  They carried out a comprehensive review of the Medline database to identify all clinical and biomechanical studies related to KneeKG system.  The KneeKG system non-invasively quantifies knee abduction/adduction, axial rotation, and relative translation of the tibia and femur.  The accuracy and reproducibility of the system have been assessed.  The average accuracy of the acquisition is 0.4° for abduction/adduction, 2.3° for axial rotation, 2.4 mm for antero-posterior translation, and 1.1 mm for axial translation.  This clinical tool enables an accurate and objective assessment of the tri-planar function of the knee joint.  The measured biomechanical parameters are sensitive to changes in gait due to OA of the knee as well as deficiency of the anterior cruciate ligament (ACL).  The authors concluded that the KneeKG system provided reliable movement analysis; it has the potential to improve the understanding of the biomechanical consequences of trauma or degenerative changes of the knee as well as more accurately quantify rotational laxity as detected by a positive pivot-shift test.

Cagnin et al (2020) stated that an important clinical gap reported by primary care physicians (PCPs) in managing patients with knee OA is the lack of validated tools to help them guide conservative treatment decision-making.  In a 6-month, cluster RCT, these researchers examined the clinical utility of adding to current medical management (CMM) by PCPs, a dynamic knee kinesiography (the KneeKG system) examination assessing biomechanical risk factors linked to progression of OA.  Primary care clinics were randomized into 3 groups: 1-CMM by PCPs, 2-CMM+KneeKG, and 3-CMM+KneeKG+Education (a self-management education session and 2 follow-up group meetings).  Primary outcomes were scores on the Knee Injury and Osteoarthritis Outcome Score (KOOS) subscales and overall score.  Of the 894 patients referred from 87 clinics, 515 participated, 449 (87.2 %) completed the study.  At 6-month follow-up, patients in both KneeKG groups reported statistically significant improvement on the KOOS overall score (Group2: +5.5; Group3: +5.0), and on the symptoms, pain, and activities of daily living (ADL) subscales compared to control group (all p < 0.05).  These researchers also reported significantly higher satisfaction levels with global care (both p < 0.01).  Group 3-CMM+KneeKG+Education showed statistically significant improvements in objective functional tests as well as greater global impression of change in pain, function, quality of life (QOL), and global condition (all p < 0.05).  the authors concluded that the findings of this study demonstrated significant improvements in terms of pain, function, and satisfaction in KneeKG groups relative to the CMM; and the addition of education and supervision further improved clinical outcomes.  These investigators stated that results of this study may support the added value of a dynamic knee functional test as they suggested that this examination enhances PCPs’ capability and knowledge base to recommend tailored conservative treatment strategies.

The authors stated that this study had several drawbacks.  First, it was carried out in a single province in Canada, which may have limited the generalizability of results to other provinces or countries.  It was however a multi-site study involving multiple health care professionals (HCPs), and educational sessions and interventions were carried out in 10 locations, supporting the feasibility of implementing KneeKG in clinical practice.  Availability of this technology is growing across Canada and internationally and it is not anticipated that the significance of these results would be altered if it were used in a broader context.  Second, data collection methods except for mechanical markers relied mostly on self-reports.  Errors may have been made during questionnaire completion; however, quality controls were programmed into the online questionnaires and the electronic database to allow instant automated data validation checks (e.g., out-of-range values, logical inconsistencies).  In addition, research assistants contacted subjects to verify unclear or incomplete answers.  In this trial, PCPs were provided with the KneeKG examination report; however, these researchers did not evaluate the extent to which they used the treatment recommendations.  However, subject adherence to their prescribed exercise program was evaluated.  The fact that objective functional tests were performed on a smaller sample represented another drawback.

Code Code Description

Information in the [brackets] below has been added for clarification purposes.   Codes requiring a 7th character are represented by "+":

CPT codes not covered for indications listed in the CPB:

96000 Comprehensive computer-based motion analysis by video-taping and 3-D kinematics
96001      with dynamic plantar pressure measurements during walking
96002 Dynamic surface electromyography, during walking or other functional activities, 1 - 12 muscles
96003 Dynamic fine wire electromyography, during walking or other functional activities, 1 muscle
96004 Review and interpretation by physician or other qualified health care professional of comprehensive computer-based motion analysis, dynamic plantar pressure measurements, dynamic surface electromyography during walking or other functional activities, and dynamic fine wire electromyography, with written report

ICD-10 codes not covered for indications listed in the CPB (not all-inclusive):

G80.0 - G80.9 Cerebral palsy
G82.20 - G83.9 Paraplegia, quadriplegia and other specified paralytic syndromes
R26.0 - R26.9 Abnormality of gait and mobility
R27.0 - R27.9 Other lack of coordination

The above policy is based on the following references:

  1. Agency for Healthcare Research and Quality (AHRQ). Effects of walking analysis on surgical outcomes. ClinicalTrials.gov Identifier: NCT00114075. Bethesda, MD: National Institutes of Health (NIH), National Library of Medicine (NLM); June 23, 2005.
