Reliability and Validity of the Force Intersection Point in the Assessment of Human Quiet Standing Balance

Abstract

Background:

This study evaluated psychometric properties of the Intersection Point Height, derived from ground-on-feet force characteristics, as a tool for assessing balance control. We compare this metric with traditional center of pressure measurements.

Methods:

Data from a public dataset of 146 participants, divided into younger (<60) and older (≥60) adults, were analyzed. Clinical tests included Short Falls Efficacy Scale-International, International Physical Activity Questionnaire-Short Form, Trails Making Tests A and B, and Mini-Balance Evaluation Systems Test. Reliability and validity were assessed through the intraclass correlation coefficient (ICC(3,1)) for mean Intersection Point Height in each test condition and Spearman’s Rho between summative Intersection Point Height (the sum of intra-condition mean values across all test conditions within one subject) and other variables of interest, respectively.

Findings:

Mean Intersection Point Height showed good to excellent reliability (ICC=0.712–0.901), similar to that of CPvel (ICC=0.733–0.922) and greater than that of varCPx (0.475–0.768). Summative Intersection Point Height exhibited strong convergent validity with Trails Making Tests A and B (rho=0.49, p<0.001) and Mini-Balance Evaluation Systems Test (rho=−0.47, p<0.001). At most, a weak to moderate (rho=0.39–0.49, p<0.001) was found between intra-condition mean Intersection Point Height with center of pressure metrics. Intra-condition mean Intersection Point Height demonstrated weak to moderate convergent validity with several clinical measures (rho=0.32–0.52, p<0.001). In contrast, at most, a weak to moderate (rho=0.39–0.49, p<0.001) association was found between intra-condition mean Intersection Point Height with center of pressure metrics

Interpretation:

The Intersection Point Height is a reliable and valid balance measure. Further, we believe that it is a more comprehensive evaluation than center of pressure metrics.

1. Introduction:

Successful balance control that maintains upright posture is dependent on the complex interaction between sensorimotor systems and the environment [1]. Balance control impairments increase the risk of falling [2], a leading cause of death in adults age 65 and older, and a leading cause of injury across age groups [3]. Isolating and defining salient features of balance control is an important step for predicting future falls and developing targeted rehabilitation techniques for high fall-risk populations.

One method of assessing balance control is static posturography, which quantifies center of pressure (CP) movements in quiet standing on a rigid support surface (e.g., CP area, velocity, and displacement; and the spectral frequency distribution of these components) [4]. Many CP metrics have demonstrated good reliability across adults [5–7]. Additionally, CP metrics have been shown to have moderate to good convergent validity with the Berg Balance Scale, a commonly used clinical measure of balance and fall risk [7].

A recent meta-analysis of the ability of the characteristics of CP displacement to differentiate fall risk in older adults concluded that several CP metrics have demonstrated success in distinguishing fallers from non-fallers [8]. Yet, another meta-analysis [9] concludes that the precise optimal conditions for such measurement (e.g., available sensory input, inclusion of a secondary task, stance position) has yet to be determined. These authors highlight the opportunity for further research to examine relative changes in CP metrics between such task conditions to provide additional information on sensory aspects underlying control. Indeed, Quijoux et al. argue that the neurophysiological determinants of the various center of pressure parameters are not well-defined”

CP metrics have an inherent limitation in describing the neuromotor response to the mechanical demands of balance. One crucial variable in balance control is the ground-on-feet force (F). However, CP metrics fail to capture the orientation of F, which is a key aspect that influences changes in angular and translational momentum of the body [10]. Relative joint torques between body segments determine F orientation, and thus it represents the final common output of the sensorimotor mechanisms that enable balance [11]. CP metrics describe only F’s point of application (the CP), and by omission of F orientation, lack a mechanistic link with control of whole-body balance. It is not surprising then that CP metrics fail to provide a comprehensive assessment of fall risk.

