Coverage Policy Manual
Policy #: 1998033
Category: Medicine
Initiated: January 1993
Last Review: July 2018
  Gait Analysis

Description: Gait analysis, or motion analysis, is the quantitative laboratory assessment of coordinated muscle function, typically requiring a dedicated facility and staff.  At its core is videotaped observation of patient walking.  Videos can be observed from several visual planes at slow speed, allowing detection of movements not detectable at normal speed.  Joint angles can be measured, and various time-distance variables can be measured including step length, stride length, cadence and cycle time.  Electromyography (EMG) assessed during walking measures timing and intensity of muscle contractions.  This allows determination of whether a certain muscle's activity is normal, out of phase, continuous or clonic.

Kinematics is the term used to describe movements of joints and limbs such as angular displacement of joints and angular velocities and accelerations of limb segments.  The central element of kinematic assessment is some type of marker system that is used to represent anatomic landmarks, which are then visualized and quantitatively assessed during analysis of videotaped observations.  Movement data is compiled by computer from cameras oriented in several planes and process the movement data so that the motion of joints and limbs can be assessed in three dimensions.  The range and direction of motion of a particular joint can be isolated from all the other simultaneous motions that are occurring during walking.  Graphic plots of individual joint and limb motion as a function of gait phase can be generated.

Kinetics is the term used to describe those factors that cause or control movement.  Evaluating kinetics involves the use of principles of physics and biomechanics to explain the kinematic patterns observed and generate analyses that describe the forces generated during normal and abnormal gait analysis.

Gait analysis has been proposed as an aid in surgical planning, primarily for cerebral palsy, and for planning for rehabilitative strategies for a variety of disorders.

Effective July 2013
Gait analysis meets primary coverage criteria that there be scientific evidence of effectiveness and is covered for either the pre-operative or post-operative assessment of patients with cerebral palsy or spina bifida who have disorders of gait.
Gait analysis for all other applications does not meet primary coverage criteria and is not covered.  The Criteria exclude coverage of interventions if there is a lack of scientific evidence regarding the intervention, or if the available scientific evidence is in conflict or the subject of continuing debate.
For members with contracts without primary coverage criteria, gait analysis for all other applications is considered investigational.  Investigational services are an exclusion in the member certificate of coverage.
Effective prior to July 2013
Gait analysis meets primary coverage criteria for effectiveness and is covered for either the pre-operative or post-operative assessment of patients with cerebral palsy who have disorders of gait.
Gait analysis for all other applications does not meet Primary Coverage Criteria  and is not covered.  The Criteria exclude coverage of interventions if there is a lack of scientific evidence regarding the intervention, or if the available scientific evidence is in conflict or the subject of continuing debate.
For members with contracts without primary coverage criteria, gait analysis for all other applications is considered investigational.  Investigational services are an exclusion in the member certificate of coverage.