  2. Aktas S, Aiona MD, Orendurff M. Evaluation of rotational gait abnormality in  the patients cerebral palsy. J Pediatr Orthop. 2000;20(2):217-220.
  3. American Association of Electrodiagnostic Medicine/American Academy of Physical Medicine and Rehabilitation. Technology review: Dynamic electromyography in gait and motion analysis. Muscle Nerve. 1999;22 (Supplement 8):S233-S238. 
  4. Andriacchi TP. Practical and theoretical considerations in the application in the development of clinical gait analysis. Biomed Mater Eng. 1998;8(3-4):137-143.
  5. Banta J. Gait analysis: Past, present, and future. Dev Med Child Neurol. 1999;41(6):363.
  6. Barkoudah E, Glader L. Cerebral palsy: Overview of management and prognosis. UpToDate [online serial]. Waltham, MA: UpToDate; reviewed January 2021a.
  7. Barkoudah E, Glader L. Cerebral palsy: Treatment of spasticity, dystonia, and associated orthopedic issues. UpToDate [online serial]. Waltham, MA: UpToDate; reviewed January 2021b.
  8. BlueCross BlueShield Association (BCBSA), Technology Evaluation Center (TEC). Gait analysis for pediatric cerebral palsy. TEC Assessment Program. Chicago, IL: BCBSA; April 2002;16(19).
  9. Buderath P, Gärtner K, Frings M, et al. Postural and gait performance in children with attention deficit/hyperactivity disorder. Gait Posture. 2009;29(2):249-254.
  10. Cagnin A, Choiniere M, Bureau NJ, et al. A multi-arm cluster randomized clinical trial of the use of knee kinesiography in the management of osteoarthritis patients in a primary care setting. Postgrad Med. 2020;132(1):91-101.
  11. Caldas R, Fadel T, Buarque F, Markert B. Adaptive predictive systems applied to gait analysis: A systematic review. Gait Posture. 2020;77:75-82.
  12. Calhoun M, Longworth M, Chester VL. Gait patterns in children with autism. Clin Biomech (Bristol, Avon). 2011;26(2):200-206.
  13. Carcreff L, Gerber CN, Paraschiv-Ionescu A, et al. Walking speed of children and adolescents with cerebral palsy: Laboratory versus daily life. Front Bioeng Biotechnol. 2020;8:812.
  14. Caserta A, Morgan P, Williams C. Identifying methods for quantifying lower limb changes in children with idiopathic toe walking: A systematic review. Gait Posture. 2019;67:181-186.
  15. Chakravorty A, Mobbs RJ, Anderson DB, et al. The role of wearable devices and objective gait analysis for the assessment and monitoring of patients with lumbar spinal stenosis: Systematic review. BMC Musculoskelet Disord. 2019;20(1):288.
  16. Chang FM, Seidl AJ, Muthusamy K, et al. Effectiveness of instrumented gait analysis in children with cerebral palsy--comparison of outcomes. J Pediatr Orthop. 2006;26(5):612-616. 
  17. Chapin KB. A focus on clinical gait analysis. Rehab Manag. 2010;23(1):10-12.
  18. Chau T. A review of analytical techniques for gait data. Part 1: Fuzzy, statistical and fractal methods. Gait Posture. 2001;13(1):49-66.
  19. Chau T. A review of analytical techniques for gait data. Part 2: Neural network and wavelet methods. Gait Posture. 2001;13(2):102-120.
  20. Chen S, Lach J, Lo B, Yang GZ. Toward pervasive gait analysis with wearable sensors: A systematic review. IEEE J Biomed Health Inform. 2016;20(6):1521-1537.
