Enhanced arm swing alters interlimb coordination during overground walking in individuals with traumatic brain injury

Human Movement Science 52 (2017) 45–54

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Enhanced arm swing alters interlimb coordination during overground walking in individuals with traumatic brain injury Ksenia I. Ustinova a,⇑, Joseph E. Langenderfer b, Nilanthy Balendra a a b

Doctoral Program in Physical Therapy, Central Michigan University, Mt. Pleasant, MI, USA School of Engineering and Technology, Central Michigan University, Mt. Pleasant, MI, USA

a r t i c l e

i n f o

Article history: Received 19 August 2016 Revised 29 December 2016 Accepted 5 January 2017

Keywords: Arm-leg coordination Neurological rehabilitation Gait

a b s t r a c t The current study investigated interlimb coordination in individuals with traumatic brain injury (TBI) during overground walking. The study involved 10 participants with coordination, balance, and gait abnormalities post-TBI, as well as 10 sex- and age-matched healthy control individuals. Participants walked 12 m under two experimental conditions: 1) at self-selected comfortable walking speeds; and 2) with instructions to increase the amplitude and out-of-phase coordination of arm swinging. The gait was assessed with a set of spatiotemporal and kinematic parameters including the gait velocity, step length and width, double support time, lateral displacement of the center of mass, the amplitude of horizontal trunk rotation, and angular motions at shoulder and hip joints in sagittal plane. Interlimb coordination (coupling) was analyzed as the relative phase angles between the left and right shoulders, hips, and contralateral shoulders and hips, with an ideal out-ofphase coupling of 180° and ideal in-phase coupling of 0°. The TBI group showed much less interlimb coupling of the above pairs of joint motions than the control group. When participants were required to increase and synchronize arm swinging, coupling between shoulder and hip motions was significantly improved in both groups. Enhanced arm swinging was associated with greater hip and shoulder motion amplitudes, and greater step length. No other significant changes in spatiotemporal or kinematic gait characteristics were found in either group. The results suggest that arm swinging may be a gait parameter that, if controlled properly, can improve interlimb coordination during overground walking in patients with TBI. Ó 2017 Elsevier B.V. All rights reserved.

1. Introduction Gait abnormalities after traumatic brain injury (TBI) are common, complex, and well documented in the literature. Abnormalities typically include slower walking speed, altered spatiotemporal characteristics and kinematics of the pelvis and lower extremities, reduced stability, and inter-joint coordination (Chiu, Osternig, & Chou, 2013; Chow, Yablon, Horn, & Stokic, 2010; Williams, Galna, Morris, & Olver, 2010; Williams, Lai, Schache, & Morris, 2015). Less is known about coordination between arms and legs during walking after brain injury. During overground walking, movements of the legs are accompanied by out-of-phase swinging of the arms. Physiologically, coordinated arm swinging is not required for walking in humans, but may remain as a residual function of quadrupedal ⇑ Corresponding author at: Doctoral Program in Physical Therapy, Central Michigan University, Mt. Pleasant, MI 48859, USA. E-mail address: [email protected] (K.I. Ustinova). http://dx.doi.org/10.1016/j.humov.2017.01.001 0167-9457/Ó 2017 Elsevier B.V. All rights reserved.

