Measuring Ambulation in Adultswith Central Neurologic Disorders

Karen J. Nolan, PhDa,b,*, Mathew Yarossi, MSa,c,Arvind Ramanujam, MSa

KEYWORDS

� Ambulation � Rehabilitation � Measurement � Gait analysis � Walking speed� Central neurologic disorder

KEY POINTS

� Examine strategies for appropriate measurements of ambulation in individuals with centralneurologic disorder within three distinct environments: 1) clinical; 2) laboratory; and 3)community.

� Discuss common challenges and solutions for clinicians and researchers when selectingappropriate outcomes for measuring ambulation.

� Description of technology-driven assessment tools for measuring ambulation to obtainobjective, quantifiable outcomes.

� Explore techniques and research related to measurement of quantitative and qualitativeambulation in the community for persons with central neurologic disorder.

INTRODUCTION

Throughout rehabilitation medicine, there is an emphasis on the ability to ambulate asa functional outcome and as an overall indicator of quality of life. When ambulation isaffected by central neurologic disorders (CND), it is typically as a result of a combina-tion of impairments and the compensatory strategies used to accommodate theseimpairments. Pathologic ambulation can result from impairments within a single

Disclaimers: None.Grant Support: Supported by Kessler Foundation.Declaration of Interest: The authors report no conflicts of interest. The authors alone areresponsible for the content and writing of the paper.a Human Performance and Engineering Laboratory, Kessler Foundation Research Center, 1199Pleasant Valley Way, West Orange, NJ 07052, USA; b Department of Physical Medicine andRehabilitation, University of Medicine and Dentistry of New Jersey - New Jersey Medical School,Newark, NJ 07103, USA; c Department of Rehabilitation &Movement Sciences, Graduate Schoolof Biomedical Sciences, University of Medicine and Dentistry of New Jersey - New Jersey MedicalSchool, Newark, NJ 07101, USA* Corresponding author. Human Performance and Engineering Laboratory, Kessler FoundationResearch Center, 1199 Pleasant Valley Way, West Orange, NJ 07052.E-mail address: knolan@kesslerfoundation.org

Phys Med Rehabil Clin N Am 24 (2013) 247–263http://dx.doi.org/10.1016/j.pmr.2012.12.001 pmr.theclinics.com1047-9651/13/$ – see front matter � 2013 Elsevier Inc. All rights reserved.

Nolan et al248

system or within a combination of systems, such as musculoskeletal, neuromuscular,sensory/perceptual, and cognitive/behavioral.1

Functional ambulation is the extent to which an individual is capable and willing tomove around in their environment.2 Measuring ambulation is a complex task involvingclinical expertise, laboratory technology, and the ability to translate information toa community setting.Measuring ambulation can be challenging, because walking occurs in multiple envi-

ronments and must be assessed at multiple time points to indicate improvement ordecline. There is an added complexity, because, in a traditional clinical environment,a single numerical value or scale score is preferred to summarize a series of complexmovements,3 whereas in a laboratory environment, researchers typically prefer quan-tifiable continuous data, which allow for complex analysis of multiple outcome vari-ables. Measuring ambulation in a community also presents unique challenges andoften requires a combination of strategies to obtain valid outcomes.4 This reviewexamines strategies for appropriate measurements of ambulation in individuals withCND within 3 distinct environments: (1) clinical, (2) laboratory, and (3) community.The purpose of this article is not to provide an exhaustive list of the measures thatcan be used to assess ambulation but rather to describe the most frequently usedmeasures and to discuss common challenges faced by clinicians and researchersin establishing an accurate picture of an individual’s ability to ambulate.

MEASURING AMBULATION IN THE CLINICAL ENVIRONMENT

In a clinical environment, it is important to select measures that can evaluate changesin impairment, function, and performance.3 Test selection is often dependent onnonclinical criteria such as cost, time to administer, and available equipment.5 It isalso important for the patient to understand the test instructions, because unfamiliaritywith the test can alter performance and outcomes.5 Some examples of typical clinicalassessments commonly used for measuring ambulation can be found in Table 1.The Functional Independence Measure (FIM) was designed to measure burden of

care in multiple rehabilitation populations.3 The scores are based on clinical observa-tion of 18 items of activities of daily living (ADLs) (13 motor and 5 cognition items).6

Although there are motor items associated with this measure (including 1 item labeledlocomotion and 1 labeled stairs), the overall FIM score is often not sensitive enough toreflect improvements in ambulation (particularly in situations in which walking perfor-mance has improved without a change in the level of assistance needed). Previousresearch also revealed a ceiling effect, and the FIM shows poor sensitivity to changein individuals with better walking abilities.13 The FIM is typically used to determineindependence at admission and discharge from inpatient rehabilitation. Anotherexample of a frequently used clinical measure of motor deficits is the Fugl-MeyerMotor Assessment, which has previously been shown to correlate with length ofstay in inpatient rehabilitation, and has been found to be predictive of dischargeFIM scores and performance in ADLs. The disadvantage of this measure is that itassesses only gross limb movement and not fine or complex movements or coordina-tion. Both the FIM and Fugl-Meyer Motor Assessment as a whole do provide reliableinformation about the results of rehabilitation, but it is difficult to specifically determineif, and how much, ambulation has improved using either of these assessment tools.The Ambulation Index (AI) is an ordinal rating scale designed to assess independent

mobility by evaluating the time and degree of assistance required to walk 7.62m (25 ft),as well as the ability to transfer.8 Previous research has found the AI to be useful asa grouping variable when measuring ambulation in individuals with stroke.14 The

Table 1Outcomes for measuring ambulation in a clinical environment

Name of Scale Description Measurement

Functional IndependenceMeasure6

A 7-level ordinal scale that describes stages of completedependence to complete independence in performance ofbasic activities of daily living

Ordinal scores based on clinical observation

Fugl-Meyer Motor Assessment7 Evaluates movement, reflexes, coordination, and speed, derivedfrom the Brunnstrom stages of poststroke recovery.Frequently used to measure motor deficits

Ordinal scores are based on clinical observationand observed movements

Ambulation Index8 An ordinal rating scale designed to assess independent mobilityby evaluating the time and degree of assistance required towalk 7.62 m (25 ft). The scale ranges from 0 (asymptomatic andfully active) to 9 (restricted to wheelchair; unable to transferindependently)

