Stroke-related motor impairments restrict mobility including reduced community ambulation, independence, and participation in life-role activities.1,2 An important factor that helps explain mobility restrictions after stroke is the perceived challenge of walking, as measured by self-reported higher levels of state anxiety,3 fear of falling,4 low mobility self-efficacy,5 and poor balance confidence.6 For instance, common measures of self-reported mobility-related balance confidence include the Activities-specific Balance Confidence (ABC) Scale7 and the Falls Efficacy Scale.8 Both are multi-item questionnaires that assess the perceived level of confidence/concern regarding balance or falls during a variety of common ambulatory activities.
The perceived challenge of walking in adults poststroke may be especially increased during complex tasks such as walking over irregular terrain or in distracting environments.2,9 Impaired performance in complex walking tasks and/or avoidance of environments requiring complex walking contributes significantly to mobility disability and restricted community ambulation.9,10 Likewise, higher levels of balance confidence and falls self-efficacy are strongly associated with perceived physical function/recovery, less frequent avoidance of challenging environments, more frequent participation in walking-related activities, and high satisfaction with community walking.5,6,11–13
Assessment of the perceived challenge of walking by self-report is a valuable approach6; however, this form of assessment can be susceptible to subjective measurement bias and error.14–16 Examples of common sources of potential bias are overreporting of positive traits and underreporting of negative traits (ie, social desirability bias), choosing extreme scores on a self-report rating scale (ie, response style effects), and providing responses that the individual thinks will be desirable to the researcher (ie, demand characteristics).17 A physiologically based measure of the perceived challenge of walking would be valuable to reduce measurement error due to bias and enhance objective assessment of this important aspect of mobility function and recovery.
One promising approach to gauge the perceived challenge of walking is to measure sympathetic nervous system (SNS) activity. Increased SNS activity is an autonomic stress response (the “fight or flight” response) that contributes to the mobilization of physiological resources to optimize behavioral performance. The SNS is responsive to physical exertion and cognitive load18 including both current and anticipated demands.19 SNS activity can be readily and noninvasively measured by recording skin conductance from the palmar surface of the hands.20,21 The validity and reliability of this approach have been discussed extensively in the 2012 committee report from the Society for Psychological Research Ad Hoc Committee on Electrodermal Measures.22 Several studies have shown that skin conductance is responsive to the increased challenge experienced during complex walking tasks in individuals without neurological deficits. Clark et al23 reported that SNS activity measured by skin conductance was increased in older adults when they performed obstacle crossing and dual-task walking (with a verbal fluency task) compared with typical walking. Adkin et al24 reported an increase in both skin conductance and self-reported state anxiety (measured by a modified version of the Sport Anxiety Scale) when healthy young adults were asked to rise to their toes while standing at the edge of an elevated platform. Similarly, Hadjistavropoulos et al25 reported increased skin conductance and self-reported state anxiety when older adults performed dual-task walking (while holding a tray) on an elevated platform relative to typical walking. Collectively, these findings suggest that complex walking tasks elicit a physiological stress response from the SNS that can be quantified by skin conductance assessment.
Despite the apparent value of using skin conductance as a measure of SNS activity to assess perceived challenge, the feasibility and validity of using this approach for people with poststroke neurological impairments has not been investigated. Furthermore, it is not known whether SNS activity is responsive to rehabilitation-induced recovery of walking function after stroke. Therefore, the purpose of this study was to test whether SNS activity measured by skin conductance during walking in adults poststroke is increased during laboratory-based assessment of complex walking tasks relative to typical walking and reduced in response to a poststroke gait rehabilitation intervention. We also measured SNS activity during the walking assessments in healthy young adults to provide a reference for how the healthy nervous system responds to the challenge of complex walking tasks. Furthermore, we tested an exploratory hypothesis that greater SNS activity during walking would be associated with worse spatiotemporal gait outcomes.