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locomotion and serve some purpose (Dietz, Fouad, & Bastiaanse, 2001; Ford, Wagenaar, & Newell, 2007a; Meyns, Bruijn, & Duysens, 2013). In part, arm swinging is frequently considered as a locomotor feature, derived from walking speed (Goudriaan, Jonkers, van Dieen, & Bruijn, 2014). Faster and longer steps produce greater trunk rotation around the vertical axis, creating greater inertial and gravity forces, and resulting in greater amplitude and frequency of arm motions. Increased arm motion helps reduce the whole-body angular momentum, thereby reducing the energy cost and increasing walking efficiency (Bruijn, Meijer, van Dieën, Kingma, & Lamoth, 2008; Collins, Adamczyk, & Kuo, 2009; Kuhtz-Buschbeck & Jing, 2012). Arm swinging also plays an important role in stabilizing the body during walking (Punt, Bruijn, Wittink, & van Dieen, 2015). Several studies showed a significantly more stable gait when arm swinging was excessive in both younger and older adults (Hu et al., 2012; Nakakubo et al., 2014). In contrast, restraining arm motions changed and, under some circumstances, even deteriorated the gait pattern (Ortega, Fehlman, & Farley, 2008; Umberger, 2008). These findings suggest that absent or altered arm swinging is a gait abnormality that needs to be addressed. Altered arm motions have been observed in people with various neurological diseases and traumas including stroke (Ford, Wagenaar, & Newell, 2007b; Wagenaar & van Emmerik, 1994), spinal cord injury (Tester, Barbeau, Howland, Cantrell, & Behrman, 2012), Parkinson’s disease (Dietz & Michel, 2008; Winogrodzka, Wagenaar, Booij, & Wolters, 2005), and cerebral palsy (Meyns et al., 2011). These patients may walk without arm swinging or with reduced swinging and coupling between arms and legs. Considering this issue, arm swinging has been incorporated into gait training in several neurological populations to improve interlimb coordination and gait pattern (Behrman & Harkema, 2000; Meyns et al., 2013; Stephenson, Lamontagne, & De Serres, 2009). For example, by increasing arm swing amplitude, patients with Parkinson’s disease were able to modify their walking patterns and normalize walking speeds and step lengths (Behrman, Teitelbaum, & Cauraugh, 1998). In patients with spinal cord injury, natural reciprocating arm movements facilitated stepping. In contrast, the stepping was inhibited when patients used their arms for weight bearing while walking (Behrman & Harkema, 2000). When arm swinging was induced in patients with stroke by asking them to hold sliding handles while walking on a treadmill, the strategy activated lower extremity muscles that are not typically active during walking and deteriorated gait (Stephenson, De Serres, & Lamontagne, 2010). Thus, preliminary evidence suggests that including arm swinging in gait training may improve walking after neurological injury. Most studies, however, have focused on increasing the amplitude of arm swinging, but not on improving the coordination or synchronization between arms and legs. Moreover, little attention was paid to patient’s neurological impairment’s at a chronic stage, when motor and functional recovery is minimal. To fill this gap, the present study investigated interlimb coordination after brain injury – specifically, the effects of arm swinging on arm and leg coordination in individuals with TBI during overground walking. Assuming that brain injury may cause gait abnormalities similar to those following stroke or cerebral palsy, we hypothesized that arm and leg coordination may be altered in individuals with TBI. Based on previous work, we further hypothesized that enhancing the amplitude and synchronization of arm swinging may improve interlimb coordination and gait patterns as a whole in these patients. 2. Methods 2.1. Subjects A convenience sample of 10 individuals with TBI (TBI group, three females) with an average (mean ± standard deviation [SD]) age of 45.2 ± 12.7 years and average time since TBI onset of 10.3 ± 6.3 years participated in the study. Individual clinical and demographic data are presented in Table 1. Eligibility criteria for the TBI group were a history of TBI sustained over 6 months previously; ability to walk at least 10 consecutive steps without assistive devices; full or near-full ranges of motion in major body joints; normal or corrected-to-normal vision; and ability to follow simple instructions. All participants with TBI presented with some degree of ataxia, postural and gait abnormalities, with the following clinical test score ranges: a) 2–18 points (mean ± SD: 7.9 ± 6.1 points) on the Ataxia Test by Klockgether, Schroth, Diener, and Dichgans (1990), where a score of 35 points indicates severe ataxia; b) 45–54 points (mean ± SD: 51.0 ± 3.6 points) on the Berg Balance test (BBS; Berg, Wood-Dauphinee, Williams, & Gayton, 1989), where a score of 45 points indicates an increased fall risk; and

Table 1 Demographic data and clinical scores of patients with TBI. Subject #

Gender M/F

Age (years)

TBI onset (years)

Ataxia (0–35 pts)

BBS (0–56 pts)

FGA (0–30 pts)