  21. Chow JW, Yablon SA, Stokic DS. Et al. Electromyogram-lengthening velocity relation in plantar flexors during stance phase of gait in patients with hypertonia after acquired brain injury. Arch Phys Med Rehabil. 2012;93(12):2287-2294.
  22. Cook RE, Schneider I, Hazlewood ME, et al. Gait analysis alters decision-making in cerebral palsy. J Pediatr Orthop. 2003;23(3):292-295.
  23. Cooper RA, Quatrano LA, Stanhope SJ, et al. Gait analysis in rehabilitation medicine: A brief report. Am J Phys Med Rehabil. 1999;78(3):278-280.
  24. Cottalorda J. Gait analysis: Matching the method to the goal. Rev Rhum Engl Ed. 1999;66(7-9):367-369.
  25. Coutts F. Gait analysis in the therapeutic environment. Man Ther. 1999;4(1):2-10.
  26. Dabney KW, Lipton GE, Miller F. Cerebral palsy. Curr Opin Pediatr. 1997;9(1):81-88.
  27. Damiano DL, Arnold AS, Steele KM, Delp SL. Can strength training predictably improve gait kinematics? A pilot study on the effects of hip and knee extensor strengthening on lower-extremity alignment in cerebral palsy. Phys Ther. 2010;90(2):269-279.
  28. D'Amico JC. The F-Scan system with EDG module for gait analysis in the pediatric patient. J Am Podiatr Med Assoc. 1998;88(4):166-175.
  29. Datta Gupta A, Tucker G, Koblar S, et al. Spatiotemporal gait analysis and lower limb functioning in foot dystonia treated with botulinum toxin. Toxins (Basel). 2018;10(12).
  30. Davids JR, Cung NQ, Pomeroy R, et al. Quantitative assessment of knee progression angle during gait in children with cerebral palsy. J Pediatr Orthop. 2018;38(4):e219-e224.
  31. Davids JR, Foti T, Dabelstein J, Bagley A. Voluntary (normal) versus obligatory (cerebral palsy) toe-walking in children: A kinematic, kinetic, and electromyographic analysis. J Pediatr Orthop. 1999;19(4):461-469.
  32. DeLuca PA, Davis RB 3rd, Ounpuu S, et al. Alterations in surgical decision making in patients with cerebral palsy based on three-dimensional gait analysis. J Pediatr Orthop. 1997;17(5):608-614.
  33. DeLuca PA, Ounpuu S, Davis RB, Walsh JH. Effect of hamstring and psoas lengthening on pelvic tilt in patients with spastic diplegic cerebral palsy. J Pediatr Orthopaed. 1998;18(6):712-718.
  34. DeLuca PA. Gait analysis in the treatment of the ambulatory child with cerebral palsy. Clin Orthopaed. 1991;264:65-75.
  35. DeLuca PA. The musculoskeletal management of children with cerebral palsy. Pediatr Clin North Am. 1996;43(5):1135-1150.
  36. Desloovere K, Molenaers G, Feys H, et al. Do dynamic and static clinical measurements correlate with gait analysis parameters in children with cerebral palsy? Gait Posture. 2006;24(3):302-313. 
  37. Dietz V. Neurophysiology of gait disorders: Present and future applications. Electroencephalogr Clin Neurophysiol. 1997;103(3):333-355.
  38. Dobson F, Morris ME, Baker R, Graham HK. Gait classification in children with cerebral palsy: A systematic review. Gait Posture. 2007;25(1):140-152.
  39. Domagalska M, Szopa A, Syczewska M, et al. The relationship between clinical measurements and gait analysis data in children with cerebral palsy. Gait Posture. 2013;38(4):1038-1043.
  40. Dreher T, Wolf S, Braatz F, et al. Internal rotation gait in spastic diplegia--critical considerations for the femoral derotation osteotomy. Gait Posture. 2007;26(1):25-31.
  41. Fabry G, Liu XC, Molenaers G. Gait pattern in patients with spastic diplegic cerebral palsy who underwent staged operations. J Pediatr Orthopaed. 1999;8(Part B):33-38.
  42. Ferrari A, Brunner R, Faccioli S, et al. Gait analysis contribution to problems identification and surgical planning in CP patients: An agreement study. Eur J Phys Rehabil Med. 2015;51(1):39-48.
  43. Filho MC, Yoshida R, Carvalho WD, et al. Are the recommendations from three-dimensional gait analysis associated with better postoperative outcomes in patients with cerebral palsy? Gait Posture. 2008;28(2):316-322.