Ordinal scores are based on clinical observation,including observed performance, and timedwalking speed

Emory Function AmbulationProfile, Modified EmoryFunctional Ambulation Profile 9,10

The time (s) to ambulate through 5 common environmentalterrains with or without an assistive device or manualassistance

The sum of the time (s) required to completethe 5 tasks

Walking Tests (2-minute, 6-minute,10-minute, and 12-minute)3

Distance walked in meters in a given time is measured toindicate performance

Distance walked (m)

Dynamic Gait Index11 A tool to assess gait, balance, and fall risk. Eight abilities aregraded on a 4-point scale (0–3) from normal performance toseverely impaired. The rating is based on the individual’sability to maintain a healthy gait pattern and pace, withoutdeviating or stumbling

Ordinal scores are based on clinical observationand observed performance

Timed Up and Go12 The time (s) required to rise from an arm chair, walk 3 m, turn,walk back, and sit down again

A categorical scale based on the time (s) requiredto accomplish the described task

10-m Walk Test, Timed 7.62 m(25-ft) Walk Test3

A measure of ambulation capacity including the time (s) to walk10 m or 7.62 m (25 ft) at maximum but safe gait speed

Time and distance to calculate maximumgait speed (m/s)

Gait Speed3 The rate (s) a given distance (m) is covered to determineself-selected or fastest gait speed

Time and distance to calculate speed (m/s)

Adapted from Finch E, Brooks D, Stratford PW, et al. Physical rehabilitation outcomemeasures. 2nd edition. Ontorio (Canada): Lippincott Williams &Wilkins; 2002.

Measurin

gAmbulatio

nin

Centra

lNeurologic

Diso

rders

249

Nolan et al250

measure is useful in rehabilitation medicine, but it is not sensitive enough to measuresmall changes in ambulation. Overall, the FIM, Fugl-Meyer, and AImeasure impairmentand function but have limited usefulness in describing and evaluating ambulation.Whenmeasuring ambulation in a clinical environment, it is important to consider that

individuals with CND may have difficulty adapting to the demands of walking in thecommunity, such as rising from a chair, stepping over an obstacle, ascending stairs,and navigating various terrains.9 The challenge is to select tasks and terrains thatmimic community ambulation that are easy to administer and provide relevant clinicalinformation. The Emory Functional Ambulation Profile (EFAP) and the modified EFAP(mEFAP) were designed to provide quantitative information about ambulation bymeasuring the time to walk over a standardized array of community obstacles andsurfaces, accounting for the use of assistive devices.9,10 The EFAP and mEFAP arereliable and valid clinical tests of ambulation that are sensitive to changes in ambula-tion speed.9,10 Research in individuals with stroke found that the mEFAP is sensitive tochanges in gait function during inpatient rehabilitation and therefore could be used tosupplement traditional subjective measures of clinical ambulation.15

Walking tests measure the distance walked in a given amount of time to indicatewalking performance. These tests are generally 2, 6, 10, or 12 minutes in duration andare widely used in clinical and research applications for individuals with CND.3 Thesemeasures are more quantitative and provide information directly related to ambulation,including walking endurance. Previous research in this area suggests that individualsmust negotiate a distance of between 332 and 360 m to access goods and servicesin the community.16,17 Walking distance is therefore a key indicator of ambulation.18

For individuals with CND, it is important to have clinical assessments that identifyindividuals who are at risk for falling and may benefit from interventions designed toimprove balance.19 The Dynamic Gait Index (DGI) was developed as a clinical toolto assess an individual’s ability to modify gait in response to changing taskdemands.11 The limitation of the DGI is that scores are reduced for those individualsusing an assistive device, regardless of performance.20 The DGI has been applied toindividuals with CND as a reliable measure of dynamic balance and potential fallrisk.20,21 The Timed Up and Go (TUG) is a screening test of balance that is commonlyused to evaluate functional mobility.19 The time to complete the test is strongly corre-lated to the level of functional mobility. Adults who complete the TUG in less than20 seconds are considered to be independent in transfer tasks associated withADLs and can maintain walking speeds sufficient for community ambulation.12,22

In individuals with CND, alterations in gait mechanics, strength, and balance havea direct effect on gait speed.5 The 10-m walk test and timed 7.62 m (25-ft) walk aremeasures of ambulation capacity in which gait speed is the primary outcome variable.These tests have been widely used in populations with CND and are easy to administerin a clinical environment.3 When measuring ambulation, it is important for clinicians toselect measures capable of detecting changes that reflect real life function.23 Gaitspeed is an important and reliable measure of ambulation for individuals, becausesafe navigation of community crosswalks mandates that an individual be able tocomplete a prescribed distance in a defined time period.16,23 Previous research instroke found that when an ankle foot orthotic was worn on the paretic limb, individualsgained the ability to modulate gait speed. The orthotic intervention provided individ-uals with stroke with the functional ability to increase speed in the community, whichis essential for negotiating obstacles and safely crossing community streets.14,16 Gaitspeed is used in clinical and research applications as the hallmark of recovery, it issimple to implement, and it has robust psychometric properties.24 Several studieshave provided evidence to support the predictive validity of gait speed, and it has

Measuring Ambulation in Central Neurologic Disorders 251

been shown to be positively correlated with level of disability, function, and quality oflife in individuals with stroke.25–27 However, what represents a clinically meaningfulchange in gait speed has not been defined in all patient populations.

MEASURING AMBULATION IN A LABORATORY ENVIRONMENT

In a laboratory, ambulation is measured using traditional clinical assessments inconjunction with technology-driven mobility assessment tools, to create quantifiableoutcomes that can be used to describe mechanisms that may lead to improved ambu-lation. For example, increased gait speedmay indicate improved ambulation, but doesnot necessarily indicate improved gait mechanisms or functional recovery. The goalsof laboratory-based outcomes for measuring ambulation are to enhance traditionalclinical measures, or to become fully integrated into routine clinical care to help directtreatment options and accurately measure changes in ambulation throughout therehabilitation process.