Our lab previously developed a metric which combines F orientation and CP to assess control of whole-body motion. When observing the lines of action of F in the sagittal plane during a period of quiet standing, these lines converge at an intersection point (IP). See Appendix A for a more detailed explanation of the IP and its relation to the mechanics of standing. Various balance challenges have been shown to alter the height of the intersection point (zIP) [12,13] and optimal control simulation has replicated zIP behavior in a standing human model [14]. By capturing the relationship between F orientation and CP, zIP is capable of fully describing F behavior, the sole factor in modifying the body’s angular momentum during quiet standing [10]. Accordingly, zIP may provide new perspective on motor control of standing in health and disease such as stroke [15].

Reliability and validity of zIP have not been established. In this study, we assessed 1) the immediate test-retest reliability of zIP alongside that of common CP metrics, 2) the association between zIP and CP metrics, and 3) the association between zIP and measures of physical activity and balance. We hypothesized that 1) zIP would have moderate to excellent reliability, 2) that zIP would be weakly to moderately correlated with CP metrics, and 3) that zIP would be at least moderately correlated with physical activity and clinical balance assessments.

2. Methods:

This study analyzed a public posturography dataset with 163 participants [16]. The database includes demographic information and scores on standardized measures of falls risk, cognition, and physical activity. Participants were excluded from analysis in cases of missing posturography data (n = 1) and those with at least one or more disabling condition(s): hearing and vestibular (n = 8), visual (n = 2), musculoskeletal (n = 3), visual and musculoskeletal (n = 1), hearing and visual (n = 1), and intellectual (n = 1). The included participants were divided into two groups, n = 83 younger (> 18 & < 60 years) and n = 63 older adults (≥ 60 years). We analyzed the Short Falls Efficacy Scale-International (SFES-I) [17], International Physical Activity Questionnaire-Short Form (IPAQ-SF) [18], Trail Making Test (TMT) [19], and the Mini-Balance Evaluation Systems Test (mini-BEST) [20] (Appendix B). Additionally, we calculated and analyzed CP metrics and zIP (Appendix B).

2.1. Posturography:

Each participant’s 60-second duration CP time-series was assessed during three trials in four conditions presented in a condition-randomized order: firm surface with eyes open (Condition 1), firm surface with eyes closed (Condition 2), foam surface with eyes open (Condition 3), and foam surface with eyes closed (Condition 4). Posturography data were collected with a commercial platform (OPT400600–1000; AMTI, Watertown, MA, USA) and amplifier (Optima Signal Conditioner; AMTI, Watertown, MA, USA) using a sampling frequency of 100 Hz (Appendix B).

Posturography data was used to calculate root mean square (RMS) of CP velocity (CPvel), and variance of CP along the x-axis (varCPx. zIP was calculated using previously reported methodology [21] which utilizes spectral decomposition of the force components (Fx, Fz, xCP) into 16 bins of 0.2 Hz width centered at 0.6 Hz through 3.8 Hz. Next, principal component analysis was applied for each bin to extract the relationship of xCP relative to Fx/Fz, which is linear for an exact IP and with the slope yielding IP’s height (zIP). Spline fitting to zIP vs frequency data compensated for occasional insufficient power in individual frequency bins. Measured forces were referenced to the surface of the force plate, necessitating accounting for the height of the foam under the feet in zIP calculations (Appendix B). Herein, zIP is reported as fraction of body height.

2.2. Statistical Analyses:

All statistical analyses were conducted using R (version 3.6.3 or later) [22]. Between-group demographic comparisons used two-tailed t-tests or chi-squared tests. A p-value cutoff of 0.001 was applied to determine statistical significance in all analyses. P-values were adjusted using Holm’s method during the comparison of zIP and CP data. Between-group differences for the SFES-I, TMT-A and TMT-B, and mini-BEST were assessed with Wilcoxon Rank Sum tests. Chi-squared tests were used to examine between-group differences for the IPAQ-SF.

