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Gait variability in older adults: A structured review of testing protocol and clinimetric properties
Gait & Posture 34 (2011) 443–450
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Review
Gait variability in older adults: A structured review of testing protocol and clinimetric properties Sue Lord a, Tracey Howe b, Julia Greenland a, Linda Simpson a, Lynn Rochester a,* a b
Institute for Ageing and Health, Newcastle University, UK HealthQWest, Glasgow Caledonian University, Glasgow, UK
A R T I C L E I N F O
A B S T R A C T
Article history: Received 14 September 2010 Received in revised form 15 July 2011 Accepted 19 July 2011
Gait variability (stride-to-stride fluctuations) is used increasingly as a marker for gait performance and future mobility status, cognitive status, and falls. This structured review explicitly examined literature that reported on the reliability, validity and responsiveness of gait variability in older adults. We searched Medline, Embase, Web of Science, Scopus, CINAHL, PEDRO, Biomechanics, SportDiscus and PsycInfo databases. Two independent reviewers undertook data extraction, with adjudication by a third reviewer in cases of disagreement. Twenty-two full papers were screened and 10 met the predefined inclusion criteria, involving 1036 participants who were mainly community dwelling older adults in their 8th decade. A wide range of gait variability parameters, testing protocols and calculations of gait variability were reported. Reliability estimates varied, but were mostly fair to moderate. Concurrent validity was established for stance time variability and change estimates were reported for stance time and swing time. Standard of reporting was generally poor, with insufficient detail provided for aspects of measurement and testing protocols. Further research is required to standardise testing procedures and establish reliability, responsiveness and validity for confident use of gait variability as a robust measure. ß 2011 Elsevier B.V. All rights reserved.
Keywords: Gait Variability Reliability Validity Responsiveness
1. Introduction Recent research suggests that gait variability (stride-to-stride fluctuations) may provide a more discriminative measure of gait performance than routine spatio-temporal measures such as average gait speed or step time. Gait variability was originally considered to represent noise of instrumentation or physiological noise. More recently it has been proposed to reflect the underlying neural control of gait with demonstrated sensitivity to pathological and ageing processes [1]. It discerns between older adults with and without mobility impairment and cognitive impairment [2,3]; identifies subtle pathological gait changes prior to more readily observed spatio-temporal changes [4]; and predicts falls [5] and future cognitive decline [6]. Gait variability has also been reported to increase when the demands of walking increase, for example under dual and multiple task conditions, emphasising the contribution of cognitive control to locomotion [7]. Different parameters are used to describe gait variability. For example, stride time variability reflects the ability to generate consistent
* Corresponding author at: Institute for Ageing and Health, Newcastle University, Clinical Ageing Research Unit, Campus for Ageing and Vitality, Newcastle Upon Tyne NE4 5PL, United Kingdom. Tel.: +44 0191 248 1291/50; fax: +44 0191 248 1251. E-mail address: [email protected] (L. Rochester). 0966-6362/$ – see front matter ß 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.gaitpost.2011.07.010
rhythmical step cycles, whilst step width and double limb support (DLS) (time spent with both feet in contact with the floor), reflects postural control mechanisms during gait [8]. There is no agreed standardised protocol for measuring gait variability, as evidenced by inconsistencies in data capture techniques, distance walked, instrumentation and analytical algorithms. Earlier reports suggest that measurement accuracy improves with longer distances [1], but this has not been verified. The situation is further confused by use of different and interchangeable terminology. For example ‘relative variability’ or coefficient of variation (CV or CoV), (SD/mean 100%), is a statistical measure of the dispersion of group data points in a data series relative to the mean, often used to evaluate reliability (stability) of measures including gait outcomes (e.g. [9]) and gait variability itself [10]. However, the concept of gait variability relevant to this review is concerned with the variability inherent in an individual’s stride-to-stride fluctuations. Calculation of gait variability (CV) is derived from the total number of steps or strides for each individual, for each gait parameter of interest. Unlike routine measures, the clinimetric properties of gait variability have not been well established. Discriminative validity and predictive validity may be inferred from a significant body of cross sectional and longitudinal studies concerned with gait and mobility outcomes for older adults (e.g. [2–7,11–13]), although these properties have not been formally examined. More recently, gait variability has been selected as a primary outcome for a
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randomised controlled trial for falls [14], although responsiveness has not been firmly established. Lack of standardisation of measurement protocol and knowledge of clinimetric properties limits the interpretation of gait variability from evaluative, diagnostic and prognostic studies. This review has three aims: (1) evaluate studies that explicitly test clinimetric properties of gait variability in older adults; (2) report on the testing protocols used and justification for their use; and (3) make recommendations concerning the utility of gait variability as a measure. 2. Methods 2.1. Search strategy Key MESH terms included ‘adult’ ‘gait’, or ‘neurologic gait disorders’ or ‘mobility limitation’, or ‘locomotion’ combined with keyword terms that included ‘motor function’, and ‘reliability’ or ‘test–retest’ or ‘variability’ with ‘stride’, or ‘step’, or ‘double limb’, or ‘double support’, or ‘swing time’ or ‘stride-to-stride gait’, with ‘validity’, or ‘responsiveness’, or ‘protocol’, or ‘procedure’. From the results of this search it became clear that the inclusion of text word terms ‘CV’, or ‘CoV’, or ‘coefficient of variation’ with ‘reliability’ or ‘test–retest’ and ‘stride’ or ‘step’ or ‘double limb’ or ‘gait’ would identify further relevant papers. The search strategy was amended to include these terms. Databases searched included Medline, Embase, Web of Science, Scopus, CINAHL, PEDRO, Biomechanics, SportDiscus, PsycInfo, from 1950 to November Week 3, 2009, and included e-pub ahead of print publications. Where the databases permitted, the search was limited to adults aged 65 and over. The search identified published conference proceedings, abstracts and theses. Hand searching was limited to inspection of reference lists from retrieved papers. 2.2. Inclusion and exclusion criteria
[15,19,20], yielding a total of 22 papers for review. The text word search yielded a further 22 abstracts which were independently screened, with none retained. Of the 22 papers screened, 10 were included (Fig. 1). Nine studies assessed the clinimetric properties of parameters of gait variability [15–23], three studies examined the effect of distance walked on gait variability (therefore the number of steps available for calculation) [16,21,23], and one study assessed concurrent validity of a tri-axial accelerometer using GAITRiteTM as the gold standard [24] (Table 1). Reasons for excluding studies included: gait variability used as a criterion (gold) standard against which an observational rating of gait variability was compared [25]; calculation of variability from group means and standard deviations [10]; variability calculated from foot imprints [26]; use of the treadmill which has been shown to influence gait kinematics [27]; and use of the term ‘variability’ to describe dispersion of a gait parameters within a sample (for example [28]). 3.2. Participants Study participants were older adults, with a mean age mostly in the 8th decade, all of whom walked independently. All study participants were community dwelling older adults apart from two studies that recruited a single cohort of frail in-patients, who were classified in the first study as demented or non demented [17], and in the subsequent study as either stable or unstable [18]. Participants for two studies by Hartmann et al. [22,24] were also from the same cohort, with minor differences. Because exact differences are unknown, we have reported them as separate groups to give a total of 1036 study participants.
The key criterion for this review was to include studies that incorporated a research design explicitly examining the clinimetric properties (reliability, validity, responsiveness) of parameters of gait variability. A second important criterion was to include only those studies in which gait variability was calculated from individual steps or strides (within-subject), rather than mean group (between-subject) values. If the derivation of gait variability was unclear, contact was made with the study authors to clarify the methods used, and the study included if appropriate. Other inclusion criteria were: adults >65 years of age with or without a neurological disease; adults >65 years of age with or without cognitive impairment; gait protocols that included over ground walking; instrumented walkways; measurement devices such as footswitches, gyroscopes, accelerometers; and assessment of commonly reported spatiotemporal parameters. Exclusion criteria were: participants with non-neurological diseases such as polyneuropathy, diabetes, arthroplasty, and cardiac conditions; gait variability derived from treadmill walking; walking on alternate floor surfaces such as soft foam; uncommon gait parameters such as medio-lateral toe in/out, vertical interstep variability, anterio-posterior step autocorrelation, and foot angle (e.g. [15]). Importantly, the large body of research indirectly supporting features of construct validity (e.g. discriminative validity) and predictive validity (e.g. [11–13]) was not critiqued for this review because these findings are embedded in larger studies whose primary aim is not concerned with testing the clinimetric properties of gait variability.