  44. Flux E, van der Krogt MM, Cappa P, et al. The human body model versus conventional gait models for kinematic gait analysis in children with cerebral palsy. Hum Mov Sci. 2020;70:102585.
  45. Fuller DA, Keenan MAE, Esquenazi A, et al. The impact of instrumented gait analysis on surgical planning: Treatment of spastic equinovarus deformity of the foot and ankle. Foot Ankle Int. 2002;22(8):738-743.
  46. Fonvig CE, Rasmussen HM, Overgaard S, Holsgaard-Larsen A. Effectiveness of instrumented gait analysis in interdisciplinary interventions on parents' perception of family-centered service and on gross motor function in children with cerebral palsy: A randomized controlled trial. BMC Pediatr. 2020;20(1):411.
  47. Gage JR, Novacheck TF. An update on the treatment of gait problems in cerebral palsy. J Pediatr Orthopaed. 2001;10(Part B):265-274.
  48. Gage JR. Editorial. The role of gait analysis in the treatment of cerebral palsy. J Pediatr Orthop. 1994;14:701-702.
  49. Goldberg SR, Ounpuu S, Arnold AS, et al. Kinematic and kinetic factors that correlate with improved knee flexion following treatment for stiff-knee gait. J Biomech. 2006;39(4):689-698.
  50. Guinet AL, Desailly E. Is physical activity of children with cerebral palsy correlated with clinical gait analysis or physical examination parameters? Comput Methods Biomech Biomed Engin. 2017 Oct;20(sup1):99-100.
  51. Jeans KA, Erdman AL, Karol LA. Plantar pressures after nonoperative treatment for clubfoot: Intermediate follow-up at age 5 years. J Pediatr Orthop. 2017;37(1):53-58.
  52. Kawamura CM, de Morais Filho MC, et al. Comparison between visual and three-dimensional gait analysis in patients with spastic diplegic cerebral palsy. Gait Posture. 2007;25(1):18-24. 
  53. Kay RM, Dennis S, Rethlefsen S, et al. The effect of preoperative gait analysis on orthopaedic decision making. Clin Orthop Relat Res. 2000;(372):217-222.
  54. Kay RM, Rethlefsen S, Reed M, et al. Changes in pelvic rotation after soft tissue and bony surgery in ambulatory children with cerebral palsy. J Pediatr Orthop. 2004;24(3):278-282.
  55. Keenan WN, Rodda J, Wolfe R, et al. The static examination of children and young adults with cerebral palsy in the gait analysis laboratory: Technique and observer agreement. J Pediatr Orthop B. 2004;13(1):1-8.
  56. Kerrigan DC, Glenn MB. An illustration of clinical gait laboratory use to improve rehabilitation management. Am J Phys Med Rehabil. 1994;73(6):421-427.
  57. Kerrigan DC, Karvosky ME, Riley PO. Spastic paretic stiff-legged gait: Joint kinetics. Am J Phys Med Rehabil. 2001;80(4):244-249.
  58. Kirkpatrick M, Wytch R, Cole G, et al. Is the objective assessment of cerebral palsy gait reproducible? J Pediatr Orthop. 1994;14:705-708.
  59. Krebs DE, Edelstein JE, Fishman S. Reliability of observational kinematic gait analysis. Phys Ther. 1985;65(7):1027-1033.
  60. Lamberts RP, Burger M, du Toit J, Langerak NG. A systematic review of the effects of single-event multilevel surgery on gait parameters in children with spastic cerebral palsy. PLoS One. 2016;11(10):e0164686.
  61. Lee EH, Goh JCH, Bose K. Value of gait analysis in the assessment of surgery in cerebral palsy. Arch Phys Med Rehabil. 1992;73:642-646.
  62. Lee EH, Nather A, Goh J, et al. Gait analysis in cerebral palsy. Ann Acad Med. 1985;14(1):37-43.
  63. Lofterod B, Terjesen T, Skaaret I, et al. Preoperative gait analysis has a substantial effect on orthopedic decision making in children with cerebral palsy: Comparison between clinical evaluation and gait analysis in 60 patients. Acta Orthop. 2007;78(1):74-80.
  64. Lofterod B, Terjesen T. Results of treatment when orthopaedic surgeons follow gait-analysis recommendations in children with CP. Dev Med Child Neurol. 2008;50(7):503-509.