1 2 3 4 5 6 7 8 9 10

M M F F F M M M M F

33 42 55 36 42 44 26 63 66 45

9 17 6 6 19 9 5 7 4 21

18 6 6 5 5 2 18 5 2 12

45 48 52 54 53 54 50 54 46 54

14 18 24 27 26 26 20 25 22 26

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c) 14–27 points (mean ± SD: 22.8 ± 4.3 points) on the Functional Gait Assessment Test (FGA; Wrisley, Marchetti, Kuharsky, & Whitney, 2004), where a cut-off score of 22 points indicates an increased fall risk. The control group comprised 10 healthy height-, sex-, and age-matched individuals (six females) without known neurological, orthopedic, or cognitive deficits. Mean age of control group participants was 43.7 ± 14.2 years. All individuals signed an approved consent form before participation. 2.2. Experimental procedure and data analysis During a single experimental session, participants walked a 12-m distance under two different conditions: 1) at their comfortable speed (normal walking), and 2) with special instructions on their arm swinging (coordinated walking). During coordinated walking, participants were instructed to increase the amplitude of arm swinging and to synchronize movements of both arms in out-of-phase mode. The experimental session began with performance of three trials of normal walking. Participants were given time (equivalent to one practice trial) to establish increased and synchronized arm swinging, followed by three trials of coordinated walking. Participants’ movements during walking were recorded with a 12-camera Vicon T160 Motion Capture system at 100 Hz with 39 markers, placed according to the Plug-in-Gait Full Body Model. Gait events were visually inputted during data post-processing. Then, the model data were used to calculate spatiotemporal and kinematic gait characteristics. Spatiotemporal gait characteristics included: gait velocity, calculated as the change in position over time of the T10 marker; step length, calculated as the anterior-posterior distance between toe markers at consecutive toe-off events; step width, calculated as the lateral distance between toe markers during each step; center of mass (COM) lateral displacement, calculated from the subject’s anthropometric measurements in the Plug-in-Gait model for each gait cycle; and double support time, defined as the cumulative time spent between initial contact and toe off of the opposing leg and represented as a percentage of the entire gait cycle. The kinematic parameters included trunk, shoulder, and hip angular displacements and were computed from trial data that were first linear length normalized for each gait cycle and interpolated at 1% increments of cycles. Trunk rotation was calculated as the angle between the laboratory forward horizontal direction and a forward directed thorax vector (determined from the midpoint of markers applied to C7 and T10 vertebrae and a marker at the jugular notch) as projected onto the laboratory overhead plane (Fig. 1). For calculation of hip flexion angle, a forward-directed thigh vector perpendicular to the plane defined by markers at lateral midpoint of thigh and medial and lateral femoral condyles was projected onto the sagittal plane of pelvis (defined as perpendicular to the vector between ASIS markers). Hip flexion was the angle between this projected vector and a vector from sacrum marker to midpoint of ASIS markers. Shoulder flexion is the angle between a vector defining the humeral long axis and the forward directed thorax sagittal plane vector corrected for zero flexion when humerus is at the side. Differences between minimal and maximal angular positions of the trunk, shoulder, and hip defined angular displacement amplitude for each joint. Interlimb coordination was analyzed by calculating relative phase angles between the left and right shoulders, hips, and contralateral shoulders and hips. Angular velocity at each joint was determined using a central difference technique and was

Frontal View

Trunk Rotation

Sagittal View

Shoulder Flexion

Hip Flexion

Fig. 1. Computation of angular displacements of the trunk (frontal view; area defined by the black circles), shoulder, and hip (sagittal view; segments defined by black circles). Trunk rotation is the angle between the laboratory forward horizontal direction and a forward directed thorax vector. Hip flexion is the angle between a forward-directed thigh vector perpendicular to the plane of thigh markers and a forward directed pelvis vector. Shoulder flexion is the angle between a vector defining the humeral long axis and the forward directed thorax sagittal plane vector.

