Control Mechanisms of Static and Dynamic Balance in Adults With and Without Vestibular Dysfunction in Oculus Virtual Environments

PM R 10 (2018) 1223-1236

www.pmrjournal.org

Review: Virtual Rehabilitation Series

Control Mechanisms of Static and Dynamic Balance in Adults With and Without Vestibular Dysfunction in Oculus Virtual Environments Anat V. Lubetzky, PT, PhD, Bryan D. Hujsak, DPT, NCS, Jennifer L. Kelly, DPT, NCS, Gene Fu, SPT, Ken Perlin, PhD

Abstract Background: Deficits in sensory integration and fear of falling in complex environments contribute to decreased participation of adults with vestibular disorders. With recent advances in virtual reality technology, head-mounted displays are affordable and allow manipulation of the environment to test postural responses to visual changes. Objectives: To develop an assessment of static and dynamic balance with the Oculus Rift and (1) to assess test-retest reliability of each scene in adults with and without vestibular hypofunction; (2) to describe changes in directional path and sample entropy in response to changes in visuals and surface and compare between groups; and (3) to evaluate the relation between balance performance and self-reported disability and balance confidence. Design: Test-retest, blocked-randomized experimental design. Setting: Research laboratory. Participants: Twenty-five adults with vestibular hypofunction and 16 age- and sex-matched adults. Methods: Participants stood on the floor or stability trainers while wearing the Oculus Rift. For 3 moving “stars” scenes, they stood naturally. For a “park” scene, they were asked to avoid a virtual ball. The protocol was repeated 1-4 weeks later. Outcome: Anteroposterior and mediolateral center-of-pressure directional path and sample entropy were derived from a force plate. Results: We observed good to excellent reliability in the 2 groups, with most intraclass correlations above 0.8 and only 2 at approximately 0.4. The vestibular group had higher directional path for the stars scenes and lower directional path for the park scene compared with controls, with large variability in the 2 groups. Sample entropy decreased with more challenging environments. In the vestibular group, less balance confidence strongly correlated with more sway for the stars scenes and less sway for the park scene. Conclusion: Virtual reality paradigms can shed light on the control mechanism of static and dynamic postural control. Clinical utility and implementation of our portable Oculus Rift assessment should be further studied. Level of Evidence: II

Introduction Healthy postural control allows for continuous, ongoing, sensory processing where the most reliable sources of sensory input are prioritized, whereas destabilizing input is “reweighted” [1,2]. It is well known that age has an important effect on balance, and aging with vestibular dysfunction magnifies that effect [3,4]. Deficits in sensory integration and fear of falling in complex visual environments contribute to increased fall risk in older adults [5] and decreased participation in adults with vestibular disorders [6].

The effect of age and vestibular dysfunction on postural control has been primarily studied within static tasks such as standing with eyes open or closed, on foam vs floor [7], and dynamic posturography using the sensory organization test (SOT) [3,8]. In the SOT, visual and somatosensory information is manipulated through various combinations of eyes closed, sway-referenced surface, and sway-referenced vision [9]. The SOT is considered the gold standard in estimating sensory contribution to balance control; however, it is not a diagnostic tool [10]. Individuals with vestibular hypofunction typically show increased postural sway with the

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most challenging conditions of the SOT, namely swayreferenced platform with eyes closed and swayreferenced vision and its clinical translation (foam and eyes-closed paradigm) [11], but so do healthy adults [3]. Postural sway per se is not a good indication of a specific underlying pathology because center-of-pressure displacements have been shown to increase with aging [4] and stroke, vestibular loss, and other neurologic conditions [10]. Perturbation-based assessment of balance and sensory weighting allows for balance testing to become more specific (ie, identifying abnormal sensory integration patterns as opposed to a general decrease in balance as a single construct) [12] and is less extreme than static posturography (eg, complete removal of vision by closing the eyes) or dynamic posturography (sway-referenced vision and surface). In advocating for better precision in balance assessment, Anson and Jeka [12] mentioned that head-mounted displays could assist with clinical adaptation of such paradigms, which typically required expensive equipment and ample space. With recent advances in virtual reality technology, head-mounted displays have become affordable and allow systematic manipulation of the environment to test postural responses to visual changes. Low-latency, lightweight, and accurate head tracking provides an opportunity to study the mechanisms of balance disorders in a unique way. Using the Oculus Rift (Oculus VR, Menlo, CA), we developed an assessment of postural control that involves a modification of a “moving room” paradigm [4,13] and an avoidance task paradigm [14]. The moving room paradigm requires an individual to maintain balance within increased oscillations of the visual environment (typically referred to as the “visual cave”). The avoidance task requires dynamic movement outside one’s base of support in response to a visual stimulus and reflects a different subdomain of balance [15]. We previously reported the feasibility and reliability of our platform in healthy young adults [16] and preliminary validity in adults with vestibular hypofunction [17]. The aims of the present were to (1) assess test-retest reliability under visual and surface conditions in adults with vestibular hypofunction and agematched controls for 2 metrics of postural sway: directional path (DP) and sample entropy (SampEn); (2) describe changes in DP and SampEn in response to changes in the visual and surface environments and compare these changes between groups; and (3) evaluate the relation between balance performance on our platform and self-reported balance confidence and dizziness-related disability. Methods Participants A convenience sample of 25 individuals with vestibular hypofunction (13 women, mean age ¼ 52.2,

standard deviation [SD] ¼ 17.5, minimum ¼ 24, maximum ¼ 81) and 16 age-matched controls (8 women, mean age ¼ 52.9, SD ¼ 17.6, minimum ¼ 24, maximum ¼ 79) participated in this pilot study. Participants in the 2 groups were excluded for peripheral neuropathy; uncorrected visual impairment; participant does not read or speak English; or pregnancy. Participants in the vestibular group presented with at least 1 of the following: 1. Medical diagnosis specific to vestibular disorder (acute or chronic, unilateral or bilateral) 2. Positive findings at vestibular function tests including electronystagmography, video nystagmography, cervical and ocular vestibular evoked myogenic potentials, video head impulse test, audio provoked brainstem response, electrocochleography, or rotary chair 3. Clinical examination that showed abnormal head thrust, subjective visual vertical, subjective visual horizontal, post head shaking nystagmus, and/or spontaneous and gaze holding nystagmus in a pattern consistent with peripheral vestibular dysfunction Self-reported balance confidence by the ActivitiesSpecific Balance Confidence Scale (ABC) [18] in the vestibular group was 41-95% (maximal score ¼ 100%, indicating full confidence), with an average score of 67.6 (SD ¼ 16.1). Self-reported disability by the Dizziness Handicap Inventory (DHI) [19] in the vestibular group was 71-76 (0 ¼ no disability), with an average score of 39.5 (SD ¼ 19.7) and with 14 patients scoring higher than 30. All participants signed a written informed consent before commencing the study. This study was approved by the institutional review board of the New York Eye and Ear Infirmary of Mount Sinai (New York, NY) and by the committee on activities involving human subjects of New York University (New York, NY). Procedures For a detailed description and illustrations of our testing protocol, see Lubetzky et al [16,17]. Participants stood hip-width apart on a laboratory forceplate (Kistler 5233A; Kistler Instrument Corp, Amherst, NY) or on 2 blue soft TheraBand (Akron, OH) stability trainers placed on top of the forceplate. They were wearing the Oculus Rift headset when 1 of the following scenes was projected: 1. ML4.5: A 60-second “stars” scene in which the stars move side to side (mediolateral [ML]) at a frequency of 0.48 Hz and an amplitude of 0.0045 meter. 2. AP5: A 60-second “stars” scene in which the stars move in the sagittal plane (anteroposterior [AP]) at a frequency of 0.2 Hz and an amplitude of 0.005 meter.