History and Evolution of Measuring Ambulation

Gait is the way ambulation is achieved using human limbs, and gait analysis is thesystematic evaluation of a person’s walking pattern.28 The origins of the science ofgait analysis began in the seventeenth century in Europe, when scientists andresearchers provided a solid scientific foundation of our current knowledge and theunderstanding of human ambulation.29 Research in the twentieth century benefitedfrom technological advancements, including the development of three-dimensional(3D) force plates, and kinesiological electromyography (KEMG), which moved themeasurement precision of gait analysis significantly forward.29 A summary of the evolu-tion of these early techniques into the systems used today is described in Table 2.29–31

Although the early principles of gait analysis are recognized as the foundation ofmeasuring ambulation, the methods used were too labor-intensive for practical appli-cation in a clinical setting.29 Technology-driven systems for measuring ambulationcontinue to evolve in the laboratory, with the goal of translating these outcomemeasures into the clinical and community setting.

Techniques for Measuring Ambulation in a Research Laboratory

Gait analysis is a widely used measurement technique to assess, plan, and treat indi-viduals with conditions affecting their ability to walk. Gait analysis emerges from thecombined interaction of the human eye (image capture) and the human brain (imageprocessing) used for recognition and identification of changes in ambulation.32 Acomplete gait analysis can be achieved using visual observation to interpret humanlocomotion. Therapists frequently use observational gait analysis to evaluate patientambulation, because instrumented measurement systems are not feasible duringtime-limited clinical visits. Observational gait analysis typically reveals compensationsor impairments from underlying diseases but it is limited by the experience of theobserver. Instrumented gait analysis is a more technical method that involves collec-tion of quantifiable information through the use of cameras, force plates, electromyog-raphy (EMG), pressure sensors, and computer analysis to objectively measure anindividual’s walking pattern.32 Quantitative state-of-the-art methods of analysis andequipment offer greater precision, specificity, and sensitivity to change. It is withinthe laboratory environment that the comparison of observational versus instrumentedgait analysis must continue to be explored to advance clinical outcomes.Healthy walking is the standard against which disease is measured. The key gait pa-

rameters that the clinician needs to compare against disease are described in Table 3.1

Table 2Techniques for gait analysis: evolution and history

KEMG J.R. Close, 1959 Studied the phasic action of muscles using a 16-mmmovie camera with a sound track and was able torecord single-channel muscle action potentials oncine film

R.W. Vreeland andD.H. Sutherland, 1961

Used a 16-mm movie camera mounted on a turntableabove an oscilloscope with a 3-channel EMG

J.V. Basmajian, 1962 Developed the technique of inserting 2 very fineelectrodes (50-mm) through a single small needle(fine-wire EMG)

J.U. Baumann, 1974 Obtained an 8-channel Honeywell tape recorder with6 channels for KEMG transmitted through telemetry

S.R. Simon, 1977 Electromyography permitted simultaneous recordingfrom 12 channels transmitted through a cable fromsubject to computer

Current Various commercially available multichannel surfaceand fine-wire EMG systems, including telemeteredand wireless EMG capability and integrated softwarefor comprehensive outcome analysis. Capable ofmeasurements inside (clinical or laboratory) andoutside (community) in various environments

Kinematics M.P. Murray, 1964 Attached reflective targets to anatomic landmarks ofsubjects walking in illumination of strobe light

P.V. Karpovich andG.P. Karpovich, 1959

Electrogoniometry

R. Linder, 1965 Vanguard motion analyzer: developed methods tomeasure yaw, pitch, and roll using mathematicalformulae, 2 or more cameras and a two-dimensionalcoordinate system

E.H. Furnee, 1967–1989 Primas system: TV/motion analysis systems withautomated recording of reflective marker positions

Current Multiple commercially available real-time opticalmotion capture systems with: (1) infrared HD cameras;(2) markerless motion capture; (3) active light-emitting diode technology; (4) wired and wirelesssystems and integrated software for comprehensiveoutcome analysis. Capable of measurements inside(clinical or laboratory) and outside (community) invarious environments

Kinetics M. Carlet, 1872 Developed and used air reservoirs to measure forceapplied to the heel and forefoot

E.J. Marey, 1894 Developed first true force plate, which measuredvertical component of ground reaction usinga pneumatic mechanism

J. Amar, 1916 Produced the world’s first 3-component (pneumatic)force plate, Trottoire Dynamique

J. Hawthorn, 1971 Dynamic force plate (piezoelectric force transducers)Current Several multichannel force (6� of freedom) and pressure

measurements systems. Capable of measurement invarious environments indoors, outdoors. and underwater

Adapted from Sutherland, DH, 2001, 2002, 2005 Refs.29–31

Nolan et al252

Table 3Outcomes for measuring ambulation in a laboratory environment

Spatial-temporalcharacteristics33

Spatial Step length, stride length, step width, velocity,cadence

Temporal IDS (initial double support time),SS (single support time), TDS (terminal doublesupport time), swing time

Symmetry Temporal-spatial symmetry ratios between limbs

Joint kinematics34–36 Rotations 3D joint angular rotationsAngular velocities Rate of change of 3D joint anglesCenter of mass Excursion of the 3D center of mass

Joint kinetics37 Forces 3D joint forces (effect of ground reaction forceson joint motions)

Torques 3D joint torques (a combination of jointrotations and forces)

EMG37 Muscle firing EMG analysis to assess muscle activation patterns(timing and amplitude) as it relates to jointkinematics

Co-contraction Simultaneous activation of agonist andantagonist muscles, if any

Pedobarography27,38,39 Plantar pressures Measuring the magnitude and timing ofpressure acting between the plantar surface ofthe foot and a supporting surface to quantifyloading, weight transfer, and area duringwalking

Measuring Ambulation in Central Neurologic Disorders 253

Measuring Ambulation Using Temporal-Spatial Characteristics

TemporalTemporal parameters are calculated using the timing characteristics within a gaitcycle, where each gait cycle is further divided into individual gait phases to makecomparisons across several cycles. The complete gait cycle includes initial doublesupport (the time between foot contact and contralateral toe off), single support(the time between contralateral toe off and contralateral foot contact), terminal doublesupport (the time between contralateral foot contact and ipsilateral toe off), and swing(the time between ipsilateral toe off and ipsilateral foot contact).33

SpatialSpatial parameters are measured using the linear measurements (displacement ofmarkers) during a gait cycle. They include step length (the forward linear distancebetween the heel markers from foot contact to subsequent contralateral foot contact),stride length (the forward linear distance between the heel markers from foot contactto ipsilateral foot contact), and step width (the linear medial-lateral distance betweenheel markers from foot contact to contralateral foot contact).33

