Comparison of a portable balance board for measures of persistence in postural sway

Journal Pre-proofs Short communication Comparison of a portable balance board for measures of persistence in postural sway Zachary S. Meade, Vivien Marmelat, Mukul Mukherjee, Takashi Sado, Kota Z. Takahashi PII: DOI: Reference:

S0021-9290(20)30003-8 https://doi.org/10.1016/j.jbiomech.2020.109600 BM 109600

To appear in:

Journal of Biomechanics

Received Date: Revised Date: Accepted Date:

30 April 2019 21 December 2019 31 December 2019

Please cite this article as: Z.S. Meade, V. Marmelat, M. Mukherjee, T. Sado, K.Z. Takahashi, Comparison of a portable balance board for measures of persistence in postural sway, Journal of Biomechanics (2020), doi: https:// doi.org/10.1016/j.jbiomech.2020.109600

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Comparison of a portable balance board for measures of persistence in postural sway. Authors: Zachary Meade, Vivien Marmelat, Mukul Mukherjee, Takashi Sado, Kota Takahashi

Zachary S. Meade, BSEE M.D. Candidate Carle Illinois College of Medicine Research Assistant University of Nebraska at Omaha Department of Biomechanics Vivien Marmelat, PhD Assistant Professor Department of Biomechanics University of Nebraska at Omaha Mukul Mukherjee, PhD Associate Professor Department of Biomechanics University of Nebraska at Omaha Takashi Sado, MS Research Assistant Department of Biomechanics University of Nebraska at Omaha Kota Z. Takahashi, PhD (Corresponding Author) Assistant Professor Department of Biomechanics University of Nebraska at Omaha 6160 University Drive, Omaha, Nebraska 68182 402-554-3228 [email protected] Keywords: Wii Balance Board; Nintendo; variability; center of pressure; detrended fluctuation analysis Abstract Words: 249

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Comparison of a portable balance board for measures of persistence in postural sway. Authors: Zachary Meade, Vivien Marmelat, Mukul Mukherjee, Takashi Sado, Kota Takahashi

Abstract: Measuring postural sway is important for determining functional ability or risk of falling. Gathering postural sway measures outside of controlled environments is desirable for reaching populations with limited mobility. Previous studies have confirmed the accuracy of the magnitude of postural sway using the Nintendo Wii Balance Board (WBB). However, it is unclear if the WBB can accurately measure persistence of postural sway, i.e., the pattern of center-of-pressure fluctuations over time. The purpose of this study was to compare measures of persistence of postural sway (through detrended fluctuation analysis) using WBB and a force platform (FP). Seventeen healthy individuals performed three standing conditions: eyes open, eyes closed, and one-leg standing. The WBB (30 Hz) was placed on top on the FP (600 Hz) to collect data simultaneously, then the FP data were downsampled to 100 Hz and 30 Hz. The agreement between WBB and FP for measures of postural sway were influenced by the sampling rate and postural sway direction. Intraclass correlation coefficient was excellent (range: 0.953 – 0.998) for long-term scaling regions in the anterior-posterior direction, but lower (range: 0.352 – 0.877) and inconsistent for medial-lateral direction and short-term scaling regions. The three comparison groups (WBB at 30 Hz, FP at 30 Hz, and FP at 100 Hz) showed dissimilar abilities in detecting differences in persistence of postural sway. In summary, the WBB is accurate for quantifying persistence of postural sway measurements in long-term scaling regions in the AP direction, but has limitations for short-term scaling regions and the ML direction.