The most common tool used to capture data was the GAITRiteTM instrumented walkway of varying lengths, used in eight studies [15–20,23,24]. Mat length and distance of active data capture were difficult to discern, with reported figures not always corresponding to manufacturer’s specifications. One study used gyroscopes with a Physilog datalogger [23] and two studies used the DynaPortMiniMod tri-axial accelerometer [22,24]. The distance walked by participants ranged from 4 m [19,21] to 295 m [16] with one study comparing interrupted and continuous walking protocols [16] (Tables 1 and 2).
2.3. Data abstraction
3.4. Methods of reporting gait variability data
Two independent reviewers (SL, JG) screened and retained titles identified from the literature search. Disagreement was resolved by third party adjudication (LR) and data were recorded using a database designed by one of the authors (TH) to support standardised extraction.
3. Results 3.1. Search yield The initial search yield of 774 citations was screened conjointly by reviewers (SL, JG), and excluded if titles were clearly outside the inclusion criteria. Agreement was reached for 164 titles. The first reviewer (SL) retained 17 abstracts, and the second reviewer (JG) retained 18 abstracts. There were 5 disagreements in total, with consensus reached for inclusion of 16 papers. Two publications were retrieved from reference lists which had not been identified in the search strategy because they were reported as Letters to the Editor [16,17], one publication was identified from personal communication with an author [18], and three papers published since the review dates were retrieved through informal searching
3.3. Methods of collecting gait variability data
Variability was described for 10 gait parameters: six temporal parameters: step time (also termed step duration), stance time, swing time, stride time, single support time, gait cycle time; three spatial characteristics: step length, step width, stride length; and gait velocity (also termed stride velocity). Step length was most frequently measured (N = 6) [15,16,19,21,22,24]. Step count was reported in four studies [19,21,22,24]; stride count in four studies [17,18,20,23], and both step and stride count in two studies [15,16]. Gait variability was most commonly reported as CV (seven studies) [15–18,20,22–24] and SD in two studies [19,21], with one study incorporating both [16] (Table 1). 3.5. Reliability Test–retest (intra-rater) reliability was examined in seven studies [15,17,18,20–23] and inter-rater reliability was examined in one study using two raters [22]. Intraclass correlation coefficients (ICCs) were reported in all studies; two studies reported limits of agreement (LoA) and ratio limits of agreement
Records identified through database searching MESH search (n = 774) Textword search (n = 22)
Screening
Identification
S. Lord et al. / Gait & Posture 34 (2011) 443–450
445
Records screened (n = 796 )
Records excluded (n = 780)
Additional records identified through other sources (n = 6)
Eligibility
Full-text articles assessed for eligibility (n = 22)
Included
Full-text articles excluded (n = 12 )
Studies included in qualitative synthesis (n = 10 )
Fig. 1. PRISMA flow chart of study design. Adapted from Moher [33].
(RLoA) (%) [22,24]. ICCs for test–retest reliability ranged from 0.11 for stride velocity [15] to 0.88 for stride length [17]. There were inconsistencies in reliability results. For example, based on accepted standards [29], ICCs for step length in two studies were low to moderate [21,22], but high in two studies [15,17]. Gait variability was tested under single task conditions in eight studies [15–19,21–23] and under single and dual task conditions in two studies [20,22]. ICCs did not decline under dual task conditions (Table 3). 3.6. Concurrent and construct validity Concurrent validity was assessed in one study comparing gait variability with six measures of health (for example self-reported health status, Activities of Daily Living, difficulty walking) [21]. Stance time variability correlated significantly with all concurrent measures, whereas step length and step width correlated only with selected measures (Table 3). Moe-Nilsson et al. examined construct validity and reported a non-significant correlation between step time and step length variability [15] (Table 3). 3.7. Responsiveness Three studies examined responsiveness using distribution based and anchor based approaches, presented as Cohen’s Effect Size (EF), Responsiveness Index (RI), Standardised Error Measurement (SEM), and Standardised Response Mean (SRM) [17–19]. Responsiveness was generally poor apart from a moderate effect
size reported for stance time and swing time variability, based on data combined from anchor and distribution based approaches (Table 3). 4. Discussion The aim of this review was to synthesise and critique research that explicitly tested the clinimetric properties of gait variability. Gait variability is increasingly reported as a marker of gait performance in older adults, with justification for its use based on findings from large, community based studies that demonstrate discriminative and predictive properties (e.g. [3,5,6]). This review examined 10 studies involving 1036 mainly community dwelling older adults with a mean group age in the 8th decade. Choice of measurement tool did not impact greatly on clinimetric properties. Test–rest ICCs obtained from gryoscopes were comparable to GAITRiteTM ICCs [23], as were ICCs derived from a tri-axial accelerometer when compared with GAITRiteTM [22]. However, for the study by Hartmann et al. [22] CV values were higher compared with other reports (e.g. [16]), suggesting that further refinement of data processing from accelerometry may be required for optimal results. Overall our findings point to the need to collect data over a reasonable distance. Hartmann et al. recommend a minimum of 20 m, or 25 steps for step duration and step length variability [24] whereas Hollman et al. recommend data from hundreds of strides for gait velocity variability [20]. Paterson et al. [16] reported higher gait variability for short interrupted walks versus continuous
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Table 1 Summary of studies reviewed. Sample characteristics
Instrument
Active recording distance
Gait parameters
Number of steps/strides
N = 558; 339 f; 219 m 79.4 4.1 years Community dwelling N = 241; 137f; 104 m 80.3 years Community dwelling N = 23; 15f; 8 m 73.4 4.3 years Community dwelling N = 23; 16f; 7 m 77.2 4.7 years Community dwelling Comparable sample to above (see text) N = 24; 11f; 13 m 72.6 4.3 years (f) 74.5 6.2 years (m) Community ambulating N = 23; 15f; 8 m 80.0 5.0 years Community dwelling
5 m GaitMat IIa (plus 1 m acceleration and deceleration) 3.66 m GAITRiteTM (plus 1 m acceleration and deceleration) DynaPortMiniModtri-axial accelerometer
4m
SD step length, step width, stance time
Maximum 12 steps
Not reported
Not reported
24 m
SD step length, step width, stance time, swing time CV step length, step duration
Not reported
13 m GAITRiteTM DynaPortMiniMod tri-axial accelerometer
7.32 m
CV step length, step duration
7–13 per trial
5.6 m GAITRiteTM (plus 2 m acceleration and deceleration)
Not reported
CV stride velocity
13 strides single task 14 strides dual task
4.88 m GAITRiteTM (plus 2 m acceleration and deceleration)
4.88
Average 34.5
Najafi [23]
N = 27; 18f; 9 m 80.3 5.0 years Preferred speed 0.5 m/s Community dwelling
4.7
Paterson [16]
N = 32; 32f 67.4 6.3 years Community dwelling
(i) Body worn sensors (gyroscopes) and Physilog datalogger (ii) 6.25 m GAITRiteTM (plus 2 m acceleration and deceleration) 8.1 m GAITRiteTM
CV step time, stride time, step length, stride length, step width, single support time, stride velocity CV gait cycle time, stride velocity
van Iersel [17]
N = 85; 46f; 40 m 75.8 7.1 years Frail in-patients Ability to walk 10 m Dementia (N = 39) No dementia (N = 46) Same cohort as above, classified according to stability of gait performance n = 59 stable; n = 8 worse; n = 18 better
5.6 m GAITRiteTM
5.6 m GAITRiteTM
Brach [19] Hartmann [22] Hartmann [24]
Hollman [20]
Moe-Nilssen [15]
van Iersel [18]
Abbreviations: N = number; f = female; m = male; CV = coefficient of variation; SD = standard deviation. a GaitMat II and GAITRiteTM distances given as reported.