Skin conductance signals were recorded using a commercially available portable data acquisition unit (Flexcomp Infiniti; Thought Technologies Ltd, Montreal, Quebec, Canada). Adhesive electrodes (10-mm Ag/AgCl recording surface) with conductive paste (0.5% saline in a neutral base) were placed on the palmar surface of the proximal phalanges of the index and ring fingers bilaterally (see Figure 1). The palmar measurement site is densely innervated by sudomotor nerves that are highly sensitive to cognitive/emotional stimuli,20,26 including greater attention, and mobility-related fear of falling and/or anxiety.23–25 Skin conductance signals were sampled at 32 Hz, acquired separately from each hand for each participant, and saved directly to an onboard flash memory card. These data were later downloaded to a computer for offline analysis.
An instrumented walkway (GAITRite Gold; CIR Systems, Franklin, New Jersey) was used to measure the spatiotemporal gait data during the walking tasks; data were sampled at 80 Hz.
All participants provided written informed consent at the time of enrollment, and the study procedures were approved by the University of Florida Institutional Review Board and the North Florida/South Georgia Veterans Affairs Human Research Protection Program. The inclusion criteria for the study included age more than 21 years; at least 6 months poststroke; medically stable; able to follow 2-stage commands; 10-m walking speed less than 0.8 m/s; Fugl-Meyer Assessment lower extremity (FMA-LE)27 score less than 30; and Mini-Mental State Examination Score28 of 21 or more. All participants were evaluated by a licensed physical therapist and were visually confirmed to exhibit a hemiparetic gait pattern. The inclusion criteria also included the ability to walk independently with or without assistive devices and/or supportive braces. Use of assistive devices was minimized, but some participants did use an ankle-foot orthosis and/or a cane when these were needed to safely complete the protocol. The total number of participants needing assistive devices is reported in Table 1. The participants recruited for this study were a sample of convenience obtained from an ongoing clinical trial.
Participants were excluded if they had any condition that would interfere with the ability to safely and appropriately participate in the gait intervention protocol, such as uncontrolled hypertension, lower extremity pain, severe obesity (body mass index >40), poor cardiopulmonary and/or renal function, severe perceptual or cognitive deficits or active drug/alcohol abuse, significant balance disturbances, lower motor neuron damage or radiculopathy, myocardial infarction or heart surgery in the prior year, bone fracture or joint replacement in the prior 6 months, or diagnosis of a terminal illness. Inclusion criteria for the young adults were age 18 to 30 years and self-reported absence of any medical conditions that affected walking ability.
Participants were instructed to walk at preferred speed around a designated path while performing 4 separate walking tasks in random order: (1) typical walking (“typical” task), (2) walking in dim lighting (“dim” task), (3) dual-task walking with a verbal fluency task (“verbal” task), and (4) walking over obstacles (“obstacles” task). The distance walked for each lap was 18 m, of which 8.2 m was performed over an electronic walkway that recorded spatiotemporal gait variables. In total, 2 or 3 laps were performed for each separate walking task, depending on the walking speed of the participant. Slower walkers (ie, 10-m walking speed = 0.38 m/s ± 0.16) performed only 2 laps to make the total task time equitable across participants and to prevent fatigue. The number of steps acquired from the instrumented walkway was similar across walking tasks for each participant, and all recorded steps were included in the data analysis.
For typical, the walking path was unobstructed and well-lit. For dim, the lights in the room were turned off but some exterior light was allowed to come in through an open door. For verbal, the participants were asked to say as many words as possible that began with a randomly assigned letter while walking. The letters for the verbal task were randomly selected from this list: B, D, G, N, P, R, and T. A new letter was assigned at the beginning of each lap. For obstacles, the participants were asked to step over 6 small objects (shoes) evenly spaced within each lap. Two of the objects were placed on the electronic walkway at approximately one-third and two-thirds of the length of the walkway. This ensured that the entire pass over the walkway included periods of planning for, executing, and/or recovering from the obstacles negotiation task. Task order was randomized for each participant. Each walking task was separated by at least 2 minutes of rest and was preceded by a quiet standing period of at least 30 seconds to provide reference baseline data for skin conductance measurements.