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Comparison of a portable balance board for measures of persistence in postural sway. Authors: Zachary Meade, Vivien Marmelat, Mukul Mukherjee, Takashi Sado, Kota Takahashi

Zachary S. Meade, BSEE M.D. Candidate Carle Illinois College of Medicine Research Assistant University of Nebraska at Omaha Department of Biomechanics Vivien Marmelat, PhD Assistant Professor Department of Biomechanics University of Nebraska at Omaha Mukul Mukherjee, PhD Associate Professor Department of Biomechanics University of Nebraska at Omaha Takashi Sado, MS Research Assistant Department of Biomechanics University of Nebraska at Omaha Kota Z. Takahashi, PhD (Corresponding Author) Assistant Professor Department of Biomechanics University of Nebraska at Omaha 6160 University Drive, Omaha, Nebraska 68182 402-554-3228 [email protected] Keywords: Wii Balance Board; Nintendo; variability; center of pressure; detrended fluctuation analysis Words: 2066 (includes titles and acknowledgements). Due to additional analysis and request for more info, we had to add a significant amount of detail.

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Comparison of a portable balance board for measures of persistence in postural sway. Authors: Zachary Meade, Vivien Marmelat, Mukul Mukherjee, Takashi Sado, Kota Takahashi

Abstract: Measuring postural sway is important for determining functional ability or risk of falling. Gathering postural sway measures outside of controlled environments is desirable for reaching populations with limited mobility. Previous studies have confirmed the accuracy of the magnitude of postural sway using the Nintendo Wii Balance Board (WBB). However, it is unclear if the WBB can accurately measure persistence of postural sway, i.e., the pattern of center-of-pressure fluctuations over time. The purpose of this study was to compare measures of persistence of postural sway (through detrended fluctuation analysis) using WBB and a force platform (FP). Seventeen healthy individuals performed three standing conditions: eyes open, eyes closed, and one-leg standing. The WBB (30 Hz) was placed on top on the FP (600 Hz) to collect data simultaneously, then the FP data were downsampled to 100 Hz and 30 Hz. The agreement between WBB and FP for measures of postural sway were influenced by the sampling rate and postural sway direction. Intraclass correlation coefficient was excellent (range: 0.953 – 0.998) for long-term scaling regions in the anterior-posterior direction, but lower (range: 0.352 – 0.877) and inconsistent for medial-lateral direction and short-term scaling regions. The three comparison groups (WBB at 30 Hz, FP at 30 Hz, and FP at 100 Hz) showed dissimilar abilities in detecting differences in persistence of postural sway. In summary, the WBB is accurate for quantifying persistence of postural sway measurements in long-term scaling regions in the AP direction, but has limitations for short-term scaling regions and the ML direction.

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Comparison of a portable balance board for measures of persistence in postural sway. Authors: Zachary Meade, Vivien Marmelat, Mukul Mukherjee, Takashi Sado, Kota Takahashi

Introduction Standing balance measurements can be invaluable in determining functional ability and risk of falling (Roman-Liu, 2018). Variability during postural sway has been shown to characterize healthy and pathological systems (Goldberger et al., 2002; Quatman-Yates et al., 2015; van Emmerik and van Wegen, 2002; Zhou et al., 2017). Specifically, persistence from center of pressure displacement (i.e., how variability of postural control fluctuates over time) is a useful description of postural control in healthy populations (Rand et al. 2015; Rhea et al., 2011) and in the investigation of pathologies such as Parkinson’s disease and developmental disorders (Deffeyes et al., 2009; Minamisawa et al., 2009; Stergiou and Decker, 2011). Obtaining measurements of postural control outside of a laboratory setting may be helpful to increase data accessibility, but portable laboratory-grade force platforms may be cost prohibitive. Previous studies have shown that a cost-effective portable device such as the Nintendo Wii Balance Board (WBB) obtains similar measures of postural sway compared to a force platform in healthy and pathological populations (Clark et al., 2010; Holmes et al., 2013; Young et al., 2011). Supporting studies examined traditional measures of postural control including center-of-pressure displacement as quantified by root-mean-square or velocity-based measures. However, it is currently unknown whether the WBB can accurately measure persistence in postural sway. The purpose of this study is to compare the accuracy of measures in persistence of postural sway (quantified by detrended fluctuation analysis [DFA], Peng et al., 1995) of centerof-pressure displacement obtained between a Wii Balance Board and a force platform. We hypothesized that the WBB and the force plate will show high agreement with intraclass

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Comparison of a portable balance board for measures of persistence in postural sway. Authors: Zachary Meade, Vivien Marmelat, Mukul Mukherjee, Takashi Sado, Kota Takahashi

correlations greater than 0.9. Lastly, we hypothesized that two devices will show similar ability to detect differences in DFA measures between various standing conditions.

