Linking gait mechanics with perceived quality of life and participation after stroke.

BACKGROUND: Individuals with hemiparesis following stroke often experience a decline in the paretic limb's anteriorly directed ground reaction force during walking (i.e., limb propulsive force). Gait speed and walking capacity have been independently associated with paretic limb propulsion, quality of life, and participation in people with stroke. However, it is unclear as to the extent that underlying limb mechanics (i.e., propulsion) play in influencing perceptions of quality of life and participation. We therefore sought to determine the role of limb propulsion during gait on the perception of quality of life and participation in people following stroke.
METHODS: This study is a secondary analysis of individuals involved in a gait retraining randomized control trial. Gait speed, walking capacity, limb propulsion, Stroke Impact Scale, and average daily step counts were assessed prior to and following 6 weeks of training. The pre-training data from 40 individuals were analyzed cross-sectionally using Pearson and Spearman correlations, to evaluate the potential relationship between limb propulsion (ratio of paretic limb propulsion to total propulsion) with gait speed, gait capacity, perceived quality of life domains, and average daily step counts. Partial correlations were used to control for gait speed. Thirty-one individuals were assessed longitudinally for the same relationships.
RESULTS: We observed a training effect for gait speed, walking capacity, and some quality of life measures. However, after controlling for gait speed, we observed no significant (p≤0.05) correlations in the cross-sectional and longitudinal analyses. SIGNIFICANCE: After controlling for the influence of gait speed, paretic limb propulsion is not directly related to perceived quality of life or participation. Although limb propulsion may not have a direct effect on participant's perceived quality of life, it appears to be an important factor to enhance gait performance, and therefore may be important to target in rehabilitation, when feasible.

Introduction

Following stroke, individuals often experience persistent unilateral motor deficits (i.e., hemiparesis) [1, 2] into the chronic phase (> six months), resulting in reductions in walking capacity and speed [3, 4] and increases in mechanical work and metabolic cost of walking [5]. Walking capacity and gait speed measures are used to document a person’s current and transitory functional recovery status, community mobility and participation level, and quality of life [3, 6, 7]. Given the relationship between gait performance and quality of life [8], it is important to identify the biomechanical mechanisms underlying disordered gait that might contribute to this relationship to ensure that rehabilitation is appropriately targeted.

The presence of motor deficits in the paretic limb following stroke can contribute to deficits throughout the gait cycle (e.g., stance phase stability, limb propulsion, and swing limb advancement). Particularly relevant to the production of forward progression during gait is a reduction in the anteriorly directed ground reaction force (i.e., limb propulsion) [9]. In individuals post-stroke, paretic limb propulsion is positively associated with both gait speed [10] and long-distance walking performance [11]. Additionally, paretic limb propulsion appears to influence a person’s community mobility, measured with metrics such as daily step count [12]. Thus, limb propulsion may provide insight into the functional contribution of the affected limb to gait, and may help elucidate whether paretic limb mechanics have recovered versus required compensations [13-15].

Despite deficits in paretic limb propulsion during typical walking, people with stroke exhibit a robust paretic propulsive reserve, suggesting that limb propulsion is modifiable [14]. In fact, the provision of feedback related to gait mechanics can significantly increase limb propulsion [16, 17], suggesting that it may serve as a therapeutic target for patient outcomes. However, a change in limb propulsion with no concomitant change in gait speed/capacity may not be meaningful to people post-stroke. How hard a person pushes off the ground may not lead to a change in mobility if there are no benefits to walking speed/capacity. Paretic limb propulsion is one of many impairments in people with stroke. In this study, we aimed to determine its relative importance beyond the improvements in gait performance.