SymmetrySymmetry measures are used to compare characteristics between the limbs.33

Temporal-spatial gait symmetry indices are calculated using mean swing (seconds)and stance times (seconds) on both limbs to calculate the following variables: overalltemporal symmetry; temporal swing symmetry; temporal stance symmetry; temporalswing stance symmetry; and spatial step symmetry (the ratio of step lengths on bothlimbs in millimeters). A normative range for temporal-spatial symmetry is assumed tobe 0.9 to 1.1.33 This technique has been effectively used in stroke to assess the

Nolan et al254

behavior of the paretic and nonparetic limbs during gait, where an overall temporalsymmetry value greater than 1 indicates a preference to rely on the nonparetic limbduring walking.33,40

Quantifying Ambulation with Kinematic and Kinetic Analysis

KinematicKinematic data consist of temporal, linear, and angular variables. They quantifythe features of gait describing the movements of the body segments and jointangles: temporal (timing), linear (displacement), and angular measurements. Temporaldata describe the stride duration and the limb coordination patterns. Distance data,computed from the coordinates of the markers, describe the stride length, thedistances between limb placements, and the flight paths of the body parts. Angulardata describe the displacements, velocities, and accelerations of the body segmentsand joints. Kinematic output from the motor control system is useful for measuring themovement variance during walking and helps to classify gait diseases.36,41 Fig. 1shows sagittal plane joint angles at the hip, knee, and ankle during healthy walking.42

Motion capture systems in a laboratory setting are routinely used to measure ambu-lation by evaluating changes in gait kinematics. Reflective markers are placed onselected anatomic landmarks and captured with the use of infrared cameras in a 3Dspace. Data reduction and kinematic modeling can then be performed to calculatelinear and angular measurements. The data may then be displayed graphically forpresentation and report generation.1,43 Deviations or changes in kinematics canhelp to identify and measure gait deficits, thereby initiating clinical interventions forrehabilitation. In an individual with CND, gait analysis may show unilateral or bilateralcircumduction, abnormal pelvic movements, and a lack of ankle dorsiflexion during

Fig. 1. Sagittal plane gait kinematics, during healthy adults walking: (A) hip, (B) knee, and(C) ankle. Each plot represents the mean � standard deviation for 1 representative subjectover 10 consecutive trials. (Data from Ramanujam A, Forrest GF, Sisto SA. Methological vari-ability in evaluating gait dynamics: a comparative study. Gait and Clinical Movement Anal-ysis Society 13th Annual Meeting. Richmond, Virginia, April 2-5, 2008. p. 278–9.)

Measuring Ambulation in Central Neurologic Disorders 255

swing (foot drop). After physical therapy or assistive device intervention, further kine-matic testing can precisely measure changes in ambulation to direct future treatmentstrategies.44

KineticKinetic analysis measures forces, both external and internal to the body. Forces devel-oped by muscles are transformed into rotations of the limb segments that producemovement. The ground reaction forces (GRF) during locomotion can be recordedusing a force plate. Intrinsic joint forces and torques are then calculated from knowl-edge of the GRFs using inverse dynamic calculations. When an individual steps on theforce plate, the force is detected by transducers or strain gauges and is converted toan electrical signal that is amplified and recorded. Variables measured by the forceplate include the stance duration, the magnitude of the vertical, longitudinal (horizontalcraniocaudal), and transverse (horizontal mediolateral) forces, the time when the peakforces occur, the impulses (area under the force time curves), and the point of appli-cation of the force (center of pressure). Displacement of the center of pressure hasbeen evaluated for individuals with stroke and has been a robust measure to predictgait velocity, a reliable indicator of both pathologic gait and general functional status inrehabilitation.45–47

Normal forces plantar to the foot during walking can also be measured using pres-sure sensor technology, such as insole sensors or pressure mats. Measuring changesin loading during ambulation provides quantitative information that cannot be obtainedwithout instrumentation. This technology has been used to evaluate weight transfer,an analytical method that quantifies linearity of loading and transfer of momentumduring gait. These outcome variables have been used to evaluate orthotic interventionand are significant factors related to the improvement of ambulation.39

Use of EMG

EMG provides information on the state of activity of the motor neurons at rest, duringreflex contraction, and during voluntary contraction. During ambulation, muscularcontraction generates forces that stabilize and move the limbs. This process ispreceded by electrical activation, which can be detected and recorded as the EMG.EMG signals are used in clinical and laboratory environments as a diagnostic tool toidentify neuromuscular diseases and disorders of motor control. When measuringambulation, the outcome variables obtained from EMG analysis provide quantifiableinformation about the coordination of complex muscle movements and can showmechanisms to compensate for muscle weakness.48 Recent research investigatingthe use of a peroneal nerve stimulator in individuals with stroke found increasedmuscle activation during gait. Use of EMG provided information about therapeuticgains after intervention that extended beyond the ankle joint and the typicallymeasured increase in dorsiflexion angle.49

Measuring Energy Consumption in a Research Laboratory

Individuals with CND who experience difficulty with ambulation typically also have anincreased energy cost during walking. Decreased walking speed associated with CNDcontributes to reduced gait efficiency by preventing efficient energy transfer.50

Previous research has suggested that faster walking speeds promote a more cost-effective gait pattern.50,51 A straightforward method of evaluating the energy cost ofwalking is the physiologic cost index (PCI). The PCI measures the effort involved inwalking as an increase in steady heart rate during movement (compared with a restingstate) divided by the velocity of that movement.52 Research has suggested a linear

Nolan et al256

relationship between heart rate during sustained walking and maximum oxygenconsumption.53 PCI can provide quantitative information of energy cost to anymobilityassessment by adding a device to measure heart rate and velocity.Alternatively, VO2 peak can be used as an indirect measure of an individual’s peak

aerobic capacity. Aerobic capacity has been defined as the maximal amount of phys-iologic work an individual can perform, measured by oxygen consumption.54

Measurements of walking economy can estimate the energy demands of an individualand characterize physiologic capacity for exercise.55 In a laboratory environment,measurement of VO2 and walking economy can be obtained using precise instrumen-tation and a standard treadmill.56