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used to create angular a position-velocity phase plane. Then, the following equation was used to calculate phase angle at each instant in time for each joint. (Tavernese, Paoloni, Massimiliano, Castelli, & Santilli, 2015):

uðtÞ ¼ tan

1

! _ hðtÞ hðtÞ

where h and h_ represent angular displacement and angular velocity respectively, normalized by the mean. Relative phase angles (u) then were calculated as differences between two phase angles of the left and right shoulders, left and right hips, and contralateral shoulder and hip joints, with an ideal out-of-phase coordination (e.g., for shouldershoulder and hip-hip pairs) equal to 180° and an ideal in-phase coordination (for shoulder-hip pairs) equal to 0°. A similar approach was used previously for analysis of arm swinging during standing in patients with unilateral stroke (Ustinova, Goussev, Balasubramaniam, & Levin, 2004). Relative phase variability was calculated as the standard deviation divided by mean, and then averaged across three repeated trials within each subject for the analysis of intra-subject variability, and within each group for the inter-subject variability. 2.3. Statistical analysis Data normality was verified with the Kolmogorov–Smirnov test (p > 0.05). Each outcome measure was averaged across four to six repeated strides within each walking trial, and then across three consecutive trials for each condition and for each participant. Two-way mixed Analysis of Variance (ANOVA) with Tukey Honest Significant Difference (HSD) post hoc test was used to compare averaged means with the factors ‘‘group” (control, TBI) and ‘‘condition” (normal walking, coordinated walking). Pearson correlation was used to determine relationship between the gait velocity and interlimb coordination in each group. A minimum significance level of p < 0.05 was set for all comparisons. 3. Results 3.1. Gait characteristics during normal walking Table 2 shows averaged means and SDs of gait characteristics for each group. ANOVA revealed that participants with TBI walked slower (F1,18 = 28.9, p = 0.000), with shorter steps (F1,18 = 9.14, p = 0.005), and greater double support time periods (F1,18 = 18.3, p = 0.000) compared to control group participants. Both groups demonstrated similar step width (post hoc, p = 0.762) and lateral displacements of the COM (post hoc, p = 0.998). There were also no significant differences between the two groups in the ranges of trunk rotation (p = 0.420), shoulder and hip flexion-extension (p = 0.075 right shoulder; p = 0.983 left shoulder; p = 0.087 right hip, and p = 0.402 left hip), in the sagittal plane during normal walking.

Table 2 Gait characteristics in the TBI and control groups. Characteristic

Gait velocity (m/s) Step length (m) Step width (m) Double support time (% gait cycle) COM lateral displacement (mm) Right shoulder ROM (°) Left shoulder ROM (°) Right hip ROM (°) Left hip ROM (°) Trunk rotation (°)

TBI

Control

Normal walking * p – between groups, within condition

Coordinated walking § p – between conditions, within group

1.05 ± 0.15* (p = 0.002) 0.62 ± 0.06* (p = 0.049) 0.11 ± 0.09 (p = 0.762) 23.2 ± 4.3* (p = 0.004) 37.8 ± 8.16 (p = 0.998) 29.2 ± 10.1 (p = 0.075) 33.6 ± 12.0 (p = 0.983) 43.4 ± 6.6 (p = 0.087) 43.4 ± 6.6 (p = 0.402) 20.1 ± 17.4 (p = 0.420)

0.99 ± 0.21 (p = 0.986) 0.69 ± 0.11§ (p = 0.007) 0.10 ± 0.09 (p = 0.210) 21.0 ± 5.7 § p = 0.304) 43.8 ± 14.0 (p = 0.096) 70.8 ± 20.3§ (p = 0.000) 70.6 ± 20.9§ (p = 0.000) 48.1 ± 6.3§ (p = 0.023) 47.9 ± 7.4§ (p = 0.049) 28.6 ± 17.9§ (p = 0.006)

Data are reported as the mean ± SD, symbols*, §, and

C

Normal walking p – between conditions, within group

Coordinated walking p – between groups, within condition

1.31 ± 0.09§ (p = 0.209) 0.69 ± 0.03§ (p = 0.003) 0.08 ± 0.05 (p = 0.094) 14.8 ± 4.6§ (p = 0.002) 37.0 ± 9.8 (p = 0.803) 34.9 ± 7.4§ (p = 0.000) 33.6 ± 10.1§ (p = 0.000) 44.2 ± 3.15§ (p = 0.003) 44.3 ± 4.1 (p = 0.112) 10.9 ± 2.3 (p = 0.031) §

1.42 ± 0.19C (p = 0.000) 0.80 ± 0.06 (p = 0.011) C 0.09 ± 0.05 (p = 0.987) 12.3 ± 4.5 C (p = 0.003) 39.2 ± 12.4 (p = 0.801) 97.2 ± 24.4C (p = 0.007) 91.6 ± 22.0C (p = 0.046) 53.0 ± 5.9 (p = 0.062) 50.4 ± 6.6 (p = 0.062) 17.8 ± 7.1 (p = 0.276)

§

indicate significant differences between groups and conditions.