A.V. Lubetzky et al. / PM R 10 (2018) 1223-1236

3. AP32: A 60-second “stars” scene in which the stars move in the sagittal plane (AP) at a frequency of 0.2 Hz and an amplitude of 0.032 meter [13]. In these 3 scenes, participants were asked to look straight ahead and to do whatever felt natural to them to maintain their balance. In scenes ML4.5 and AP5, the movement of the stars is mild and was not perceived by most participants. These scenes have been used to study the ability to process (“weight”) visual cues during static postural control [13,20,21]. Young adults and older children [13,21] have been shown to down-weight their response to the AP32 scene in which the movement of the 3-wall display is larger. 4. A 120-second “park” scene [14] was used to test participants’ dynamic balance. In this scene, participants were asked to avoid a ball approaching their head (ball appearance was randomized every 2-4 seconds) in whatever strategy they chose, but without moving their feet. The scene had 4 30-second segments: (I) no background movement; (II) background movement of cars (0.7 m/s) and cubes (to represent pedestrians) at a speed of 0.9-3.2 m/s; (III) increased speed of background movement (cars at 2.2 m/s and cubes at 2.8-4.7 m/s); and (IV) same parameters as III in a dark environment to simulate night time. All scenes were developed in Unity 5.2.1f (Unity Technologies, San Francisco, CA). Each scene was repeated twice per surface in a randomized order. The design of the study was block randomized: participants always started on the floor and progressed to the stability trainer. The order of the conditions was randomized for each participant but kept the same for the 2 surfaces. Two patients could not perform the scenes on the stability trainer because of balance difficulty. Calibration of height was done using the Oculus headtracking sensor. We created a calibration scene in which a gray line was aligned between the participant’s feet and a “thank you” sign was positioned in the center of a rectangle to confirm that all scenes were projected at the correct height. To monitor for cyber-sickness symptoms, we administered the Simulator Sickness Questionnaire (SSQ) [22] after every 5 trials (vestibular group) or 10 trials (control group). The SSQ contains 15 items. The participant is asked, “Are you experiencing any” (eg, fatigue, nausea, dizziness) and can respond with “none,” “slight,” “moderate,” or “severe” [22]. All participants returned for a retest session within 1-4 weeks when they repeated the same protocol. Outcome Measures We calculated 1 traditional parameter of postural sway (DP) and 1 nonlinear measure (SampEn), which

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were found to be reliable in healthy young adults performing our protocol [16]. The center-of-pressure signal was sampled at 100 Hz and we used separate custommade Matlab (Matlab_R2017a for Macintosh) software (MathWorks, Natick, MA) to process and calculate each measure. To calculate DP, we applied a traditional low-pass, fourth order, 0-lag, Butterworth digital filter using a forward and reverse pass with a cutoff at 10 Hz [23]. Then we measured the length of the position curve in either direction (AP or ML) as the sum of the absolute value of the position change at every time stamp [24]. In contrast, SampEn is a measure of postural sway regularity [25], and a filter or other smoothing procedures can remove unique features of a time series [26,27]. SampEn is defined as the negative natural logarithm of the conditional probability that a subsequence of length m, which matches pointwise within a tolerance r, also matches at the next point. For the calculation of SampEn, we down-sampled the original dataset by 2 and used m ¼ 2 and r ¼ 0.15 [16,27]. Statistical Analysis We first counted the total number of “none,” “slight,” “moderate,” and “severe” responses on the SSQ. We present the data in a figure per session for each group. Aim 1 We estimated test-retest reliability using intraclass correlations (ICCs) as described by Shrout and Fleiss [28]. We used the average-measures ICC2,1 (2-way, random) derived from a 2-way analysis of variance calculated for the mean of 2 trials. In this model, consistency in within-subject variability is compared with between-subject variability, where random effects are considered over time. ICCs were calculated separately for each group (vestibular and control) and each outcome (DP and SampEn) and direction (AP and ML) per surface (floor and stability trainers) per scene (park  4 segments, ML4.5, AP5, AP32) for a total of 112 estimates. We report the ICC2,1 point estimate and its corresponding 95% confidence interval (CI). We also calculated the standard error of measurement (SEM) as cumulative SD  (1 e ICC)½ and from that derived the minimal detectable chance as 1.96  SEM  2½ [29,30]. Aim 2 DP and SampEn were calculated every 30 seconds (park) or 60 seconds (stars). We ran 6 repeatedmeasures analysis of variance models of group (between factors, 2 levels)  visual (within factor, 4 levels for park, 3 levels for stars). This was done for DP and SampEn in the AP and ML directions per surface. When the Mauchly test result of sphericity was significant, we reported the P value and F values of the Greenhouse-

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Geisser correction. We used the Shapiro-Wilk test to test for normality. Most variables were normally distributed. Some variables for the stability trainers showed deviation from normality. In small samples, violation of normality could lead to an inflated type I error. Therefore, if a violation occurred, then we used the nonparametric equivalent of the test (Friedman test for comparison of visual effect within a group and MannWhitney U-test for comparison between groups). Partial h2 effect size (ES) estimates were calculated. All analyses were conducted using SPSS 24 for Macintosh (IBM Corp, Armonk, NY). For the normally distributed variables, if a significant main effect of visual was observed, then we ran a post hoc Bonferroni analysis of pairwise comparison. Aim 3 We estimated the correlation coefficients between DP (AP and ML) in each scene (ML4.5, AP5, AP32, and park) and age (for the 2 groups) and self-reported ABC and DHI scores (vestibular dysfunction group). Because of the presence of outliers, we used Spearman correlations. Results Figure 1 displays the total number of symptoms reported on the SSQ [22] per group (vestibular and control) for each session. Symptoms were minimal and transient for the 2 groups in the 2 sessions. The most common response was “none.”