Methods Participants Seventeen healthy volunteers were recruited (3 females; mean age 24.5±4.5 years; height 1.75±0.08m; body weight 78.7±10.0kg). Exclusion criteria included self-reported pregnancy, gait or balance abnormality, and below 19 or above 35 years old. The local Institutional Review Board approved the study and all participants provided informed consent.

Equipment A Wii Balance Board (Nintendo; Kyoto, Japan) was placed centrally upon a flush mounted laboratory grade force platform (AMTI; Watertown, MA, USA). This allowed for simultaneous data collection which has been shown to negate subject-to-subject variability while acquiring highly similar results (Huurnink et al., 2013). Both devices were zeroed and the WBB was calibrated each time by placing a known weight (20.4 kg) on the board and utilizing software by Cooper et al. (2014). The WBB has a plastic composite build with four load sensors on each corner and measures vertical forces. Freely available software was used to interface the WBB via Bluetooth to a laptop and extrapolate data (Cooper et al., 2014). As a previous study demonstrated measures of persistence varies with sampling rate (Rhea, et al., 2011; Rhea et al., 2015), the FP was sampled at 600 Hz and downsampled to 100 Hz and 30 Hz for comparison to the WBB which was sampled at 30 Hz. The frequency 30 Hz was chosen due to similar 6

Comparison of a portable balance board for measures of persistence in postural sway. Authors: Zachary Meade, Vivien Marmelat, Mukul Mukherjee, Takashi Sado, Kota Takahashi

frequencies (10 – 50 Hz) used in prior studies (Haworth and Stergiou 2018; Huisinga 2012; Kent 2012; Rand 2015; Rand and Mukherjee 2018; Schneipp et al., 2013). A railing was placed around the force platforms for the safety of the participants.

Procedures Participants conducted standing trials in randomized order for three different conditions: quiet standing on both legs with eyes open (i.e., Eyes Open), quiet standing on both legs with eyes closed (i.e., Eyes Closed) and standing on one leg with eyes open (i.e., One-leg). Each trial lasted three minutes and participants were asked to stand comfortably while maintaining eye contact on an ‘X’ placed at each participant’s preferred eye level. During the One-leg trial, participants were asked to stand on their right leg (regardless of dominance) in a comfortable manner. Outlines of each participant’s feet were traced to ensure consistent foot placement between trials. Trials were marked invalid and repeated if the indicated standing position was violated.

Data Processing and Analysis Center of pressure (CoP) displacement data for the WBB was extracted from freely available software (Cooper et al., 2014). A custom-written Matlab (MathWorks, Natick, RI, USA) script was created to analyze and compare data between the WBB and the FP. The FP data was downsampled from 600 Hz to 100 Hz and 30 Hz. Data between WBB and FP were timesynchronized using a pre-built MATLAB function (AlignSignals), which utilizes crosscorrelation, allowing the lagging or leading signal to be trimmed accordingly. The data were not

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Comparison of a portable balance board for measures of persistence in postural sway. Authors: Zachary Meade, Vivien Marmelat, Mukul Mukherjee, Takashi Sado, Kota Takahashi

