Minimal Clinically Important Differences of Spatiotemporal Gait Variables in Parkinson Disease

Abstract

Background:

Assessment of gait function in People with Parkinson Disease (PwPD) is an important tool for monitoring disease progression in PD. While comprehensive gait analysis has become increasingly popular, only one study, Hass et al. (2014), has established minimal clinically important differences (MCID) for one spatiotemporal variable (velocity) in PwPD.

Research Question:

What are the MCIDs for velocity and additional spatiotemporal variables, including mean, variability, and asymmetry of step length, time, and width?

Methods:

As part of a larger clinic-based initiative, 382 medicated, ambulatory PwPD walked on an instrumented walkway during routine clinical visits. Distribution and anchor-based methods (Unified Parkinson’s Disease Rating Scale-III, Modified Hoehn and Yahr, and the mobility subsection of the Parkinson Disease Questionnaire) were used to calculate MCIDs for variables of interest in a cross-sectional approach.

Results:

Distribution measures for all variables are presented. Of nine gait variables, four were significantly associated with every anchor and pooled to the following values: velocity (8.2cm/s), step length mean (3.6cm), step length variability (0.7%), and step time variability (0.67%).

Significance:

The finalized MCID for velocity (8.2cm/s) was nearly half of the MCID of 15cm/s reported by Hass et al., potentially due to differences in calculations. These results allow for evaluations of effectiveness of interventions by providing values that are specific to changes in gait for PwPD. Alterations of methodology including different versions of clinical or walking assessments, and/or different calculation and selection of gait variables necessitate careful reasoning when using presented MCIDs.

Introduction

Declines in walking ability are ranked amongst the most important outcomes for people with Parkinson disease (PwPD).[1] Gait dysfunction can be observed in the prodromal stage,[2] inevitably declining over the course of the disease.[3] A clinical scale for quantifying motor symptoms, including gross assessment of gait, is the Unified Parkinson Disease Rating Scale Section III (UPDRS-III).[4] The gait assessment therein relies on subjective observation and has limitations, especially pertaining to evaluating walking dysfunction.[5] Thus, more objective and sensitive measures of gait are recommended.[5,6] Comfortable walking speed, a “clinical vital sign”, is associated with quality of life and mortality in PwPD,[7,8] but does not provide a complete view of an individual’s walking pattern.[5,6] Other spatiotemporal parameters, such as step length/time and variability, are more sensitive than gait speed in determining dysfunction in early disease and monitoring severity.[6,9] While spatiotemporal parameters are valuable outcomes for PwPD, responsiveness of these variables is limited.[5,6] Measures for responsiveness, such as minimal detectable change (MDC) or minimal clinically important difference (MCID), can be helpful in determining sample sizes and potential effectiveness of clinical trials and aid in clinical decision-making.[10]

While there are various terms associated with psychometric responsiveness calculations,[11] differences in calculations alter interpretation of determined values. MDC is the smallest amount of change observed unrelated to measurement error,[11] but is reliant on the methods of the study and does not provide information regarding clinical meaningfulness.[12] In contrast, MCID relies on commonly accepted and/or clinically relevant values to estimate whether the magnitude of change reflects meaningful difference.[12] To our knowledge, only one study established a MCID for any spatiotemporal variable in PwPD.[13] Hass et al. used distribution-based and anchor-based methods to determine a range of MCIDs for gait speed in 324 PwPD.[13] The purpose of the present study is to determine MCIDs for velocity and additional spatiotemporal variables in PwPD in a different, larger sample than Hass et al.. Additionally, we compare our results to those found by Hass et al.

Methods

Participants

Data were collected as part of a clinic-based research initiative approved by the Washington University Human Research Protection Office (Saint Louis, USA). Ambulatory PwPD, who had taken their normally prescribed medication, completed a walking assessment as part of normal clinical care between [4/15/2015–5/31/2018]. Exclusion criteria included individuals who: had undergone deep brain stimulation surgery or used an assistive device. Chart reviews were conducted to exclude individuals with history of other disorders (e.g., dementia, stroke, multiple sclerosis) or issues (e.g., recent injuries/surgeries) influencing walking.