The ICC coefficients and their accompanying 95% CIs are presented in Figure 2 (vestibular group) and Figure 3 (control group). DP ICCs were generally excellent [31] in the 2 groups, with the exception of AP5 ML stability trainer, where the 2 groups had moderate ICCs with wide CIs. The vestibular group also had wide CIs in the park scene (AP stability trainer and ML floor). SampEn also showed excellent ICCs. The 2 groups had wide CIs at ML4.5 (AP stability trainer). The vestibular group had wide CIs on all park segments in the 2 directions on the 2 surfaces. The control group also had wide CIs on all park segments but only in the AP direction on the floor. The control group also had wide CIs for AP32 (AP floor). The Appendix lists descriptive statistics of all test and retest scores and the SEM and minimal detectable chance per group and outcome measure. DP changes in a given group, scene, and surface for the stars scenes are presented in Figure 4 (top row) and Table 1. Overall, patients’ DP was consistently higher, with large variability in the 2 groups (Table 1). In the AP direction, we observed a significant main effect of visual (F1.3,40 ¼ 5.44, P ¼ .02, ES ¼ 0.12) on the floor driven by a difference between AP32 and each of the other 2 scenes (P < .001 for ML4.5 and P ¼ .05 for AP5). A main effect of visual also was observed on the stability trainer (Friedman c2 ¼ 19, P < .001 for the 2 groups). The between-group difference approached significance on the floor (F1,40 ¼ 3.72, P ¼ .06, ES ¼ 0.09). Although patients increased their sway more than controls with the AP32 vs AP5 and ML4.5, this interaction was not significant. The groups were significantly different on the stability trainer in the AP5 scene (P ¼ .008 by

Figure 1. Pie chart displaying the number of “none,” “slight,” “moderate,” or “severe” responses reported on the Kennedy SSQ by participants in the (top) vestibular group and (bottom) control group during the test (left-hand side) and retest (right-hand side) sessions. SSQ, Simulator Sickness Questionnaire.

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ICC

Vesbular Floor AP: Direconal Path

Vesbular Floor AP: Sample Entropy

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AP32 Park I Park II Park III Park IV

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Vesbular Floor ML: Direconal Path 1

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Park I Park II Park III Park IV

ML4.5

AP5

AP32

Park I Park II Park III Park IV

Vesbular Floor ML: Sample Entropy

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Park I Park II Park III Park IV

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Park I Park II Park III Park IV

Figure 2. ICC coefficients and their accompanying 95% confidence intervals for the vestibular group during all scenes on the 2 surfaces for directional path (left-hand side) and sample entropy (right-hand side). AP, anteroposterior; ICC, intraclass correlation; ML, mediolateral.

Mann-Whitney U-test). In the ML direction, there were no significant main effects of visual or group or interaction on the floor (P > .05, minimal ES  0.05). Differences between groups were somewhat larger on the stability trainer (Figure 4, Table 1). We observed a significant main effect of visual in the control group (Friedman c2 ¼ 13.5, P ¼ .001) but not in the vestibular group (c2 ¼ 1.8, P ¼ .41). The groups were significantly different on the stability trainer for the AP5 scene (P ¼ .02 by Mann-Whitney U-test). SampEn in the stars scene was consistently lower in the vestibular dysfunction group (Figure 4, bottom row, Table 2). In the AP direction, there were no significant

main effects of visual or group on the floor. Participants in the vestibular group demonstrated a larger decrease with the AP32 scene, but the interaction was not significant because the 2 groups showed decreased SampEn with this scene (Table 2). Values on the stability trainer were lower for the 2 groups, such that mean SampEn was 0.4-0.47 (SD ¼ 0.02-0.03) for the 2 groups. In the ML direction on the floor, we observed a significant main effect of visual (F1.8,40 ¼ 4.73, P ¼ .01, ES ¼ 0.11), such that SampEn decreased in the 2 groups, slightly more so for patients, from ML4.5 (P ¼ .01) and AP5 (P ¼ .048) to AP32. On the stability trainer, no significant main effect or interaction was observed, but SampEn in patients

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ICC

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Control Floor AP: Sample Entropy

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Figure 3. ICC coefficients and their accompanying 95% confidence intervals for the control group during all scenes on the 2 surfaces for directional path (left-hand side) and sample entropy (right-hand side). AP, anteroposterior; ICC, intraclass correlation; ML, mediolateral.

remained somewhat lower throughout (control group ¼ 0.48 [0.05] and 0.52-0.49 [0.06-0.05]; vestibular dysfunction group ¼ 0.42 [0.04] and 0.47-0.45 [0.050.04]). In the park scene, where participants moved away from a virtual ball, DP was overall consistently lower for the vestibular group compared with the control group (Figure 5, top, Table 3). The groups became, on average, closer when standing on the stability trainer (Table 3, Figure 5, top). Although there also were no significant main effects on the floor in the ML direction, the pattern was such that the vestibular group decreased their sway at the last 2 segments (mean ¼

2044 [SD ¼ 119] on I to 1825.6 [SD ¼ 156.2] on IV), whereas the control group remained consistent (mean ¼ 2095.3 [SD ¼ 145.7] on I to 2157.42 [SD ¼ 191.4] on IV). The groups were quite similar in magnitude of sway in the ML direction on the stability trainer (Figure 5). A significant main effect of visual was observed for SampEn in the park scene in the AP direction on the floor (F2.8,40 ¼ 16.31, P < .001, ES ¼ 0.3). Segment I was significantly different than the other 3 segments (P < .001 for each). Although the vestibular group was consistently lower, the 2 groups had the exact same pattern across segments (see Table 4). A main effect of visual in the AP direction also was observed on the

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Figure 4. Changes in (top) directional path (millimeters) and (bottom) sample entropy according to the type of star movement (ML4.5, AP5, AP32) on the floor and the stability trainer in the anteroposterior and mediolateral directions for the 2 groups. Green line indicates the vestibular group and blue line represents the control group. A significant main effect of visual changes is denoted by the asterisk. A significant main effect of group is denoted by the dagger. For easier visualization, the y-axis scale differs between graphs (postural sway on the stability trainer was at least twice as high as on the floor and sample entropy was lower).

stability trainer (Friedman c2 ¼ 12.83, P ¼ .005 for control group; c2 ¼ 5.9, P ¼ 0.12 for vestibular group). In the ML direction, a main effect of visual was observed on the floor (F1.5,40 ¼ 6.01, P ¼ .001, ES ¼ 0.13; segment

I was significantly different than segments IV [P ¼ .007] and II [P ¼ .001]), but not on the stability trainer. No significant main effect of group was observed in the ML direction (Figure 5, bottom).

Table 1 Directional path (millimeters) on stars scenes per group, surface, and direction 95% CI Direction

Group

Condition

Floor AP

Control

ML4.5 AP5 AP32 ML4.5 AP5 AP32 ML4.5 AP5 AP32 ML4.5 AP5 AP32

623.18 659.81 679.07 750.03 751.98 826.89 400.07 412.93 393.72 423.85 448.95 449.78

57.35 47.14 53.81 45.88 37.71 43.05 37.15 44.34 34.62 29.72 35.47 27.69

507.18 564.47 570.22 657.23 657.70 739.81 324.93 323.24 323.70 363.74 377.20 393.77

739.18 755.16 787.92 842.83 828.25 913.97 475.21 502.62 463.74 483.96 520.70 505.80

ML4.5 AP5 AP32 ML4.5 AP5 AP32 ML4.5 AP5 AP32 ML4.5 AP5 AP32

1383.82 1363.17 1801.87 1765.63 1833.03 2320.68 697.12 580.73 686.41 795.28 790.27 845.06

144.54 126.38 237.87 126.17 110.31 207.63 62.85 72.70 74.70 54.86 63.46 65.20

1090.38 1106.61 1318.97 1509.49 1609.09 1899.16 569.52 433.13 534.77 683.91 661.44 712.70

1677.26 1619.72 2284.78 2021.76 2056.97 2742.19 824.71 728.32 838.06 906.66 919.10 977.43

Vestibular

ML

Control

Vestibular

Stability trainer AP

Control

Vestibular

ML

Control

Vestibular

Mean

SE

CI ¼ confidence interval; SE ¼ standard error; AP ¼ anteroposterior; ML ¼ mediolateral.