ZIP analysis metrics were mean zIP to summarize zIP across frequency bins for each standing trial, intra-condition mean zIP to summarize across all three trials for each participant within each condition, and summative zIP to summarize across the four conditions for each participant (Appendix A Fig. A3). Reliability of mean zIP across the repeated trials within each condition was assessed with the intraclass correlation coefficient (ICC[3,1]) [23]. Using the 95% confidence interval of the ICC estimate as a guide, values less than 0.5 = poor, between 0.5 and 0.75 = moderate, between 0.75 and 0.9 = good, and greater than 0.90 = excellent [24].

Due to non-normality of the datasets, convergent validity of zIP was evaluated with Spearman’s correlations for each condition’s summative zIP and intra-condition mean zIP separately with SFES-I total score, TMT-A and TMT-B times, and mini-BEST total score. Associations between zIP and CP metrics were assessed using Spearman’s correlations for intra-condition mean zIP with CPvel and varCPx for each condition. P-values for these tests are unadjusted.

3. Results:

Data from 146 participants; (83 younger [< 60 years] and 63 older [≥ 60 years] adults) were analyzed (Table 1). Intra-condition mean zIP values for each condition (p < 0.001) and summative zIP value (p < 0.001) were higher for older adults compared to younger adults (Table 2). Older adults had significantly higher mean CPvel (p < 0.001) and significantly greater mean varCPx than younger adults for conditions 3 (p < 0.001) and 4 (p < 0.001), which involved standing on foam.

Table 1.

Demographics

Variable	Older (n = 63)	Younger (n = 83)	p-value
Age (years)a	71.4 (6.4)	27.5 (7.6)	< 0.001
Sex (female)b	51 (81.0%)	53 (63.9%)	0.03
Body Mass Index (kg/m2)a	25.5 (3.1)	22.2 (2.9)	< 0.001
Taking medicationsb	55 (87.3%)	38 (45.8%)	< 0.001
Reported at least 1 fallb	15 (23.8%)	20 (24.1%)	0.99
Reported multiple fallsb	2 (3.2%)	9 (10.8%)	0.15
Number of Fallsa	0.3 (0.5)	0.7 (2.4)	0.12

^a= mean (standard deviation) with a p-value from a two-tailed t-test.

^b= n (%) with a p-value from a chi-square test.

Table 2.

Between-group Differences for zIP and CP Metrics

Variable	All	Older(n = 63)	Younger (n = 83)	p-value
zIP Metrics
Intra-condition mean zIP (Condition 1)	0.612 (0.065)	0.651 (0.043)	0.582 (0.063)	< 0.001
Intra-condition mean zIP (Condition 2)	0.618 (0.071)	0.658 (0.047)	0.588 (0.071)	< 0.001
Intra-condition mean zIP (Condition 3)	0.617 (0.054)	0.655 (0.041)	0.589 (0.044)	< 0.001
Intra-condition mean zIP (Condition 4)	0.626 (0.051)	0.660 (0.038)	0.601 (0.044)	< 0.001
Summative zIP	2.473 (0.208)	2.623 (0.121)	2.359 (0.187)	< 0.001
CP Metrics
CPvel (Condition 1)	0.010 (0.004)	0.012 (0.004)	0.008 (0.003)	< 0.001
CPvel (Condition 2)	0.011 (0.005)	0.013 (0.006)	0.009 (0.004)	< 0.001
CPvel (Condition 3)	0.028 (0.010)	0.036 (0.008)	0.022 (0.007)	< 0.001
CPvel (Condition 4)	0.035 (0.013)	0.043 (0.011)	0.030 (0.010)	< 0.001
varCPx (Condition 1)	0.027 (0.029)	0.029 (0.028)	0.026 (0.030)	0.49
varCPx (Condition 2)	0.030 (0.030)	0.027 (0.021)	0.032 (0.035)	0.31
varCPx (Condition 3)	0.114 (0.054)	0.136 (0.049)	0.098 (0.053)	< 0.001
varCPx (Condition 4)	0.132 (0.066)	0.168 (0.076)	0.105 (0.041)	< 0.001

Abbreviations: zIP = height of the intersection point (fraction of body height); CPvel = root mean square of the center of pressure velocity; varCPx = variance of the center of pressure along the anterior-posterior axis.