Given that limb propulsion is related to gait speed and walking capacity [10, 11] and these outcomes are both related to quality of life and participation [3, 4, 6, 7], we sought to investigate the role of limb propulsion as a potential mechanism influencing quality of life measures for people with chronic stroke directly. We assessed the potential influence of limb propulsion on quality-of-life measures following stroke both 1) cross-sectionally, and 2) longitudinally. We hypothesized that paretic limb propulsion is associated with quality of life and participation after controlling for walking speed. Likewise, we hypothesized that an improvement in limb propulsion following gait training is associated with quality of life and participation after controlling for changes in walking speed. Knowledge of the relative importance of paretic limb propulsion on quality-of-life and participation measures would inform the relative therapeutic efficacy of targeting limb propulsion in the rehabilitation process. The presence of such a relationship would give merit to targeting gait speed and walking capacity through enhanced paretic limb propulsion rather than targeting other impairments or allowing compensatory strategies to develop.

Materials and methods

Participants

Participants in this study were part of a motor learning randomized control trial described elsewhere [18]. Participants were included if they experienced an ischemic or hemorrhagic stroke more than six months prior with resulting asymmetric gait (step length asymmetry index of > 0.537 or stance time asymmetry index of > 0.524) [14]. All included subjects had a comfortable overground gait speed of less than 1.0 m/s, with or without an assistive device and bracing. Participants were excluded if they were concurrently in physical therapy during the study, obtained botulinum toxin to the lower limb within the 6 months leading up to or during the study, had uncontrolled cardiac, respiratory, or metabolic disorders, had neurologic disorders other than stroke, or had a cerebellar lesion. Participants provided informed consent as approved by the Institutional Review Board (IRB) at University of North Carolina at Chapel Hill (IRB # 11–1240). The trial was listed on ClinicalTrials.gov (NCT01598675).

We used the participant’s pre-test data to perform a cross-sectional analysis (Cohort 1). Because the hypotheses did not consider the impact of a particular intervention and the primary study did not find significant differences among groups, we chose not to separate participants based on treatment groups. Rather, all participants with data who completed the first training session are included in one group in the cross-sectional analysis (Cohort 1) and all participants that completed the full training are included as one group in the longitudinal analysis (Cohort 2).

Data collection

We collected outcome measures related to gait speed/walking capacity and quality of life one week prior to training (pre-test), and again one week following the final training session (post-test). We assessed participation from the average daily step count, which is a measure of community engagement and daily activity [3, 19]. We determined the step count from a StepWatch Activity Monitor (Modus Health, Edmonds, WA) that was worn for 4–6 days between the pre-training assessment and the start of training, and from the end of training and the post-training assessment. Comfortable gait speed was measured as participants made three passes across a 14-foot GaitRite pressure mat (CIR Systems, Havertown, PA). Participants were instructed to walk at their preferred, comfortable gait speed, as if they were walking outside of the laboratory setting and not being monitored. We measured walking capacity using the six-minute walk test (6MWT) in which participants were instructed to cover as much distance as possible in six minutes. Participants walked between two tape marks placed 100 feet apart in a hallway as the investigator measured the distance traveled with a measuring wheel. We used the Stroke Impact Scale (SIS) as a quality-of-life measure.

Ground reaction force data from treadmill walking (Bertec Corp, Columbus, OH) were collected at the first training session and during the final (18th) training session. Although these gait training sessions lasted up to 20 minutes, only data from the first two minutes of walking were used. This decision was made because, for two of the three groups, the treadmill belts moved at different speeds from each other starting in minute three. Using data from only the first two minutes ensured that participants always had a belt speed difference (between belts) of zero. Because participants generally improved their walking speed over the 18 sessions of training, all but two subjects in the longitudinal analysis were walking at a faster speed during the final session compared to the initial session.

Gait cycle events (i.e., heel strike and toe off) from the first two minutes of treadmill walking were determined using Visual3D (ver 6, C-Motion, Germantown, MD) and then exported for further analysis using custom Labview code (National Instruments, Austin, TX). Anterior-posterior ground reaction forces were first standardized by percent bodyweight. Then, ground reaction forces were time normalized to the percent of gait cycle to create an ensemble average [20]. We extracted the propulsive impulse (using ‘raw’ timeframes) from both the paretic and non-paretic limbs’ ensemble averages. Propulsive impulse was calculated as the integral of the anteriorly directed (positive) component of the ensemble averaged time-series. Paretic propulsion impulse ratio was then calculated as a ratio of paretic propulsive impulse divided by the sum of paretic and nonparetic propulsive impulses [9].