Lower Bound

Upper Bound

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Table 2 Sample entropy on stars scenes per group, surface, and direction 95% CI Direction

Group

Condition

Mean

SE

Lower Bound

Upper Bound

Floor AP

Control

ML4.5 AP5 AP32 ML4.5 AP5 AP32 ML4.5 AP5 AP32 ML4.5 AP5 AP32

0.64 0.66 0.63 0.60 0.58 0.53 0.66 0.70 0.56 0.72 0.63 0.61

0.06 0.06 0.06 0.04 0.05 0.04 0.08 0.09 0.07 0.07 0.08 0.06

0.53 0.53 0.52 0.51 0.48 0.44 0.50 0.51 0.42 0.59 0.48 0.49

0.76 0.79 0.74 0.69 0.69 0.62 0.83 0.89 0.70 0.85 0.78 0.72

ML4.5 AP5 AP32 ML4.5 AP5 AP32 ML4.5 AP5 AP32 ML4.5 AP5 AP32

0.47 0.45 0.40 0.44 0.45 0.41 0.48 0.52 0.49 0.42 0.47 0.45

0.03 0.03 0.03 0.03 0.03 0.02 0.05 0.06 0.05 0.04 0.05 0.04

0.41 0.39 0.35 0.39 0.40 0.37 0.38 0.40 0.40 0.33 0.37 0.37

0.53 0.51 0.46 0.49 0.50 0.46 0.58 0.64 0.59 0.51 0.58 0.54

Vestibular

ML

Control

Vestibular

Stability trainer AP

Control

Vestibular

ML

Control

Vestibular

CI ¼ confidence interval; SE ¼ standard error; AP ¼ anteroposterior; ML ¼ mediolateral.

Lower ABC scores showed consistent negative moderate to strong correlations with longer DP on the floor in all 3 stars scenes, primarily in the AP direction (Figure 6). When participants were standing on the

stability trainer, longer DP on the stars scenes was moderately correlated with increased age in the control group in the 2 directions and to a lesser extent in the vestibular group in the AP direction only (Figure 7). In

Figure 5. Changes in (top) directional path (millimeters) and (bottom) sample entropy according to the time segment of the park scene (segments I-IV) on the floor and the stability trainer in the anteroposterior and mediolateral directions for the 2 groups. Green line indicates the vestibular group and blue line represents the control group. A significant main effect of visual changes is denoted by the asterisk. A significant main effect of group is denoted by the dagger.

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Table 3 Directional path (millimeters) on the park scene per group, surface, and direction 95% CI Direction

Group

Condition

Mean

SE

Lower Bound

Upper Bound

Floor AP

Control

I II III IV I II III IV I II III IV I II III IV

1606.13 1630.07 1532.55 1668.01 1504.97 1468.68 1421.51 1486.39 2095.29 2098.15 2091.23 2157.42 2043.99 1955.88 1854.02 1825.62

125.18 154.02 155.33 157.79 102.21 125.76 126.83 128.83 145.74 191.04 194.44 191.41 119.00 155.99 158.76 165.28

1352.70 1318.27 1218.11 1348.59 1298.05 1214.09 1164.77 1225.58 1800.25 1711.40 1697.62 1769.94 1803.09 1640.10 1532.64 1509.24

1859.55 1941.88 1847.00 1987.44 1711.89 1723.27 1678.26 1747.20 2390.34 2484.89 2484.84 2544.90 2284.89 2271.66 2175.41 2142.00

I II III IV I II III IV I II III IV I II III IV

2274.62 1630.07 2265.88 2299.56 2280.34 1482.15 2290.93 2466.58 1960.60 2006.26 2014.28 2047.73 2123.32 2051.64 2191.07 2212.73

241.49 157.65 299.82 276.96 215.99 141.01 268.17 247.72 232.04 238.86 258.58 275.35 207.54 213.65 231.28 246.28

1783.86 1309.68 1656.57 1736.71 1841.39 1195.58 1745.95 1963.16 1489.03 1520.83 1488.78 1488.15 1701.53 1617.46 1721.05 1712.23

2765.38 1950.46 2875.20 2862.41 2719.29 1768.71 2835.92 2970.01 2432.16 2491.69 2539.79 2607.30 2545.10 2485.82 2661.10 2712.23

Vestibular

ML

Control

Vestibular

Stability trainer AP

Control

Vestibular

ML

Control

Vestibular

CI ¼ confidence interval; SE ¼ standard error; AP ¼ anteroposterior; ML ¼ mediolateral.

addition, greater ML sway in the park scene was associated with greater balance confidence in the vestibular group, primarily on the stability trainer (Figure 8). SampEn decreased with increased age in controls (all correlations ¼ 0.4 to 0.5 except for floor park AP and all stability trainer AP) but not in the vestibular group. The following 3 moderate correlations also were observed, but because they did not fit a consistent pattern, they could be spurious findings: ML SampEn on the floor AP32 with ABC (R ¼ 0.57); AP SampEn on the stability trainer with DHI (0.6); and AP DP on the floor AP5 with DHI (þ0.52). All other correlations were weak to nonexistent (Rs < 0.4). Discussion We compared the performance of 25 individuals with vestibular hypofunction and 16 age-matched control subjects on a newly developed Oculus Rift virtual reality assessment of static balance (within scenes of moving stars) and dynamic balance (within a park scene where

participants are asked to avoid a virtual ball). Cybersickness was minimal to none. This was expected from our past work [16,17,21] because of the low latency, mild nature of the scenes, and short duration of each scene. The reliability of balance performance was overall excellent. Wide CIs on the park scene in the vestibular group can be explained by 3 outliers: 3 patients decreased their postural sway at the retest session compared with the test session. Below we discuss the different patterns of behavior we observed on the different tasks (static and dynamic) and the 2 different measurement paradigms (traditional and nonlinear). Given the known effect of aging and vestibular dysfunction on sensory integration for balance, we expected the 2 groups to be affected by the visual manipulations [3,4]. Indeed, the 2 groups, on average, had greater postural sway (indicated by a longer DP) in the 2 directions compared with healthy young adults performing the floor part of the same protocol in a previous study [16]. Specifically, healthy young adults had, on average, an ML DP of approximately 250 mm and an AP

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Oculus Virtual Environments for Studying Balance

Table 4 Sample entropy on the park scene per group, surface, and direction 95% CI Direction

Group

Condition

Mean

SE

Lower Bound

Upper Bound

Floor AP

Control

I II III IV I II III IV I II III IV I II III IV

0.37 0.43 0.43 0.41 0.33 0.41 0.40 0.39 0.22 0.27 0.26 0.28 0.21 0.27 0.30 0.30

0.02 0.03 0.03 0.03 0.02 0.02 0.02 0.02 0.02 0.02 0.04 0.04 0.02 0.02 0.03 0.03

0.32 0.37 0.37 0.35 0.29 0.36 0.35 0.34 0.19 0.22 0.17 0.21 0.18 0.23 0.24 0.24

0.42 0.48 0.49 0.47 0.37 0.45 0.45 0.44 0.26 0.31 0.37 0.35 0.24 0.31 0.37 0.36

I II III IV I II III IV I II III IV I II III IV

0.40 0.44 0.43 0.43 0.40 0.41 0.41 0.42 0.29 0.31 0.30 0.29 0.29 0.29 0.29 0.29

0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.03 0.03 0.03 0.02 0.02 0.02 0.02

0.36 0.40 0.39 0.39 0.37 0.38 0.38 0.38 0.24 0.26 0.24 0.24 0.25 0.24 0.24 0.25

0.43 0.47 0.47 0.47 0.43 0.44 0.45 0.46 0.34 0.37 0.35 0.35 0.33 0.34 0.34 0.34

Vestibular

ML

Control

Vestibular

Stability trainer AP

Control

Vestibular

ML

Control

Vestibular

CI ¼ confidence interval; SE ¼ standard error; AP ¼ anteroposterior; ML ¼ mediolateral.