Literature published through 2001 offered the following observations and conclusions regarding gait analysis for pediatric cerebral palsy:
    • There are no generally recognized standards of performance and interpretation of gait analysis. Different labs use different computer systems, and there are no standards for training in gait analysis techniques and interpretation. Comparison between laboratories is difficult, and there could be many interpretations of the same data.
    • Gait analysis has been used extensively as an outcome tool in research on gait, however, much is still unknown about the specific correlation of gait analysis parameters to overall functional status.
    • Gait analysis can be evaluated in terms of accuracy relative to some reference standard, but the available comparators only allow evaluation in a very limited sense. For example, accuracy of gait analysis in determining some specific parameters of gait such as joint flexion could be compared to clinical observations, and likely show that gait analysis is most reliable and valid. However, such information is of limited utility in making diagnostic decisions. The purpose of both clinical assessment and gait analysis is not to determine specific quantifiable deficits in gait but to interpret the whole clinical picture and make clinical decisions that result in the best patient outcomes.
    • The scientific evidence directly addressing the question of improved patient outcomes due to gait analysis consists of a single retrospective study of 23 pediatric patients. In the absence of any well-designed observational or randomized controlled trials, no conclusions can be drawn about whether gait analysis in routine clinical management has an effect on health outcomes.
This policy focused on the use of gait analysis in cerebral palsy.  Even less literature exists on gait analysis used in other musculoskeletal disorders.
Periodic reviews of the peer-reviewed literature on MEDLINE revealed no clinical trial studies that would alter the precious conclusions. In 1 study of 10 healthy individuals, Peters and colleagues (2002) evaluated the reproducibility of gait analysis using 1-step versus 3-step methods.  The authors concluded that both methods had comparable repeatability. However, each step analysis led to different results, demonstrating the need for further studies to identify standardized, reliable, and repeatable methods of data collection for gait analysis. In another study by Suda et al (2002), gait analysis was compared in 60 patients with neurogenic intermittent claudication to 50 healthy controls. The authors concluded that gait analysis provided useful quantitative and objective information to evaluate postsurgical treatment. However, the study does not address how the gait analysis influenced treatment decisions or effected health outcomes. In a retrospective study by Schwartz et al (2004), data were reviewed on 135 children with spastic diplegia subtype of cerebral palsy from an existing database.  Children had undergone either orthopaedic surgery, selective dorsal rhizotomy, or both, and had pre- and postoperative gait analysis to assess functional outcomes. The authors concluded that preoperative gait analysis can be used to guide surgical intervention. However, how this would occur is unclear and, as the authors also note, this study design restricts interpretation of results.
2007 Update
A search of the MEDLINE database was performed from March 2006 through June 2007. One prospective study assessed the relation between blinded gait analysis data and clinical measurements in 200 randomly selected patients. (Desloovere, 2006)  The study found only fair to moderate correlations between the measures (r 2 <= 0.60), none of the correlations were considered good. The authors suggested that gait analysis can provide different information than clinical measurement, but no data were presented to indicate that this additional information improved outcomes.
A prospective single-institution study evaluated the effect of gait analysis on surgical planning. (Lofterod, 2007)  Preoperative surgical plans derived from clinical assessments were found to have been modified in 70% of patients following multi-disciplinary team gait assessment. Thirty-nine (65%) of the 60 patients had been referred by an orthopedic surgeon who was a member of the gait laboratory. A retrospective study of the influence of gait analysis recommendations reported that the surgeries performed matched those recommended in 23 (77%) of 30 consecutive patients who underwent orthopedic surgery at the author’s institution. (Wren, 2005)  The gait laboratory physician was also the referring physician for nearly 65% of the 30 patients.
Although these studies indicate that gait analysis can influence clinical decision making, results cannot be generalized beyond these institutions. In a 2003 study funded by the United Cerebral Palsy Foundation, 4 different gait analysis centers gave different treatment recommendations after evaluating the same 11 patients. (Noonan, 2003)  Thus, there appears to be little consistency in gait analysis recommendations between centers. Questions remain, therefore, about both the reliability and the validity of gait analysis recommendations. Multicenter controlled studies are needed to determine whether gait analysis can improve clinical outcomes. The policy statement remains unchanged.
2008 Update
Review of peer reviewed medical literature through August 2008 provided no information which would change the above coverage policy.
2011 Update
A search of the MEDLINE database was conducted through January 2011.  There was no literature identified that would prompt a change in the coverage statement.
A study was published by Cimolin and colleagues in Italy (Cimolin, 2011). It included 19 children with cerebral palsy scheduled for gastrocnemius fascia lengthening surgery and 20 healthy controls (for establishment of preoperative normative values). Patient evaluation included videotaping and three-dimensional gait analysis. The study used the Gait Deviation Index (GDI) to summarize data; this is a measure derived from comparing nine kinematic variables of a person’s gait to those of a control group. A GDI value of approximately 100 or higher indicates an absence of gait pathology. Every decrease in 10 points below 100 indicates 1 standard deviation from normal kinematics. All participants completed the study. The mean preoperative GDI value among the 19 children with cerebral palsy was 70.4 +/- 14.8 (i.e., three standard deviations away from healthy children). After surgery, the mean GDI was 82.9 +/-7.4. The improvement in GDI was statistically significant compared to the presurgery value (p<0.05). The study did not evaluate whether there was incremental value with use of the postoperative GDI compared to postoperative observation alone.
2012 Update
A literature search was conducted through January 2012.  There was no additional literature identified that would prompt a change in the coverage statement.
2013 Update
A literature search was conducted using the MEDLINE database through January 2013.  