Clinical Assessments for the Participants Poststroke
The ABC Scale7 was administered to assess the self-reported balance confidence preintervention and postintervention. The FMA-LE27 was also conducted to characterize the participants' motor impairment preintervention and postintervention.
Gait Rehabilitation Intervention for the Participants Poststroke
To test whether SNS activity is reduced in response to a poststroke gait rehabilitation intervention, a subset of individuals poststroke participated in a 12-week rehabilitation intervention focusing on enhancing coordination (60 sessions, by a licensed physical therapist, 3 hours per day, 5 times per week). Also included were therapeutic exercises to improve balance and strength. The training paradigm included progression from simple to complex movement tasks (ie, moving from in-synergy to out-of-synergy lower extremity movement patterns) to facilitate motor learning of appropriate coordination patterns. Positions of side-lying, prone, supine, and seated were used to mitigate the effects of gravity and abnormal muscle tone in order to facilitate the practice of high-quality movement patterns. The standing position was used to practice coordination of movements in the upright position. The newly acquired coordinated movements were then integrated into the practice of gait. Gait coordination training focused on ankle dorsiflexion, knee flexion, and hip flexion during the swing phase; knee flexion at toe-off and knee extension before heel strike; knee and hip extension during the stance phase; and whole body balance control during weight shifting. The motor learning principles used in the gait training protocol included movement practice to produce kinematics as close as possible to walking patterns typical of individuals with no disability, a high number of repetitions, attention to the motor task, and rapid progression of task difficulty while maintaining the integrity of the task movements. The intervention protocol used in this study has been successfully implemented in prior studies.29–31
Data analysis was conducted with commercial software (MATLAB version R2015a, Ledalab v3.4.7; Mathworks, Natick, Massachusetts) using custom analysis programs. The raw skin conductance data were down-sampled to 8 Hz and visually examined for the presence of motion artifact, as indicated by high-frequency fluctuations in the signal amplitude. Relatively few artifacts were identified, and the ones that were found were removed and replaced by linear interpolation. Continuous decomposition analysis was performed in Ledalab v3.4.7 to separate the tonic component (skin conductance level, or SCL) and the phasic component (skin conductance response, or SCR) of the signal. A minimum amplitude criterion of 0.04 μS was applied to achieve a balance between sensitive detection of SCRs and minimizing the effects of movement artifacts.22,32
For each walking task, two values were extracted from the tonic SCL data. The first value was the minimum SCL during the baseline period of quiet standing that preceded each walking task. The minimum SCL was selected to capture the most relaxed state of the SNS. The second value was the mean SCL during the duration of the walking task. The primary outcome variable for SCL is the percent change in SCL (denoted by ΔSCL) between the resting and walking phases of each task using the following formula:
A similar approach was used for analysis of SCR. The rate of SCRs was calculated during the duration of the resting phase that preceded the walking task, as well as during the duration of the walking task. The rate of SCRs refers to the number of SCRs detected, divided by the duration of the recording period. The primary outcome for SCR is the change in the rate of SCR (denoted by ΔSCR) from the resting to the walking phase of each task using the following formula:
Statistical analysis was conducted using statistical software (JMP 11; SAS Institute Inc, Cary, North Carolina). Pearson's correlation coefficient was used to test the consistency of skin conductance measurements acquired from each recording site for the stroke and young groups. For the stroke group, associations were examined between paretic versus nonparetic ΔSCL and paretic versus nonparetic ΔSCR. For the young group, associations were examined between left versus right ΔSCL and left versus right ΔSCR. The association was examined for the data from each task, as well as the data averaged from all tasks. The false discovery rate procedure was applied to account for multiple comparisons.33
To test whether SNS activity measured by skin conductance is increased during laboratory-based assessment of complex walking tasks relative to typical walking, task-dependent differences in SNS activity were assessed with separate 2-way repeated-measures analysis of variance (ANOVA) models (Task × Recording site) for each skin conductance variable (ΔSCL and ΔSCR) and for each group (stroke and young). Recording site refers to the side that the skin conductance signal was recorded from (ie, paretic and nonparetic hands for the stroke group and left and right hands for the young group). In addition, task-dependent differences in spatiotemporal gait outcomes in the adults poststroke were assessed by 1-way repeated-measures ANOVA for walking speed and step width, and by 2-way repeated-measures ANOVA (Task × Limb) for step length and step length variability.