DP of approximately 500 mm for the stars scene. Participants in the present study had an average ML DP of 400 mm (control) or 450 mm (vestibular) and an average AP DP of 650 mm (controls) and 750 mm (vestibular). In the present study, although postural sway of the vestibular group was consistently greater during the static stance, the 2 groups were affected similarly by the visual manipulations. A main effect of group in the AP direction emerged only when participants were standing on a compliant surface. This supports the notion that visual dependence increases with age and vestibular dysfunction, but adults with vestibular dysfunction are more challenged with the further decrease of somatosensory input because the vestibular system is important for upright control, especially as the support surface becomes more challenging [32]. Challenges in sensory integration with vestibular dysfunction have been reported in the literature, where differences between healthy adults and adults with vestibular dysfunction have been reported primarily on the more challenging conditions of the SOT [3,8]. One aspect that

is unique in our paradigm is its mild nature. The visual cues are simple, abstract, and quite minimal (eg, most participants did not perceive visual movement in scenes ML4.5 and AP5); and the stability trainers provide a milder manipulation of surface compared with a swayreferenced platform. Dizziness or nausea was rarely reported and certainly did not exceed the level participants in the vestibular group experience in their daily life. In addition, the combination of a portable headmounted display and stability trainers makes this assessment easily accessible in the clinic. SampEn is commonly used in postural control research [33-36] to study the effect of aging and disease on postural sway regularity. It has been suggested that postural sway tends to become more predictable with any environmental (eg, challenging surface, eyes closed) or personal (eg, older age, disease) restrictions [7]. In our protocol, the vestibular group had somewhat lower SampEn compared with the control group, but the 2 groups had more regular sway compared with young adults [16] (0.4-0.6 in present study and 0.8-1 in healthy

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1233

Figure 6. Scatterplots illustrating the relation between ABC score and directional path during the 3 stars scenes when performed on the floor. The ABC score has a range of 0-100% (higher indicates more confidence of not losing balance in various activities). Directional path is measured in millimeters. Top row presents anteroposterior sway and bottom row presents mediolateral sway. Each scatterplot is accompanied by the Spearman correlation quantifying the observed relation. ABC, Activities-Specific Balance Confidence Scale.

young adults study). Note that the comparison of values is made possible, although the assessment was done in 2 separate studies, because we processed the signal in the same manner in the 2 studies (down-sampling by 2, no

smoothing applied) [26] and used the same parameters to calculate SampEn (r, m) [27]. Our findings are in agreement with previous studies on environmental constraints [7,8]: regularity increases (ie, SampEn

Figure 7. Scatterplots illustrating the relation between age (years) and directional path (millimeters) during the stars scenes when performed on the stability trainer for the (top) control group and (bottom) vestibular group. Each scatterplot is accompanied by the Spearman correlation quantifying the observed relation.

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Oculus Virtual Environments for Studying Balance

Figure 8. Scatterplots illustrating the relation between ABC score and directional path during all park segments (I-IV) when participants were standing on the stability trainer. The Spearman correlation coefficients accompanying the figures suggest that participants in the vestibular group who were more confident in their balance also moved more in the mediolateral plane (to avoid the ball). ABC, Activities-Specific Balance Confidence Scale.

decreases) when participants are standing on a compliant surface compared with a stable floor and when visual complexity increases (ie, dark vs light, larger sway of stars compared with minimal sway). In fact, the effect of the support surface on regularity was so dominant that any between-group differences vanished when participants were standing on the stability trainer. It is possible that the vestibular group could not decrease their degrees of freedom any further because of the constraints induced by the vestibular dysfunction, whereas the healthy group had a greater range of movement strategies available. The constraints induced by the stability trainer were primarily evident in the AP plane. In the ML plane, controls did not decrease their SampEn as much, possibly because the stance position (feet hip-width apart) provides more stability in the frontal plane. This hypothesis needs to be tested in future research comparing groups with known large differences. Note that in the present study, it is possible that the difference between groups were mild because the 2 groups involved young and older adults and some participants in the vestibular group had minimal selfreported disability. Within static stance tasks, increased DP is equated with poor postural steadiness and reflects lower balance performance [7]. Indeed, in our study, we found moderate to excellent correlations between increased DP on the stars scene and decreased self-reported

balance confidence. However, in a dynamic task, longer DP might actually reflect better balance performance because it indicates that participants can move their center of mass outside their base of support and back to center without losing their balance or fear of doing so. In our park scene, we asked participants to try to avoid a virtual ball from hitting their head. Most participants chose to move in the frontal plane (side to side) to avoid the ball. As expected, ML DP in the park scene was descriptively longer in the control group, and the negative correlation observed in the vestibular group between DP and ABC score was reversed: participants with greater balance confidence also moved away from the ball more (positive moderate correlation). Taken together, these findings suggest that a simple metric of balance, such as DP, can reflect different domains of postural control (postural steadiness and limits of stability) when applied to unique specific postural tasks. Our study included a small and diverse sample. The 2 groups were matched for age, but we also did not specify the level of severity or functional impact of vestibular dysfunction. Several participants in the vestibular group were young with mild disability and their balance performance was similar to that of controls. Given that, we are particularly excited about the observed relations between performance on our platform and self-reported balance confidence on the ABC

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in the vestibular group. The ABC is 1 of the 4 most commonly used patient-reported measures in vestibular rehabilitation [37]. Lower ABC scores have been associated with increased fall risk [18,38] in older adults. Balance confidence is a complex construct that has been associated with functional tasks of balance (Rs ¼ 0.4), duration of vestibular symptoms, general health-related quality of life, and other comorbidities [39]. According to our findings, visual dependence (operationally defined as amount of sway induced by moving stars when participants are standing on the floor) was associated with less balance confidence, whereas amount of sway induced by the stability trainer (related to somatosensory processing) increased with age. Although this finding suggests one potential underlying mechanism for less balance confidence, a causal relation cannot be inferred from an observational correlation. Future studies should examine whether interventions to improve visual integration within complex visual environments can lead to increased balance confidence and increased participation in these patients. Higher ABC scores were associated with greater ML postural sway in the park scene. However, within our paradigm, we cannot determine whether participants with less balance confidence could not move outside their base of support (ie, they would lose their balance) or simply chose not to do so for fear of falling.

their SampEn decreased. A diverse vestibular group demonstrated greater postural sway on static tasks, primarily on a mildly unstable surface, and less postural sway on a dynamic task, compared with age-matched controls. Participants with less balance confidence also displayed greater sway in response to the moving stars and less sway when asked to avoid a virtual ball. These responses were reliable, and the testing did not induce cyber-sickness. Future studies should develop norms of performance across various severities of vestibular dysfunction and age groups. The clinical implications of this novel assessment and its ability to classify severity of sensory dysfunction also should be investigated.