Not reported
Not reported
Not reported
Not reported
CV and SD gait velocity, step length, step width, step time, stride time, stride length CV stride time, stride length
Not reported
Not reported
CV stride time, stride length
Not reported
S. Lord et al. / Gait & Posture 34 (2011) 443–450
Principal author Brach [21]
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Table 2 Property tested and testing protocol. Clinimetric property
Principal author
Testing protocol
Test–retest (intra-rater) reliability
Brach [21]
Within session testing. 4 trials in total. Reliability calculated in two ways: (i) from a single 4 m trial (5–6 steps), and from two, 4 m trials (10–12 steps). Instructions regarding speed not reported. Between session testing 24 m walk (18 m assessed) in four conditions at preferred speed; gym floor, gym floor dual task, rubber walkway, rubber walkway dual task. Repeated 3: once on first occasion (T1) and twice 5–10 days later T2, T3 (20 min apart). Within session testing. 3 trials at preferred speed under single task conditions (mean 13 strides per subject) and dual task conditions (mean 14 strides per subject), repeated 30 min later. Within session testing. 2 trials at preferred speed, repeated after a short rest. Average number of steps = 34.5 SD 4.7. Within session testing. Test–retest reliability calculated from 5 m GAITRite walk and 20 m Physilog system walk. Both walked at preferred speeds, repeated after 15 min rest Between session testing N = 39 dementia patients; N = 46 no dementia. Walked 2 over mat at preferred speed. Clinical change in gait determined by video gait analysis (N = 3 experts); rated as stable or relevant change (non-stable) over 2 week period. Between session testing; same protocol as above. For overall stride time and for stable patients (stride time only).
Hartmann [22]
Hollman [20]
Moe-Nilssen [15] Najafi [23] van Iersel [17]
van Iersel [18]
Inter-rater reliability
Hartmann [22]
Concurrent validity
Brach [21]
Construct validity
Moe-Nilssen [15]
Responsiveness
Brach [19]
van Iersel [17] van Iersel [18]
Effect of walking protocol on variability
Concurrent validity of DynaPortMiniMod with GAITRite as gold standard
Paterson [16]
Reliability protocol as outlined above. Data from T2, T3 calculated for inter-rater reliability (N = 2 raters) Established by comparison with 5 self-reported levels of health perception, and dichotomous levels for ADLs, IADLs, difficulty walking, balance and physical activity 2 trials at slow, preferred and fast speeds. Gait parameters selected for construct validity from reliability study (see above) if ICCs 0.80 Anchor-based responsiveness estimates based on decline or improvement in self-reported walking difficulty (dichotomous response) and distance walked (4 level question) over one year. Responses classified as no change, improvement or decline. Distribution-based responsiveness derived from two trials (10–12 steps) at self-selected speed, repeated a year later Mean change in test score over 2/52 in non-stable patients with changes to SD of change in stable patients Anchor based responsiveness estimates based on expert opinion of gait (SRM, RI). Distribution based responsiveness reported as ES and SEM
Hollman [20]
Two protocols randomly presented with a brief rest in-between: 10 repeated single trials on GAITRiteTM and 10 laps of curvilinear circuit that included 2 straight sections of 8.1 m: total distance walked between 281 m and 295 m, depending on participant’s height. ES used to examine differences between protocols Number of strides required for reliable variability.
Hartmann [24]
4 trials at slow, preferred and fast speed with 7–13 steps recorded per trial
Abbreviations: ADLs = Activities of Daily Living; IADLs = Instrumented Activities of Daily Living; t = time; ES = effect size; SRM = Standardised Response Mean; RI = Responsiveness Index; SEM = standard error of the mean.
walking, suggesting that during interrupted walks spatiotemporal rhythms do not have time to become established. This concurs with previous research that shows long-range, power-law correlations for stride data captured over long distances and the need to establish steady state walking for measurement of gait variability [31]. However, during habitual walking gait largely occurs in short, interrupted bursts of walking. The distance walked during testing may reflect different aspects of motor control (e.g. attention may play a greater role in short, interrupted walks), and rationale for selection needs to be explicit within the test protocol. Requisite distance may also vary according to the gait parameter under consideration. Findings from this review reflect a limited theoretical framework to guide selection of gait variability parameters, as evidenced by the range of parameters used. Earlier reports suggest the importance of capturing two broad domains of gait: rhythmicity (e.g. stride time variability) and dynamic balance control (e.g. step width variability, DLS variability) [8], although this is not a unified aim. Recent evidence supports this twodomain view, pointing to distinct neural mechanisms and predictive characteristics for stride time variability and DLS time variability in people with Parkinson’s disease [32]. Studies in this review reported both step and stride analysis, although the
rationale for selecting one or other, or both, was unclear. Variability can be calculated for the left and right limbs separately using step data, allowing for the calculation of the asymmetry of gait variability. An important consideration to make when analysing step data is to avoid taking the standard deviation of pooled left and right steps because this measurement may be confounded by underlying spatiotemporal asymmetries. Another approach is to calculate the standard deviation from the residuals of each step around the mean over its respective limb. This advantage of this approach is that it allows double the number of steps to be used in the analysis, resulting in a more precise measure of gait variability. An issue related to selection of gait parameter concerns the calculation of gait variability itself, most commonly reported by CV although SD was also used. CV has an advantage in that it is a dimensionless unit, and has comparability with other studies. CV and SD values vary for different parameters, requiring care with interpretation. For example, low CV and SD indicate that gait is consistent for parameters associated with rhythmicity and progression (e.g. step time and step length), whereas high CV and SD are more likely to indicate a consistent gait for parameters that reflect postural control (e.g. step width). Excessive or reduced step width variability reflect an inability to adapt postural control
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448 Table 3 Clinimetric properties of gait variability. Clinimetric property
Principal author
Results
Comments
Test–retest (intra-rater) reliability
Brach [21]
Single 4 m walk showed poor to fair test–retest reliability (all ICCs): stance time SD .37, step length SD .48, step width SD .22 Two 4-m walks showed fair to good reliability: stance time SD .63, step length SD .5, step width SD .4 Intra-rater reliability gym floor single task (i) step duration ICC 0.78, LOA 0.89, RLOA (%) 38.6; dual task ICC 0.46, LOA 2.57, RLOA (%) 78.6 (ii) step length single task ICC 0.31, LOA 4.10, RLOA (%) 84.1; dual task ICC 0.37, LOA 5.27, RLOA (%) 93
Reliability of gait variability recorded over 10–12 steps greater than 5–6 steps
Hartmann [22]
Moe-Nilssen [15]
Test–retest reliability ICCs step time 0.64, step length 0.81, stride length 0.50, step width 0.22, single support time 0.68, stride velocity 0.11
Najafi [23]
Test–retest reliability gait cycle time 5 m GAITRiteTM ICCs 0.42, 5 m Physilog 0.42, 20 m Physilog 0.10; test–retest reliability stride velocity 5 m GAITRiteTM ICC 0.37, 5 m Physilog 0.37, 20 m Physilog 0.50 N = 85 (39 dementia and 46 no dementia) Test–retest reliability: ICCs dementia (N = 39) stride time 0.56, stride length 0.88; no dementia (N = 46) stride time 0.84, stride length 0.88 N = 85 (59 stable and 26 unstable) Test–retest reliability high for all variables (0.82 to 0.98) apart from overall stride time ICC 0.46, which increased to 0.72 when stable patients only were assessed
van Iersel [17]
Van Iersel [18]
Inter-rater reliability
Hartmann [22]
Inter-rater reliability gym floor single task (i) step duration ICC 0.59, LOA 1.16, RLOA (%) 49.6; dual task ICC 0.72, LOA 0.1.93, RLOA (%) 59.4 (ii) step length single task ICC 0.69, LOA 2.80, RLOA (%) 59.3; dual task ICC0.71, LOA 3.48, RLOA (%) 64.2
Validity
Brach [21]
Concurrent validity assessed by comparison of groups; independent t-tests for 2 groups; 1-way ANOVA for >2 groups. Largest effect found for stance time variability (P < 0.0001 for 6 health status criteria). Step length significant for 4 criteria (P < 0.05); step width significant for 3 criteria (P < 0.05) Construct validity established through correlation: Pearsons r for step time and step length = 0.28 (NS)
Moe-Nilssen [15]
Responsiveness
Brach [19]
Anchor based through self report: (i) no change: all variability measures stable; (ii) improvement: step length SD improvement; (iii) decline: inconsistent results Distribution based ES (moderate): stance time SD (s) 0.01, swing time SD (s) 0.009, step length SD (cm) 0.61, step width SD (cm) 0.08 Combining anchor and distribution approaches meaningful change estimate: stance time SD and swing time SD = 0.01(s) and 0.25 (cm) step length SD (moderate ES)
van Iersel [17]
RI dementia stride time 0.4, stride length 2.9; no dementia stride time 0.2, stride length 0.5 Anchor based responsiveness stride length SRM 0.6, RI 2.6; stride time SRM 0.5, RI 0.7 Distribution based stride time ES 0.1, SEM 1.3; stride time ES 0.1, SEM 1.6
van Iersel [18]
Intra-rater reliability (test–retest) lower than inter-rater reliability, although neither support the use of DynaPortMiniMod tri-axial accelerometer for gait variability measures Reliability for step parameters higher than stride parameters due to number of data points (mean steps = 34.5 c/f mean strides = 17) Test–retest reliability improved slightly over distance, but overall poor irrespective of walking distance Moderate to excellent reliability demonstrated in older people with and without dementia Poor reliability for stride time
Inter-rater reliability higher than intra-rater reliability (test–retest), although neither support the use of DynaPortMiniMod tri-axial accelerometer for gait variability measures Increased step length and stance time variability and decreased step width variability associated with poorer health status, impaired functional status and inactivity Spatial and temporal variability may represent different constructs Preliminary findings provide estimates for meaningful change for swing time and stance time variability Step width SD problematic as an outcome because of non-linear relationship with mobility decline and falls (too much and too little step width variability may be optimal for control of dynamic equilibrium during gait) RI low for CV Small effect sizes may be partly explained by large number of stable patients
Abbreviations: CV = coefficient of variation, ES = effect size, ICC = intraclass correlation coefficient, L = left, LOA = limits of agreement, R = right, RI = responsiveness index, RLOA = ratio limits of agreement, SD = standard deviation, SEM = standard error of the mean, SRM = standardised response mean.