Procedures

Participants walked at their “normal, comfortable pace” across a 5-m GAITRite^® instrumented walkway (120Hz; Spatial accuracy: ±1.27cm, temporal accuracy: ±1 sample; CIR Systems Inc, Havertown, PA). Participants completed three trials, starting a few steps before the beginning of the mat and stopping a few steps at the end of the mat. Demographics and clinical assessments, UPDRS-III and modified Hoehn and Yahr scale (mH&Y), were completed same day.

Gait variables were selected to represent spatial and temporal measures in the anterior-posterior and medial-lateral directions. As such, we focused on velocity, step length, time, and width calculated by GAITRite^® software. Variability (coefficient of variation (CV)) and asymmetry of these measures were calculated as these measures have sensitivity in distinguishing specific aspects of the disease.[10,14,15] CV was selected over standard deviation (SD) for measures of variability due to its ubiquitous use.[10] Asymmetry measures (mean step length and step time) were calculated via Equation 1.

$$\frac{\mathit{\text{mean}}\mathit{\text{step}}\mathit{\text{length}}\mathit{\text{of}}\mathit{\text{more}}\mathit{\text{affected}}\mathit{\text{limb}}}{\mathit{\text{mean}}\mathit{\text{step}}\mathit{\text{length}}\mathit{\text{of}}\mathit{\text{less}}\mathit{\text{affected}}\mathit{\text{limb}}}\times \mathrm{100\%=}\mathit{\text{MA/LA}}\mathit{\text{Length}}\mathit{\text{Asymmetry}}$$ Eq.1 (example: step length):

While past studies primarily use right/left comparison for asymmetry,[6,14] more-affected/less-affected provides greater clinical utility. [15] Measures were reported as percentages: 100% represents perfect symmetry, greater than 100% represent larger values of the more-affected limb compared to less-affected limb. More-affected/less-affected side were determined based on lateralized UPDRS-III items 20–26[4,16], which includes upper and lower limb measures.

Statistical Analysis

Distributions of gait variables were checked for normality. As calculations may be affected by outliers,[17] outliers greater than three SD from the group mean were eliminated in a pairwise fashion for each variable. In total, 30 unique individuals displayed at least one outlier.

Distribution-based

Means and SDs were calculated for the entire sample to calculate effect sizes based on estimated change from the mean. Effect sizes of 0.2/0.5/0.8 were considered small/medium/large changes, respectively.[18] Effect sizes (0.2/0.5/0.8) were multiplied by the SD of each gait variable[13] to include a range around 0.5(SD), a common threshold for estimated change.[19]

Anchor-based

We used three anchors: mH&Y, UPDRS-III, and the mobility subsection of the Parkinson Disease Questionnaire (mPDQ). A smaller subset of 223 out of 382 individuals completed the mPDQ.

First, mH&Y is a measure of disease stage, encompassing evaluations of disease involvement and, in later stages, balance and walking.[20] While the majority of clinicians use the mH&Y scale (Stages:1, 1.5, 2, 2.5, 3, 4, 5, higher stage indicates worse severity) in place of the standard version, there are no clinometric values established for this version.[20] In the present sample, one SD in mH&Y was 0.37. Rounding this to the appropriate unit, 0.5 is considered a meaningful effect.

Second, UPDRS-III is a common scale to measure the motor symptoms of PD and was used by Hass et al.(Cronbach’s alpha=.88–.96, test-retest reliability=.82).[4,21] Scores range from 0–108 (13 items, higher scores indicating worse severity) and include subjective items scored by a trained rater including speech, tremor, and gait. For UPDRS-III, scores of 2.7/6.7/10.8 were previously established as minimal/moderate/large important differences.[22]

Third, mPDQ is a subsection of a PD-specific health-related questionnaire (Scale:0–100, 10 items, higher is worse perceived mobility). The mPDQ is valid and reliable as a standalone measure(Cronbach’s alpha=.95, test-retest reliability=.93).[23] Items include how often individuals note difficulty completing various tasks including “walking 100 yards” and “getting around the house”. Peto et al. established the mPDQ[24] minimal important difference at 3.2 points.