MEASURING AMBULATION IN THE COMMUNITY

Reintegration into the community and maintenance of ADLs are the goal of rehabilita-tion programs for persons with CND. In a free-living population, technology-drivenambulation assessment measures often cannot be effectively used because of theconstraints of the home/community environment. Measurement of community ambu-lation involves an individual’s interaction within their environment and therefore musttake into account the psychosocial aspects of that interaction as well as the physicalimpairment.4,57 It is the patient’s home environment that sets the context for evalua-tions of individual goals as they relate to quality of life. Complex measures that areboth qualitative and quantitative are required, making measurement of ambulationin the community perhaps the most challenging environment in which to define mean-ingful outcomes for measuring ambulation.58

Additional levels of complexity in measuring community ambulation are found in theinteraction with other impairments such as cognition that affect spatial recognition,memory, and attention.59,60 In clinical and laboratory environments, there is diminishedattention paid to cognitive impairments, to focus on the effects of physical impairmentson ambulation. In the community, an individual must be able to ambulate in combina-tion with cognitive tasks, therefore the cognitive and physical are interrelated whenmeasuring community ambulation.22,61,62 Although several clinical measures havebeen developed to simulate community ambulation activities, such as those requiringgait over uneven surfaces or dual task assessments requiring additional cognitive orattentional involvement during gait, the ability to measure the interaction with otherimpairments in real situations in the community are virtually nonexistent.Previous research has concentrated on several areas related to measurement of

mobility impairment in the community such as measurement of impairment, activity,participation, and quality of life.4 The following section highlights several of the impor-tant techniques and findings related to measurement of ambulation in the communityfor persons with CND.

Qualitative Measurements of Ambulation

Most often, qualitative measurements of community ambulation are obtained throughthe use of self-report survey instruments that seek to categorize an individual’s abilityto ambulate in different situations. Measures of community ambulation and participa-tion are often aimed at categorically identifying the ADLs that an individual is capableof performing, as well as the level of assistance needed, in terms of use of assistivedevices and care from another person.In the population with stroke, both Perry and colleagues46 and Lord and

colleagues61 have conducted studies in which participants rated their ability toperform such tasks as getting to appointments or walking from their homes to the

Measuring Ambulation in Central Neurologic Disorders 257

mailbox. Based on their ability to accomplish such tasks independently, participantswere classified as noncommunity, limited community, or full community ambulators.In a 2005 review, Lord and colleagues61 described many of the classification

schemes to assess community participation in stroke such as the Subjective Indexof Physical and Social Outcome,63 the Reintegration to Normal Living Index,64 theSickness Impact Profile,65 the Frenchay Activities Index,66 the Nottingham ExtendedActivities of Daily Living Index,67 and the Stroke Impact Scale.68 The UnifiedParkinson’s Disability Rating Scale is the gold standard instrument used to measuredisease severity in Parkinson disease and includes questions about the ability tosuccessfully ambulate in various community environments.69 Although these scaleshave been validated for the classification of community ambulation ability, few couldbe considered measurement tools that can successfully assess the quantity or qualityof ambulation in the community. The Spinal Cord Injury-Functional Index measure-ment system uses a 5-factor model, comprising basic mobility, ambulation, wheel-chair mobility, self-care, and fine motor function, with the goal of comprehensivelyassessing physical function for individuals with spinal cord injury.70

Although many of these classification systems are specific to 1 population, theInternational Classification of Functioning (ICF) from the World Health Organization71

has been used across many populations with CND. The ICF has multiple compo-nents that aim to assess the way an individual’s disability affects all aspects oflife. To assess the effect of limited community ambulation, as it relates to overallquality of life, the ICF uses 3 domains: (1) body functions and structures (impair-ment); (2) activity; and (3) participation.72 Using this framework, deficits in the abilityto ambulate in the community can be categorized as impairments of body functionsand structures (such as weakness or pain), environmental factors (including acces-sibility of the home and available transportation), as well as personal factors(including motivation and support). Included in this assessment are questions aboutself-efficacy, which represents the individual’s perception of their own ability. Oftenindividuals avoid activities that they perceive are too difficult, and therefore it isimportant to understand the perception of disability when evaluating the effect ofdisability as a whole.71 The goal of the ICF is to provide a framework that considersnot only physical impairment but also social barriers to accurately classify commu-nity ambulation.71,72

Other broadly used scales across multiple populations are the Ambulatory Self-Confidence Questionnaire,73 aimed at specifically examining self-efficacy, whichhas been used in patient populations with both Parkinson disease and multiple scle-rosis. The Environmental Analysis of Mobility Questionnaire, developed in nonneuro-logically impaired geriatric populations, has also been validated to study communityambulation in persons with CND.62,74

Quantitative Measurements of Community Ambulation

Quantitative measurements of community ambulation provide more reliableoutcomes, compared with qualitative measures, when used in various populationswith CND. Many of the laboratory technologies described earlier cannot be used inthe community. Often, assessments of community ambulation must depend oncomparatively simple technologies that result in different outcomes/goals from thoseof laboratory and clinical assessments. Unlike in the laboratory, where greater spec-ificity and detail are paramount, community-based technologies must be able tocollect data for long periods and not impede ADLs.75 However, the need to under-stand commonly used clinical and laboratory ambulation measures is essential toeffectively adapt quantitative measures to evaluate community outcomes.

Nolan et al258

Common technologies used for quantitative assessment of community ambulationinclude pedometers, accelerometers, and GPS (Global Positioning System)-baseddevices.58,75 The goal of these instruments is to provide long-term, remote, unobtru-sive monitoring of ambulatory activity.76,77 It is also important to provide data ona timescale that allows the observation of changes in activity patterns.78

Pedometer-based activity monitors report data as number of steps per unit timeusing sensors that measure a combination of acceleration, position, and time(Fig. 2). Outcomemeasures include daily step counts, measures of peak activity, step-ping bout duration quantified by the amount of time spent continuously stepping, andthe amount of time ambulating at predefined activity levels.77 In a study of individualswith chronic hemiplegia secondary to stroke, Roos and colleagues79 found that thosewith a self-reported status of “unlimited community ambulators” ambulated more inthe community than “limited community ambulators.” Ambulation by all participantswas found to be in short bouts, and sustained ambulation was uncommon.75,79 Usingsimilar techniques in individuals with Parkinson disease to assess declines in commu-nity ambulation, Cavanaugh and colleagues80 found that participants had significantdeclines in the amount and intensity of daily ambulatory activity, but not in itsfrequency and duration. This research indicates that it is not only important to assessthe amount of ambulation in terms of step counts or distance traveled but also to trackthe variation in day-to-day ambulatory activity. Fig. 2 shows a sample of data from anaccelerometer-based activity monitor, representing steps taken per day for 1 indi-vidual with stroke and an analysis of community ambulation by intensity for 17 partic-ipants with foot drop secondary to stroke.78