Data analysis

We performed cross-sectional and longitudinal analyses with SPSS (ver 27, IBM, Armonk, NY). Given the known confounding influence of gait speed in limb propulsion, we performed partial correlations, controlling for overground gait speed. For the cross-sectional analysis, we used only the pre-training data from Cohort 1 to perform partial correlations between paretic propulsion impulse ratio and SIS domains most reflective of gait (Activity, Mobility, Participation, Recovery) [21]. We then used pre-training data from Cohort 1 to perform partial correlation between paretic propulsion impulse ratio and average daily step count.

For the longitudinal analysis, we assessed the change in each outcome measure (i.e., limb propulsion, gait speed and 6MWT, SIS domains, average daily step counts) from pre-training to post-training from Cohort 2 with a paired samples t-test. We then used partial correlational analyses (controlled for change in gait speed) to assess for potential relationships between changes in limb propulsion and changes in SIS domains and changes in average daily step counts.

To better visualize the relationship between limb propulsion and participation, we completed a partial regression between paretic impulse ratio and average daily step count in the pre-training data, after the model accounted for gait speed. Additionally, we assessed for a similar relationship with the change variables (i.e., longitudinal analysis) in a similar manner.

Results

Although we had enrolled 48 subjects in the initial study, eight were excluded here from Cohort 1 for incomplete or unusable gait data. Thirty-seven subjects completed training, but six subjects were excluded from Cohort 2 for incomplete or unusable gait data either in the pre-training or post-training data. Two subjects had unusable daily step count data, and were further excluded from Cohort 1 and 2 for those analyses only. The subject’s demographics for both Cohort 1 and 2 are found in Table 1.

Table 1

Subject demographics.

	Cohort 1 (cross-sectional analysis pre-training, N = 40)	Cohort 2 (longitudinal analysis, N = 31)
Sex	16 F, 24 M	11 F, 20 M
Age, y	59±11	58±11
Height, cm	170±9	170±9
Weight, kg	81.3±16.5	81.7±14.2
Time since stroke, y	4.7±5.0	3.7±3.5
Assistive device	15 N, 25 Y	12 N, 19 Y
Ankle foot orthosis	16 N, 24 Y	15 N, 16 Y
Paretic limb	26 L, 14 R	18 L, 13 R
6MWT pre-training (m)	166.1±108.5	187.3±108.1
Comfortable gait speed pre-training (m/s)	0.41±0.23	0.45±0.23

Abbreviations: N, Number; M, Male; F, Female; y, Years; cm, centimeters; kg, kilograms; N, No; Y, Yes; L, Left; R, Right; y, Years; m, meters; m/s, meters per second

Cross sectional analysis

From the pre-training (Cohort 1) data, we observed no significant relationships between paretic impulse ratio and quality of life or participation measures when controlling for gait speed (all p > .05, Table 2). This can be further visualized in the partial regression plot between paretic impulse ratio residuals and average daily step count residuals, where there appears to be no significant relationship (Fig 1).

Table 2

Partial correlation of propulsion with quality-of-life (Spearman’s Rho, N = 40) and participation (Pearson’s R, N = 38) measures, controlling for comfortable gait speed.

	SIS Activity	SIS Mobility	SIS Participation	SIS Recovery	Average Daily Step Count
Paretic Impulse Ratio (%)	rho = .19	rho = -.23	rho = -.02	rho = .01	R = .02
	p = .25	p = .16	p = .89	p = .96	p = .89

Fig 1

Partial regression residual plot of pre-training average steps per day against paretic impulse ratio, controlling for comfortable gait speed.

Despite the two apparent outliers in the lower right quadrant, their removal does not change the results.