To determine estimated change in each gait measure associated with each anchor, curve estimation procedures were completed. Following Hass et al., we included linear, quadratic, cubic, and logarithmic functions to identify the best-fitting model with the anchor modeled as the predictor of the gait variable.[13,17] When fit was similar, we chose the simplest, significant model. A p-value of p<.006 was used to correct for the number of variables (nine) calculated within each anchor. We calculated each gait MCID by multiplying the unstandardized coefficient (B) by the anchor variable’s MCID. For example, if B for velocity is −0.56 and the MCID for mPDQ is 3.2,[24] the estimated MCID for velocity would be −1.79cm/s. That is, if an individual were to undergo a meaningful change in mPDQ, we would estimate a decrease in gait velocity by 1.79cm/s.

As mH&Y is an ordinal variable, we tested for potential differences between stages. Due to the relatively small sample size individual mH&Y stages, Independent-Samples Kruskal-Wallis Tests were performed (pairwise p-value: p<.05 with Bonferroni corrections).

Triangulation of Methods

While multiple methods of calculation are suggested to determine MCIDs, methods of triangulation are recommended to allow for a single value that is “empirically sound and clinically relevant”.[25] We triangulated (by averaging)[25–28] four MCIDs for each gait variable: distribution-based measure of 0.5(SD) identified as “Medium” in Table 2; the anchor-based MCIDs of mH&Y; the “moderate” improvement based on UPDRS-III; and the mPDQ. Only gait variables that had calculated MCIDs for all four methods were included.

Table 2.

Distribution-based clinically important differences

	n	Mean ± SE	SD	“Small”	“Medium”	“Large”
Mean
Velocity (cm/s)	382	89.74 ± 1.14	22.37	4.47	11.19	17.90
Step Length (cm)	380	52.67 ± 0.53	10.25	2.05	5.12	8.20
Step Time (s)	375	0.59 ± 0.00	0.07	0.01	0.03	0.05
Step Width (cm)	378	10.70 ± 0.19	3.64	0.73	1.82	2.91
Variability
CV Step Length (%)	374	5.99 ± 0.13	2.55	0.51	1.27	2.04
CV Step Time (%)	377	5.44 ± 0.11	2.22	0.44	1.11	1.78
CV Step Width (%)	373	22.29 ± 0.57	11.04	2.21	5.52	8.83
MA/LA Asymmetry
Step Length (%)	379	100.09 ± 0.32	6.22	1.24	3.11	4.98
Step Time (%)	377	101.54 ± 0.28	5.37	1.07	2.69	4.30

Descriptive information on gait variables of the sample and calculated distribution-based clinically important differences based on Cohen et al. 1988. Final sample number per variable displayed in n. Standard error (SE), standard deviation (SD), coefficient of variation (CV), more-affected/less-affected (MA/LA).

Results

Demographics

In total, 382 individuals were included in the analysis (Table 1). The frequency of mH&Y was: Stage 1 (n=10), Stage 1.5 (n=10), Stage 2 (n=192), Stage 2.5 (n=154), Stage 3 (n=16). Frequency of more-affected side included: right side (n=198) and left side (n=184). Twenty individuals did not have a more-affected side according to UPDRS-III, so the more-affected side was identified by chart reviews of side of onset.

Table 1.

Clinical assessments of the sample.

	n	Mean ± SE	SD	Range
Sex	382	253 M, 129 F	-	-
Age (years)	382	65.9 ± 0.49	9.51	[36,86]
UPDRS-III Total	382	25.26 ± 0.52	10.12	[5,57]
PDQ-39 Total	255	31.89 ± 1.41	22.54	[0,122]
mPDQ subscore	223	25.07 ± 1.37	20.48	[0,88]

Final sample number per variable displayed in n. Standard error (SE), standard deviation (SD), Males (M), Females (F), Modified Hoehn and Yahr (mHoehn and Yahr), Unified Parkinson’s Disease Rating Scale motor subsection (UPDRS-III), 39-question Parkinson’s Disease Questionnaire (PDQ-39), 39-question Parkinson’s Disease Questionnaire mobility subsection (mPDQ)

Distribution-based

Mean, standard error, SD, and distribution-based metrics for “small”, “medium”, and “large” estimated meaningful change are in Table 2.