To test whether SNS activity is reduced in response to a poststroke gait rehabilitation intervention, a separate 3-way repeated-measures ANOVA model (Time × Task × Recording site) was used to compare intervention-induced changes in SNS activity for each skin conductance variable (ΔSCL and ΔSCR). Post hoc analysis using Tukey's HSD was conducted to further investigate significant main effects. The assumption of sphericity for the ANOVA models was tested by Mauchly's test, and any violations were corrected using the Huynh-Feldt correction (if ε > 0.75) or the Greenhouse-Geisser correction (if ε < 0.75). Intervention induced changes in the spatiotemporal gait parameters measured preintervention and postintervention were statistically compared by paired t tests for each walking task.
To conduct a preliminary assessment of whether greater task-related SNS activity is associated with deterioration of gait performance, each participant's ΔSCL value from typical walking was subtracted from the ΔSCL value from each complex walking task. An analogous procedure was used to calculate task-related differences for ΔSCR and for gait parameters (speed, step width, step length, and step length variability). Pearson's correlation coefficient was used to test the association between the task-related differences in each skin conductance measure (ie, ΔSCL and ΔSCR) and gait parameters.
Thirty-one adults with chronic poststroke hemiparesis participated in the cross-sectional walking assessments. A subset of 9 participants with stroke underwent the intervention protocol. Demographic and clinical information for all participants with stroke is shown in Table 1. A group of 8 healthy young adults (age = 22.4 ± 3.7 years) was also tested on each walking assessment to provide reference data on task-related differences in SNS activity for a healthy unimpaired nervous system.
For both groups, the skin conductance data acquired from each recording site were significantly and positively associated for most walking tasks (P < 0.05), as reported in Table 2.
SNS Activity Measured by Skin Conductance for Each Walking Task
Within the stroke group, there was a significant main effect of Task on ΔSCL (P < 0.0001, Figure 2A). Post hoc analysis revealed that ΔSCL was significantly greater for obstacles (12.4%) than for verbal (9.0%, P = 0.04), dim (5.4%, P = 0.0005), and typical (4.3%, P < 0.0001). ΔSCL for verbal was significantly greater than typical (P = 0.02). The main effect of recording site was not significant (P = 0.82).
Within the young group, there was a significant main effect of Task on ΔSCL (P = 0.009, Figure 2B). Post hoc analysis revealed that ΔSCL was significantly greater for obstacles (21.0%) than for verbal (13.1%, P = 0.03), dim (3.8%, P = 0.01), and typical (3.4%, P = 0.04). ΔSCL for verbal was significantly greater than dim (P = 0.04). A trend toward greater ΔSCL was observed for verbal than for typical (P = 0.07). The main effect of recording site was not significant (P = 0.97). Resting and walking SCL for each group and for each walking task has been shown in Table 3.
Within the stroke group, there was a significant main effect of Task on ΔSCR (P = 0.002, Figure 2C). Post hoc analysis revealed that ΔSCR was significantly greater for obstacles (0.13 responses per second, P = 0.007) and verbal (0.13 responses per second, P = 0.003) than for typical (0.04 responses per second). ΔSCR for verbal was significantly greater than for dim (0.06 responses per second, P = 0.02). A trend toward greater ΔSCR was observed for obstacles than for dim (P = 0.09). The main effect of recording site was not significant (P = 0.79). Within the young group, the main effect of Task on ΔSCR was not significant (P = 0.39, Figure 2D). Resting and walking SCR for each group and for each walking task has been shown in Table 4.