and as such are harbingers of falls, although this may to some extent be dependent on speed [30]. For gait variability test–retest reliability, ICCs were low apart from studies conducted on in-patients where ICCs were moderate to excellent for stable patients [18], even those presenting with dementia [17]. Reliability estimates varied for different gait parameters. As a measure of dynamic stability, step width variability was overall less reliable than step length variability as expected. Hartmann et al. [22] reported higher test–retest reliability for temporal parameters (step duration)
than spatial parameters (step length), although this was not the case for all studies and may reflect the use of accelerometery derived data. An important consideration is the period of time for test–retest reliability. Four of the 10 included studies of withinsession test–retest reliability [15,21–23], and ICCs from these studies were not as high as studies that reported a 7 day or 14 day interval between tests [17,18,22], which is a more realistic time frame over which stability can be measured. The single study that investigated inter-rater reliability for accelerometry yielded slightly superior ICCs to test–retest reliability but rater error
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overall was not considered to contribute significantly to low test–retest ICCs [22]. Reliability estimates under dual task conditions were comparable to single task conditions, which is surprising given the impact of an added cognitive task on gait variability [7]. Only one study tested concurrent (discriminative) validity and reported superiority of stance time over other parameters tested [21]. Our search strategy failed to locate any studies designed specifically to test predictive validity, although as noted earlier, this is indirectly implied from a large body of longitudinal and cross-sectional research. Anchor-based and distribution based approaches were used to measure responsiveness [18,19], however comparisons are limited because statistics and variability descriptors differed. Brach et al. provided point estimates for meaningful change for swing time and stance time variability as a guide, although confidence intervals were not reported [19]. Responsiveness is closely linked to reliability and will need to be examined further once protocols have been established to improve reliability. Standards of reporting for the studies in this review were generally poor, with insufficient detail for aspects of measurement and testing protocol such as distance walked, effective data capture area, total number of steps or strides, rationale for selection of CV or SD, and clarity concerning the use of individual or group mean/SD to calculate variability. 4.1. Review limitations Our search strategy was limited to English language publications. We did not search grey literature other than that revealed through the search strategy, and we may have missed relevant theses and conference abstracts. Inclusion criteria were limited to studies overtly designed to test the clinimetrics of gait variability, thereby excluding a large body of research that supports its predictive and discriminatory properties. Also, because we were unable to locate a standardised data extraction tool that suited the purposes of this review, we developed and piloted our own tool which may have introduced bias. 5. Summary This review indicates that further work is required to determine the clinimetric properties of gait variability. However, it is important to place the challenge of achieving this in context. Routine gait measures such as gait speed and stride amplitude are based on mean estimates, which are more stable (and therefore robust) than variance estimates. Measurement of gait variability enhances gait analysis, and future research will compare its utility with other non-linear metrics which may prove even more sensitive to early gait pathology. Recommendations for the use of gait variability are tentative until consensus is achieved for selection of gait parameters, testing protocol and method of analysis. Further research is required to examine clinimetric properties, including studies that formally evaluate predictive validity. However, based on this review we suggest the following: Provide a rationale for selection of gait parameters Report SD and CV Ensure at least 12 steps are collected Data from continuous walking is more reliable than interrupted walking, although the two possibly test different aspects of gait control Report the number of steps and the distance Pilot reliability data before using gait variability as an outcome measure.
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Acknowledgements We acknowledge the support of the UK NIHR Biomedical Research Centre for Ageing and Age-related disease award to the Newcastle upon Tyne Hospitals NHS Foundation Trust. Sue Lord is the Peggy Coates Fellow, Clinical Ageing Research Unit. Conflict of interest: The authors do not have any financial or personal relationships with other people or organizations that have inappropriately influenced or biased this work. References [1] Hausdorff J. Gait dynamics, fractals and falls: finding meaning in the stride-tostride fluctuations of human walking. Hum Mov Sci 2007;26:557–89. [2] Hausdorff J, Cudkowicz M, Firtion B, Wei J, Goldberger A. Gait variability and basal ganglia disorders: stride-to-stride variations of gait cycle timing in Parkinson’s disease and Huntington’s disease. Mov Disord 1998;13(3):428–37. [3] Verghese J, Robbins T, Holtzer R, Zimmerman M, Wang C, Xan X, et al. Gait dysfunction in mild cognitive impairment syndromes. J Am Geriatr Soc 2008;56:1244–51. [4] Baltadjieva R, Giladi N, Gruendinger L, Peretz C, Hausdorff J. Marked alterations in the gait timing and rhythmicity of patients with de novo Parkinson’s disease. Eur J Neurosci 2006;24:1815–20. [5] Herman T, Mirelman A, Giladi N, Schweiger A, Hausdorff J. Executive control deficits as a prodrome to falls in healthy older adults: a prospective study linking thinking, walking, and falling. J Gerontol A Biol Sci Med Sci 2010;65(10):1086–92. [6] Verghese J, Wang C, Holtzer R, Lipton R, Wang C. Quantitative gait markers and risk of cognitive decline and demention. J Neurol Neurosurg Psychiatry 2007;78:929–35. [7] Yogev-Seligmann G, Giladi N, Peretz C, Springer S, Simon E, Hausdorff J. Dual tasking, gait rhythmicity, and Parkinson’s disease: which aspects are attention demanding. Eur J Neurosci 2005;22:124–1256. [8] Gabell A, Nayak U. The effect of age on variability in gait. J Gerontol 1984;39(6):662–6. [9] Shimada S, Kobayashi S, Wada M, Sasaki S, Kawahara H, Uchida K, et al. Effect of compensation procedures for velocity on repeatability and variability of gait parameters in normal subjects. Clin Rehabil 2006;20(3):239–45. [10] Montero-Odasso M, Casas A, Hansen K, Bilski P, Gutmanis R, Wells J, et al. Quantitative gait analysis under dual-tak in older people with mild cognitive impairment: a reliability study. J Neuroeng Rehabil 2009;6(35). [11] Brach J, Studenski S, Perera S, VanSwearingen J, Newman A. Gait variability and the risk of incident mobility disability in community-dwelling older adults. J Gerontol Med Sci 2007;62A(9):983–8. [12] Maki B. Gait changes in older adults: predictors of falls or indicators of fear. J Am Geriatr Soc 1997;45:313–20. [13] Springer S, Giladi N, Peretz C, Yogev G, Simon E, Hausdorff J. Dual-tasking effects on gait variability: the role of aging, falls and executive function. Mov Disord 2006;7:950–7. [14] Montero-Odasso M, Wells J, Borrie M, Speechley M. Can cognitive enhancers reduce the risk of falls in older people with Mild Cognitive Impairment? A protocol for a randomised controlled double blind trial. BMC Neurol 2009;9(1471):42. [15] Moe-Nilssen R, Aaslund M, Hodt-Billington C, Helbostad J. Gait variability measures may represent different constructs. Gait Posture 2010;32(1): 98–101. [16] Paterson K, Lythgo N, Hill K. Gait variability in younger and older adult women is altered by overground walking protocol. Age Ageing 2009;38(6):745–8. [17] van Iersel M, Benraad C, Olde Rikkert M. Validity and reliability of quantitative gait analysis in geriatric patients with and without dementia. J Am Geriatr Soc 2007;55(4):632–4. [18] van Iersel M, Munneke M, Esselink R, Benraad C, Olde Rikkert M. Gait velocity and the timed-up-and-go test were sensitive to changes in mobility in frail elderly patients. J Clin Epidemiol 2008;61(2):186–91. [19] Brach J, Perera S, Studenski S, Katz M, Hall C, Verghese J. Meaningful change in measures of gait variability in older adults. Gait Posture 2010;31(2):175–9. [20] Hollman J, Childs K, McNeil M, Mueller A, Quilter C, Youdas J. Number of strides required for reliable measurements of pace, rhythm and variability parameters of gait during normal and dual task walking in older individuals. Gait Posture 2010;32(1):23–8. [21] Brach J, Perera S, Studenski S, Newman A. The reliability and validity of measures of gait variability in community-dwelling older adults. Arch Phys Med Rehabil 2008;89:2293–6. [22] Hartmann A, Murer K, de Bie R, de Bruin E. Reproducibility of spatio-temporal gait parameters under different conditions in older adults using a trunk triaxial accelerometer system. Gait Posture 2009;30(3):351–5. [23] Najafi B, Helbostad J, Moe-Nilssen R, Zijlstra W, Aminian K. Does walking strategy in older people change as a function of walking distance. Gait Posture 2009;29(2):261–6. [24] Hartmann A, Luzi S, Murer K, de Bie R, de Bruin E. Concurrent validity of a trunk tri-axial accelerometer system for gait analysis in older adults. Gait Posture 2009;29(3):444–8.