filtered in any other way. The CoP data were then plotted and compared against each other for accuracy (Figure 1). [Insert Figure 1] Figure 1: (A) Representative example of center of pressure in AP direction (CoP) plotted across time for force platform (FP) data at 30 Hz (dashed line), 100 Hz (solid line), and WBB (dotted line). The WBB (dotted line) is highly correlated and overlaps significantly with the FP signal, thus a zoomed in section is provided for clarity. (B) Example anterior-posterior detrended fluctuation analysis (DFA) plots. The slopes represent the DFA alpha values for the short-term and long-term regions Root-mean-square (RMS) was used to quantify the magnitude of postural sway from the CoP displacement. To quantify the persistence of postural sway, detrended fluctuation analysis (DFA) was applied on the CoP displacement (Rand et al., 2015). Persistence of CoP signals have been shown to have multiple scaling regions (i.e. areas of significance at different time lengths). Using the ginput function in Matlab, an inflection point was chosen through a visual inspection of the log/log plot where the slope became less steep (Figure 1B). On average, the inflection point was at a time scale of 1.43s. The persistence of postural sway was assessed on each side of the inflection point for two scaling regions (Collins and De Luca,1994; Kuznetsov et al., 2013; Rand and Mukherjee, 2018): a ‘short-term’ region (ranging from 0.16 to 0.82s) characterizing CoP fluctuations happening in less than a second, and a ‘long-term’ region (ranging from 2.15 to 11.85s), characterizing CoP fluctuations happening on a longer time scale over a few seconds. The algorithm for DFA was constructed in MATLAB using descriptions in prior studies (Peng, et al. 1995; Damouras, et al. 2010; Mirzayof and Ashkenazy, 2010). Statistical analysis was done using SPSS software (IBM, Subscription Version). Twoway mixed absolute agreement intraclass correlation, also referred to as ICC(3,1) (Bartko, 1966; Koo and Li 2016) was calculated between the WBB and FP at both sampling rates for RMS and 8

Comparison of a portable balance board for measures of persistence in postural sway. Authors: Zachary Meade, Vivien Marmelat, Mukul Mukherjee, Takashi Sado, Kota Takahashi

DFA measures. Normal distribution of the data was confirmed with the Shapiro-Wilk test. Oneway repeated measures ANOVA was used to compare differences in measures of RMS and DFA across the three standing conditions (Eyes Open, Eyes Closed, and One-leg) for the WBB and FP data (sampled at 30 and 100 Hz). This test was used to examine the ability of each device to detect differences in postural sway measurements among the standing conditions. For statistical differences additional ANOVA analyses were conducted as post hoc test, and a Bonferroni correction was used. Level of statistical significance was set at p < 0.05.

Results For RMS measures, the averaged intraclass correlation coefficient between WBB and FP was 0.979 for both 30 Hz and 100 Hz (Table 1). For DFA measures, the agreement between the WBB and FP was dependent on the FP sampling rate, scaling region, and the direction of postural sway. The averaged intraclass correlation coefficients were 0.989 and 0.971 for longterm regions in the anterior-posterior (AP) direction at 30 Hz and 100 Hz respectively, but decreased for all other conditions, notably in all of the medial-lateral (ML) and short-term regions (Table 1). [Insert Table 1] Table 1: Intraclass correlation using a two-way mixed absolute agreement methods, also known as ICC(3,1) (Koo and Li 2016) were conducted between the Wii Balance Board at 30 Hz and the Force Platform at 30 Hz, and 100 Hz for root-mean-square, and both short-term and long-term scaling regions of detrended fluctuation analysis in the anterior-posterior (AP) and mediallateral (ML) directions. One-way repeated measures ANOVA showed no statistically significant differences of RMS values at any sampling rate (p>0.132) in both directions. One-way repeated measures ANOVA showed there were significant differences of DFA measures between the WBB and the 9

Comparison of a portable balance board for measures of persistence in postural sway. Authors: Zachary Meade, Vivien Marmelat, Mukul Mukherjee, Takashi Sado, Kota Takahashi