  65. Lustig S, Magnussen RA, Cheze L, Neyret P. The KneeKG system: A review of the literature. Knee Surg Sports Traumatol Arthrosc. 2012;20(4):633-638.
  66. Maquet D, Lekeu F, Warzee E, et al. Gait analysis in elderly adult patients with mild cognitive impairment and patients with mild Alzheimer's disease: Simple versus dual task: A preliminary report. Clin Physiol Funct Imaging. 2010;30(1):51-56.
  67. McMulkin ML, Gulliford JJ, Williamson RV, Ferguson RL. Correlation of static to dynamic measures of lower extremity range of motion in cerebral palsy and control populations. J Pediatr Orthop. 2000;20(3):366-369.
  68. Michelini A, Eshraghi A, Andrysek J. Two-dimensional video gait analysis: A systematic review of reliability, validity, and best practice considerations. Prosthet Orthot Int. 2020;44(4):245-262.
  69. Michlitsch MG, Rethlefsen SA, Kay RM. The contributions of anterior and posterior tibialis dysfunction to varus foot deformity in patients with cerebral palsy. J Bone Joint Surg Am. 2006;88(8):1764-1768.
  70. Miller F, Cardoso Dias R, Lipton GE, et al. The effect of rectus EMG patterns on the outcome of rectus femoris transfers. J Pediatr Orthop. 1997;17(5):603-607.
  71. Molenaers G, Desloovere K, Fabry G, De Cock P. The effects of quantitative gait assessment and botulinum toxin a on musculoskeletal surgery in children with cerebral palsy. J Bone Joint Surg Am. 2006;88(1):161-170.
  72. Morton R. New surgical interventions for cerebral palsy and the place of gait analysis. Dev Med Child Neurol. 1999;41(6):424-428.
  73. Mueske NM, Ounpuu S, Ryan DD, et al. Impact of gait analysis on pathology identification and surgical recommendations in children with spina bifida. Gait Posture. 2019;67:128-132.
  74. Mutoh T, Mutoh T, Tsubone H, et al. Impact of serial gait analyses on long-term outcome of hippotherapy in children and adolescents with cerebral palsy. Complement Ther Clin Pract. 2018;30:19-23.
  75. Narayanan UG. Management of children with ambulatory cerebral palsy: An evidence-based review. J Pediatr Orthop. 2012;32 Suppl 2:S172-S181.
  76. Narayanan UG. The role of gait analysis in the orthopaedic management of ambulatory cerebral palsy. Curr Opin Pediatr. 2007;19(1):38-43.
  77. Nene AV, Evangs GA, Patrick JH. Simultaneous multiple operations for spastic diplegia. J Bone Joint Surg. 1993;75-B(3):488-494.
  78. Niklasch M, Dreher T, Döderlein L, et al. Superior functional outcome after femoral derotation osteotomy according to gait analysis in cerebral palsy. Gait Posture. 2015;41(1):52-56.
  79. Noonan KJ, Halliday S, Browne R, et al. Interobserver variability of gait analysis in patients with cerebral palsy. J Pediatr Orthop. 2003;23:279–287; discussion 288–291.
  80. Olney SJ, Griffin MP, McBride ID. Temporal, kinematic, and kinetic variables related to gait speed in subjects with hemiplegia: A regression approach. Phys Ther. 1994;74(9):872-885.
  81. Onpuu S, Davis RB, DeLuca PA. Joint kinetics: Methods, interpretation and treatment decision-making in children with cerebral palsy and myelomeningocele. Gait Posture. 1996;4(1):62-78.
  82. Orendurff MS, Chung JS, Pierce RA. Limits to passive range of joint motion and the effect on crouch gait in children with cerebral palsy [abstract]. Gait Posture. 1998;7(2):165.
  83. Ornetti P, Maillefert JF, Laroche D, et al. Gait analysis as a quantifiable outcome measure in hip or knee osteoarthritis: A systematic review. Joint Bone Spine. 2010;77(5):421-425.
  84. O'Sullivan R, Munir K, Keating L. Idiopathic toe walking-A follow-up survey of gait analysis assessment. Gait Posture. 2018;68:300-304.
  85. Ounpuu S, Davis RB, DeLuca PA.  Joint kinetics: Methods, interpretation and treatment decision-making in children with cerebral palsy and myelomeningocele. Gait Posture. 1996;4(1):62-78.