These data are presented as mean (standard deviation) with a p-value from a two-tailed t-test comparing older to younger.

3.1. Immediate Test-retest Reliability of the Mean zIP and CP Metrics:

Across all participants, mean zIP within each condition had ICCs = 0.809 – 0.901 (Table 3). Additionally, ICC values for mean zIP within each condition ranged from 0.741 to 0.886 and 0.712 to 0.851 for younger and older participants, respectively. Across all participants, ICCs for CPvel ranged from 0.803 to 0.922 and ICC values were 0.502 to 0.768 for varCPx within each condition. The ICC values for CPvel were 0.866 to 0.917 for younger and 0.733 to 0.829 for older participants. VarCPx had the lowest ICC values, from 0.475 to 0.666 for younger and 0.501 to 0.736 for older participants.

Table 3.

Reliability for zIP and CP Metrics

Test Conditions	Mean zIP ICC(3,1) (95% CI)	CPvel ICC(3,1) (95% CI)	varCPx ICC(3,1) (95% CI)
All Participants (N = 146)
Condition 1	0.809 (0.759, 0.853)	0.803 (0.760, 0.841)	0.625 (0.556, 0.689)
Condition 2	0.901 (0.872, 0.924)	0.856 (0.823, 0.884)	0.502 (0.422, 0.579)
Condition 3	0.838 (0.794, 0.875)	0.922 (0.903, 0.938)	0.625 (0.556, 0.689)
Condition 4	0.834 (0.789, 0.872)	0.893 (0.868, 0.915)	0.768 (0.719, 0.812)
Younger Participants (n = 83)
Condition 1	0.763 (0.681, 0.831)	0.866 (0.824, 0.901)	0.606 (0.511, 0.693)
Condition 2	0.886 (0.841, 0.921)	0.890 (0.855, 0.919)	0.475 (0.367, 0.580)
Condition 3	0.741 (0.653, 0.814)	0.917 (0.890, 0.939)	0.658 (0.571, 0.737)
Condition 4	0.784 (0.707, 0.846)	0.877 (0.837, 0.909)	0.666 (0.580, 0.743)
Older Participants (n = 63)
Condition 1	0.712 (0.603, 0.803)	0.733 (0.647, 0.806)	0.666 (0.566, 0.754)
Condition 2	0.851 (0.785, 0.902)	0.825 (0.763, 0.875)	0.615 (0.507, 0.713)
Condition 3	0.790 (0.702, 0.859)	0.813 (0.748, 0.867)	0.501 (0.378, 0.618)
Condition 4	0.747 (0.647, 0.829)	0.829 (0.768, 0.878)	0.736 (0.650, 0.808)

Abbreviations: ICC = intraclass correlation coefficient; CI = confidence interval; zIP = height of the intersection point; CPvel = root mean square of the center of pressure velocity; varCPx = variance of the center of pressure along the anterior-posterior axis.

3.2. Associations Between zIP and Other Metrics:

Associations between intra-condition mean zIP values for each condition and other metrics were found to be as follows: TMT-A (Condition 1: rho = 0.39, p < 0.001, Condition 2: rho = 0.36, p < 0.001, Condition 3: rho = 0.49, p < 0.001, Condition 4: rho = 0.45, p < 0.001), TMT-B (Condition 1: rho = 0.37, p < 0.001, Condition 2: rho = 0.32, p < 0.001, Condition 3: rho = 0.52, p < 0.001, Condition 4: rho = 0.48, p < 0.001), and mini-BEST (Condition 1: rho = −0.36, p < 0.001, Condition 2: rho = −0.32, p < 0.001, Condition 3: rho = −0.46, p < 0.001, Condition 4: rho = −0.49, p < 0.001). There was no association between intra-condition mean zIP values and SFES-I (Condition 1: rho = −0.09, p = 0.268, Condition 2: rho = −0.14, p = 0.099, Condition 3: rho = −0.2, p = 0.014, Condition 4: rho = −0.19, p = 0.019). (Table 4)

Table 4.