FP across sampling rates for the short-term region in both directions (AP and ML). Pairwise comparison demonstrated significant differences between both devices and all frequencies. Comparison of the long-term regions showed no statistical differences in the AP direction (p = 0.313) but differences in the ML direction (p =0.005). Comparison of the short-term regions showed differences in both the AP (p<0.001) and ML (p<0.001). Pairwise comparison demonstrated difference between the WBB at 30 Hz compared to the FP at 30 Hz and 100 Hz. The WBB and FP had differences in their ability to detect significant differences in DFA measures between various standing conditions (Table 2, Figure 2). The FP at 30 Hz detected four significant differences when the FP at 100 Hz did not. The WBB at 30 Hz detected two significant differences when the FP at 100 Hz did not, but the WBB did not detect one difference that the FP at 100 Hz did. [Insert Figure 2] Figure 2: The Wii Balance Board shows identical ability to detect differences in RMS across three standing conditions (Eyes Open, Eyes Closed, and One-leg) when compared to the force plate at 30 Hz or 100 Hz. However, the DFA-derived measures differed between all three devices in their ability to detect various standing conditions. Brackets and ‘*’ denote significant difference between conditions (p<0.05). [Insert Table 2] Table 2: Comparison of detrended fluctuation analysis alpha value mean differences between the Wii Balance Board (WBB) and force platform (FP) at 30Hz and 100Hz across three different standing conditions: eyes open, eyes closed, and one-leg. Significant values are bolded.

Discussion The main purpose of this study was to investigate the WBB’s ability to measure persistence in postural control. First, our results are consistent with previous literature with respect to the WBB’s ability to assess magnitude of sway across sampling rates (Clark, et al., 10

Comparison of a portable balance board for measures of persistence in postural sway. Authors: Zachary Meade, Vivien Marmelat, Mukul Mukherjee, Takashi Sado, Kota Takahashi

2010). We also found that i) the WBB provides excellent ability to measure persistence in postural sway (measured with DFA) for long-term regions in the AP direction compared to a FP at 30 Hz or 100 Hz, but has less accuracy in the short-term regions and ML directions; and ii) the ability to quantify differences between standing tasks when considering persistence in postural sway differs between a WBB at 30 Hz, a FP at 30 Hz and a FP at 100 Hz. We found that the estimates of DFA using the WBB were comparable to that of the FP at both sampling rates (30 and 100 Hz) in the long-term regions of the AP direction, but differed greatly in short-term regions and in the ML direction. This may necessitate a visual inspection of the data, in particular where the crossover region occurs which was on average 1.42s, and longterm regions which occurred between 2.15 – 11.85s on average. Differences in alpha values of the scaling regions could be due to the temporal nature of DFA which may be altered using various processing techniques, specifically downsampling (Rhea et al., 2015). There was no clear pattern of detecting differences between standing conditions (i.e. eyes open vs. eyes closed vs. one leg) when analyzing long-term and short-term scaling regions, different devices and sampling rate. This is an interesting result given that posture data is typically collected between 10 Hz and 50 Hz (Rand et al., 2015). In these cases, it should be noted that previous research suggests temporal structure may be altered when downsampling (Rhea et al., 2015). When utilizing the WBB for postural sway measurements, several limitations should be considered. There is a general consensus that the Wii is not interchangeable, but rather a comparable alternative for certain circumstances that should be used with caution and consideration (Bonnechère et al. 2016; Clark et al., 2010; Holmes et al., 2013; Pagnacco et al., 2011; Ruff et al., 2015; Young et al., 2011; Young 2014). Specifically, there has been debate 11

Comparison of a portable balance board for measures of persistence in postural sway. Authors: Zachary Meade, Vivien Marmelat, Mukul Mukherjee, Takashi Sado, Kota Takahashi

about the Wii’s lack of FDA approval, inter-device reliability due to manufacturing standards (i.e. systematic bias and standard error of measure), and overall quality of center of pressure measurements (Clark et al., 2010; Pagnacco et al., 2011; Reed-Jones, 2013; Young et al., 2011; Young 2014). In summary, the WBB is an accurate tool to quantify measures of postural sway using traditional measures (e.g., RMS of CoP displacement), but measures of persistence (derived via DFA) have notable limitations. Persistence measures from WBB have excellent ICC values (range: 0.953 – 0.998) in the AP direction for long-term scaling regions, mostly moderate ICC values (range: 0.517 – 0.877 ) for AP direction for short-term scaling regions, and poor to moderate ICC values (range: 0.352 – 0.603) in the ML direction for both long-term and shortterm regions (Koo and Li 2016). Given our data, we fail to fully support our hypothesis that the FP and WBB would be able to detect similar differences in standing conditions. Persistence data collected by the WBB is sufficient in the AP long-term scaling regions which can provide useful information; however, caution should be used if information from the short-term region or ML direction is desired.