One randomized, controlled trial  comparing post-surgery health outcomes in children with cerebral palsy who were managed with and without gait analysis was identified (Wren, 2012). This was a single-center, single-blind study. The trial included 186 ambulatory children with cerebral palsy who were candidates for lower extremity surgery to improve their gait. All participants underwent gait analysis at a gait laboratory. Patients were randomized to a treatment group in which the surgeon received the gait analysis report or a control group in which the surgeon did not receive the report. The reports included a summary of test results and treatment recommendations from the gait laboratory physician. The same surgeons treated the intervention and control patients i.e., they received gait reports for half of the patients. Patients were re-examined the day before surgery (i.e., following gait analysis) for pre-operative treatment planning. Outcomes were assessed pre-operatively and approximately 1 year post-surgery. There were 3 primary outcomes: pre- to post-surgical change between groups in the walking scale of the Gillete Functional Assessment Questionnaire (FAQ), the Gait Deviation Index (GDI) and the oxygen cost of walking, a measure of the energy expended while walking (oxygen, cost). A total of 156 of 186 (84%) participants returned for the follow-up examination; analysis was not intention to treat. There was not a statistically significant difference between groups in any of the 3 primary outcomes. For example, the proportion of patients improved according to the FAQ was 31% in the intervention group and 25% in the control group (p=0.38). There were significant differences between groups at the p=0.05 level for 2 of 19 secondary outcome variables; p values were not adjusted for multiple comparisons. The authors noted that physicians followed only 42% of recommendations in the gait analysis report for patients in the treatment group, which may partially explain the lack of significant differences between groups in the primary outcomes and most of the secondary outcomes. They further noted that there was a positive relationship between gait outcomes and following gait analysis recommendations.
Two reviews discussing the clinical utility of gait analysis for the orthopedic management of patients with spina bifida are summarized below.
Swaroop and Dias (Swaroop, 2009) published a review on the orthopedic management of patients with spina bifida. Gait analysis was discussed as an important part of the orthopedic evaluation particularly in pre-operative planning for ambulatory patients. The authors conclude, “Computerized gait analysis should be employed as an important part of the examination of patients with spina bifida, especially when surgical treatment is being considered”.
Thompson and colleagues (Thompson, 2010) discuss the dramatic changes over the past 10 years in the orthopedic management of spina bifida. There is a greater appreciation for the effects that spasticity, poor balance and the tethered cord syndrome have on ambulation. Additionally, more emphasis is now placed on the function of the knee and the prevention of knee pain. The authors note, “Gait analysis has significantly increased our ability to evaluate knee and leg function and to better understand factors which affect the knee”.
Although there were no randomized trials identified, gait analysis has been reported in the literature for analysis of many manifestations of spina bifida (Dunteman, 2000; Gabrieli, 2003; Moen, 2005). Despite the absence of randomized controlled trials in this rare condition, the use of gait analysis appears to be a valuable component of the orthopedic management of patients with spina bifida.  The coverage statement has been changed to include spina bifida.
2014 Update
A literature search conducted through June 2014 did not reveal any new information that would prompt a change in the coverage statement. The key identified literature is summarized below.
In 2009, McGinley et al published a systematic review of studies of intersession and interassessor reliability of 3-dimensional kinematic gait analysis that included 15 full manuscripts and 8 abstracts (McGinley, 2009). Similar to the Dobson systematic review, the authors noted variability in methodologic quality across the studies, but concluded that most studies demonstrated interassessor error of between 2 and 5 degrees of measurement, which the authors considered was “reasonable but may require consideration in data interpretation.” Benedetti et al conducted an analysis of between-site consistency in gait analysis measurements of 1 healthy subject at 7 different laboratories (Benedetti, 2013). The authors concluded that there was generally high concordance of segment and joint kinematics, except in the knee and the hip.
In 2013, Wren et al published a secondary analysis of data from the RCT previously described to evaluate the impact of gait analysis on the correction of excessive internal hip rotation among ambulatory children with cerebral palsy (Wren, 2013). In the secondary analysis, the authors included the subset of children for whom the gait laboratory recommended external femoral derotation osteotomy (FDRO) to correct excessive passive and active internal hip rotation and who had both pre- and postoperative data available. As in the primary study, the intervention was receipt of the gait analysis report by the treating orthopedic surgeon for participants in the intervention group; in this subset of patients, all patients had had FDRO recommended by the gait analysis report, but the decision to actually perform surgery was up to the treating surgeon. Physical measurements for this subanalysis included femoral anteversion, maximum hip internal and external rotation range of motion, and rotational alignment during gait. The primary outcome variables included femoral anteversion and mean hip rotation and foot progression in the stance phase of gait. Outcomes postsurgery and change in variables pre- to postsurgery were compared between intervention and control groups, with additional analyses based on whether patients in the gait report (intervention) group had had the gait report recommendations followed. This subanalysis included 44 children (65 limbs) in whom FDRO was recommended. FDRO was performed in 7/39 limbs in which it was recommended in the gait report (intervention group); it is not clear how many children in the control group for whom FDRO was recommended received surgery. There were no significant differences in outcomes between the gait report and control groups on intent-to-treat analysis. However, among children in the intervention group who had FDRO done (n=7 limbs), the limbs demonstrated greater improvements in femoral anteversion (-32.9° vs -12.2°; p=0.01), dynamic hip rotation (-25.5° vs -7.6°; p=0.001), and foot progression (-36.2° vs -12.4°; p=0.02) than limbs in the control group. The discrepancy between the intent-to-treat and per-protocol results may be related to generally poor compliance with the gait report recommendations, as only 7 of 39 recommended FDROs performed in the gait analysis group. Interpretation of this study’s significance is limited by its subgroup analysis design and the small number of patients who received gait analysis and FDRO.
Schwartz et al published an evaluation of the role of a random forest algorithm (a statistical method used to predict an outcome for a particular observation based on a series of predictor values) that included gait analysis to predict outcomes after single-event, multilevel surgery for patients with ambulatory cerebral that either did or did not include psoas lengthening (Schwartz, 2013). The study authors report that their random forest algorithm was able to generate criteria that are predictive of good outcomes for patients undergoing a single-event, multilevel orthopedic surgery. However, the study based on a retrospective analysis of a motion analysis center database and is thus subject to bias. In addition, the complexity of the random forest decision algorithm makes it is difficult to determine the degree to which gait analysis independently predicts outcomes.
Gait analysis has been used in the assessment of multiple other conditions (eg, knee pain in older patients with osteoarthritis, (Asay, 2013) gait after acute stroke, (Ferrarello, 2013) and of frailty in older patients (NCT00419432)); however, the evidence linking the use of gait analysis to outcomes in these conditions is limited.
2015 Update
A literature search conducted using the MEDLINE database did not reveal any new information that would prompt a change in the coverage statement.
2016 Update
A literature search conducted through April 2016 did not reveal any new information that would prompt a change in the coverage statement.
2017 Update
A literature search conducted using the MEDLINE database through June 2017 did not reveal any new literature that would prompt a change in the coverage statement.
2018 Update
A literature search was conducted through June 2018.  There was no new information identified that would prompt a change in the coverage statement.  