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3.2. Interlimb coordination Arm and leg coordination patterns employed during normal walking may differ in some participants. Stick figures in Fig. 2 illustrate the relative positions of arms and legs at different phases of the gait cycle in two participants: one control and one individual with TBI. Trajectories of angular shoulder and hip displacements in the same two participants are shown beneath the stick figures. Normal walking of the participant with TBI is characterized by decreased arm movement amplitude and out-of-phase synchronization between arms (u = 117°) or legs (u = 161°). The control individual demonstrates much more prominent out-of-phase arm or leg synchronization (u = 164° and u = 168°, respectively). When required to increase and synchronize arm swinging during the coordinated walking condition, both participants established greater arm movement amplitude and stronger coupling (u = 166° in the TBI participant; and u = 174° in the control participant). However, the TBI patient did not reach the same level of out-of-phase synchronization as the control individual. On average, the TBI group demonstrated much weaker out-of-phase coupling between shoulder joints or hip joints than the control group (Fig. 3). ANOVA showed significant between group differences in relative phase means for the hips

TBI

CONTROL Normal walking

Hip

25° F

-25° E

Shoulder

25° F

-10° E

Coordinated walking

Hip

25° F

-25° E

Shoulder

25° F

-40° E

Gait cycles

Gait cycles Hip and shoulder:

right

left; F –flexion, E - extension

Fig. 2. Angular displacements of the right (gray lines) and left (black lines) hips and shoulders in one control and one individual with TBI during normal (upper panel) and coordinated (lower panel) walking. Stick figures illustrates several arm and leg positions during one gait cycle.

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Fig. 3. Means and standard deviations of relative phase angles for the left and right shoulders hips, and for contralateral shoulder and hip joint motions in participants with TBI (grey bars) and control individuals (open bars). Short horizontal lines represent individual subject means across the three trials for each condition.

(F1,18 = 13.7, p = 0.001) and shoulders (F1,18 = 6.17, p = 0.023) with the mean values ranging from 113° to 166° in the TBI group and from 149° to 172° in the control group. In-phase coordination between the left shoulder and right hip was also decreased in the TBI group compared to the control group (F1,18 = 9.47, p = 0.006) with means for relative phase ranging from 22° to 70° and from 24° to 48°, respectively. Participants in the TBI group showed a tendency toward less coupling between the right shoulder and left hip (F1,18 = 2.79, p = 0.111) compared to control individuals (see Fig. 3). Synchronized arm swinging influenced interlimb coupling in both groups. Relative phase angles increased for the hip joints (Fig. 3; F1,18 = 5.52, p = 0.030) and shoulder joints (F1,18 = 7.94, p = 0.011), with mean values approaching the ideal 180° angle, and ranging from 149° to 172° in the TBI group and from 164° to 178° in the control group (Fig. 3). A similar positive change in the relative phase angle was observed for the left shoulder and right hip in both groups (F1,18 = 4.84, p = 0.041), with means approaching the ideal 0° angle and ranging from 27° to 65° in the TBI group and from 19° to 37° in the control group. Changes in the right shoulder and left hip coupling did not reach the level of significance in both groups (p = 0.228). No significant between group differences were found in the relative phase means during coordinated walking for the hips (post hoc, p = 0.065), or shoulders (post hoc, p = 0.840). Coordination between the left shoulder and right hip remained weaker in the TBI than in the control group during the coordinated walking (post hoc, p = 0.036). 3.3. Variability of interlimb coordination Interlimb coupling was very variable in both groups for all joints and conditions. The intra-subject relative phase variability means ranged in the TBI group: 0.006 – 0.032 (mean ± SD: 0.016 ± 0.010 during normal walking and 0.020 ± 0.008 during coordinated walking) for the hips; 0.009–0.232 (0.066 ± 0.068 during normal walking, and 0.032 ± 0.022 during coordinated walking) for the shoulders; 0.023–0.891 (0.191 ± 0.253 and 0.262 ± 0.202) for the right hip and left shoulder; and 0.060–0.518 (0.180 ± 0.135 and 0.249 ± 0.167) for the left hip and right shoulder. In the control group, the intra-subject variability means the following ranges respectively: 0.004–0.038 (0.016 ± 0.008 during normal walking, and 0.022 ± 0.009 during coordinated walking) for the hips; 0.003–0.037 (0.022 ± 0.016 during normal walking, and 0.015 ± 0.009 during coordinated walking) for the shoulders; 0.058–0.377 (0.146 ± 0.090 and 0.142 ± 0.070) for the right hip and left shoulder;