Study Limitations

1. Peterka RJ. Sensorimotor integration in human postural control. J Neurophysiol 2002;88:1097-1118. 2. Jeka J, Oie KS, Kiemel T. Multisensory information for human postural control: Integrating touch and vision. Exp Brain Res 2000; 134:107-125. 3. Pedalini MEB, Cruz OLM, Bittar RSM, Lorenzi MC, Grasel SS. Sensory organization test in elderly patients with and without vestibular dysfunction. Acta Otolaryngol 2009;129:962-965. 4. Toledo DR, Barela JA. Age-related differences in postural control: Effects of the complexity of visual manipulation and sensorimotor contribution to postural performance. Exp Brain Res 2014;232: 493-502. 5. Barr CJ, McLoughlin JV, van den Berg MEL, Sturnieks DL, Crotty M, Lord SR. Visual field dependence is associated with reduced postural sway, dizziness and falls in older people attending a falls clinic. J Nutr Health Aging 2016;20:671-676. 6. Alghwiri AA, Whitney SL, Baker CE, et al. The development and validation of the vestibular activities and participation measure. Arch Phys Med Rehabil 2012;93:1822-1831. 7. Strang AJ, Haworth J, Hieronymus M, Walsh M, Smart LJ Jr. Structural changes in postural sway lend insight into effects of balance training, vision, and support surface on postural control in a healthy population. Eur J Appl Physiol 2011;111:1485-1495. 8. Hoffmann CP, Seigle B, Fre `re J, Parietti-Winkler C. Dynamical analysis of balance in vestibular schwannoma patients. Gait Posture 2017;54:236-241. 9. Cohen H, Heaton LG, Congdon SL, Jenkins HA. Changes in sensory organization test scores with age. Age Ageing 1996;25:39-44. 10. Mancini M, Horak FB. The relevance of clinical balance assessment tools to differentiate balance deficits. Eur J Phys Rehabil Med 2010;46:239-248. 11. Park MK, Kim K-M, Jung J, Lee N, Hwang SJ, Chae SW. Evaluation of uncompensated unilateral vestibulopathy using the modified clinical test for sensory interaction and balance. Otol Neurotol 2013; 34:292-296.

This is a small pilot study of a new protocol developed for this population. Therefore, we urge the reader to consider the descriptive results presented in the tables and figures and take significant findings with caution. Additional limitations of this study, beyond the sample size, should be noted and addressed in future research. We did not record ABC scores of the control group, although the ABC was originally developed for community-dwelling older adults [38]. Future studies should test whether the observed relation also holds in adults without vestibular dysfunction. We did not document the side participants chose to move toward within the park scene or compare it with the side of vestibular lesion. The different avoidance strategies adopted by participants should be further described using head kinematics and reaction time. It is possible that we did not observe strong relations with DHI scores because several participants in the vestibular group did not experience disability from their symptoms (see range of scores). Conclusion Virtual reality paradigms can shed light on control mechanisms of static and dynamic postural control. In the 2 groups, participants’ postural sway increased with changes in visual and surface environments, whereas

Acknowledgment The authors thank David Lobser and Wenbo Lan (NYU Media Research Laboratory) for the design of the scenes and technical support and Erinn Kary, DPT, and Helene Darmanin, SPT, for their assistance with data collection. This study was funded by the Steinhardt School Research Development Award, New York University, 2016. References

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12. Anson E, Jeka J. Perspectives on aging vestibular function. Front Neurol 2015;6:269. 13. Polastri PF, Barela JA. Adaptive visual re-weighting in children’s postural control. PloS One 2013;8:e82215. 14. Eikema DJA, Hatzitaki V, Konstantakos V, Papaxanthis C. Elderly adults delay proprioceptive reweighting during the anticipation of collision avoidance when standing. Neuroscience 2013;234:22-30. 15. Alvarez-Otero R, Perez-Fernandez N. The limits of stability in patients with unilateral vestibulopathy. Acta Otolaryngol 2017;137: 1051-1056. 16. Lubetzky AV, Kary E, Harel D, Hujsak BD, Perlin K. Feasibility and reliability of a virtual reality oculus platform to measure sensory integration for postural control in young adults. Physiother Theo Pract 2018;24:1-16. 17. Lubetzky AV, Hujsak BD, Kary EE, Darmanin H, Perlin K. An Oculus platform to measure sensory integration for postural control in patients with vestibular dysfunction. In: 2017 International Conference on Virtual Rehabilitation (ICVR); 2017, 1-7. 18. Myers AM, Fletcher PC, Myers AH, Sherk W. Discriminative and evaluative properties of the Activities-Specific Balance Confidence (ABC) scale. J Gerontol Biol Sci Med Sci 1998;53:M287-M294. 19. Jacobson GP, Newman CW. The development of the Dizziness Handicap Inventory. Arch Otolaryngol Head Neck Surg 1990;116: 424-427. 20. Lubetzky-Vilnai A, McCoy SW, Price R, Ciol MA. Young adults largely depend on vision for postural control when standing on a BOSU ball but not on foam. J Strength Cond Res 2015;29: 2907-2918. 21. Lubetzky AV, Harel D, Darmanin H, Perlin K. Assessment via the Oculus of visual “weighting” and “re-weighting” in young adults. Motor Control 2017;21:468-482. 22. Kennedy RS, Fowlkes JE, Berbaum KS, Lilienthal MG. Use of a motion sickness history questionnaire for prediction of simulator sickness. Aviat Space Env Med 1992;63:588-593. 23. Aoki M, Tokita T, Kuze B, Mizuta K, Ito Y. A characteristic pattern in the postural sway of unilateral vestibular impaired patients. Gait Posture 2014;40:435-440. 24. Quatman-Yates CC, Lee A, Hugentobler JA, Kurowski BG, Myer GD, Riley MA. Test-retest consistency of a postural sway assessment protocol for adolescent athletes measured with a force plate. Int J Sports Phys Ther 2013;8:741-748. 25. Richman JS, Moorman JR. Physiological time-series analysis using approximate entropy and sample entropy. Am J Physiol Heart Circ Physiol 2000;278:H2039-H2049.