Anchor-based

Velocity (B=−27.44, p<.001, r=−.449), mean step length (B=−12.00, p<.001, r=−.427), CV step length (B=2.21, p<.001, r=.311), and CV step time (B=2.25, p<.001, r=.372) demonstrated significant linear relationships with mH&Y stage. Velocity, step length, CV step length, and CV step time were significantly different (all univariate p<.001) between stages. When pairwise differences were significant (Table 3), the differences were in the same direction: higher disease stage was associated with slower walking with shorter, more variable step lengths and longer, more variable step times (Figure 1). Full statistical output including correlations between anchors and model parameters, means, SD, and pairwise significance are in Supplementary 1.

Table 3.

Pairwise significance between Modified Hoehn & Yahr Independent-Samples Kruskal-Wallis Test Analysis

Modified Hoehn & Yahr
	1v1.5	1v2	1v2.5	1v3	1.5v2	1.5v2.5	1.5v3	2v2.5	2v3	2.5v3
Velocity	>.999	.147	<.001	<.001	.612	<.001	.001	<.001	.001	>.999
Step Length	>.999	.840	<.001	<.001	>.999	.015	.011	<.001	<.001	>.999
Step Time	>.999	.186	.020	.701	.086	.008	.429	.241	>.999	>.999
CV Step Length	>.999	.122	<.001	.043	>.999	.059	.985	<.001	>.999	>.999
CV Step Time	>.999	.041	<.001	<.001	.381	.002	.001	<.001	.007	>.999

Pairwise significance between Modified Hoehn & Yahr ANOVA Analysis with Bonferroni correction applied. Coefficient of variation (CV). Bolded values denote significance.

Figure 1.

Spatiotemporal differences between Modified Hoehn and Yahr Stages. Pairwise significant differences can be found in Table 3.

As with mH&Y, further progression (higher UPDRS-III) was related to slower velocity (B=−0.88, p<.001, r=−.398), shorter step length (B=−0.36, p<.001, r=−.356), longer step time (B=0.001, p<.001, r=.203), higher step length variability (B=0.06, p<.001, r=.257), and higher step time variability (B=0.05, p<.001, r=.222). Step width mean (p=.308), step width variability (p=.71), and asymmetry measures were not associated with UPDRS-III (length asymmetry p=.648, time asymmetry p=.462).

While clinicians scored mH&Y and UPDRS-III, the mPDQ represents the participant’s perspective of their own mobility with higher scores representing worse mobility. Again, slower velocity (B=−0.56, p<.001, r=−.509), shorter step length (B=−0.29, p<.001, r=−.557), higher CV step length (B=0.05, p<.001, r=.399), and higher CV step time (B=0.04, p<.001, r=.385) were associated with worse perceived mobility. Step time mean (p=.079), step width mean (p=.023), CV step width (p=.064), and measures of asymmetry (length asymmetry p=.231, time asymmetry p=.522) were not associated with mPDQ.

The range of values associated with the anchors is listed within Table 4. In general, the MCIDs calculated using mH&Y were the largest and those associated with mPDQ were smallest.

Table 4.

Anchor-based clinically important differences.

	mH&Y		UPDRS-III			mPDQ
	Minimal CID	95% CI	Minimal CID	Moderate CID	Large CID	Minimal CID	95% CI
Velocity (cm/s)	−13.72	[−16.47,−10.97]	−2.38	−5.90	−9.51	−1.79	[−2.19,−1.39]
Step Length (cm)	−6.00	[−7.28,−4.71]	−0.98	−2.43	−3.91	−0.91	[−1.09,−0.73]
Step Time (s)	0.02	[0.01,0.03]	0.003	0.007	0.01	ns
Step Width (cm)	ns		ns			0.08	[0.01,0.16]
CV Step Length (%)	1.10	[0.76,1.45]	0.17	0.43	0.69	0.17	[0.12,0.22]
CV Step Time (%)	1.13	[0.84,1.41]	0.13	0.33	0.53	0.12	[0.08,0.16]
MA/LA Time Asym (%)	−1.00	[−0.07,−1.74]	ns			ns

Calculated anchor-based clinically important differences. Italicized numbers indicate values that should be rounded according to resolution sensitivity. Clinically important difference (CID), 95% confidence interval (95% CI), not significant (ns), coefficient of variation (CV), more-affected/less-affected (MA/LA).