Conflicts of interest statement There are no conflicts of interest.

Acknowledgements Funding was provided by the Funding for Undergraduate Scholarly Experience (FUSE) at the University of Nebraska at Omaha, and the Barry Goldwater Scholarship and Excellence in Education Foundation for ZM. This study was also supported by the Center of Biomedical Research Excellence grant (1P20GM109090-01) from NIGMS/NIH for KT, VM and MM, a NASA EPSCoR grant (80NSSC18M0076), and an American Heart Association award (18AIREA33960251) for MM. The content is solely the responsibility of the authors and does not necessarily represent the official views of NASA, NIH or AHA. 12

Comparison of a portable balance board for measures of persistence in postural sway. Authors: Zachary Meade, Vivien Marmelat, Mukul Mukherjee, Takashi Sado, Kota Takahashi

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Comparison of a portable balance board for measures of persistence in postural sway. Authors: Zachary Meade, Vivien Marmelat, Mukul Mukherjee, Takashi Sado, Kota Takahashi

Huisinga, J.M., Yentes, J.M., Filipi, M.L., Stergiou, N., 2012. Postural control strategy during standing is altered in patients with multiple sclerosis. Neuroscience Letters 524, 124-128. Huurnink, A., Fransz, D.P., Kingma, I., van Dieën, J.H., 2013. Comparison of a laboratory grade force platform with a Nintendo Wii Balance Board on measurement of postural control in single-leg stance balance tasks. Journal of Biomechanics 46(7), 1392-1395. Kent, J.S., Hong, S.L, Rolbecker, A.R., Klaunig, M.J., Forsyth, J.K., O’Donnel, B.F., Hetrick, W.P., 2012. Motor Deficits in Schizophrenia Quantified by Nonlinear Analysis of Postural Sway. PLOS One 7(8), e41808. Koo, Terry K., Li, Mae Y., 2016. A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research. Journal of Chiropractic Medicine, 15(2), 155-163. Kuznetsov, N., Bonnette, S., Gao, J., Riley, M.A., 2013. Adaptive Fractal Analysis Reveals Limits to Fractal Scaling in Center of Pressure Trajectories. Annals of Biomedical Engineering 41, 16461660. Minamisawa, T., Takakura, K. & Yamaguchi, T., 2009. Detrended Fluctuation Analysis of Temporal Variation of the Center of Pressure (CoP) during Quiet Standing in Parkinsonian Patients. Journal of Physical Therapy Science 21, 287–292. Mirzayof, D., Ashkenazy, Y., 2010. Preservation of long range temporal correlations under extreme random dilution. Physica A: Statistical Mechanics and its Applications 24, 5573-5580. Pagnacco, G., Oggero, E., Wright, C.H.G., 2011. Biomedical instruments versus toys: a preliminary comparison of force platforms and the Nintendo Wii Balance Board. Biomedical Sciences Instrumentation 47, 12–17. Peng, C.K., Havlin, S., Stanley, H.E., Goldberger, A.L., 1995. Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. Chaos 5, 82-87. Quatman-Yates, C.C., Bonnettee, S., Hugentobler, J.A., Médé, B., Kiefer, A.W., Kurowski, B.G., Riley, M.A., 2015. Postconcussion Postural Sway Variability Changes in Youth: The Benefit of Structural Variability Analyses. Pediatric Physical Therapy 27, 316-327. Rand, T. J., Myers, S. A., Kyvelidou, A. & Mukherjee, M., 2015. Temporal Structure of Support Surface Translations Drive the Temporal Structure of Postural Control During Standing. Annals of Biomedical Engineering 43, 2699–2707. Rand, T.J., Mukherjee, M 2018. Transitions in persistence of postural dynamics depend on the velocity and structure of postural perturbations. Experimental Brain Research 236, 1491. 14