96000Comprehensive computer-based motion analysis by video-taping and 3D kinematics;
96001Comprehensive computer-based motion analysis by video-taping and 3D kinematics; with dynamic plantar pressure measurements during walking
96002Dynamic surface electromyography, during walking or other functional activities, 1-12 muscles
96003Dynamic fine wire electromyography, during walking or other functional activities, 1 muscle
96004Review 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

References: Asay JL, Boyer KA, Andriacchi TP.(2013) Repeatability of gait analysis for measuring knee osteoarthritis pain in patients with severe chronic pain. J Orthop Res 2013; 31(7):1007-12.

Benedetti MG, Merlo A, Leardini A.(2013) Inter-laboratory consistency of gait analysis measurements. Gait Posture 2013; 38(4):934-9.

Cimolin V, Galli M, Vimercati SL et al.(2011) Use of the Gait Deviation Index for the assessment of gastrocnemius fascia lengthening in children with cerebral palsy. Res Dev Disabil 2011; 32(1): 377-81.

DeLuca PA, Davis RB, et al.(1997) Alterations in surgical decision making in patients with cerebral palsy based on three dimensional gait analysis. J Pediatr Orthop 1997; 17:608-14.

Desloovere K, Molenaers G, et al.(2006) Do dynamic and static clinical measurements correlate with gait analysis parameters in children with cerebral palsy? Gait Posture, 2006; 24(3):302-13.