Fig. 2. Sample community ambulation data from an accelerometer-based activity monitor.(A) One representative day of total steps taken for 1 subject with stroke; (B) daily commu-nity ambulation data analyzed by intensity for 17 participants with foot drop secondary tostroke. (Data from Yarossi M, Franco B, Nolan KJ. StepWatch measures of community ambu-lation in individuals with hemiplegia following stroke. Archives of Physical Medicine andRehabilitation–ACRM/ASNR Annual Conference 2011;92(10):1720.)

Measuring Ambulation in Central Neurologic Disorders 259

Reports of agreement between self-report and objective measures of communityambulation are mixed.61,81 Average daily steps were measured for individuals withchronic incomplete spinal cord injury (n 5 50), and a significant difference was foundbetween self-reported community and noncommunity walkers.81 In contrast, a studyby Lord and colleagues61 concluded that persons with stroke showed greater activityon self-report measures than what was measured objectively. Self-report measuresinherently depend on an individual’s perception of their own health and impairmentand are often useful only in the context of the individual and are difficult to evaluateat the group level.Understanding the strength of the relationship between commonly used clinical and

laboratory measures and ambulatory activity in the community is essential for the useof clinical measurement to predict community outcomes. Research in chronic strokereported on correlation and predictive ability of several clinical measures of ambula-tion including the Rivermead Mobility Index, Rivermead Motor Assessment, 6-minutewalk test (6MWT), 10-m walk test (10MWT), and data collected from step activitymonitors (SAM) worn in the community by participants with chronic stroke.77 Onlythe 6MWT and the 10MWT were found to correlate with SAM data, and only the6MWT was a significant predictor of step counts. In recent research in individualswith stroke, step activity was analyzed by intensity (see Fig. 2), and no significantrelationships were found between clinical measurements of gait speed and endurance(timed 7.62-m [25-ft] walk and 6MWT), the number of steps taken per day, or theamount of the day spent active.78,82 Clinical measures were correlated to measuresof peak activity only during community ambulation, which indicated that individualswith hemiplegia who were able to sustain activity greater than 20 steps per minutewalked more in the community.78 Future work to better understand and measurecommunity ambulation will have a valuable impact when evaluating individuals fortreatment, which will provide more independence in performing ADLs.

SUMMARY

The goal of most rehabilitation programs is successful reintegration into the commu-nity. The ability to accurately measure ambulation, one of the most important metricsused to show transition into a community environment, is essential to measure treat-ment effectiveness and rehabilitation outcomes in populations with CND.Measurements of ambulation in populations with CND should: (1) be predictive of

disease state or progression; (2) be useful for evaluating recovery or intervention; (3) bediagnostic; (4) indicate the need for assistance in the community; (5) be able to provideinformation related to quality of life; and (6) use state-of-the-art technology. In addition,measurements of ambulation should be carefully examined for administration withina particular environment (clinical, laboratory, or community). Even the most robustmeasure of ambulation is affected by the environment in which it is implemented. Typeof measurement or environment selected depends on the clinical or research questionand the specificity of the hypothesis investigated.

REFERENCES

1. Perry J, Burnfield JM. Gait analysis: normal and pathological function. 2ndedition. Thorofare (NJ): Slack; 2010.

2. Coleman KL, Smith DG, Boone DA, et al. Step activity monitor: long-term, contin-uous recording of ambulatory function. J Rehabil Res Dev 1999;36(1):8–18.

3. Finch E, Brooks D, Stratford PW, et al. Physical rehabilitation outcome measures.2nd edition. Ontario (Canada): Lippincott Williams & Wilkins; 2002.

Nolan et al260

4. Lord SE, Rochester L. Measurement of community ambulation after stroke:current status and future developments. Stroke 2005;36(7):1457–61.

5. Jackson AB, Carnel CT, Ditunno JF, et al. Outcome measures for gait andambulation in the spinal cord injury population. J Spinal Cord Med 2008;31(5):487–99.

6. Keith RA, Granger CV, Hamilton BB, et al. The functional independence measure:a new tool for rehabilitation. Adv Clin Rehabil 1987;1:6–18.

7. Fugl-Meyer AR, Jaasko L, Leyman I, et al. The post-stroke hemiplegic patient. 1.A method for evaluation of physical performance. Scand J Rehabil Med 1975;7(1):13–31.

8. Hauser SL, Dawson DM, Lehrich JR, et al. Intensive immunosuppression inprogressive multiple sclerosis. A randomized, three-arm study of high-dose intra-venous cyclophosphamide, plasma exchange, and ACTH. N Engl J Med 1983;308(4):173–80.

9. Wolf SL, Catlin PA, Gage K, et al. Establishing the reliability and validity ofmeasurements of walking time using the Emory Functional Ambulation Profile.Phys Ther 1999;79(12):1122–33.

10. Baer HR, Wolf SL. Modified Emory Functional Ambulation Profile: an outcomemeasure for the rehabilitation of poststroke gait dysfunction. Stroke 2001;32(4):973–9.

11. Shumway-Cook A, Woollacott M. Motor control: theory and applications.Baltimore (MD): Wilkins & Wilkins; 1995.

12. Podsiadlo D, Richardson S. The timed “Up & Go”: a test of basic functionalmobility for frail elderly persons. J Am Geriatr Soc 1991;39(2):142–8.

13. Cohen ME, Marino RJ. The tools of disability outcomes research functional statusmeasures. Arch Phys Med Rehabil 2000;81(12 Suppl 2):S21–9.

14. Nolan KJ, Savalia KK, Lequerica AH, et al. Objective assessment of functionalambulation in adults with hemiplegia using ankle foot orthotics after stroke.PM R 2009;1(6):524–9.

15. Liaw LJ, Hsieh CL, Lo SK, et al. Psychometric properties of the modified EmoryFunctional Ambulation Profile in stroke patients. Clin Rehabil 2006;20(5):429–37.