Longitudinal analysis

Following gait training, the participants in Cohort 2 made improvements in all propulsion measures (all p ≤ .03, Table 3). Training also yielded a significant improvement in quality-of-life measures, as noted by increases in the SIS Mobility (p < .01) and Recovery (p = .02) domains. No improvement occurred in SIS Activity (p = .06) or Participation (p = .48) domains. Additionally, no improvement occurred in average daily step count (p = 0.51).

Table 3

Paired samples t-tests for outcome measures and propulsion measures (N = 31).

Measure	Pre-testing	Post-testing	t	Sig, p (2-tailed)
Gait Speed (m/s)	0.45 ± 0.23	0.57 ± 0.27	5.82	<0 .01
6MWT (m)	187.3 ± 108.1	223.0 ± 115.8	5.54	< 0.01
Paretic propulsive impulse (%BW*s)	1.0 ± 1.1	1.7 ± 1.1	3.68	< 0.01
Nonparetic propulsive impulse (%BW*s)	4.6 ± 2.8	3.3 ± 1.1	-2.24	0.03
Paretic Impulse Ratio (%)	19 ± 17	32 ± 18	4.88	< 0.01
SIS Activity	48.6 ± 14.0	51.7 ± 12.6	1.94	0.06
SIS Mobility	50.9 ± 14.1	60.7 ± 12.3	4.33	< 0.01
SIS Participation	38.4 ± 16.6	40.8 ± 16.6	.71	0.48
SIS Recovery	47.7 ± 17.5	53.3 ± 16.5	2.56	0.02
Average Daily Step Count (N = 30)	1663.0 ± 1230.0	1791.5 ± 1161.0	1.40	0.17

Abbreviations: BW, body weight; s, seconds

In the longitudinal analysis (Cohort 2), we also observed no significant relationships between change in paretic impulse ratio and change in quality of life or participation measures, when controlling for gait speed (all p > .05, Table 4). This is also visualized in the partial regression plot between change in paretic impulse ratio residuals and change in average daily step count residuals, where we do not observe the presence of any significant relationship (Fig 2).

Table 4

Partial correlation of change in propulsion with change in quality-of-life (Spearman’s Rho, N = 31) and participation (Pearson’s R, N = 30) measures, controlling for change in comfortable gait speed.

	ΔSIS Activity	ΔSIS Mobility	ΔSIS Recovery	ΔSIS Participation	ΔAverage Daily Step Count
ΔParetic Impulse Ratio (%)	rho = .27	rho = .22	rho = -.06	rho = .20	R = .16
	p = .16	p = .23	p = .78	p = .29	p = .41

Fig 2

Partial regression residual plot of change in average steps per day against change in paretic impulse ratio, controlling for change in comfortable gait speed.

Despite the two apparent outliers in the left upper and lower quadrants, their removal does not change the results.

Discussion

We sought the presence of a potential relationship between limb propulsion with both quality of life and participation measures in people following stroke, to identify a meaningful biomechanical gait target during rehabilitation. Specifically, we had hypothesized that 1) paretic limb propulsion would be associated with participation and perceived quality of life measures, and 2) a change in paretic limb propulsion with gait training would be correlated to a change in participation and quality of life measures. Although paretic limb propulsion is important for walking performance (e.g., gait speed, walking capacity) [19, 22, 23], after we control for walking performance, we failed to observe any significant relationship between limb propulsion and either quality of life or participation. This finding suggests that limb propulsion does not influence perceptions of quality of life and participation beyond that arising from walking performance. This finding was consistent in both a cross-sectional sample, and longitudinally after a 6-week gait training intervention.

Paretic limb propulsion has an established relationship with gait speed [19, 22, 23]. Limb propulsion can accelerate the body forward through manipulations to hip extension power [24], ankle plantarflexion power [14, 25], and/or trailing limb posture [14, 25, 26]. Rehabilitation approaches can either target true recovery or how to compensate for reduced limb forces [13]. For example, someone who increases gait speed through enhanced limb propulsion can be considered to have recovered limb function. In particular, people post-stroke may achieve higher gait speeds via enhanced paretic limb propulsion through increased ankle plantarflexor torque or trailing limb posture [10, 11, 15, 27]. In contrast, a person who uses alternative strategies to increase walking speed has learned to compensate [19]. Compensations may take the form of redistributions of mechanical work to either the contralateral limb [28-30] or to more proximal joints on the paretic limb [31, 32]. Our findings support the idea that people post-stroke perceive their gait performance to be more strongly related to their quality of life, rather than the means by which they achieved that gait performance (i.e., recovery versus compensation of gait speed and walking capacity) [4, 8]. In fact, people with chronic stroke have noted the importance of walking performance, including how far and how fast they can walk, in navigating their daily lives [33].

