Observational gait analysis is often performed in the clinic or therapy setting to assess the walking pattern of a patient. Advantages of this technique include its low cost and convenience; however, there are significant disadvantages when considering a complex gait pattern. Often the clinician does not have an ideal view of the patient, who might be walking in a narrow hallway. Even with the help of a videotape, research has shown that the repeatability of a clinical impression by observation has been shown to be moderate, and that the experience level of the clinician is a factor.1 In light of the limitations of observation, computerized gait analysis (CGA) has emerged as the gold standard for the objective assessment of gait. Preintervention, the data can be used as one component of the treatment planning process. Postintervention CGA data are the most objective means for quantifying outcome.
In most laboratories, the technology involved with CGA is, in many ways, similar to that employed by the animation industry to create digital video and movie characters. You may have seen television commercials or extra footage on a DVD depicting actors or athletes wearing black suits and silver balls on their body or face to capture their movements. The same is true in the clinical setting, though generally the patient wears these reflective markers attached directly to the skin over significant bony landmarks (locations specified by the biomechanical model used by the lab) to track the motion of the feet, legs, pelvis, and trunk. High-speed, high-resolution cameras are placed around the room and are focused on a center walkway. Most labs use somewhere between six and 12 cameras, synchronized to take around 120 frames per second. The raw three-dimensional coordinate data generated by the cameras is used by the data reduction software to calculate the motion of the joints, or kinematics. Though some joint motion can be observed by clinicians at some oblique view, CGA provides the information at a much higher level of detail in simultaneous, three-dimensional representation of motion, breaking out the sagittal, frontal or coronal, and transverse plane motion.
Generating Valuable Data
Computerized gait analysis also generates valuable data that are not visible to the clinician in a typical patient/clinic setting. Most laboratories employ force plates that are embedded in the walkway, quantifying all the forces between the foot and the floor during walking. When combined with the kinematic data, this allows for the calculation of joint moments, or kinetics, involved in gait. Joint moments represent the net turning force acting about a joint that is generated by muscles as well as structural properties of the joint (eg, tendons, ligaments). Abnormal forces acting through a joint may relate to current complaints of pain, or of future joint deterioration. Muscle activity data are also collected as a standard component of CGA, and constitute another component of the analysis that is not available to the naked eye. Dynamic electromyography (EMG) is a common technique for recording the electrical activity of muscles during movement. Recorded through the skin, using surface electrodes, or by indwelling electrodes inserted with a needle, the pattern of muscle activity for key muscles can be evaluated. The information may be used to better understand which muscles are performing abnormally and perhaps be amenable to intervention.
At the Motion Analysis Center (MAC) (at Mary Free Bed Rehabilitation Hospital, Grand Rapids, Mich), the patient population referred for CGA is predominantly cerebral palsy and pediatric; however, the lab also receives referrals for children with myelomeningocele, as well as children, adolescents, and adults who have experienced a traumatic brain injury or stroke. In general, the gait analysis session involves a detailed physical examination by a licensed physical therapist, split screen videotaping while walking and performing other appropriate gross motor skills, marker placement and a number of walking trials with the reflective markers, placement of EMG electrodes, and additional walking. Appropriate patient selection is important, given that the test session alone can last 3 to 4 hours depending on the cooperation and endurance of the patient. For example, patient selection can impact the ability to collect the data: a very young patient may not tolerate the markers and EMG electrodes taped to the skin, and may also have difficulty following directions during the session. Another patient selection factor that affects data interpretation, rather than collection, is repeatability of the gait pattern. A patient with dystonia, for example, may exhibit an extremely variable gait pattern that may compromise interpretation and complicate resultant treatment recommendations.
A MultiDisciplinary Team
The MAC, as with most CGA laboratories, is staffed by a multidisciplinary team of individuals with complementary expertise in the areas of pediatric orthopedics, kinesiology/biomechanics, physical therapy, and engineering. Using a team conference setting, the video, clinical examination, kinematic, and kinetic data are reviewed for each patient. Primary and compensatory gait deviations are identified, and, at the MAC, the medical director formulates a list of treatment recommendations that may include therapy, orthotics, medical management of spasticity, and surgery. An evidence-based approach for making treatment recommendations based on all available data is used to the extent that is possible. Using this approach has become one of the primary advantages of incorporating CGA into clinical practice, because a comprehensive treatment plan can be generated, addressing multiple impairments at many joints that are having an adverse impact on a patient’s gait pattern. This single-event, multilevel surgical (SEMLS) approach, also known as the multiple lower-extremity procedure (MLEP) approach, avoids the more traditional approach of performing interventions to address one deformity at a time.2 That traditional approach has been referred to as the “birthday syndrome” because of the repeated surgical interventions and bouts of rehabilitation required to recover.3 Recent evidence suggests that the SEMLS approach that includes the use of pre- and post-operative gait analysis can result in much lower costs than estimated charges for multiple surgical interventions (incorporating the same procedures) over years without the use of CGA, when considering total costs of surgery, recovery, anesthesia, inpatient stay, and therapy.4 Other research has demonstrated that the cost over years was comparable, despite the SEMLS group experiencing more procedures (on average).5 However, one cannot quantify and incorporate the “cost” of decreased disruption to a patient’s life when using the SEMLS approach; what is the value of a single bout of surgery, recovery, and intense therapy to a family, as opposed to multiple impositions of this process on life?
Despite all of the advantages of using CGA, one of the ongoing challenges for its systematic utilization is cost. Typical charges for a standard gait analysis range between $1,500 and $2,000. Specific CPT codes exist that accurately describe the services offered (see 96000-96004); however, some payors continue to consider the service “experimental” and have developed medical policies that deny payment for these codes. Other payors may have medical policies that support payment for a particular patient population (eg, cerebral palsy), but deny payment for other diagnoses. Many labs operate at a loss, or are affiliated with institutions that perform charity care and are therefore not subjected to the practices of third-party payors. Still other labs have incorporated the study of other motions, such as baseball pitching and golf swings, where self-pay from individuals interested in performance improvement can support the clinical mission of the lab. Additional clinical research documenting the value of incorporating CGA in the treatment planning process may ultimately change the payment patterns of insurers.
Krisanne B. Chapin, PhD, is the manager and clinical biomechanist for the Motion Analysis Center at Mary Free Bed Rehabilitation Hospital in Grand Rapids, Mich. She holds a master’s degree in biomechanics from Purdue University, and a PhD in exercise and movement science from the University of Oregon. Chapin has been practicing gait analysis for 12 years. For more information, go to www.MaryFreeBed.com.
- Brunnekreef JJ, van Uden C, van Moorsel S, Kooloos JG. Reliability of videotaped observational gait analysis in patients with orthopedic impairments. BMC Musculoskelet Disord. 2005;6:17-25.
- Gage JR, ed. The Treatment of Gait Problems in Cerebral Palsy. London: Mac Keith, distributed by Cambridge University Press; 2004:180-204.
- Rang M. Cerebral palsy. In: Morrissey R, ed. Pediatric Orthopaedics. Philadelphia: JB Lippincott; 1990:465-506.
- Ounpuu S, et al. Proceedings of the 2007 Annual Meeting of the Gait and Clinical Movement Analysis Society.
- Wren TAL, Kalisvaart MM, Ghatan CE, et al. Effects of preoperative gait analysis on costs and amount of surgery. J Pediatr Orthopaedics. 2009;29:558-563.