26. Rhea CK, Kiefer AW, Wright WG, Raisbeck LD, Haran FJ. Interpretation of postural control may change due to data processing techniques. Gait Posture 2015;41:731-735. 27. Lubetzky AV, Harel D, Lubetzky E. On the effects of signal processing on sample entropy for postural control. PLoS One 2018;13: e0193460. 28. Shrout P, Fleiss J. Intraclass correlations: Uses in assessing rater reliability. Psychol Bull 1979;86:420-428. 29. Lee P, Liu C-H, Fan C-W, Lu C-P, Lu W-S, Hsieh C-L. The test-retest reliability and the minimal detectable change of the Purdue pegboard test in schizophrenia. J Formos Med Assoc 2013;112: 332-337. 30. Haley SM, Fragala-Pinkham MA. Interpreting change scores of tests and measures used in physical therapy. Phys Ther 2006;86: 735-743. 31. Portney LG, Watkins MP. Foundations of Clinical Research: Applications to Practice. 3rd ed. Upper Saddle River, NJ: Prentice Hall; 2009. 32. Horak FB. Postural compensation for vestibular loss. Ann Natl Acad Sci 2009;1164:76-81. 33. da Costa Barbosa R, Vieira MF. Postural control of elderly adults on inclined surfaces. Ann Biomed Eng 2017;45:726-738. 34. Degani AM, Santos MM, Leonard CT, et al. The effects of mild traumatic brain injury on postural control. Brain Inj 2017;31: 49-56. 35. Yamagata M, Ikezoe T, Kamiya M, Masaki M, Ichihashi N. Correlation between movement complexity during static standing and balance function in institutionalized older adults. Clin Interv Aging 2017;12:499-503. 36. Cone BL, Goble DJ, Rhea CK. Relationship between changes in vestibular sensory reweighting and postural control complexity. Exp Brain Res 2017;235:547-554. 37. Fong E, Li C, Aslakson R, Agrawal Y. Systematic review of patientreported outcome measures in clinical vestibular research. Arch Phys Med Rehabil 2015;96:357-365. 38. Lajoie Y, Gallagher SP. Predicting falls within the elderly community: Comparison of postural sway, reaction time, the Berg balance scale and the Activities-specific Balance Confidence (ABC) scale for comparing fallers and non-fallers. Arch Gerontol Geriatr 2004; 38:11-26. 39. Marchetti GF, Whitney SL, Redfern MS, Furman JM. Factors associated with balance confidence in older adults with health conditions affecting the balance and vestibular system. Arch Phys Med Rehabil 2011;92:1884-1891.

Disclosure A.V.L. New York University, Department of Physical Therapy, Steinhardt School of Culture Education and Human Development, 380 2nd Ave, New York, NY 10010. Address correspondence to: A.V.L.; e-mail: [email protected] Disclosure: nothing to disclose. B.D.H. Vestibular Rehabilitation, The Ear Institute, Hearing and Balance Center, The New York Eye and Ear Infirmary of Mount Sinai, New York, NY Disclosure: nothing to disclose. J.L.K. Vestibular Rehabilitation, The Ear Institute, Hearing and Balance Center, The New York Eye and Ear Infirmary of Mount Sinai, New York, NY Disclosure: nothing to disclose.

G.F. New York University, Department of Physical Therapy, Steinhardt School of Culture Education and Human Development, New York, NY Disclosure: nothing to disclose. K.P. New York University, Computer Science Department, Courant Institute of Mathematical Sciences, New York, NY Disclosure: nothing to disclose. Submitted for publication March 30, 2018; accepted July 8, 2018.

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1236.e1

Appendix SEM and MDC per group and outcome measure

A: Directional path in vestibular group AP Floor

Stability trainer

ML Floor

Stability trainer

B: Directional path in control group AP Floor

Stability trainer

ML Floor

Stability trainer

ICC

Mean Test

SD Test

Mean Retest

SD Retest

ML4.5 AP5 AP32 Park I Park II Park III Park IV ML4.5 AP5 AP32 Park I Park II Park III Park IV

0.77 0.93 0.89 0.73 0.86 0.89 0.92 0.86 0.78 0.92 0.55 0.66 0.62 0.74

750.03 751.98 826.89 1504.97 1468.68 1421.51 1486.39 1765.63 1833.03 2320.68 2280.34 2307.18 2290.93 2466.58

276.28 217.67 249.63 368.27 502.57 491.35 550.32 660.46 601.77 1107.68 972.48 1026.31 1102.84 1049.52

727.41 729.78 819.37 1386.10 1390.73 1327.49 1377.81 1632.17 1800.15 2088.94 1850.92 1918.23 1857.20 1975.95

193.60 195.29 325.15 480.70 548.37 563.49 491.62 538.13 726.29 728.20 617.36 607.98 552.48 676.86

ML4.5 AP5 AP32 Park I Park II Park III Park IV ML4.5 AP5 AP32 Park I Park II Park III Park IV

0.81 0.97 0.82 0.85 0.75 0.80 0.71 0.74 0.38 0.84 0.85 0.87 0.86 0.86

423.85 448.95 449.78 2043.99 1955.88 1854.02 1825.62 795.28 790.27 845.06 2123.31 2051.64 2191.07 2212.73

158.20 200.42 153.24 591.97 784.25 795.02 753.29 295.23 372.36 363.10 1010.24 984.54 1130.40 1203.86

428.59 429.01 433.57 1910.90 1837.29 1804.40 1835.19 703.49 761.91 762.49 1698.63 1778.99 1743.31 1862.28

ML4.5 AP5 AP32 Park I Park II Park III Park IV ML4.5 AP5 AP32 Park 1 Park 2 Park 3 Park 4

0.92 0.89 0.89 0.95 0.96 0.96 0.92 0.87 0.85 0.91 0.99 0.95 0.92 0.94

623.18 659.81 679.07 1606.13 1630.07 1532.55 1668.01 1383.82 1363.17 1801.87 2274.62 2341.07 2265.88 2299.56

121.22 128.93 144.09 653.65 757.82 779.60 738.10 445.41 336.75 690.29 957.62 1278.60 1311.33 1177.57

ML4.5 AP5 AP32 Park I Park II Park III Park IV ML4.5 AP5 AP32 Park I Park II Park III Park IV

0.93 0.94 0.94 0.83 0.92 0.9 0.9 0.91 0.62 0.84 0.92 0.97 0.97 0.95

400.07 412.93 393.72 2095.29 2098.15 2091.23 2157.42 697.11 580.73 686.41 1960.60 2006.26 2014.28 2047.73

131.78 132.39 110.81 568.91 732.32 750.47 784.17 176.84 111.63 180.34 812.38 917.30 898.01 955.98

Cum SD

SEM

MDC

238.82 207.08 289.88 432.29 527.41 530.74 524.61 606.10 667.14 944.48 842.34 865.62 898.77 916.52

114.53 54.79 96.14 224.63 197.34 176.03 148.38 226.78 312.92 267.14 565.06 504.74 554.04 467.33

317.47 151.87 266.50 622.63 547.00 487.92 411.30 628.60 867.36 740.47 1566.25 1399.05 1535.71 1295.38

192.92 186.39 176.16 780.46 798.56 863.24 732.48 183.33 388.90 260.01 691.28 871.44 807.75 890.95

176.43 193.79 165.30 695.84 793.65 830.20 742.97 249.98 380.98 318.48 891.25 939.65 1007.60 1073.42

76.90 33.57 70.13 269.50 396.83 371.28 400.10 127.47 299.99 127.39 345.18 338.80 377.01 401.64