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and 0.007 – 0.418 (0.116 ± 0.110 and 0.167 ± 0.104) for the left hip and right shoulder. ANOVA found no significant between group differences in the relative phase variability for all joint pairs, except shoulders. Coordination between shoulders was much more variable in the TBI group compared to the control group (F1,18 = 6.17, p = 0.023). Coordinated walking did not change relative phase variability in either group. 3.4. Gait characteristics during coordinated walking Walking with synchronized arm swinging changed the gait kinematics in both groups. Angular range of motion increased for the trunk and the left and right shoulders and hips (Table 2; post hoc test p < 0.05), with the exception of the left hip in the control group only (p = 0.112). Participants with TBI walked with smaller angular amplitude at the right (p = 0.007) and left (p = 0.046) shoulders than the control individuals. No difference in the hip range of motion was revealed between groups (p > 0.05, Table 2). Spatiotemporal gait characteristics were not affected by exaggerated arm swinging, with some parameters changing while others remained unchanged (Table 2). During coordinated walking, step length increased in the TBI (p = 0.007) and control (p = 0.003) groups. However, this increase did not cause any significant changes in the gait velocity. COM lateral displacements remained unchanged regardless of the group and walking condition (p > 0.05), while the double support time period was significantly decreased in the control group only (p = 0.002). 3.5. Effects of gait velocity on interlimb coordination In participants with TBI Pearson correlation analysis showed moderate relationship between the gait velocity and relative phase angle of the left shoulder and right hip (r = 0.691, p = 0.032), and of the right shoulder and left hip (r = 0.714, p = 0.012) during normal walking. Subjects who walked faster demonstrated stronger interlimb coupling at these joints pairs. No significant effects of the gait velocity on the interlimb coordination at other joint pairs and during coordinated walking were revealed in the TBI group (Table 3). In the control participants gait speed had no effects on relative phase angles in either condition (Table 3). 4. Discussion The study showed that TBI affects arm swinging and coordination between arms and legs during overground walking. When arm swinging was emphasized as a control parameter, coordination was improved not only between arms, but also between legs and between the contralateral arm and leg in both groups. Enhanced arm swinging resulted in greater hip and shoulder motion amplitudes, but did not significantly change spatiotemporal characteristics of gait in either group. Absent or abnormal arm swinging is often seen in patients with stroke, spinal cord injury, Parkinson disease, normally aged individuals, and children with spastic form of cerebral palsy (Ford et al., 2007b; Krasovsky, Lamontagne, Feldman, & Levin, 2014; Sterling et al., 2015; Tester et al., 2012; Meyns et al., 2011). When walking, these individuals may swing their arms with decreased amplitude, weaker coupling, less symmetry between arms, or limited dependency of the arm and leg phase relationship on the gait speed (Craik, Herman, & Finley, 1976; Tester et al., 2012). Such abnormalities are caused by various neurological impairments, including, but not limited to, muscle weakness, bradykinesia, paresis, and altered muscle tone (Meyns et al., 2013). None of those impairments manifested severely in our TBI participants, who had mild-to-moderate coordination and balance problems, without muscle strength or tone issues. As a result, the arm and leg movement amplitudes were similar in both groups. The only marked abnormality seen in TBI patients was reduced coupling between the arms and legs, which remained regardless of the walking condition. This effect was anticipated, as a consequence of brain damage to the widely distributed network of motor centers involved in control of motor coordination. Partially supporting these results, other studies reported impaired coordination between legs and between arms and legs in high-functioning and moderately affected patients with stroke and mild TBI (Chiu et al., 2013; Ford et al., 2007b; Krasovsky, Lamontagne, Feldman, & Levin, 2013). Our participants with TBI walked more slowly than controls, with shorter steps and longer double support time periods. Gait speed could potentially be a factor influencing interlimb coupling. It has been shown that at slower speed, healthy people reduce arm swinging and may even alter arm-leg coordination patterns from performing one arm swing per step Table 3 Pearson’s correlation coefficients determining the relationship between the gait velocity and different relative phase angles in the TBI and control groups. TBI