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[25] Huang W-N, VanSwearingen J, Brach J. Gait variability in older adults: observational rating validated by comparison with a computerized walkway gold standard. Phys Ther 2008;88(10):1146–53. [26] Stolze H, Friedrich H-J, Steinauer K, Vieregge P. Stride parameters in healthy young and old women—measurement variability on a simple walkway. Exp Aging Res 2000;26(2):159–68. [27] Owings T, Grabiner M. Measuring step kinematic variability on an instrumented treadmill: how many steps are enough? J Biomech 2003;36(8):1215–8. [28] Menz H, Latt M, Tiedemann A, Kwan M, Lord S. Reliability of the GAITRiteR walkway system for the quantification temporo-spatial parameters of gait in young and older people. Gait Posture 2004;1:20–5. [29] Portney L, Watkins M. Foundations of clinical research: Applications to practice, 3rd ed., CT, USA: Appleton & Lange; 2003.
[30] Brach J, Berlin J, VanSwearingen J, Newman A, Studenski S. Too much or too little step width variability is associated with a fall history in older persons who walk at or near normal gait speed. J Neuroeng Rehabil 2005;2(21). [31] Lindemann U, Najafi B, Zijlstra W, Hauer K, Muche R, Becker C, et al. Distance to achieve steady state walking speed in frail elderly persons. Gait Posture 2008;27(1):91–6. [32] Lord S, Baker K, Nieuwboer A, Burn D. Gait variability in Parkinson’s disease: an indicator of non-dopaminergic contributors to gait dysfunction? J Neurol 2011;258(4):566–72. [33] Moher D, Liberati A, Tetzlaff J, Altman DG. The PRISMA group preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Open Med 2009;3(3):123–30.
Contents lists available at ScienceDirect
Gait & Posture journal homepage: www.elsevier.com/locate/gaitpost
Review
Gait variability in older adults: A structured review of testing protocol and clinimetric properties Sue Lord a, Tracey Howe b, Julia Greenland a, Linda Simpson a, Lynn Rochester a,* a b
Institute for Ageing and Health, Newcastle University, UK HealthQWest, Glasgow Caledonian University, Glasgow, UK
A R T I C L E I N F O
A B S T R A C T
Article history: Received 14 September 2010 Received in revised form 15 July 2011 Accepted 19 July 2011
Gait variability (stride-to-stride fluctuations) is used increasingly as a marker for gait performance and future mobility status, cognitive status, and falls. This structured review explicitly examined literature that reported on the reliability, validity and responsiveness of gait variability in older adults. We searched Medline, Embase, Web of Science, Scopus, CINAHL, PEDRO, Biomechanics, SportDiscus and PsycInfo databases. Two independent reviewers undertook data extraction, with adjudication by a third reviewer in cases of disagreement. Twenty-two full papers were screened and 10 met the predefined inclusion criteria, involving 1036 participants who were mainly community dwelling older adults in their 8th decade. A wide range of gait variability parameters, testing protocols and calculations of gait variability were reported. Reliability estimates varied, but were mostly fair to moderate. Concurrent validity was established for stance time variability and change estimates were reported for stance time and swing time. Standard of reporting was generally poor, with insufficient detail provided for aspects of measurement and testing protocols. Further research is required to standardise testing procedures and establish reliability, responsiveness and validity for confident use of gait variability as a robust measure. ß 2011 Elsevier B.V. All rights reserved.
Keywords: Gait Variability Reliability Validity Responsiveness
1. Introduction Recent research suggests that gait variability (stride-to-stride fluctuations) may provide a more discriminative measure of gait performance than routine spatio-temporal measures such as average gait speed or step time. Gait variability was originally considered to represent noise of instrumentation or physiological noise. More recently it has been proposed to reflect the underlying neural control of gait with demonstrated sensitivity to pathological and ageing processes [1]. It discerns between older adults with and without mobility impairment and cognitive impairment [2,3]; identifies subtle pathological gait changes prior to more readily observed spatio-temporal changes [4]; and predicts falls [5] and future cognitive decline [6]. Gait variability has also been reported to increase when the demands of walking increase, for example under dual and multiple task conditions, emphasising the contribution of cognitive control to locomotion [7]. Different parameters are used to describe gait variability. For example, stride time variability reflects the ability to generate consistent
* Corresponding author at: Institute for Ageing and Health, Newcastle University, Clinical Ageing Research Unit, Campus for Ageing and Vitality, Newcastle Upon Tyne NE4 5PL, United Kingdom. Tel.: +44 0191 248 1291/50; fax: +44 0191 248 1251. E-mail address: [email protected] (L. Rochester). 0966-6362/$ – see front matter ß 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.gaitpost.2011.07.010
rhythmical step cycles, whilst step width and double limb support (DLS) (time spent with both feet in contact with the floor), reflects postural control mechanisms during gait [8]. There is no agreed standardised protocol for measuring gait variability, as evidenced by inconsistencies in data capture techniques, distance walked, instrumentation and analytical algorithms. Earlier reports suggest that measurement accuracy improves with longer distances [1], but this has not been verified. The situation is further confused by use of different and interchangeable terminology. For example ‘relative variability’ or coefficient of variation (CV or CoV), (SD/mean 100%), is a statistical measure of the dispersion of group data points in a data series relative to the mean, often used to evaluate reliability (stability) of measures including gait outcomes (e.g. [9]) and gait variability itself [10]. However, the concept of gait variability relevant to this review is concerned with the variability inherent in an individual’s stride-to-stride fluctuations. Calculation of gait variability (CV) is derived from the total number of steps or strides for each individual, for each gait parameter of interest. Unlike routine measures, the clinimetric properties of gait variability have not been well established. Discriminative validity and predictive validity may be inferred from a significant body of cross sectional and longitudinal studies concerned with gait and mobility outcomes for older adults (e.g. [2–7,11–13]), although these properties have not been formally examined. More recently, gait variability has been selected as a primary outcome for a
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randomised controlled trial for falls [14], although responsiveness has not been firmly established. Lack of standardisation of measurement protocol and knowledge of clinimetric properties limits the interpretation of gait variability from evaluative, diagnostic and prognostic studies. This review has three aims: (1) evaluate studies that explicitly test clinimetric properties of gait variability in older adults; (2) report on the testing protocols used and justification for their use; and (3) make recommendations concerning the utility of gait variability as a measure. 2. Methods 2.1. Search strategy Key MESH terms included ‘adult’ ‘gait’, or ‘neurologic gait disorders’ or ‘mobility limitation’, or ‘locomotion’ combined with keyword terms that included ‘motor function’, and ‘reliability’ or ‘test–retest’ or ‘variability’ with ‘stride’, or ‘step’, or ‘double limb’, or ‘double support’, or ‘swing time’ or ‘stride-to-stride gait’, with ‘validity’, or ‘responsiveness’, or ‘protocol’, or ‘procedure’. From the results of this search it became clear that the inclusion of text word terms ‘CV’, or ‘CoV’, or ‘coefficient of variation’ with ‘reliability’ or ‘test–retest’ and ‘stride’ or ‘step’ or ‘double limb’ or ‘gait’ would identify further relevant papers. The search strategy was amended to include these terms. Databases searched included Medline, Embase, Web of Science, Scopus, CINAHL, PEDRO, Biomechanics, SportDiscus, PsycInfo, from 1950 to November Week 3, 2009, and included e-pub ahead of print publications. Where the databases permitted, the search was limited to adults aged 65 and over. The search identified published conference proceedings, abstracts and theses. Hand searching was limited to inspection of reference lists from retrieved papers. 2.2. Inclusion and exclusion criteria
[15,19,20], yielding a total of 22 papers for review. The text word search yielded a further 22 abstracts which were independently screened, with none retained. Of the 22 papers screened, 10 were included (Fig. 1). Nine studies assessed the clinimetric properties of parameters of gait variability [15–23], three studies examined the effect of distance walked on gait variability (therefore the number of steps available for calculation) [16,21,23], and one study assessed concurrent validity of a tri-axial accelerometer using GAITRiteTM as the gold standard [24] (Table 1). Reasons for excluding studies included: gait variability used as a criterion (gold) standard against which an observational rating of gait variability was compared [25]; calculation of variability from group means and standard deviations [10]; variability calculated from foot imprints [26]; use of the treadmill which has been shown to influence gait kinematics [27]; and use of the term ‘variability’ to describe dispersion of a gait parameters within a sample (for example [28]). 3.2. Participants Study participants were older adults, with a mean age mostly in the 8th decade, all of whom walked independently. All study participants were community dwelling older adults apart from two studies that recruited a single cohort of frail in-patients, who were classified in the first study as demented or non demented [17], and in the subsequent study as either stable or unstable [18]. Participants for two studies by Hartmann et al. [22,24] were also from the same cohort, with minor differences. Because exact differences are unknown, we have reported them as separate groups to give a total of 1036 study participants.