Task-Related Differences in Spatiotemporal Measurements of Gait
Gait data for stroke participants are shown in Table 5. There was a significant main effect of Task on gait speed (P < 0.001), step length (P < 0.0001), step length variability (P < 0.0001), and step width (P = 0.008). Post hoc analyses were conducted for each gait variable. Gait speed was significantly slower for obstacles than for verbal, dim, and typical (P < 0.001). Gait speed was significantly slower for verbal than for dim (P = 0.0003) and typical (P = 0.0001). Step length was significantly shorter for obstacles than for verbal, dim, and typical (P < 0.001). Step length was significantly shorter for verbal than for dim (P < 0.0002) and typical (P < 0.0001). Step length variability was significantly greater for obstacles than for verbal, dim, and typical (P < 0.01). Step width was significantly greater for verbal (P = 0.02) and dim (P = 0.016) than for typical. The main effect of Limb was not significant for any comparisons (P > 0.05).
Skin Conductance Before and After the Gait Rehabilitation Intervention
For ΔSCL, there was a significant main effect of Time (P = 0.02) and Task (P = 0.03) such that ΔSCL was significantly lower at postintervention than at preintervention during walking assessments (Figure 3A). Post hoc analysis revealed that ΔSCL was significantly lower for obstacles at postintervention than at preintervention (P = 0.04). ΔSCL did not change significantly for verbal (P = 0.35), dim (P = 0.23), and typical (P = 0.23). The main effect of recording site was not significant (P = 0.21). The interaction effect of Time × Task was not significant (P = 0.33).
For ΔSCR, there was a significant main effect of Time (P = 0.02) such that ΔSCR was significantly lower at postintervention than at preintervention during walking assessments (Figure 3B). Post hoc analysis revealed that ΔSCR was significantly lower for obstacles (P = 0.008) and verbal (P = 0.01) at postintervention than at preintervention. ΔSCR did not change significantly for dim (P = 0.59) and typical (P = 0.29). The main effect of recording site was not significant (P = 0.44). The interaction effect of Time × Task was not significant (P = 0.47).
Gait and Clinical Outcomes Before and After the Rehabilitation Intervention
The preintervention and postintervention spatiotemporal gait data are reported in Table 5. Statistical comparisons of gait parameters measured preintervention and postintervention revealed that step width decreased significantly postintervention for obstacles (P = 0.004), verbal (P = 0.03), dim (P = 0.02), and typical (P = 0.006) tasks. However, gait speed, step length, and step length variability did not change significantly postintervention (P > 0.05 for all measures).
Self-reported balance confidence measured by the ABC Scale7 improved significantly following the poststroke gait rehabilitation intervention (70.41% ± 11.01 preintervention vs 79.29% ± 9.91 postintervention, P = 0.04). FMA-LE27 score did not change significantly (21.0 ± 4.74 preintervention vs 20.88 ± 5.94 postintervention).
Association Between SNS Activity and Spatiotemporal Measurements of Gait
The test of association between skin conductance measurements of SNS activity (ΔSCL and ΔSCR) and gait measures of speed, step width, step length, and step length variability did not yield statistically significant findings. However, several trends were observed. For the dim task, greater ΔSCL showed a trend toward an association with 3 out of the 4 measured gait metrics including slower gait speed (P = 0.13), shorter step length (P = 0.08), and higher step length variability (P = 0.09). Likewise, for the obstacles task, greater ΔSCL showed a trend toward an association with slower gait speed (P = 0.05) and shorter step length (P = 0.08).
The results of this study support the feasibility of measuring SNS activity with skin conductance to gauge the perceived challenge of walking tasks in people poststroke. First, SNS activity was found to be acutely increased during complex walking tasks relative to typical walking. The increase in SNS activity is consistent with a heightened physiological stress response due to the greater perceived challenge experienced during the performance of complex walking tasks. Second, SNS activity during complex walking tasks was attenuated following a gait rehabilitation intervention. This finding suggests that the tasks were perceived as less challenging after the intervention. The study also evaluated potential associations between SNS activity and walking performance outcomes, but the findings were generally weak and not statistically significant.