Triangulation

Four variables were triangulated based on the results from the distribution-based and anchor-based calculations: velocity at 8.2cm/s, step length at 3.6cm, CV step length at 0.7%, and CV step time at 0.67%.

Discussion

The present study extends research by Hass et al.[13] providing distribution-based and anchor-based metrics for basic spatiotemporal measures for medicated PwPD. Velocity, step length, CV step length, and CV step time displayed significant associations with all three anchors and were triangulated to a single MCID value providing a useful measure of estimated clinically important differences associated with the MCIDs of both clinician-reported and patient-reported outcomes.

Comparison of velocity MCID

Compared to other populations, our triangulated MCID for velocity (8.2cm/s) exceeds the “small meaningful change” estimate for older adults (4cm/s) and subacute stroke survivors (6cm/s).[29] As the present study and Hass et al. used the UPDRS-III as an anchor, we confirmed similar anchor-based measures with 1cm/s difference between the “Large” MCIDs of the two studies (side-by-side comparison: Supplementary 2). We also used the same distribution-based cut-offs, finding our MCIDs were slightly smaller in the present sample compared to Hass et al. The range of scores for Hass et al. was wider, including nine individuals with mH&Y Stage 4: “severe disability”.[20] The smaller MCID values in the present study reflect the sensitivity needed to adequately describe the disability presented in our sample. Our mH&Y-based MCID of 14cm/s approximately matches the MCID of 15cm/s from Hass et al., who used a full stage as clinically important.[13] When calculating the MCID using the methods closest to Hass et al. (i.e., single-stage H&Y),[13] the MCID for velocity in the present sample would be 18cm/s (Supplementary 2). Care should be taken when making inferences specific to the original H&Y or the modified version, as conclusions may vary.

Heterogeneity of MCID Values

There are very few, if any, published MCIDs on gait measures for any population,[5] thus this study represents an important first step to identifying clinically important differences specific to gait variables other than velocity. While triangulation of measures provides a single value for each variable, the range of values suggest MCIDs are dependent on methods of calculation and should always be used and interpreted with caution.

When comparing MCIDs across anchors, the values obtained using the mH&Y anchor were consistently the largest while those obtained based upon UPDRS-III and mPDQ were considerably smaller. While all anchors were significantly correlated to each other (Supplementary 1), suggesting shared variance, the three anchors have scales with different values considered “meaningful”. For mH&Y, meaningful change is 14% of the total (1/7), more than double what is meaningful for UPDRS-III (6%=6.7/108) and quadruple the change associated with mPDQ (3%=3.2/100).[30] The progression from one mH&Y stage to the next represents a larger expected change in gait than a meaningful change in UPDRS-III or mPDQ and this difference is reflected in our range of MCIDs. Beyond differences in scale, underlying constructs specific to measuring mobility are different. Considering the UPDRS-III includes the widest array of components (e.g., tremor, speech), correlations between gait measures and the UPDRS-III were consistently lower compared to mH&Y and mPDQ. To provide a single MCID per variable, we triangulated measures similar to past studies.[27,28] The averages represent a middle ground between the various methods, while all four methods provide the range of values suggested for MCIDs.[26]

Triangulated MCIDs: Step Length, CV Step Length, and CV Step Time

Compared to velocity, the remaining three variables have even less available comparisons related to MCIDs in other populations, limiting comparisons to published differences of mean values between groups and within interventions. MCIDs reported herein for step length, CV step length, and CV step time are greater than the associated change observed in 12–18 months among those with PD. [3,31] Levodopa, the most common medication for PwPD, consistently increases step length and decreases variability.[32–34] Son et al. found levodopa increased step length by 3cm[33] with a corresponding five point decrease in UPDRS-III, matching our triangulated measure of 3.6cm. Statistically significant decreases to variability with levodopa are consistently around 1% for both CV length and CV time.[32,34] Our triangulated measures for CV length and CV time are smaller than changes noted with levodopa, at 0.7% and 0.67%, respectively.