213.17 93.04 194.39 747.01 1099.94 1029.13 1109.03 353.32 831.52 353.11 956.78 939.10 1045.01 1113.28

640.48 656.94 705.58 1381.62 1424.70 1392.50 1401.69 1362.46 1359.81 1715.14 1944.35 2014.34 2016.95 2001.09

123.51 117.23 151.12 676.80 729.01 715.79 702.98 391.74 348.75 483.31 842.27 911.30 895.10 885.58

122.67 123.22 148.24 674.73 750.61 751.65 732.95 419.57 342.81 597.43 916.79 1122.20 1129.55 1052.49

34.70 40.87 49.17 150.87 150.12 150.33 207.31 151.28 132.77 179.23 91.68 250.93 319.48 257.81

96.18 113.28 136.28 418.20 416.12 416.69 574.63 419.32 368.02 496.80 254.12 695.54 885.56 714.60

388.63 404.43 406.24 1726.39 1800.85 1759.16 1800.14 707.87 641.86 713.06 1724.03 1753.67 1771.47 1799.09

130.61 126.04 149.23 757.59 928.68 863.52 965.41 260.82 202.28 208.62 593.11 725.48 772.86 711.18

131.32 129.33 131.58 694.85 849.39 825.84 897.43 222.89 166.21 195.45 721.02 836.56 846.53 851.64

34.74 31.68 32.23 286.49 240.24 261.15 283.79 66.87 102.46 78.18 203.93 144.90 146.62 190.43

96.31 87.81 89.34 794.12 665.92 723.88 786.63 185.34 283.99 216.70 565.28 401.63 406.42 527.85

(continued on next page)

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Oculus Virtual Environments for Studying Balance

Appendix (continued )

C: Sample entropy in vestibular group AP Floor

Stability trainer

ML Floor

Stability trainer

D: Sample entropy in control group AP Floor

Stability trainer

ML Floor

Stability trainer

ICC

Mean Test

ML4.5 AP5 AP32 Park I Park II Park III Park IV ML4.5 AP5 AP32 Park I Park II Park III Park IV

0.78 0.84 0.71 0.21 0.3 0.52 0.67 0.65 0.87 0.75 0.84 0.72 0.59 0.73

0.60 0.58 0.53 0.33 0.41 0.40 0.39 0.44 0.45 0.41 0.40 0.41 0.41 0.42

ML4.5 AP5 AP32 Park I Park II Park III Park IV ML4.5 AP5 AP32 Park I Park II Park III Park IV

0.81 0.72 0.73 0.74 0.42 0.85 0.65 0.89 0.86 0.93 0.84 0.8 0.72 0.63

ML4.5 AP5 AP32 Park I Park II Park III Park IV ML4.5 AP5 AP32 Park I Park II Park III Park IV ML4.5 AP5 AP32 Park I Park II Park III Park IV ML4.5 AP5 AP32 Park I Park II Park III Park IV

SD Test

Mean Retest

SD Retest

Cum SD

SEM

MDC

0.22 0.25 0.20 0.07 0.10 0.12 0.12 0.11 0.09 0.10 0.06 0.06 0.08 0.06

0.54 0.54 0.57 0.39 0.43 0.44 0.46 0.44 0.44 0.43 0.38 0.41 0.40 0.41

0.21 0.24 0.24 0.11 0.11 0.16 0.18 0.12 0.11 0.09 0.08 0.08 0.07 0.07

0.22 0.24 0.22 0.10 0.11 0.14 0.15 0.11 0.10 0.10 0.07 0.07 0.08 0.06

0.10 0.10 0.12 0.09 0.09 0.10 0.09 0.07 0.04 0.05 0.03 0.04 0.05 0.03

0.28 0.27 0.34 0.25 0.25 0.27 0.24 0.19 0.10 0.14 0.08 0.10 0.13 0.09

0.72 0.63 0.61 0.21 0.27 0.30 0.30 0.42 0.47 0.45 0.29 0.29 0.29 0.29

0.32 0.31 0.24 0.07 0.09 0.19 0.17 0.20 0.21 0.19 0.09 0.09 0.08 0.10

0.60 0.63 0.63 0.22 0.25 0.32 0.33 0.45 0.45 0.47 0.28 0.28 0.29 0.29

0.25 0.32 0.32 0.08 0.11 0.29 0.27 0.20 0.19 0.23 0.10 0.10 0.13 0.11

0.29 0.32 0.28 0.08 0.10 0.25 0.22 0.20 0.20 0.21 0.10 0.10 0.11 0.10

0.13 0.17 0.15 0.04 0.08 0.10 0.13 0.07 0.08 0.06 0.04 0.04 0.06 0.06

0.35 0.46 0.40 0.11 0.22 0.26 0.37 0.18 0.21 0.15 0.11 0.12 0.16 0.17

0.91 0.93 0.65 0.85 0.8 0.73 0.83 0.41 0.85 0.84 0.88 0.82 0.8 0.83

0.64 0.66 0.63 0.37 0.42 0.43 0.41 0.47 0.45 0.40 0.40 0.44 0.43 0.43

0.23 0.27 0.25 0.13 0.12 0.12 0.12 0.13 0.14 0.11 0.08 0.08 0.09 0.10

0.65 0.64 0.65 0.39 0.41 0.41 0.41 0.45 0.44 0.42 0.39 0.44 0.43 0.43

0.23 0.26 0.32 0.10 0.11 0.10 0.10 0.09 0.12 0.09 0.09 0.08 0.07 0.09

0.23 0.26 0.29 0.11 0.12 0.11 0.11 0.11 0.13 0.10 0.08 0.08 0.08 0.10

0.07 0.07 0.17 0.04 0.05 0.06 0.05 0.08 0.05 0.04 0.03 0.03 0.03 0.04

0.19 0.19 0.47 0.12 0.14 0.16 0.13 0.24 0.14 0.11 0.08 0.09 0.09 0.11

0.96 0.77 0.81 0.94 0.96 0.92 0.89 0.81 0.89 0.88 0.93 0.93 0.89 0.92

0.66 0.70 0.56 0.22 0.27 0.25 0.28 0.48 0.52 0.49 0.29 0.31 0.30 0.29

0.34 0.45 0.34 0.08 0.09 0.09 0.09 0.19 0.26 0.19 0.10 0.12 0.13 0.11

0.64 0.61 0.59 0.23 0.26 0.25 0.26 0.47 0.46 0.42 0.29 0.30 0.29 0.28

0.28 0.30 0.29 0.10 0.10 0.10 0.09 0.20 0.20 0.19 0.09 0.09 0.10 0.09

0.31 0.39 0.32 0.09 0.09 0.09 0.09 0.20 0.24 0.19 0.10 0.11 0.11 0.10

0.06 0.19 0.14 0.02 0.02 0.03 0.03 0.09 0.08 0.07 0.03 0.03 0.04 0.03

0.17 0.52 0.38 0.06 0.05 0.07 0.09 0.24 0.22 0.19 0.07 0.08 0.10 0.08

SEM ¼ standard error of measurement; MDC ¼ minimal detectable change; ICC ¼ intraclass correlation; SD ¼ standard deviation; Cum SD ¼ cumulative standard deviation; AP ¼ anteroposterior; ML ¼ mediolateral.