The key criterion for this review was to include studies that incorporated a research design explicitly examining the clinimetric properties (reliability, validity, responsiveness) of parameters of gait variability. A second important criterion was to include only those studies in which gait variability was calculated from individual steps or strides (within-subject), rather than mean group (between-subject) values. If the derivation of gait variability was unclear, contact was made with the study authors to clarify the methods used, and the study included if appropriate. Other inclusion criteria were: adults >65 years of age with or without a neurological disease; adults >65 years of age with or without cognitive impairment; gait protocols that included over ground walking; instrumented walkways; measurement devices such as footswitches, gyroscopes, accelerometers; and assessment of commonly reported spatiotemporal parameters. Exclusion criteria were: participants with non-neurological diseases such as polyneuropathy, diabetes, arthroplasty, and cardiac conditions; gait variability derived from treadmill walking; walking on alternate floor surfaces such as soft foam; uncommon gait parameters such as medio-lateral toe in/out, vertical interstep variability, anterio-posterior step autocorrelation, and foot angle (e.g. [15]). Importantly, the large body of research indirectly supporting features of construct validity (e.g. discriminative validity) and predictive validity (e.g. [11–13]) was not critiqued for this review because these findings are embedded in larger studies whose primary aim is not concerned with testing the clinimetric properties of gait variability.
The most common tool used to capture data was the GAITRiteTM instrumented walkway of varying lengths, used in eight studies [15–20,23,24]. Mat length and distance of active data capture were difficult to discern, with reported figures not always corresponding to manufacturer’s specifications. One study used gyroscopes with a Physilog datalogger [23] and two studies used the DynaPortMiniMod tri-axial accelerometer [22,24]. The distance walked by participants ranged from 4 m [19,21] to 295 m [16] with one study comparing interrupted and continuous walking protocols [16] (Tables 1 and 2).
2.3. Data abstraction
3.4. Methods of reporting gait variability data
Two independent reviewers (SL, JG) screened and retained titles identified from the literature search. Disagreement was resolved by third party adjudication (LR) and data were recorded using a database designed by one of the authors (TH) to support standardised extraction.
3. Results 3.1. Search yield The initial search yield of 774 citations was screened conjointly by reviewers (SL, JG), and excluded if titles were clearly outside the inclusion criteria. Agreement was reached for 164 titles. The first reviewer (SL) retained 17 abstracts, and the second reviewer (JG) retained 18 abstracts. There were 5 disagreements in total, with consensus reached for inclusion of 16 papers. Two publications were retrieved from reference lists which had not been identified in the search strategy because they were reported as Letters to the Editor [16,17], one publication was identified from personal communication with an author [18], and three papers published since the review dates were retrieved through informal searching
3.3. Methods of collecting gait variability data
Variability was described for 10 gait parameters: six temporal parameters: step time (also termed step duration), stance time, swing time, stride time, single support time, gait cycle time; three spatial characteristics: step length, step width, stride length; and gait velocity (also termed stride velocity). Step length was most frequently measured (N = 6) [15,16,19,21,22,24]. Step count was reported in four studies [19,21,22,24]; stride count in four studies [17,18,20,23], and both step and stride count in two studies [15,16]. Gait variability was most commonly reported as CV (seven studies) [15–18,20,22–24] and SD in two studies [19,21], with one study incorporating both [16] (Table 1). 3.5. Reliability Test–retest (intra-rater) reliability was examined in seven studies [15,17,18,20–23] and inter-rater reliability was examined in one study using two raters [22]. Intraclass correlation coefficients (ICCs) were reported in all studies; two studies reported limits of agreement (LoA) and ratio limits of agreement
Records identified through database searching MESH search (n = 774) Textword search (n = 22)
Screening
Identification
S. Lord et al. / Gait & Posture 34 (2011) 443–450
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Records screened (n = 796 )
Records excluded (n = 780)
Additional records identified through other sources (n = 6)
Eligibility
Full-text articles assessed for eligibility (n = 22)
Included
Full-text articles excluded (n = 12 )
Studies included in qualitative synthesis (n = 10 )
Fig. 1. PRISMA flow chart of study design. Adapted from Moher [33].