16. Cohen JJ, Sveen JD, Walker JM, et al. Establishing a criteria for communityambulation. Top Geriatr Rehabil 1987;3(1):71–7.

17. Lerner-Frankiel MB, Vargas S, Brown M, et al. Functional community ambulation:what are your criteria? Clinical Management 1986;6(2):12–5.

18. Fulk GD, Echternach JL, Nof L, et al. Clinometric properties of the six-minute walktest in individuals undergoing rehabilitation poststroke. Physiother Theory Pract2008;24(3):195–204.

19. Shumway-Cook A, Brauer S, Woollacott M. Predicting the probability for falls incommunity-dwelling older adults using the Timed Up & Go Test. Phys Ther2000;80(9):896–903.

20. Jonsdottir J, Cattaneo D. Reliability and validity of the dynamic gait index inpersons with chronic stroke. Arch Phys Med Rehabil 2007;88(11):1410–5.

21. McConvey J, Bennett SE. Reliability of the Dynamic Gait Index in individuals withmultiple sclerosis. Arch Phys Med Rehabil 2005;86(1):130–3.

22. Smulders K, van Swigchem R, de Swart BJ, et al. Community-dwelling peoplewith chronic stroke need disproportionate attention while walking and negotiatingobstacles. Gait Posture 2012;36(1):127–32.

23. Fulk GD, Echternach JL. Test-retest reliability and minimal detectable change ofgait speed in individuals undergoing rehabilitation after stroke. J Neurol PhysTher 2008;32(1):8–13.

Measuring Ambulation in Central Neurologic Disorders 261

24. Tilson JK, Sullivan KJ, Cen SY, et al. Meaningful gait speed improvement duringthe first 60 days poststroke: minimal clinically important difference. Phys Ther2010;90(2):196–208.

25. Goldie PA, Matyas TA, Evans OM, et al. Maximum voluntary weight-bearing bythe affected and unaffected legs in standing following stroke. Clin Biomech(Bristol, Avon) 1996;11(6):333–42.

26. Schmid A, Duncan PW, Studenski S, et al. Improvements in speed-based gaitclassifications are meaningful. Stroke 2007;38(7):2096–100.

27. Nolan KJ, Yarossi M. Preservation of the first rocker is related to increases in gaitspeed in individuals with hemiplegia and AFO. Clin Biomech 2011;26(6):655–60.

28. Chambers HG, Sutherland DH. A practical guide to gait analysis. J Am AcadOrthop Surg 2002;10(3):222–31.

29. Sutherland DH. The evolution of clinical gait analysis. Part I: kinesiological EMG.Gait Posture 2001;14(1):61–70.

30. Sutherland DH. The evolution of clinical gait analysis. Part II: kinematics. GaitPosture 2002;16(2):159–79.

31. Sutherland DH. The evolution of clinical gait analysis. Part III: kinetics and energyassessment. Gait Posture 2005;21(4):447–61.

32. Whittle MW. Gait analysis an introduction. 4th edition. New York: ButterworthHeinemann; 2007.

33. Patterson KK, Parafianowicz I, Danells CJ, et al. Gait asymmetry in community-ambulating stroke survivors. Arch Phys Med Rehabil 2008;89(2):304–10.

34. Wu G, Siegler S, Allard P, et al. ISB recommendation on definitions of joint coor-dinate system of various joints for the reporting of human joint motion–part I:ankle, hip, and spine. International Society of Biomechanics. J Biomech 2002;35(4):543–8.

35. Wu G, van der Helm FC, Veeger HE, et al. ISB recommendation on definitions ofjoint coordinate systems of various joints for the reporting of human joint motion–part II: shoulder, elbow, wrist and hand. J Biomech 2005;38(5):981–92.

36. Lee HJ, Chou LS. Detection of gait instability using the center of mass and centerof pressure inclination angles. Arch Phys Med Rehabil 2006;87(4):569–75.

37. Winter DA. Biomechanics and motor control of human movement. 4th edition.Hoboken (NJ): John Wiley; 2009.

38. Soames RW. Foot pressure patterns during gait. J Biomed Eng 1985;7(2):120–6.39. Nolan KJ, Yarossi M. Weight transfer analysis in adults with hemiplegia using

ankle foot orthosis. Prosthet Orthot Int 2011;35(1):45–53.40. Esquenazi A, Ofluoglu D, Hirai B, et al. The effect of an ankle-foot orthosis on

temporal spatial parameters and asymmetry of gait in hemiparetic patients.PM R 2009;1(11):1014–8.

41. Burnfield JM, Powers CM. The role of center of mass kinematics in predicting peakutilized coefficient of friction during walking. J Forensic Sci 2007;52(6):1328–33.

42. Ramanujam A, Forrest GF, Sisto SA. Methodological variability in evaluating gaitdynamics: a comparative study. Gait and Clinical Movement Analysis Society13th Annual Meeting. Richmond, Virginia, April 2-5, 2008. p. 278–9.

43. Bensoussan L, Viton JM, Barotsis N, et al. Evaluation of patients with gait abnor-malities in physical and rehabilitation medicine settings. J Rehabil Med 2008;40(7):497–507.

44. Nolan KJ, Savalia KK, Yarossi M, et al. Evaluation of a dynamic ankle foot orthosisin hemiplegic gait: a case report. NeuroRehabilitation 2010;27(4):343–50.

45. Mizelle C, Rodgers M, Forrester L. Bilateral foot center of pressure measurespredict hemiparetic gait velocity. Gait Posture 2006;24(3):356–63.

Nolan et al262

46. Perry J, Garrett M, Gronley JK, et al. Classification of walking handicap in thestroke population. Stroke 1995;26(6):982–9.

47. Nolan KJ, Yarossi M. Changes in COP displacement with the use of a foot dropstimulator in individuals with stroke 13th Emed Scientific Meeting (ESM). Aalborg,Denmark, August 1–4, 2012.

48. Brunner R, Romkes J. Abnormal EMG muscle activity during gait in patientswithout neurological disorders. Gait Posture 2008;27(3):399–407.

49. Yarossi M, Ramanujam A, Pilkar R, et al. Therapeutic gains after utilization ofa foot drop stimulator in stroke extend beyond the ankle joint: a case report.Archives of Physical Medicine and Rehabilitation– ACRM/ASNR Annual Confer-ence 2012;93(10):e19.