Skin conductance components SCL and SCR are well-established measurements of SNS activity.22 SCL represents slow changes in skin conductivity over several seconds. SCR represents fast changes in skin conductance22,34 that may be more closely time locked to underlying activity of the sudomotor nerves.35 Representative data depicting SCL and SCR during the walking tasks are shown in Figures 4A and 4B, respectively. An interesting finding is that skin conductance data acquired from the paretic and nonparetic hands were strongly correlated. This finding supports that the present results are driven by central SNS activity and are relatively robust despite potential deterioration of measurement conditions after stroke, such as due to spastic clenched fist or altered skin health.
We tested whether SNS activity is increased during laboratory-based assessment of complex walking tasks relative to typical walking in adults poststroke. We found that both ΔSCL and ΔSCR increased during laboratory-based complex walking tasks compared with the typical task (ie, obstacles and verbal > typical). The young group also demonstrated increased ΔSCL during complex walking compared with the typical task, including a significant increase for the obstacles and a trend for verbal. Cumulatively, these results demonstrate the responsiveness of skin conductance to both physical and cognitive sources of physiological stress, consistent with prior studies.23,25,36
The rationale for including a healthy young group was to provide additional context for interpreting the stroke group data by also observing how an unimpaired nervous system responds to the same walking tasks. We did not make direct statistical comparisons between the young and stroke groups because we did not hypothesize that there would be a group difference. Rather, our primary objective was to assess task-dependent changes in SNS activity within each group. In general, visual observation of data indicates that the SNS of adults poststroke and healthy adults behaves similarly in response to complex walking tasks. A notable difference was that the stroke group exhibited a significant increase of both ΔSCL and ΔSCR for complex walking tasks whereas in the young group, only ΔSCL was significantly increased. The reason for a lack of increase in ΔSCR for the young group is unclear because both ΔSCL and ΔSCR are measures of SNS activity. It may be that SCRs are more indicative of specific anxiety-provoking events. For example, a moment of unsteadiness might trigger an acute SNS response (ie, SCR) that would be expected to occur more often for individuals with hemiparetic gait.37
We also tested whether SNS activity is reduced in response to a gait rehabilitation intervention. We found that ΔSCL and ΔSCR were reduced significantly in response to the gait rehabilitation intervention. Furthermore, in agreement with reduced SNS activity, step width was reduced during the walking assessments postintervention. A narrower step width suggests a less cautious gait pattern and is consistent with improved balance confidence and less fear of falling.38 Indeed, there was an increase in the self-reported balance confidence after the intervention, measured by the ABC Scale. A future randomized controlled trial will be needed to confirm and expand upon these preliminary findings of rehabilitation-induced changes in the perceived challenge of walking after stroke.
Study Considerations and Limitations
A consideration for this study is that we were not able to control for walking speed across participants due to the overground nature of the walking tasks. Overground assessment of walking is advantageous in terms of allowing participants to engage naturally in each task but might also allow them to reduce the challenge level of some tasks by walking more slowly. This could lead to underestimation of SNS activity for a given task. The effect of walking speed on SNS activity can be addressed in future studies such as by using a treadmill to control speed. If anything, we expect that standardizing speed across tasks would further strengthen task-related differences in SNS activity.
The present study investigated whether a gait intervention designed to enhance walking coordination and function would also decrease the perceived challenge of walking. The postintervention findings are based on a secondary analysis performed with a convenience sample from an ongoing clinical trial of neurorehabilitation of walking. Therefore, a limitation of the study design is that the intervention was not specifically designed to reduce the perceived challenge of walking after stroke. Although the findings of this study support robust reductions in SNS activity, especially for the most difficult walking tasks, we cannot be certain about the aspects of the intervention that contributed to the decrease in SNS activity. Furthermore, the lack of a nonintervention control group and the participants' exposure to the walking assessment tasks preintervention and postintervention limit the ability to attribute the reduction in SNS activity solely to the intervention. Future studies would be needed to address the limitations of this design.