These data suggest that, while paretic limb propulsion is important for gait and can be a target in rehabilitation, it may not be any more important than other biomechanical components of gait. Instead, the importance to a person’s life and participating in activities appears to be driven by walking performance, not the targeted impairment (e.g., propulsion). These data support the framework that while propulsion may be important for improving walking abilities, on its own it is not important for perceptions of quality of life or participation. Rehabilitation practices should be focused on gait performance through task practice [34], which may include propulsion training for improved limb propulsion. However, therapy should not strictly treat observed impairments without considering the overall goal of improved mobility [34].

Limitations

There are several limitations that need to be acknowledged. First, the gait training intervention was not designed to target limb propulsion, which may have yielded smaller propulsive changes than otherwise expected. Therefore, the changes to limb mechanics may not have been large enough to elicit improvements in quality of life and participation in the longitudinal analysis. Nevertheless, we elicited similar responses in gait speed as others who have explicitly targeted limb propulsion through gait training [35]. Additionally, all but two individuals were walking faster at the end of training in the longitudinal analysis, which may have resulted from improvement in walking mechanics other than paretic limb propulsion. In fact, we observed only small improvements in outcome measures such as gait speed and paretic limb propulsion, suggesting that we may not have elicited large enough changes to gait measures to have an impact on quality-of-life or participation measures. We observed small but significant improvements in gait speed, walking capacity, limb propulsion, and quality of life measures after gait training. Despite improvements within the entire cohort, the observed changes were often small, as only 12 (out of 31) exceeded minimal detectable changes (MDCs) for gait speed [36]. Interestingly, there was no increase in daily step count, suggesting that despite an increase in walking performance, participants did not use this increased function in a meaningful way. Behavioral changes in daily activity may require additional intervention beyond gait training and/or physical therapy. Additionally, we did not observe improvements in several SIS domains (Participation, Activity) after gait training. Although we intentionally chose SIS domains most closely related to gait function and step count as a metric for community and activity participation, we acknowledge that many other factors in individuals’ daily lives could influence these measures. Finally, we only assessed for linear relationships, however, it is possible that these data could be subjected to non-linear relationships.

Conclusions

Paretic limb propulsion does not appear to be important for quality-of-life or participation measures in individuals with chronic hemiparesis following stroke beyond its impact on gait performance. However, this relationship does not reveal that paretic propulsion is not important. Rather, it appears that how hard people with stroke push off the ground does not have an impact on their perceptions or participation in a vacuum. Changes in limb propulsion can influence gait performance, which is important for quality of life and participation. How we achieve this improved gait performance (e.g., through limb propulsion or through other biomechanical targets) does not appear to be important.

29 Jun 2022

28 Jul 2022

Comments are attached below. For a more clear response, we have attached a Response to Reviewers as a .doc file, with red text and indenting to highlight our responses. It is included below as well.

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We thank the reviewers for their thoughtful critique of our manuscript. We have responded to each of the comments below on a point-by-point basis. Each of the reviewers comments is in italics, with our response directly below.

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These data have not been published previously. The work here represents an analysis of previously unpublished secondary outcome measures from a clinical trial. The conclusions were presented at the American Physical Therapy Association’s Combined Sections Meeting (CSM) as a 10-minute platform talk. This was accompanied by an abstract, but a full paper has not been published with these findings. Therefore there is no risk of dual publication.

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Reference list has been checked, and updated to reflect the proper format (Vancouver).