Other Variables: Step Time, Step Width, CV Step Width, and Asymmetry

The remaining variables, which were unable to be triangulated or were not significantly associated with anchors, tend to be less utilized and/or understood with mixed results in the literature. Changes in step/stride time are conflicting across interventions[33] and disease progression.[9,31] Morris et al. hypothesized changes in step time may be a “compensatory mechanism” for reduced step length.[35] As such, unless there is a specific research question aimed at improving step time mean, step time should be interpreted in the context of step length and velocity. Additionally, step width changes may represent a compensation strategy not entirely specific to PwPD, as step width mean and variability are often measured in relation to cognition and balance in PwPD and in healthy aging[10]. Gait asymmetry measures are also popular measures of dual-task walking.[14] Yet, methods to calculate asymmetry continue to be inconsistent[15,33] causing confusion on the direction or relevance of asymmetry.[33] As changes in step time, step width, and asymmetry may indicate compensatory strategies to overcome mobility dysfunction related to PD,[15,35] alterations of step time, step width, or asymmetry should not be overlooked, but further explored.

Methodological Considerations

Similar to Hass et al., we used a gait assessment commonly used in clinical and/or research settings. Dual-tasking or testing individuals in their “off” medication state and comparing to the results herein would be an important future step. As technology advances to integrate “real-world” data into the clinical setting, these MCIDs should also be re-evaluated in these specific contexts.

In addition to the apparent range of MCIDs across anchors, equipment considerations pertaining to accuracy and validity are of particular importance when calculating and interpreting MCIDs. Validity and reliability of the GAITRite are considered to be “excellent”,[36] thus it is often utilized in the research and clinical settings, including to validate new systems. Still, researchers and clinicians should be careful when extending these MCIDs to systems with different accuracy, such as accelerometry. The triangulated MCID values reported herein are larger than measurement error inherent in the GAITRite system but may fall within the range of measurement error for less accurate methodologies.

Limitations

There are important limitations to the presented MCIDs. First, the MCIDs are from a cross-sectional, or “between-person” analysis, calculating the estimated/associated change of the gait variable per meaningful change in published MCID of the specified anchor. A “between-person” MCID analysis provides a reasonable estimate of a “within-person” analysis of change, but may underestimate longitudinal change.[37] Second, the sample represents individuals mostly in Stages 2–2.5. While these individuals represent the stages most commonly seen for ambulatory care, expanding the number of individuals in earlier and later stages would provide a wider application. As individuals with a mH&Y score of 3 have noted “postural instability”, the inclusion of these individuals is of particular interest from a clinical intervention perspective. Each mH&Y group had at least ten individuals, meeting the minimum threshold for group inclusion for this type of analysis.[38] Third, the average number of steps per individual was 24±8. Thus many individuals did not meet the 30-step minimum recommended for measurement of gait variability[39]. While the reliability of step time variability is not as sensitive to the number of steps as step length variability[39], the varying number of collected steps among subjects is a limitation. Lastly, other derivations of the analyzed gait measures are used in the literature. MCIDs for SD measures and stride measures are in Supplementary 2. While the MCIDs may be similar, the MCIDs are not identical nor interchangeable.

Conclusions

To our knowledge, we are the first to present MCIDs for spatiotemporal variables beyond velocity in medicated, ambulatory PwPD. Four variables, velocity, step length, CV step length, and CV step time, were significantly associated with all three anchors (mH&Y, UPDRS-III, and mPDQ). The smallest anchor-based metrics (minimal change for UPDRS-III and the mPDQ) were nearly identical across all gait measures, providing general consistency across clinician-rated and patient-rated anchors. While we suggest using the triangulated values as a conservative middle-ground value, we also provide all anchor-based and distribution-based measures as a range of values is recommended[26]. Researchers and clinicians should always provide adequate explanation and reasoning when using MCIDs.[40]