  86. Ounpuu S, DeLuca PA, Bell KJ, Davis RB. Using surface electrodes for the evaluation of the rectus femoris, vastus medialis and vastus lateralis muscles in children with cerebral palsy. Gait Posture. 1997;5(3):211-216.
  87. Park TS, Owen JH. Surgical management of spastic diplegia in cerebral palsy. N Engl J Med. 1992;326(11):745-749.
  88. Perry J, Hoffer MM. Preoperative and postoperative dynamic electromyography as an aid in planning tendon transfers in children with cerebral palsy. J Bone Joint Surg Am. 1977;59(4):531-537.
  89. Perry J. The use of gait analysis for surgical recommendations in traumatic brain injury. J Head Rehabil. 1999;14(2):116-135.
  90. Petis S, Howard J, Lanting B, et al. Comparing the anterior, posterior and lateral approach: Gait analysis in total hip arthroplasty. Can J Surg. 2018;61(1):50-57.
  91. Pinzone O, Schwartz MH, Thomason P, Baker R. The comparison of normative reference data from different gait analysis services. Gait Posture. 2014;40(2):286-290.
  92. Radler C, Kranzl A, Manner HM, et al. Torsional profile versus gait analysis: Consistency between the anatomic torsion and the resulting gait pattern in patients with rotational malalignment of the lower extremity. Gait Posture. 2010;32(3):405-410.
  93. Rasmussen HM, Pedersen NW, Overgaard S, et al. Gait analysis for individually tailored interdisciplinary interventions in children with cerebral palsy: A randomized controlled trial. Dev Med Child Neurol. 2019;61(10):1189-1195. 
  94. Rasmussen HM, Pedersen NW, Overgaard S, et al. The use of instrumented gait analysis for individually tailored interdisciplinary interventions in children with cerebral palsy: A randomised controlled trial protocol. BMC Pediatr. 2015;15:202. 
  95. Rathinam C, Bateman A, Peirson J, Skinner J. Observational gait assessment tools in paediatrics--a systematic review. Gait Posture. 2014;40(2):279-285.
  96. Rechtien JJ, Gelblum JB, Haig AJ, et al. Technology assessment: Dynamic electromyography in gait and motion analysis. Muscle Nerve. 1996;19(3):396-402.
  97. Rogan S, de Bie R, Douwe de Bruin E. Sensor-based foot-mounted wearable system and pressure sensitive gait analysis: Agreement in frail elderly people in long-term care. Z Gerontol Geriatr. 2017;50(6):488-497. 
  98. Ronthal M. Gait disorders of elderly patients. UpToDate [online serial]. Waltham, MA: UpToDate; reviewed December 2013.
  99. Rose SA, DeLuca PA, Davis RB 3rd, et al. Kinematic and kinetic evaluation of the ankle after lengthening of the gastrocnemius fascia in children with cerebral palsy. J Pediatr Orthop. 1993;13(6):727-732.
  100. Saraph V, Zwick E, Zwick G, et al. Multilevel surgery in spastic diplegia: Evaluation by physical examination and gait analysis in 25 children. J Pediatr Orthopaed. 2002;22:150-157.
  101. Scheidt S, Endreß S, Gesicki M, Hofmann UK. Using video rasterstereography and treadmill gait analysis as a tool for evaluating postoperative outcome after lumbar spinal fusion. Gait Posture. 2018;64:18-24.
  102. Schwartz MH, Rozumalski A, Novacheck TF. Femoral derotational osteotomy: Surgical indications and outcomes in children with cerebral palsy. Gait Posture. 2014;39(2):778-783.
  103. Schwartz MH, Viehweger E, Stout J, et al. Comprehensive treatment of ambulatory children with cerebral palsy. An outcome assessment. J Pediatr Orthop. 2004;24(1):45-53.
  104. Scott AC, Chambers C, Cain TE. Adductor transfers in cerebral palsy: Long-term results studied by gait analysis. J Pediatr Orthop. 1996;16(6):741-746.
  105. Scott AC, Scarborough N. The use of dynamic EMG in predicting the outcome of split posterior tibial tendon transfers in spastic hemiplegia. J Pediatr Orthop. 2006;26(6):777-780.
  106. Sees JP, Truong WH, Novacheck TF, et al. What's new in the orthopaedic treatment of ambulatory children with cerebral palsy using gait analysis. J Pediatr Orthop. 2020;40(6):e498-e503.