Correlational Analyses

	Spearman Rho (95% CI)	p-value
Associations with Intra-condition Mean zIP
Condition 1
Age	0.58 (0.46, 0.68)	< 0.001
SFES-I (Total)	−0.09 (−0.25, 0.07)	0.268
TMT-A (Time)	0.39 (0.24, 0.52)	< 0.001
TMT-B (Time)	0.37 (0.22, 0.5)	< 0.001
Mini-BEST (Total)	−0.36 (−0.49, −0.21)	< 0.001
Condition 2
Age	0.53 (0.4, 0.64)	< 0.001
SFES-I (Total)	−0.14 (−0.29, 0.03)	0.099
TMT-A (Time)	0.36 (0.21, 0.49)	< 0.001
TMT-B (Time)	0.32 (0.17, 0.46)	< 0.001
Mini-BEST (Total)	−0.32 (−0.46, −0.17)	< 0.001
Condition 3
Age	0.69 (0.59, 0.76)	< 0.001
SFES-I (Total)	−0.2 (−0.35, −0.04)	0.014
TMT-A (Time)	0.49 (0.35, 0.6)	< 0.001
TMT-B (Time)	0.52 (0.4, 0.63)	< 0.001
Mini-BEST (Total)	−0.46 (−0.58, −0.32)	< 0.001
Condition 4
Age	0.6 (0.49, 0.7)	< 0.001
SFES-I (Total)	−0.19 (−0.35, −0.03)	0.019
TMT-A (Time)	0.45 (0.31, 0.57)	< 0.001
TMT-B (Time)	0.48 (0.34, 0.59)	< 0.001
Mini-BEST (Total)	−0.49 (−0.6, −0.35)	< 0.001
Associations with Summative zIP
Age	0.69 (0.59, 0.76)	< 0.001
SFES-I (Total)	−0.18 (−0.33, −0.02)	0.031
TMT-A (Time)	0.49 (0.35, 0.6)	< 0.001
TMT-B (Time)	0.49 (0.36, 0.61)	< 0.001
Mini-BEST (Total)	−0.47 (−0.58, −0.33)	< 0.001
Association between Intra-condition Mean zIP and CPvel
Condition 1	0.39 (0.24, 0.52)	< 0.001
Condition 2	0.42 (0.28, 0.55)	< 0.001
Condition 3	0.49 (0.36, 0.61)	< 0.001
Condition 4	0.4 (0.26, 0.53)	< 0.001
Association between Intra-condition Mean zIP and varCPx
Condition 1	0 (−0.17, 0.16)	0.969
Condition 2	−0.05 (−0.21, 0.11)	0.519
Condition 3	0.13 (−0.03, 0.29)	0.109
Condition 4	0.25 (0.09, 0.39)	0.003

Abbreviations: CI = confidence interval; zIP = intersection point height; SFES-I = Short Falls Efficacy Scale - International; TMT =, Trail Making Test; mini-BEST = mini Balance Evaluation Systems Test; CPvel = root mean square of the CP velocity; varCPx = variance of the center of pressure along the anterior-posterior axis.

The associations between summative zIP and other metrics found to be as follows: TMT-A (rho = 0.49, p < 0.001) and TMT-B (rho = 0.49, p < 0.001), as well as with mini-Best (rho = −0.47, p < 0.001). There was no association between summative zIP and SFES-I (rho = −0.18, p = 0.031). (Table 4)

For all conditions, intra-condition mean zIP was associated with mean CPvel (rho = 0.39–0.49, p < 0.001), but it was not associated with varCPx (rho = −0.05–0.25, p > 0.001) (Table 4). Intra-condition mean zIP for each condition and summative zIP were associated with age (rho = 0.53–0.69, p < 0.001) and rho = 0.69, p < 0.001, respectively).