Comparison of a portable balance board for measures of persistence in postural sway. Authors: Zachary Meade, Vivien Marmelat, Mukul Mukherjee, Takashi Sado, Kota Takahashi

Reed-Jones, R.J., 2014. Current limitations of the Wii’s validity and what should be considered for the future. Gait and Posture 39, 1149-1161. Rhea, C.K., Silver, T.A., Hong, S.L., Ryu, J.H., Studenka, B.E., Hughes, C.M.L., Haddad, J.M., 2011. Noise and Complexity in Human Postural Control: Interpreting the Different Estimations of Entropy. PLoS ONE 6. Rhea, C.K., Kiefer, A.W., Wright, W.G., Raisbeck, L.D., Haran, F.J., 2015. Interpretation of postural control may change due to data processing techniques. Gait & Posture 41, 731–735. Roman-Liu, Danuta, 2018. Age-Related Changes in the Range and Velocity of Postural Sway. Archives of Gerontology and Geriatrics 77, 68–80. Ruff, J., Wang, T.L., Quatman-Yates, C.C., Phieffer, L.S., Quatman, C.E., 2015. Commercially available gaming systems as clinical assessment tools to improve value in the orthopaedic setting: a systematic review. International Journal of the Care of the Injured 46, 178–183. Schneipp, R., Wuehr, M., Pradhan, C., Novozhilov, S., Krafczyk, S., Brandt, T., Jahn K., 2013. Nonlinear Variability of Body Sway in Patients with Phobic Postural Vertigo. Frontiers in Neuroscience 15, 115. Stergiou N., Decker L.M., 2011. Human movement variability, nonlinear dynamics, and pathology: is there a connection? Human Movement Science 30, 869–888. van Emmerik, R.E.A., van Wegen, E.E.H., 2002. On the Functional Aspects of Variability in Postural Control. Exercise and Sport Sciences Reviews 30, 177-183. Young, W.R., Ferguson, S., Brault, S., Craig, C., 2011. Assessing and training standing balance in older adults: A novel approach using the ‘Nintendo Wii’ Balance Board. Gait and Posture 33, 303-305. Young, W.R., 2014. We need a balanced perspective in Wii-search. Gait and Posture 39, 11561157. Zhou, J., Habtemariam, D., Iloputaife, I., Lipsitz, L.A., Manor, B., 2017. The Complexity of Standing Postural Sway Associates with Future Falls in Community-Dwelling Older Adults: The MOBILIZE Boston Study. Scientific Reports 7, 2924.

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Comparison of a portable balance board for measures of persistence in postural sway. Authors: Zachary Meade, Vivien Marmelat, Mukul Mukherjee, Takashi Sado, Kota Takahashi

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Comparison of a portable balance board for measures of persistence in postural sway. Authors: Zachary Meade, Vivien Marmelat, Mukul Mukherjee, Takashi Sado, Kota Takahashi