This study did not include a concurrent second measure of self-reported perceived challenge upon completion of the experimental walking task. A concurrent measure of self-report could provide additional context by which to interpret task-related differences in skin conductance. Future study designs should include a second measure of self-reported challenge and also compare the self-report and skin conductance measurement approaches. Future studies should also further investigate the potential relationship between SNS activity and walking performance outcomes.
Finally, this study presents promising findings that support the feasibility of measuring skin conductance to assess the perceived challenge of walking. However, it will be important for future studies to establish the test-retest reliability of the skin conductance measure for people poststroke and for the specific walking tasks performed here (and/or for other walking tasks that could be included as part of future clinical trials).
1. Danks KA, Pohlig RT, Roos M, Wright TR, Reisman DS. Relationship between walking
capacity, biopsychosocial factors, self-efficacy, and walking
activity in persons poststroke. J Neurol Phys Ther. 2016;40(4):232–238.
2. Balasubramanian CK, Clark DJ, Fox EJ. Walking
adaptability after a stroke and its assessment in clinical settings. Stroke Res Treat. 2014;2014:591013.
3. Elf M, Eriksson G, Johansson S, von Koch L, Ytterberg C. Self-reported fatigue and associated factors six years after stroke. PLoS One. 2016;11(8):e0161942.
4. Hellstrom K, Lindmark B. Fear of falling in patients with stroke: a reliability study. Clin Rehabil. 1999;13(6):509–517.
5. Robinson CA, Shumway-Cook A, Ciol MA, Kartin D. Participation in community walking
following stroke: subjective versus objective measures and the impact of personal factors. Phys Ther. 2011;91(12):1865–1876.
6. Torkia C, Best KL, Miller WC, Eng JJ. Balance confidence: a predictor of perceived physical function, perceived mobility, and perceived recovery 1 year after inpatient stroke rehabilitation
. Arch Phys Med Rehabil. 2016;97(7):1064–1071.
7. Powell LE, Myers AM. The Activities-specific Balance Confidence (ABC) Scale. J Gerontol Ser A Biol Sci Med Sci. 1995;1(34):M28–M34.
8. Tinetti ME, Richman D, Powell L. Falls efficacy as a measure of fear of falling. J Gerontol. 1990;45(6):239–243.
9. Patla AE, Shumway-Cook A. Dimensions of mobility: defining the complexity and difficulty associated with community mobility. J Aging Phys Act. 1999;7(1):7.
10. Shumway-Cook A, Patla A, Stewart A, Ferrucci L, Ciol MA, Guralnik JM. Environmental components of mobility disability in community-living older persons. J Am Geriatr Soc. 2003;51(3):393–398.
11. Robinson CA, Matsuda PN, Ciol MA, Shumway-Cook A. Participation in community walking
following stroke: the influence of self-perceived environmental barriers. Phys Ther. 2013;93(5):620–627.
12. Robinson CA, Shumway-Cook A, Matsuda PN, Ciol MA. Understanding physical factors associated with participation in community ambulation following stroke. Disabil Rehabil. 2011;33(12):1033–1042.
13. Lord SE, Weatherall M, Rochester L. Community ambulation in older adults: which internal characteristics are important? Arch Phys Med Rehabil. 2010;91(3):378–383.
14. Donaldson SI, Grant-Vallone EJ. Understanding self-report bias in organizational behavior research. J Bus Psychol. 2002;17(2):245–260.
15. Paulhus DL, Vazire S. The self-report method. In: Handbook of Research Methods in Personality Psychology. Robins R. W., Fraley R. C., Krueger R. F., Eds. New York, NY, US: Guilford Press, 2007.