Dunteman RC, Vankoski SJ, Dias LS.(2000) Internal derotation osteotomy of the tibia: pre- and postoperative gait analysis in persons with high sacral myelomeningocele. J Pediatr Orthop. 2000;20(5):623. J Pediatr Orthop. 2000;20(5):623.

Ferrarello F, Bianchi VA, Baccini M et al.(2013) Tools for observational gait analysis in patients with stroke: a systematic review. Phys Ther 2013; 93(12):1673-85.

Gabrieli AP, Vankoski SJ, Dias LS, et al.(2003) Gait analysis in low lumbar myelomeningocele patients with unilateral hip dislocation or subluxation. J Pediatr Orthop. 2003;23(3):330.

Gait analysis for pediatric cerebral palsy. Blue Cross Blue Shield Association Technology Evaluation Center, 2001, tab 19.

Kay RM, Dennis S, et al.(2000) Impact of postoperative gait analysis on orthopaedic care. Clin Orthop 2000; 374:259-64.

Lofterod B, Terjesen T, et al.(2007) Preoperative gait analysis has a substantial effect on orthopedic decision making in children with cerebral palsy: comparison between clinical evaluation & gait analysis in 60 patients. Acta Orthop, 2007; 78(1):74-80.

McGinley JL, Baker R, Wolfe R et al.(2009) The reliability of three-dimensional kinematic gait measurements: a systematic review. Gait Posture 2009; 29(3):360-9.

Moen T, Gryfakis N, Dias L, Lemke L.(2005) Crouched gait in myelomeningocele: a comparison between the degree of knee flexion contracture in the clinical examination and during gait. J Pediatr Orthop. 2005;25(5):657.

Narayanan UG.(2007) The role of gait analysis in the orthopedic management of ambulatory cerebral palsy/ Curr Opin Pediatr, 2007; 19(1):38-43.

Neuromuscular disorders. In: Tachdjian's Pediatric orthopedics. Herring, J (Ed), Saunders Elsevier, Philadelphia 2008. p.1405.

Noonan KJ, Halliday S, et al.(2003) Interobserver variability of gait analysis in patients with cerebral palsy. J Pediatr Orthop, 2003; 23(3):279-87.

Peters EJ, Urukalo A, et al.(2002) Reproducibility of gait analysis variables: one-step versus three-step method of data acquisition. J Foot Ankle Surg, 2002; 41(4):206-12.

Pullman SL, Goodin DS, et al.(2000) Clinical utility of surface EMG: Report of the Therapeutics & Technology Assessment Subcommittee of the American Academy of Neurology. Neurology, 2000; 55:171-7.

Rechtien JJ, Gelblum JB, et al.(1999) Technology Review: Dynamic electromyography in gait and motion analysis. American Association of Electrodiagnostic Medicine. Muscle and Nerve 1999; 22; S233-S238.

Schwartz MH, Viehweger E, et al.(2004) Comprehensive treatment of ambulatory children with cerebral palsy: an outcome assessment. J Pediatr Orthop, 2004; 24(1):45-53.

Schwenk M, Howe C, Saleh A et al.(2014) Frailty and technology: a systematic review of gait analysis in those with frailty. Gerontology 2014; 60(1):79-89.

Suda Y, Saitou M, et al.(2002) Gait analysis of patients with neurogenic intermittent claudication. Spine, 2002; 27(22):2509-13.

Swaroop VT and Luciano Dias.(2009) Orthopedic management of spina bifida. Part I: hip, knee, and rotational deformities. J Child Orthop. 2009; 2:441-449.

Thompson JD and Segal LS.(2010) Orthopedic management of spina bifida. Developmental Disabilities Research Reviews.2010; 16:96-103.

Wren TA, Lening C, Rethlefsen SA et al.(2013) 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):919-25.

Wren TA, Otsuka NY, Bowen RE et al.(2012) 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 2012 [Epub ahead of print].

Wren TA, Woolf K, Kay RM.(2005) How closely do surgeons follow gait analysis recommendations and why? J Pediatr Orthop B, 2005; 14(3):202-5.

Group specific policy will supersede this policy when applicable. This policy does not apply to the Wal-Mart Associates Group Health Plan participants or to the Tyson Group Health Plan participants.
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