(RLoA) (%) [22,24]. ICCs for test–retest reliability ranged from 0.11 for stride velocity [15] to 0.88 for stride length [17]. There were inconsistencies in reliability results. For example, based on accepted standards [29], ICCs for step length in two studies were low to moderate [21,22], but high in two studies [15,17]. Gait variability was tested under single task conditions in eight studies [15–19,21–23] and under single and dual task conditions in two studies [20,22]. ICCs did not decline under dual task conditions (Table 3). 3.6. Concurrent and construct validity Concurrent validity was assessed in one study comparing gait variability with six measures of health (for example self-reported health status, Activities of Daily Living, difficulty walking) [21]. Stance time variability correlated significantly with all concurrent measures, whereas step length and step width correlated only with selected measures (Table 3). Moe-Nilsson et al. examined construct validity and reported a non-significant correlation between step time and step length variability [15] (Table 3). 3.7. Responsiveness Three studies examined responsiveness using distribution based and anchor based approaches, presented as Cohen’s Effect Size (EF), Responsiveness Index (RI), Standardised Error Measurement (SEM), and Standardised Response Mean (SRM) [17–19]. Responsiveness was generally poor apart from a moderate effect
size reported for stance time and swing time variability, based on data combined from anchor and distribution based approaches (Table 3). 4. Discussion The aim of this review was to synthesise and critique research that explicitly tested the clinimetric properties of gait variability. Gait variability is increasingly reported as a marker of gait performance in older adults, with justification for its use based on findings from large, community based studies that demonstrate discriminative and predictive properties (e.g. [3,5,6]). This review examined 10 studies involving 1036 mainly community dwelling older adults with a mean group age in the 8th decade. Choice of measurement tool did not impact greatly on clinimetric properties. Test–rest ICCs obtained from gryoscopes were comparable to GAITRiteTM ICCs [23], as were ICCs derived from a tri-axial accelerometer when compared with GAITRiteTM [22]. However, for the study by Hartmann et al. [22] CV values were higher compared with other reports (e.g. [16]), suggesting that further refinement of data processing from accelerometry may be required for optimal results. Overall our findings point to the need to collect data over a reasonable distance. Hartmann et al. recommend a minimum of 20 m, or 25 steps for step duration and step length variability [24] whereas Hollman et al. recommend data from hundreds of strides for gait velocity variability [20]. Paterson et al. [16] reported higher gait variability for short interrupted walks versus continuous
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Table 1 Summary of studies reviewed. Sample characteristics
Instrument
Active recording distance
Gait parameters
Number of steps/strides
N = 558; 339 f; 219 m 79.4 4.1 years Community dwelling N = 241; 137f; 104 m 80.3 years Community dwelling N = 23; 15f; 8 m 73.4 4.3 years Community dwelling N = 23; 16f; 7 m 77.2 4.7 years Community dwelling Comparable sample to above (see text) N = 24; 11f; 13 m 72.6 4.3 years (f) 74.5 6.2 years (m) Community ambulating N = 23; 15f; 8 m 80.0 5.0 years Community dwelling
5 m GaitMat IIa (plus 1 m acceleration and deceleration) 3.66 m GAITRiteTM (plus 1 m acceleration and deceleration) DynaPortMiniModtri-axial accelerometer
4m
SD step length, step width, stance time
Maximum 12 steps
Not reported
Not reported
24 m
SD step length, step width, stance time, swing time CV step length, step duration
Not reported
13 m GAITRiteTM DynaPortMiniMod tri-axial accelerometer
7.32 m
CV step length, step duration
7–13 per trial
5.6 m GAITRiteTM (plus 2 m acceleration and deceleration)
Not reported
CV stride velocity
13 strides single task 14 strides dual task
4.88 m GAITRiteTM (plus 2 m acceleration and deceleration)
4.88
Average 34.5
Najafi [23]
N = 27; 18f; 9 m 80.3 5.0 years Preferred speed 0.5 m/s Community dwelling
4.7
Paterson [16]
N = 32; 32f 67.4 6.3 years Community dwelling
(i) Body worn sensors (gyroscopes) and Physilog datalogger (ii) 6.25 m GAITRiteTM (plus 2 m acceleration and deceleration) 8.1 m GAITRiteTM
CV step time, stride time, step length, stride length, step width, single support time, stride velocity CV gait cycle time, stride velocity
van Iersel [17]
N = 85; 46f; 40 m 75.8 7.1 years Frail in-patients Ability to walk 10 m Dementia (N = 39) No dementia (N = 46) Same cohort as above, classified according to stability of gait performance n = 59 stable; n = 8 worse; n = 18 better
5.6 m GAITRiteTM
5.6 m GAITRiteTM
Brach [19] Hartmann [22] Hartmann [24]
Hollman [20]
Moe-Nilssen [15]
van Iersel [18]
Abbreviations: N = number; f = female; m = male; CV = coefficient of variation; SD = standard deviation. a GaitMat II and GAITRiteTM distances given as reported.
Not reported
Not reported
Not reported
Not reported
CV and SD gait velocity, step length, step width, step time, stride time, stride length CV stride time, stride length
Not reported
Not reported
CV stride time, stride length
Not reported
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Principal author Brach [21]
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Table 2 Property tested and testing protocol. Clinimetric property
Principal author
Testing protocol
Test–retest (intra-rater) reliability
Brach [21]
Within session testing. 4 trials in total. Reliability calculated in two ways: (i) from a single 4 m trial (5–6 steps), and from two, 4 m trials (10–12 steps). Instructions regarding speed not reported. Between session testing 24 m walk (18 m assessed) in four conditions at preferred speed; gym floor, gym floor dual task, rubber walkway, rubber walkway dual task. Repeated 3: once on first occasion (T1) and twice 5–10 days later T2, T3 (20 min apart). Within session testing. 3 trials at preferred speed under single task conditions (mean 13 strides per subject) and dual task conditions (mean 14 strides per subject), repeated 30 min later. Within session testing. 2 trials at preferred speed, repeated after a short rest. Average number of steps = 34.5 SD 4.7. Within session testing. Test–retest reliability calculated from 5 m GAITRite walk and 20 m Physilog system walk. Both walked at preferred speeds, repeated after 15 min rest Between session testing N = 39 dementia patients; N = 46 no dementia. Walked 2 over mat at preferred speed. Clinical change in gait determined by video gait analysis (N = 3 experts); rated as stable or relevant change (non-stable) over 2 week period. Between session testing; same protocol as above. For overall stride time and for stable patients (stride time only).
Hartmann [22]
Hollman [20]
Moe-Nilssen [15] Najafi [23] van Iersel [17]
van Iersel [18]
Inter-rater reliability
Hartmann [22]
Concurrent validity
Brach [21]
Construct validity
Moe-Nilssen [15]
Responsiveness
Brach [19]
van Iersel [17] van Iersel [18]
Effect of walking protocol on variability
Concurrent validity of DynaPortMiniMod with GAITRite as gold standard
Paterson [16]
Reliability protocol as outlined above. Data from T2, T3 calculated for inter-rater reliability (N = 2 raters) Established by comparison with 5 self-reported levels of health perception, and dichotomous levels for ADLs, IADLs, difficulty walking, balance and physical activity 2 trials at slow, preferred and fast speeds. Gait parameters selected for construct validity from reliability study (see above) if ICCs 0.80 Anchor-based responsiveness estimates based on decline or improvement in self-reported walking difficulty (dichotomous response) and distance walked (4 level question) over one year. Responses classified as no change, improvement or decline. Distribution-based responsiveness derived from two trials (10–12 steps) at self-selected speed, repeated a year later Mean change in test score over 2/52 in non-stable patients with changes to SD of change in stable patients Anchor based responsiveness estimates based on expert opinion of gait (SRM, RI). Distribution based responsiveness reported as ES and SEM
Hollman [20]
Two protocols randomly presented with a brief rest in-between: 10 repeated single trials on GAITRiteTM and 10 laps of curvilinear circuit that included 2 straight sections of 8.1 m: total distance walked between 281 m and 295 m, depending on participant’s height. ES used to examine differences between protocols Number of strides required for reliable variability.
Hartmann [24]
4 trials at slow, preferred and fast speed with 7–13 steps recorded per trial
Abbreviations: ADLs = Activities of Daily Living; IADLs = Instrumented Activities of Daily Living; t = time; ES = effect size; SRM = Standardised Response Mean; RI = Responsiveness Index; SEM = standard error of the mean.