Shou-Shou Hip-Hip LHip-Rshou RHip-LShou *

Control

Normal walking (n = 10)

Coordinated walking (n = 10)

Normal walking (n = 10)

Coordinated walking (n = 10)

0.531 (p = 0.343) 0.023 (p = 0.457) 0.691* (p = 0.032) 0.714* (p = 0.012)

0.405 0.331 0.472 0.259

0.436 (p = 0.073) 0.290 (p = 0.811) 0.019 (p = 0.087) 0.030 (p = 0.564)

0.302 (p = 0.162) 0.477 (p = 0.098) 0.488 (p = 0.066) 0.612 (p = 0.055)

Indicate significant correlation.

(p = 0.064) (p = 0.101) (p = 0.080) (p = 0.277)

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to two swings per step (Craik et al., 1976; Donker, Beek, Wagenaar, & Mulder, 2001). This speed-coordination dependency is limited, however in patients with neurological conditions, especially in those able to walk within a speed range of 1.0–1.3 m/ s (Tester et al., 2012) typically seen in individuals without neurological injury (Waters, Lunsford, Perry, & Byrd, 1988). Drastic changes in interlimb coordination occur when the walking speed falls below the 0.7–0.8 m/s threshold (Donker et al., 2001) used to differentiate household from community walkers (Middleton, Fritz, & Lusardi, 2015; Perry, Garrett, Gronley, & Mulroy, 1995). In our study, participants in both groups walked within a range of comfortable speed for the healthy individuals (at 1.05 m/s on average in the TBI group and at 1.31 m/s in the control group). That is probably why the significant speed-coordination relationship was revealed for the two joints pairs and during normal walking only. It is also important to mention that the gait speed was not a parameter that was intentionally manipulated or fixed during the experiment. Thus, the slower speed in the TBI groups was unlikely a factor affecting their interlimb coordination. This may be a question for the future investigations. Interestingly, when asked to increase the arm swinging amplitude and focus on its out-of-phase coordination, participants in both groups improved coupling not only between arms, but also between legs and between contralateral arm and leg. This effect was not related to increased gait speed or other gait characteristics, as for example was reported in patients with stroke (Kwakkel & Wagenaar, 2002). We did not observe any significant changes in gait speed with increased arm swinging, and unlikely this factor could contribute to changes in interlimb coordination. These facts may suggest that the neuronal circuits interconnecting arm and leg muscles for locomotion are preserved after brain injury. Furthermore, neuronal coupling between the four extremities could still be adapted in a task-dependent way similarly to healthy individuals (Dietz et al., 2001). This observation raises a logical question of why patients with brain injuries walk with less synchronous interlimb coupling, unless they are reminded to control this parameter. What prevents them from utilizing their neural and motor capacities to full extent? Unfortunately, the similar strategies of underutilization are frequently observed during performance of other motor skills in many patient populations. For example, patients with Parkinson’s disease perform movements slower and with smaller amplitude, unless they are forced to improve the movements (Farley & Koshland, 2005). Similarly, individuals with mild-to-moderate post-stroke hemiparesis tend to shift their body weight toward the less affected leg during standing, even when their paretic leg is capable of accepting more weight (Ustinova, Ioffe, & Chernikova, 2003). They also tend to minimize involvement of the paretic arm/hand in performance of activities of daily living, despite the fact that functional capacities of the paretic arm could be partially retained after stroke. Such type of behavior is not unusual and was described by Taub (1980), as ‘‘learned non-use”. When an injury disrupts a regular, well established movement pattern, so that it becomes awkward and inconvenient, the CNS finds a way to compensate with a newly developed movement. This new compensatory pattern often replaces the ‘‘broken” movement components or synergies, unless they are necessary for survival, to minimize a risk of error or failure. Arm swinging is a locomotor feature that may be beneficial, but not required for walking in the desired direction. Thus, the CNS may select not to use it rather than to make an attempt to repair this function. Another possible explanation of arm swinging non-use may be an associated with gait and perceived risk of falling. Patients with brain injuries may want to keep their arms free from other activities to be able to grab a supporting surface in case they anticipate fall. Finally, a lack of arm swinging could be a result of rehabilitation processes most patients go through. Although reduced or absent arm swinging is recognized as a gait deviation, recovery of this function very rare becomes priority during gait training, for example in case of Lokomat therapy or gait practice on treadmills (Ustinova, Chernikova, Dull, & Perkins, 2015). Our research finding illustrates the fact that enhanced arm swinging can improve coordination between arms and legs during walking, and that may be taken into consideration when planning gait training activities in this patient population. This observation seems to be therapeutically relevant and important, considering the ambiguity of previous findings on interlimb coordination during walking in other patient populations. In line with our results, Ford et al. (2007a,2007b) reported improvement of the phase relationship between arms and legs and between transverse pelvic and thoracic rotations in patients with stroke when they walked on a treadmill with out-of-phase arm movements, or when they matched their arm and leg movements with auditory signals. In contrast, no strong relationship between arm and leg coordination and enhanced arm swinging was found in high-functioning stroke subjects walking on a treadmill while using sliding handrails to enhance arm swinging (Stephenson et al., 2009). Such inconsistency in results might be explained by differences between experimental patient populations. Although stroke and trauma may affect similar brain structures, clinical manifestations and responsiveness to therapeutic exercises may be different after these injuries. Study outcomes could also be influenced by experimental and instructional settings. For instance, participants showed improvement in the area that they focused on. When asked to focus on holding rails, participants improved in arm swinging, but not in coordination that, in contrast, became stronger when attention was paid to interlimb synchronization. To date, too few studies of arm and leg coordination after TBI have been performed, limiting a more solid justification for the results. Enhanced arm swinging and coupling between limbs in our participants were not associated with significant changes in gait characteristics. Step length increased but gait stability remained practically unchanged in both groups. Similar to our study, a previous paper reported no association between stronger out-of-phase arm coordination and stride parameters or gait stability in high-functioning patients with stroke (Ford, Wagenaar, & Newell, 2007c; Krasovsky et al., 2013; Stephenson et al., 2009). In contrast, some patients with incomplete spinal cord injuries were able to improve their stepping pattern on a treadmill by reciprocating arm swinging (Behrman & Harkema, 2000). The relationship between walking speed and arm and leg coordination was not always obvious for all patients and all speeds, however (Tester et al., 2012). Excessive arm swing was found to increase walking speed and decrease cadence in patients with Parkinson’s disease (Behrman et al.,