50. Lamontagne A, Stephenson JL, Fung J. Physiological evaluation of gait distur-bances post stroke. Clin Neurophysiol 2007;118(4):717–29.

51. Hesse S, Werner C, Paul T, et al. Influence of walking speed on lower limb muscleactivity and energy consumption during treadmill walking of hemiparetic patients.Arch Phys Med Rehabil 2001;82(11):1547–50.

52. MacGregor J. The evaluation of patient performance using long-term ambulatorymonitoring technique in thedomiciliaryenvironment.Physiotherapy1981;67(2):30–3.

53. Wieler M, Stein RB, Ladouceur M, et al. Multicenter evaluation of electrical stim-ulation systems for walking. Arch Phys Med Rehabil 1999;80(5):495–500.

54. Resnick B, Michael K, Shaughnessy M, et al. Inflated perceptions of physicalactivity after stroke: pairing self-report with physiologic measures. J Phys ActHealth 2008;5(2):308–18.

55. Macko RF, Smith GV, Dobrovolny CL, et al. Treadmill training improves fitnessreserve in chronic stroke patients. Arch Phys Med Rehabil 2001;82(7):879–84.

56. Michael K, Macko RF. Ambulatory activity intensity profiles, fitness, and fatigue inchronic stroke. Top Stroke Rehabil 2007;14(2):5–12.

57. Donovan K, Lord SE, McNaughton HK, et al. Mobility beyond the clinic: the effectof environment on gait and its measurement in community-ambulant strokesurvivors. Clin Rehabil 2008;22(6):556–63.

58. Gebruers N, Vanroy C, Truijen S, et al. Monitoring of physical activity after stroke:a systematic review of accelerometry-based measures. Arch Phys Med Rehabil2010;91(2):288–97.

59. Lord SE, Rochester L, Weatherall M, et al. The effect of environment and task ongait parameters after stroke: a randomized comparison of measurement condi-tions. Arch Phys Med Rehabil 2006;87(7):967–73.

60. Brauer SG, Morris ME. Can people with Parkinson’s disease improve dual taskingwhen walking? Gait Posture 2010;31(2):229–33.

61. Lord SE, McPherson K, McNaughton HK, et al. Community ambulation afterstroke: how important and obtainable is it and what measures appear predictive?Arch Phys Med Rehabil 2004;85(2):234–9.

62. Blennerhassett JM, Dite W, Ramage ER, et al. Changes in balance and walkingfrom stroke rehabilitation to the community: a follow-up observational study.Arch Phys Med Rehabil 2012;93(10):1782–7.

63. Trigg R, Wood VA. The Subjective Index of Physical and Social Outcome (SIPSO):a new measure for use with stroke patients. Clin Rehabil 2000;14(3):288–99.

64. Wood-Dauphinee SL, Opzoomer MA, Williams JI, et al. Assessment of globalfunction: the Reintegration to Normal Living Index. Arch Phys Med Rehabil1988;69(8):583–90.

65. BergnerM, Bobbitt RA, CarterWB, et al. The Sickness Impact Profile: developmentand final revision of a health status measure. Med Care 1981;19(8):787–805.

Measuring Ambulation in Central Neurologic Disorders 263

66. Holbrook M, Skilbeck CE. An activities index for use with stroke patients. AgeAgeing 1983;12(2):166–70.

67. Nouri FM, Lincoln NB. An extended activities of daily living scale for strokepatients. Clin Rehabil 1987;1(4):301–5.

68. Duncan PW, Wallace D, Lai SM, et al. The stroke impact scale version 2.0. Eval-uation of reliability, validity, and sensitivity to change. Stroke 1999;30(10):2131–40.

69. Movement Disorder Society Task Force on Rating Scales for Parkinson’s Disease.The Unified Parkinson’s Disease Rating Scale (UPDRS): status and recommenda-tions. Mov Disord 2003;18(7):738–50.

70. Tulsky DS, Jette AM, Kisala PA, et al. Spinal cord injury-functional index: itembanks to measure physical functioning in individuals with spinal cord injury.Arch Phys Med Rehabil 2012;93(10):1722–32.

71. Stucki G, Ewert T, Cieza A. Value and application of the ICF in rehabilitationmedicine. Disabil Rehabil 2003;25(11–12):628–34.

72. Jette AM. Toward a common language for function, disability, and health. PhysTher 2006;86(5):726–34.

73. Asano M, Miller WC, Eng JJ. Development and psychometric properties of theambulatory self-confidence questionnaire. Gerontology 2007;53(6):373–81.

74. Shumway-Cook A, Patla A, Stewart AL, et al. Assessing environmentally deter-mined mobility disability: self-report versus observed community mobility. J AmGeriatr Soc 2005;53(4):700–4.

75. Orendurff MS, Schoen JA, Bernatz GC, et al. How humans walk: bout duration,steps per bout, and rest duration. J Rehabil Res Dev 2008;45(7):1077–89.

76. Halstead LS. Longitudinal unobtrusive measurements in rehabilitation. Arch PhysMed Rehabil 1976;57(4):189–93.

77. Mudge S, Stott NS. Timed walking tests correlate with daily step activity inpersons with stroke. Arch Phys Med Rehabil 2009;90(2):296–301.

78. Yarossi M, Franco B, Nolan KJ. StepWatch measures of community ambulation inindividuals with hemiplegia following stroke. Archives of Physical Medicine andRehabilitation–ACRM/ASNR Annual Conference 2011;92(10):1720.

79. Roos MA, Rudolph KS, Reisman DS. The structure of walking activity in peopleafter stroke compared with older adults without disability: a cross-sectional study.Phys Ther 2012;92(9):1141–7.

80. Cavanaugh JT, Ellis TD, Earhart GM, et al. Capturing ambulatory activity declinein Parkinson’s disease. J Neurol Phys Ther 2012;36(2):51–7.

81. Saraf P, Rafferty MR, Moore JL, et al. Daily stepping in individuals with motorincomplete spinal cord injury. Phys Ther 2010;90(2):224–35.

82. van de Port IG, Kwakkel G, Lindeman E. Community ambulation in patients withchronic stroke: how is it related to gait speed? J Rehabil Med 2008;40(1):23–7.