walking, suggesting that during interrupted walks spatiotemporal rhythms do not have time to become established. This concurs with previous research that shows long-range, power-law correlations for stride data captured over long distances and the need to establish steady state walking for measurement of gait variability [31]. However, during habitual walking gait largely occurs in short, interrupted bursts of walking. The distance walked during testing may reflect different aspects of motor control (e.g. attention may play a greater role in short, interrupted walks), and rationale for selection needs to be explicit within the test protocol. Requisite distance may also vary according to the gait parameter under consideration. Findings from this review reflect a limited theoretical framework to guide selection of gait variability parameters, as evidenced by the range of parameters used. Earlier reports suggest the importance of capturing two broad domains of gait: rhythmicity (e.g. stride time variability) and dynamic balance control (e.g. step width variability, DLS variability) [8], although this is not a unified aim. Recent evidence supports this twodomain view, pointing to distinct neural mechanisms and predictive characteristics for stride time variability and DLS time variability in people with Parkinson’s disease [32]. Studies in this review reported both step and stride analysis, although the
rationale for selecting one or other, or both, was unclear. Variability can be calculated for the left and right limbs separately using step data, allowing for the calculation of the asymmetry of gait variability. An important consideration to make when analysing step data is to avoid taking the standard deviation of pooled left and right steps because this measurement may be confounded by underlying spatiotemporal asymmetries. Another approach is to calculate the standard deviation from the residuals of each step around the mean over its respective limb. This advantage of this approach is that it allows double the number of steps to be used in the analysis, resulting in a more precise measure of gait variability. An issue related to selection of gait parameter concerns the calculation of gait variability itself, most commonly reported by CV although SD was also used. CV has an advantage in that it is a dimensionless unit, and has comparability with other studies. CV and SD values vary for different parameters, requiring care with interpretation. For example, low CV and SD indicate that gait is consistent for parameters associated with rhythmicity and progression (e.g. step time and step length), whereas high CV and SD are more likely to indicate a consistent gait for parameters that reflect postural control (e.g. step width). Excessive or reduced step width variability reflect an inability to adapt postural control
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448 Table 3 Clinimetric properties of gait variability. Clinimetric property
Principal author
Results
Comments
Test–retest (intra-rater) reliability
Brach [21]
Single 4 m walk showed poor to fair test–retest reliability (all ICCs): stance time SD .37, step length SD .48, step width SD .22 Two 4-m walks showed fair to good reliability: stance time SD .63, step length SD .5, step width SD .4 Intra-rater reliability gym floor single task (i) step duration ICC 0.78, LOA 0.89, RLOA (%) 38.6; dual task ICC 0.46, LOA 2.57, RLOA (%) 78.6 (ii) step length single task ICC 0.31, LOA 4.10, RLOA (%) 84.1; dual task ICC 0.37, LOA 5.27, RLOA (%) 93
Reliability of gait variability recorded over 10–12 steps greater than 5–6 steps
Hartmann [22]
Moe-Nilssen [15]
Test–retest reliability ICCs step time 0.64, step length 0.81, stride length 0.50, step width 0.22, single support time 0.68, stride velocity 0.11
Najafi [23]
Test–retest reliability gait cycle time 5 m GAITRiteTM ICCs 0.42, 5 m Physilog 0.42, 20 m Physilog 0.10; test–retest reliability stride velocity 5 m GAITRiteTM ICC 0.37, 5 m Physilog 0.37, 20 m Physilog 0.50 N = 85 (39 dementia and 46 no dementia) Test–retest reliability: ICCs dementia (N = 39) stride time 0.56, stride length 0.88; no dementia (N = 46) stride time 0.84, stride length 0.88 N = 85 (59 stable and 26 unstable) Test–retest reliability high for all variables (0.82 to 0.98) apart from overall stride time ICC 0.46, which increased to 0.72 when stable patients only were assessed
van Iersel [17]
Van Iersel [18]
Inter-rater reliability
Hartmann [22]
Inter-rater reliability gym floor single task (i) step duration ICC 0.59, LOA 1.16, RLOA (%) 49.6; dual task ICC 0.72, LOA 0.1.93, RLOA (%) 59.4 (ii) step length single task ICC 0.69, LOA 2.80, RLOA (%) 59.3; dual task ICC0.71, LOA 3.48, RLOA (%) 64.2
Validity
Brach [21]
Concurrent validity assessed by comparison of groups; independent t-tests for 2 groups; 1-way ANOVA for >2 groups. Largest effect found for stance time variability (P < 0.0001 for 6 health status criteria). Step length significant for 4 criteria (P < 0.05); step width significant for 3 criteria (P < 0.05) Construct validity established through correlation: Pearsons r for step time and step length = 0.28 (NS)
Moe-Nilssen [15]
Responsiveness
Brach [19]
Anchor based through self report: (i) no change: all variability measures stable; (ii) improvement: step length SD improvement; (iii) decline: inconsistent results Distribution based ES (moderate): stance time SD (s) 0.01, swing time SD (s) 0.009, step length SD (cm) 0.61, step width SD (cm) 0.08 Combining anchor and distribution approaches meaningful change estimate: stance time SD and swing time SD = 0.01(s) and 0.25 (cm) step length SD (moderate ES)
van Iersel [17]
RI dementia stride time 0.4, stride length 2.9; no dementia stride time 0.2, stride length 0.5 Anchor based responsiveness stride length SRM 0.6, RI 2.6; stride time SRM 0.5, RI 0.7 Distribution based stride time ES 0.1, SEM 1.3; stride time ES 0.1, SEM 1.6
van Iersel [18]
Intra-rater reliability (test–retest) lower than inter-rater reliability, although neither support the use of DynaPortMiniMod tri-axial accelerometer for gait variability measures Reliability for step parameters higher than stride parameters due to number of data points (mean steps = 34.5 c/f mean strides = 17) Test–retest reliability improved slightly over distance, but overall poor irrespective of walking distance Moderate to excellent reliability demonstrated in older people with and without dementia Poor reliability for stride time
Inter-rater reliability higher than intra-rater reliability (test–retest), although neither support the use of DynaPortMiniMod tri-axial accelerometer for gait variability measures Increased step length and stance time variability and decreased step width variability associated with poorer health status, impaired functional status and inactivity Spatial and temporal variability may represent different constructs Preliminary findings provide estimates for meaningful change for swing time and stance time variability Step width SD problematic as an outcome because of non-linear relationship with mobility decline and falls (too much and too little step width variability may be optimal for control of dynamic equilibrium during gait) RI low for CV Small effect sizes may be partly explained by large number of stable patients
Abbreviations: CV = coefficient of variation, ES = effect size, ICC = intraclass correlation coefficient, L = left, LOA = limits of agreement, R = right, RI = responsiveness index, RLOA = ratio limits of agreement, SD = standard deviation, SEM = standard error of the mean, SRM = standardised response mean.
and as such are harbingers of falls, although this may to some extent be dependent on speed [30]. For gait variability test–retest reliability, ICCs were low apart from studies conducted on in-patients where ICCs were moderate to excellent for stable patients [18], even those presenting with dementia [17]. Reliability estimates varied for different gait parameters. As a measure of dynamic stability, step width variability was overall less reliable than step length variability as expected. Hartmann et al. [22] reported higher test–retest reliability for temporal parameters (step duration)
than spatial parameters (step length), although this was not the case for all studies and may reflect the use of accelerometery derived data. An important consideration is the period of time for test–retest reliability. Four of the 10 included studies of withinsession test–retest reliability [15,21–23], and ICCs from these studies were not as high as studies that reported a 7 day or 14 day interval between tests [17,18,22], which is a more realistic time frame over which stability can be measured. The single study that investigated inter-rater reliability for accelerometry yielded slightly superior ICCs to test–retest reliability but rater error
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overall was not considered to contribute significantly to low test–retest ICCs [22]. Reliability estimates under dual task conditions were comparable to single task conditions, which is surprising given the impact of an added cognitive task on gait variability [7]. Only one study tested concurrent (discriminative) validity and reported superiority of stance time over other parameters tested [21]. Our search strategy failed to locate any studies designed specifically to test predictive validity, although as noted earlier, this is indirectly implied from a large body of longitudinal and cross-sectional research. Anchor-based and distribution based approaches were used to measure responsiveness [18,19], however comparisons are limited because statistics and variability descriptors differed. Brach et al. provided point estimates for meaningful change for swing time and stance time variability as a guide, although confidence intervals were not reported [19]. Responsiveness is closely linked to reliability and will need to be examined further once protocols have been established to improve reliability. Standards of reporting for the studies in this review were generally poor, with insufficient detail for aspects of measurement and testing protocol such as distance walked, effective data capture area, total number of steps or strides, rationale for selection of CV or SD, and clarity concerning the use of individual or group mean/SD to calculate variability. 4.1. Review limitations Our search strategy was limited to English language publications. We did not search grey literature other than that revealed through the search strategy, and we may have missed relevant theses and conference abstracts. Inclusion criteria were limited to studies overtly designed to test the clinimetrics of gait variability, thereby excluding a large body of research that supports its predictive and discriminatory properties. Also, because we were unable to locate a standardised data extraction tool that suited the purposes of this review, we developed and piloted our own tool which may have introduced bias. 5. Summary This review indicates that further work is required to determine the clinimetric properties of gait variability. However, it is important to place the challenge of achieving this in context. Routine gait measures such as gait speed and stride amplitude are based on mean estimates, which are more stable (and therefore robust) than variance estimates. Measurement of gait variability enhances gait analysis, and future research will compare its utility with other non-linear metrics which may prove even more sensitive to early gait pathology. Recommendations for the use of gait variability are tentative until consensus is achieved for selection of gait parameters, testing protocol and method of analysis. Further research is required to examine clinimetric properties, including studies that formally evaluate predictive validity. However, based on this review we suggest the following: Provide a rationale for selection of gait parameters Report SD and CV Ensure at least 12 steps are collected Data from continuous walking is more reliable than interrupted walking, although the two possibly test different aspects of gait control Report the number of steps and the distance Pilot reliability data before using gait variability as an outcome measure.
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