REVIEWER #1

Reviewer #1: The article is interesting and well-written. Moreover, the statistical analysis is well-conducted. I have only the following comments.

1. The statistical methods considered by the authors rely on assumptions about the nature of the underlying data. If the data do not meet those assumptions, then the results often are not valid. Therefore, it is important for authors to check that those assumptions are satisfied for the data at hand, at least, approximately.

As requested, we have verified that our dataset met the six standard assumptions for partial regression.

2. Figures 1 and 2 need to be further commented. Just as an example, the shape of the scatter plot in Figure 1 is “particular” (it seems there is more than one group and leverage points) while from Figure 2 it seems to me that there are some outliers.

We agree that Figure 1 appears to have two ‘leverage’ points. To test this, we removed the two points on the lower right quadrant (i.e., those > 0.40 % Impulse Ratio residuals), which revealed no change in the statistical outcome or interpretation of the data. Likewise, we used Cook’s distance to determine that those two data points were not considered outliers (Cook’s distance = 0.20 and 0.33, respectively for the two data points). Similarly, in Fig 2, we removed apparently outliers/leverage points and, again, no change in the interpretation of the data or significance was noted. As a result, we have opted to leave these data points in the analysis with a note in the Figure captions to indicate to the reader that we considered the possibility that the data points were outliers and/or leverage points, but our analysis rejected that contention.

3. All the statistical symbols should be: 1) defined for the reader the first time they are used and 2) written in italic. Just as an example, define N at line 97.

Modified as suggested.

4. Consider including the following paper in your introduction and discussion (PMCID: PMC7390624 ).

Included as suggested.

REVIEWER #2

Reviewer #2: The authors present a study that indicates that it is part or segment of another study, and the same number is observed in the clinical trial registration. Which in no way diminishes the scientific relevance. The complementary data informing how the training protocols were performed are clear in the studies mentioned as the basis for this one. It is suggested, however, that the subjects' database (without naming them) and collection data be made available in complementary material.

Please see response above regarding lack of dual-publication, and how we have now made data available.

REVIEWER #3

Reviewer #3: The present study determines the role of limb propulsion during gait on the perception of quality of life and participation in people following stroke. After controlling for gait speed, paretic limp propulsion is not correlated to perceived quality of life or participation. Although may not have a related effect, limp propulsion appears to be an important factor to enhance gait performance, and this highlights the importance of further investigations in the rehabilitation area.

Below, some considerations:

An important factor was presented in the introduction and deserves discussion: a change in limb propulsion without a concomitant change in speed or gait ability may not be representative for people after stroke. How hard a person pushes the ground may not lead to a change in mobility if it is not related to benefits in speed or walking ability. In this scenario, the self-selected walking speed and maximum walking speed bring us a better understanding of the functional improvement of gait, as it is related to both energy and mechanical efficiency. (https://doi.org/10.1016/j.jstrokecerebrovasdis.2021.106023 - “Comfortable and Maximum Gait Speed in Individuals with Chronic Stroke and Community-Dwelling Controls” and 10.4103/2468-5658.184750 – “A new integrative approach to evaluate pathological gait: locomotor rehabilitation index”)

We agree with the reviewer, as this was the aim of our paper. Our conclusions coincide with the reviewer’s comment regarding the fact that how a person achieves increased gait speed really should not matter, but that they are able to do so. This point is discussed in the Discussion section, to tie together the point that propulsion only matters if it leads to a benefit to gait speed/walking capacity.

#Materials and Methods

*Participants

Page 5, Line 97-105: This section is specifically to describe materials and methods used in the research, as well as to describe participants characteristics, without any results on the number of included/excluded. You can describe that you used pre-test data to perform the cross-sectional analysis, however, I suggest not putting the N as well for the post-training analysis. I suggest leaving all results in relation to N included/excluded to the RESULTS section, presenting a flowchart for both cross-sectional and post-training analysis, it would be clearer for readers. About table 1, it should also be placed in the RESULTS section.

As recommended, we have now left only the description of the participant characteristics, and have moved the numbers (sizes) of the cohorts to the results section.