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1998), and to increase dynamic stability in healthy subjects (Nakakubo et al., 2014; Punt et al., 2015). However, the greater stability was not linked to changes in spatiotemporal characteristics, such as step-width ratio. Thus, there is no common point of view on the interrelation among multiple gait characteristics. Human gait is a very complex movement that could be described via different experimental paradigms and computational algorithms (Stenum, Bruijn, & Jensen, 2014). This movement concurrently serves several functional purposes, including propelling the body forward, maintaining stability, and adapting the pattern to constantly changing environmental demands. Successful performance requires control of multiple variables through a widely distributed neural network. In this situation, there is always a possibility of trade-off when some movement characteristics are ‘‘favored” by the controlling systems to maximize performance efficiency in a given environment. This explanation, although relatively speculative, may account for some of the inconsistency in results. In summary, the present study discovered additional post-injury gait deviations, such as altered arm swinging and reduced coordination between arms and legs, although with some limitations. When properly addressed, this abnormality could be easily corrected, although it is unclear how long this effect may last without additional reinforcement. Moreover, our study included a relatively small sample of patients with chronic TBI and mild-to-moderate ataxia and balance impairments. Their gait pattern was only mildly affected, which left very little room for improvement. Most patients might have already developed long-term compensatory strategies for maintaining balance and might be less able to adapt their comfortable and reproducible gait pattern to the arm swinging condition. 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