Figure Legend Figure 1: (A) Representative example of center of pressure in AP direction (CoP) plotted across time for force platform (FP) data at 30 Hz (dashed line), 100 Hz (solid line), and WBB (dotted line). The WBB (dotted line) is highly correlated and overlaps significantly with the FP signal, thus a zoomed in section is provided for clarity. (B) Example anterior-posterior detrended fluctuation analysis (DFA) plots. The slopes represent the DFA alpha values for the short-term and long-term regions Figure 2: The Wii Balance Board shows identical ability to detect differences in RMS across three standing conditions (Eyes Open, Eyes Closed, and One-leg) when compared to the force plate at 30 Hz or 100 Hz. However, the DFA-derived measures differed between all three devices in their ability to detect various standing conditions. Brackets and ‘*’ denote significant difference between conditions (p<0.05). Table 1: Intraclass correlation using a two-way mixed absolute agreement methods, also known as ICC(3,1) (Koo and Li 2016) were conducted between the Wii Balance Board at 30 Hz and the Force Platform at 30 Hz, and 100 Hz for root-mean-square, and both short-term and long-term scaling regions of detrended fluctuation analysis in the anterior-posterior (AP) and medial-lateral (ML) directions. Table 2: Comparison of detrended fluctuation analysis alpha value mean differences between the Wii Balance Board (WBB) and force platform (FP) at 30Hz and 100Hz across three different standing conditions: eyes open, eyes closed, and one-leg. Significant values are bolded.

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Comparison of a portable balance board for measures of persistence in postural sway. Authors: Zachary Meade, Vivien Marmelat, Mukul Mukherjee, Takashi Sado, Kota Takahashi

Intraclass Correlation Coefficient

N = 17 Root-Mean-Square

Detrended Fluctuation Analysis Long

Detrended Fluctuation Analysis Short

Force Platform 30Hz

Force Platform 100Hz

Force Platform 30Hz

Force Platform 100Hz

Force Platform 30Hz

Force Platform 100Hz

Eyes Open

AP: 0.992 ML: 0.991

AP: 0.992 ML: 0.991

AP: 0.995 ML: 0.561

AP: 0.989 ML: 0.364

AP: 0.701 ML: 0.461

AP: 0.660 ML: 0.381

Eyes Closed

AP: 0.977 ML: 0.984

AP: 0.977 ML: 0.984

AP: 0.998 ML: 0.582

AP: 0.971 ML: 0.569

AP: 0.657 ML: 0.375

AP: 0.640 ML: 0.522

One Leg

AP: 0.981 ML: 0.951

AP: 0.981 ML: 0.951

AP: 0.975 ML: 0.352

AP: 0.953 ML: 0.398

AP: 0.517 ML: 0.603

AP: 0.877 ML: 0.468

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Comparison of a portable balance board for measures of persistence in postural sway. Authors: Zachary Meade, Vivien Marmelat, Mukul Mukherjee, Takashi Sado, Kota Takahashi

Long Region DFA Measures Between Standing Conditions Anterior-Posterior DFA Mean Difference

Medial-Lateral DFA Mean Difference

WBB 30 Hz

FP 30 Hz

FP 100 Hz

WBB 30 Hz

FP 30 Hz

FP 100 Hz

Eyes Open vs Eyes Closed

0.128 (p = 0.065)

0.112 (p = 0.102)

0.118 (p = 0.151)

0.073 (p = 0.201)

0.1­39 (p = 0.013)

0.101 (p = .131)

Eyes Open vs One Leg

0.128 (p 0.056)

0.125 (p = 0.035)

0.131 (p = 0.061)

0.106 (p = 0.028)

0.075 (p = 0.783)

0.062 (p = 1.00)

Eyes Closed vs One Leg

0.00007 (p = 1.00)

0.013 (p = 1.00)

0.013 (p = 1.00)

0.033 (p = 0.602)

0.065 (p = 0.609)

0.039 (p = 1.00)

N = 17

Short Region DFA Measures Between Standing Conditions Eyes Open vs Eyes Closed

0.015 (p = 1.00)

0.017 (p = 1.00)

0.004 (p = 1.00)

0.026 (p = 0.157)

0.035 (p = 0.696)

0.004 (p = 1.00)

Eyes Open vs One Leg

0.040 (p 0.407)

0.133 (p = 0.003)

0.018 (p = 1.00)

0.048 (p = 0.132)

0.112 0.062 (p < 0.001) (p = 0.049)

Eyes Closed vs One Leg

0.056 (p = 0.114)

0.116 (p = 0.004)

0.015 (p = 1.00)

0.075 (p = 0.025)

0.077 0.058 (p = 0.143) (p = 0.159)

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