#Data Collection

Page 6, Line 118-119: Please, this information refers to the result, insert in the RESULTS section.

Page 7, Line 131-133: Please enter this information in the RESULTS section.

Page 8, Line 148-149: Please delete the N=40 information, just leave it described Cohort 1 so readers will know what it is.

Page 8, Line 155: “(N = 29 for step count, N = 31 for all others)”, you can remove this information, leave it to the RESULTS section.

Page 8, Line 161: “(N=38), remove this information, leave it to the RESULTS section.

Page 8, Line 162-163: “(N=29), remove this information, leave it to the RESULTS section.

All above have been integrated into the paper and corrected.

*Is comfortable gait speed equivalent to self-selected walking speed? I ask this question because it is described like this: “Comfortable gait speed was measured as participants made three passes across a 14-foot GaitRite pressure mat (CIR Systems, Havertown, PA)”. Were participants instructed to walk at the most comfortable speed? This is a very important factor as they can often walk slower or faster than their usual comfortable speed during a test.

This has been clarified in this section – participants were instructed to walk at a self-selected comfortable pace. Although not reported, after recording the comfortable pace, we asked participants to self-select their fastest (but safe) gait speed. Thus, we are well aware that self-selected speeds can range from slow to fast. Here, however, we only report on participant’s self-selected comfortable gait speed.

RESULTS

Participants reduced an average of 125 steps for the Step Count variable after the training period. I believe that 125 steps in a stroke population is relevant. Can you discuss this and bring possible explanations for this fact? Since they have improved walking speed and walking ability, why have they reduced the number of steps? I would expect the number of steps to increase with rehab.

For this aspect, it would be interesting to calculate the effect size and see the magnitude of this result, because apparently 125 steps less are clinically relevant in my opinion, despite not having presented a statistically significant difference, the p value is very close to that.

We are grateful that the reviewer brought up this point as it caused us to go back to our original dataset to determine why step count decreased. We had inadvertently pulled data cells from the wrong column initially. The data have been checked and double-checked at this point and we are confident that the data are accurate. With the correct numbers, we see a non-significant increase in daily step counts of ~128 steps/day. This change is fairly small with a small effect size (0.256). We regret the mistake, but are grateful that the reviewer alerted us to the possibility of this error. All analyses have been rerun, and tables/figures/text updated with the correct values.

DISCUSSION

Page 12, Line 213-221: In this scenario, your training was performed only on the treadmill. I believe it will be interesting for the future to investigate precisely the effect of specific strength exercises to improve limb propulsion, as well as trunk and balance postural control exercises. I fully agree with the rationale that “For example, someone who increases gait speed through enhanced limb propulsion can be considered to have recovered limb function”, however, it is possible to improve gait speed without improving limb function. Would it be more important to worry about gait speed, as it would be more related to functionality? This discussion is very important, because according to the findings of the present study, they perceive that gait performance is strongly related to quality of life, rather than the means by which they achieved, and in my opinion, the quality of life of individuals is very important.

We are happy that the reviewer agrees with our statements and note that the reviewer’s points are well illustrated in our Discussion section: “Our findings support the idea that people post-stroke perceive their gait performance to be more strongly related to their quality of life, rather than the means by which they achieved that gait performance (i.e., recovery versus compensation of gait speed and walking capacity).” and “while paretic limb propulsion is important factor for gait and can be a target in rehabilitation, it may not be any more important than other biomechanical subcomponents of gait.”

Submitted filename: Response to Reviewers.docx

Click here for additional data file.

30 Aug 2022

Linking gait mechanics with perceived quality of life and participation after stroke

PONE-D-22-07362R1

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Kind regards,

Leonardo A. Peyré-Tartaruga, Ph.D.

Academic Editor

PLOS ONE

1 Sep 2022

PONE-D-22-07362R1

Linking gait mechanics with perceived quality of life and participation after stroke

Dear Dr. Rowland:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

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Kind regards,

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on behalf of

Professor Leonardo A. Peyré-Tartaruga

Academic Editor

PLOS ONE