  107. Selge C, Schoeberl F, Zwergal A, et al. Gait analysis in PSP and NPH: Dual-task conditions make the difference. Neurology. 2018;90(12):e1021-e1028.
  108. Shapiro A, Susak Z, Malkin C, et al. Preoperative and postoperative gait evaluation in cerebral palsy. Arch Phys Med Rehabil. 1990;71:236-240.
  109. Simon SR. Quantification of human motion: Gait analysis-benefits and limitations to its application to clinical problems. J Biomech. 2004;37(12):1869-1880.
  110. Skaggs DL, Rethlefsen SA, Kay RM, et al. Variability in gait analysis interpretation. J Pediatr Orthop. 2000;20:759–764.
  111. Stefko RM, de Swart RJ, Dodgin DA, et al. Kinematic and kinetic analysis of distal derotational osteotomy of the leg in children with cerebral palsy. J Pediatr Orthop. 1998;18(1):81-87.
  112. The Hospital for Sick Children. Outcomes of orthopaedic surgery using gait laboratory versus observational gait analysis in children with cerebral palsy. ClinicalTrials.gov Identifier:  NCT00419432. Bethesda, MD: National Institutes of Health (NIH), National Library of Medicine (NLM); January 5, 2007.
  113. Thomason P, Baker R, Dodd K, et al. Single-event multilevel surgery in children with spastic diplegia: A pilot randomized controlled trial. J Bone Joint Surg Am. 2011;93(5):451-460.
  114. Thomason P, Selber P, Graham HK. Single event multilevel surgery in children with bilateral spastic cerebral palsy: A 5 year prospective cohort study. Gait Posture. 2013;37(1):23-28.
  115. Tomie J, Hailey D. Computerized gait analysis in the rehabilitation of children with cerebral palsy and spina bifida. Health Technology Assessment. HTA 5. Edmonton, AB: Alberta Heritage Foundation for Medical Research; October 1997.
  116. Trivedi J, Srinivas S, Trivedi R, et al. Preoperative and postoperative, three-dimensional gait analysis in surgically treated patients with high-grade spondylolisthesis. J Pediatr Orthop. 2021;41(2):111-118.
  117. Watts HG. Editorial. Gait laboratory analysis for preoperative decision making in spastic cerebral palsy: Is it all it's cracked up to be? J Pediatr Orthop. 1994;14:703-704.
  118. Wren TA, Elihu KJ, Mansour S, et al. Differences in implementation of gait analysis recommendations based on affiliation with a gait laboratory. Gait Posture. 2013;37(2):206-209.
  119. Wren TA, Gorton GE 3rd, Ounpuu S, Tucker CA. Efficacy of clinical gait  analysis: A systematic review. Gait Posture. 2011;34(2):149-153.
  120. Wren TA, Kalisvaart MM, Ghatan CE, et al. Effects of preoperative gait analysis on costs and amount of surgery. J Pediatr Orthop. 2009;29(6):558-563.
  121. Wren TA, Lening C, Rethlefsen SA, Kay RM. Impact of gait analysis on correction of excessive hip internal rotation in ambulatory children with cerebral palsy: A randomized controlled trial. Dev Med Child Neurol. 2013;55(10):919-925.
  122. Wren TA, Otsuka NY, Bowen RE, et al. Influence of gait analysis on decision-making for lower extremity orthopaedic surgery: Baseline data from a randomized controlled trial. Gait Posture. 2011;34(3):364-369.
  123. Wren TA, Otsuka NY, Bowen RE, et al. Outcomes of lower extremity orthopedic surgery in ambulatory children with cerebral palsy with and without gait analysis: Results of a randomized controlled trial. Gait Posture. 2013;38(2):236-241.
  124. Wren TA, Woolf K, Kay RM. How closely do surgeons follow gait analysis recommendations and why? J Pediatr Orthop B. 2005;14(3):202-205.
  125. Yoo JI, Cha YH, Kim KJ, et al. Gait analysis after total hip arthroplasty using direct anterior approach versus anterolateral approach: A systematic review and meta-analysis. BMC Musculoskelet Disord. 2019;20(1):63.
  126. Zugner R, Tranberg R, Lisovskaja V, et al.  Validation of gait analysis with dynamic radiostereometric analysis (RSA) in patients operated with total hip arthroplasty. J Orthop Res. 2017;35(7):1515-1522.