16. Dowling NM, Bolt DM, Deng S, Li C. Measurement and control of bias in patient reported outcomes using multidimensional item response theory. BMC Med Res Methodol. 2016;16(1):63.
17. Orne MT. On the social psychology of the psychological experiment: with particular reference to demand characteristics and their implications. Am Psychol. 1962;17(11):776.
18. Frith CD, Allen HA. The skin conductance orienting response as an index of attention. Biol Psychol. 1983;17(1):27–39.
19. Dawson ME, Schell AM, Courtney CG. The skin conductance response, anticipation, and decision-making. J Neurosci Psychol Econ. 2011;4(2):111–116.
20. Boucsein W. Electrodermal activity (2nd Ed). New York: Springer; 2012.
21. van Dooren M, Janssen JH. Emotional sweating across the body: comparing 16 different skin conductance measurement locations. Physiol Behav. 2012;106(2):298–304.
22. Boucsein W, Fowles DC, Grimnes S, et al Publication recommendations for electrodermal measurements. Psychophysiology. 2012;49(8):1017–1034.
23. Clark DJ, Rose DK, Ring SA, Porges EC. Utilization of central nervous system resources for preparation and performance of complex walking
tasks in older adults. Front Aging Neurosci. 2014;6:217.
24. Adkin AL, Frank JS, Carpenter MG, Peysar GW. Fear of falling modifies anticipatory postural control. Exp Brain Res. 2002;143(2):160–170.
25. Hadjistavropoulos T, Carleton RN, Delbaere K, et al The relationship of fear of falling and balance confidence with balance and dual tasking performance. Psychol Aging. 2012;27(1):1–13.
26. Critchley HD. Electrodermal responses: what happens in the brain. Neuroscientist. 2002;8(2):132–142.
27. Fugl-Meyer AR, Jaasko L, Leyman I, Olsson S, Steglind S. The post-stroke hemiplegic patient, part 1: a method for evaluation of physical performance. Scand J Rehabil Med. 1975;7(1):13–31.
28. Folstein MF, Folstein SE, McHugh PR. “Mini-mental state.” A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res. 1975;12(3):189–198.
29. Daly JJ, Zimbelman J, Roenigk KL, et al Recovery of coordinated gait
. Neurorehabil Neural Repair. 2011;25(7):588–596.
30. Pundik S, Holcomb J, McCabe J, Daly JJ. Enhanced life-role participation in response to comprehensive gait
training in chronic stroke survivors. Disabil Rehabil. 2012;34(26):2264–2271.
31. Daly JJ, McCabe JP, Gansen J, et al Gait
coordination protocol for recovery of coordinated gait
, function, and quality of life following stroke. J Rehabil Res Dev. 2012;49(8):xix–xxviii.
32. Braithwaite JJ, Watson DG, Jones R, Rowe M. A guide for analysing electrodermal activity (EDA) & skin conductance responses (SCRs) for psychological experiments. Psychophysiology. 2013;49:1017–1034.
33. Curran-Everett D. Multiple comparisons: philosophies and illustrations. Am J Physiol Regul Integr Comp Physiol. 2000;279(1):R1–R8.
34. Figner B., Murphy R. O. Using skin conductance in judgment and decision making research. In Schulte-Mecklenbeck M., Kuehberger A., Ranyard R. (Eds.), A Handbook of Process Tracing Methods for Decision Research. New York, NY: Psychology Press; 2011.
35. Benedek M, Kaernbach C. A continuous measure of phasic electrodermal activity. J Neurosci Methods. 2010;190(1):80–91.
36. Brown LA, Gage WH, Polych MA, Sleik RJ, Winder TR. Central set influences on gait
. Age-dependent effects of postural threat. Exp Brain Res. 2002;145(3):286–296.
37. Forster A, Young J. Incidence and consequences of falls due to stroke: a systematic inquiry. BMJ. 1995;311(6997):83–86.
38. Dunlap P, Perera S, VanSwearingen JM, Wert D, Brach JS. Transitioning to a narrow path: the impact of fear of falling in older adults. Gait