4. Discussion:

This is the first study to investigate the psychometric properties of zIP. Our data demonstrates that zIP is a reliable and valid measure of balance and that it has comparable or superior reliability as common CP metrics. Along with the greater potential explanatory power of zIP due to the incorporation of both CP location and F orientation, these data support our contention that zIP may provide additional value in quantifying balance ability compared to CP metrics.

4.1. Immediate Test-retest Reliability:

Among all conditions for all participants, the reliability of mean zIP ranged from good to excellent whereas the reliability of CP metrics ranged from moderate to excellent. Mean zIP had moderate to good reliability in younger and older adults. Comparatively, the reliability of CP metrics ranged from poor to excellent in the younger and from moderate to good in the older group.

Our results agree with previous studies that demonstrated the reliability of CP measures. For instance, the reliability of the Sensory Organization Test, which utilizes CP data to assess balance in various sensory conditions, has been established for healthy adults (ICC = 0.9) [25,26]. In healthy adults, CP path length has excellent reliability under conditions of rigid platform eyes open, rigid platform eyes closed, and foam pad eyes open (ICC = 0.93, 0.90, and 0.90, respectively) [5]. Establishing the reliability of zIP achieves an important prerequisite for translation of zIP to clinical use, as the clinician can be confident in the precision of zIP when re-testing the same person in the same conditions.

4.2. Associations Between zIP and Other Metrics:

Summative zIP demonstrates moderate convergent validity with each of the capacity-based outcome measures (i.e., the ability to perform a given task) including TMT-A, TMT-B, and mini-BEST. Intra-condition mean zIP shows similar strength in conditions 3 and 4 (foam surface), but weak validity for conditions 1 and 2 (firm surface).

Summative zIP is similar to the TMT and the mini-BEST in that each is a global measure. Summative zIP comprehensively assesses balance across sensory contexts, while the mini-BEST evaluates multiple aspects of balance, and the TMT captures diverse cognitive functions. In contrast, intra-condition mean zIP reflects condition-specific demands. Foam introduces additional postural challenge by altering proprioceptive input and requiring greater motor control. The moderate convergent validity in foam conditions suggests sensitivity to such factors. Together, summative and intra-condition mean zIP provide a comprehensive assessment of balance as a global metric while also having the capability to capture specific aspects related to challenging conditions.

The associations between zIP and the TMT as well as the mini-BEST support zIP as a potential falls risk metric. The TMT assesses visual-motor processing speed, attention, and executive functioning and the mini-BEST is an assessment of balance and mobility performance. TMT results in combination with a Random Forest Model have been found to be a good predictor of falls in the acute neurologic population [27]. These authors posed that the relationship between TMT performance and falls may stem from the ability of the TMT to measure executive function and processing speed. The mini-BEST involves challenges to cognition (dual-tasking) and attention (obstacle negotiation) and has a demonstrated ability to identify fallers in post-stroke and Parkinson’s populations [20]. These common domains between the mini-BEST and the TMT and the association performance on these tests with zIP suggests that zIP may capture the influence of executive functioning on balance.

Because of the moderate to good convergence of CP metrics in adult populations with established measures of balance such as the Timed Up-and-Go and Berg Balance Scale [28], it is important to consider the relationships between intra-condition mean zIP and CP metrics. Examining the 11 comparisons between intra-condition mean zIP and CP metrics, six show moderate correlations, one shows a weak correlation, and four show no correlation. The tenuous relationship between zIP and CP metrics supports our contention that intra-condition mean zIP and CP metrics measure different aspects of balance and that zIP captures specific aspects of balance that go beyond what traditional CP metrics measure [7,28]. The ability of zIP to provide a direct link to the angular motion mechanics required for standing, which CP lacks by itself, further supports our assertion that zIP taps into unique motor control characteristics.

4.3. Implications for Further Research and Clinical Translation:

The differences in measurement capability between CP metrics and zIP, along with their similar reliability, justifies further investigation of zIP’s utility as a balance metric. By representing not just CP movement but control of F orientation as well, zIP provides potential for greater sensitivity to small changes in balance control. Currently used measures of balance and fall risk are limited by their lack of responsiveness to small differences in ability.

Considering that zIP can be measured with relatively inexpensive equipment and requires 60 seconds (or less) of quiet standing, broad clinical adoption is feasible from a cost and time standpoint. Further examination is needed to determine the strength of zIP as a fall risk assessment. Specifically focusing on its ability to capture and differentiate small differences in motor control ability would provide valuable insights into the responsiveness of zIP as a balance metric and its potential clinical utility.

4.4. Limitations:

The current study utilizes a large dataset with multiple accepted measures of balance for study of reliability and validity, including the opportunity to derive any CP metric desired; however, we chose a small representative sample of possible CP metrics based on their high occurrence of utilization across balance studies. When analyzing zIP in trials requiring standing on foam, it was necessary to estimate the amount of foam displacement based on subject mass and foot length to determine zIP (Supplement B). While this method was used consistently across subjects, it is a possible source of variability. There were unexpected trends across age in this study population; surprisingly, the older group reported a similar level of fear of falling on SFES-I, higher levels of activity on IPAQ-SF, and fewer falls compared to the younger group (Table 5). This is despite significant differences observed between groups on TMT-A, TMT-B, and mini-BEST.

Table 5.

Between-group Differences for Self-report and Capacity-based Measures

Variable	Older (n = 63)	Younger (n = 83)	p-value
SFES-I (Total)a	9.0 (7.0 – 11.0)	10.0 (8.0 – 12.5)	0.06
IPAQ-SF (Low)b	7 (11.1%)	19 (22.9%)	< 0.001
IPAQ-SF (Moderate)b	23 (36.5%)	45 (54.2%)	< 0.001
IPAQ-SF (High)b	33 (52.4%)	19 (22.9%)	< 0.001
TMT-A (Time)a	49.4 (34.6 – 60.5)	19.8 (16.7 – 24.9)	< 0.001
TMT-A (Errors)a	0.0 (0.0 – 0.0)	0.0 (0.0 – 0.0)	0.56
TMT-B (Time)a	163.0 (91.0 – 238.9)	46.5 (36.7 – 59.0)	< 0.001
TMT-A (Errors)a	2.0 (1.0 – 3.0)	0.0 (0.0 – 1.0)	< 0.001
Mini-BEST (Total)a	18.0 (17.0 – 21.0)	24.0 (23.0 – 26.0)	< 0.001

Abbreviations: SFES-I = Short Falls Efficacy Scale-International; IPAQ-SF = International Physical Activity Questionnaire - Short Form; TMT = Trail Making Test; mini-BEST = mini Balance Evaluation Systems Test.

^a= presented as median (inter-quartile range) with a p-value from a Wilcoxon Rand Sum test.

^b= presented as N (%) with a p-value from a chi-square test.

5. Conclusion:

zIP is a reliable measure of balance with a demonstrated ability to distinguish between healthy younger and older adults. The larger potential for description of motor control of balance contained in zIP versus traditional CP metrics, paired with the finding of comparable or better psychometric properties, suggests that zIP should be considered as an improvement upon traditional measurement of balance in that it may be a more comprehensive measure of motor control. Further psychometric testing is warranted to determine reliability of zIP across longer spans of time, mimicking clinically feasible testing intervals. Additional studies should examine the responsiveness of zIP to small changes in balance ability associated with aging, disease, or response to interventions, further development of zIP as a tool for falls prevention, and the usefulness of zIP to drive referrals for rehabilitation.

• Valid and reliable, the zIP metric provides a robust measure for evaluating balance

• The reliability and validity of zIP are comparable to or surpass center of pressure (CP) metrics

• zIP has the potential to provide a more comprehensive balance assessment than CP metrics