Generalizability of Stabilogram Diffusion Analysis of center of pressure measures
Introduction
Understanding the control processes functioning during quiet standing has lead to numerous techniques of data collection and analysis. In terms of data collection and interpretation, the force platform has surfaced as the preferred tool for assessment. The force platform output is usually reported as some quantification of the center of pressure (COP). Typically summary measures, such as standard deviation, sway velocity, or swept area are used to quantify the COP profile [1]. Although these type of measures are the most frequently reported, many researchers also include measurements that address the dynamic nature of COP motion. Stabilogram Diffusion Analysis (SDA) has emerged as a common technique for this specific purpose. SDA uses the tools of statistical mechanics to extract more meaningful physiological information from the COP profile [2]. This type of analysis produces several measures that describe the stochastic and deterministic nature of the COP profile. Numerous studies have used measures of SDA as an indication of postural control, e.g., [3], [4], [5], [6], [7], [8], however little information about the reliability of SDA has been reported [2], [9]. The purpose of the current study was to investigate more rigorously the reliability of SDA measures during quiet standing using Generalizability Theory (G-Theory) [10], [11], in order to propose optimal experimental protocols that produce acceptable levels of reliability.
SDA assumes that the COP during quiet stance can be modeled as a system of coupled, correlated random walks [2]. SDA provides several measures that quantify the stochastic behavior of the COP profile and these measures are intended to provide information on the underlying control processes at work during quiet standing. The diffusion coefficient (D) is an average measure of the stochastic activity of a random walker and can be thought of as an indicator of the relative stability of the system. The scaling exponent (H) provides an indication if the motion of a particle (i.e., COP) is more or less likely to continue moving in the same direction that it is currently moving. If the squared distance between two points on the COP profile is plotted against the time interval that they are separated by, it becomes apparent that there are at least two distinct regions on the plot (a short-term region, which tends to be below 1–2 s, and a long-term region of larger time intervals). This transition point is referred to as the critical point. It is quantified by the critical point coordinates—the critical time interval (CT) and critical value (CV). The critical point has been suggested to give an indication of when postural control changes from a primarily open-loop to a primarily closed-loop control process [2]. Diffusion coefficients and scaling exponents can be determined for both the short- and long-term regions of the plot. For a detailed review see Ref. [2]. It should be acknowledged that alternative methods of investigating the dynamic nature of the COP profile exist (e.g., [12], [13], [14]). We focused our investigation on the reliability of the measures calculated using the SDA approach proposed by Collins and De Luca [2].
Studies that use SDA as the primary analysis technique have employed a variety of methods to collect data. In particular, the number of trials used in past research has ranged from 1 to 10 trials and the individual trials have varied from 30 to 90 s in length [2], [3], [15]. In addition to the inconsistent methodology employed in different studies, relatively little reliability information has been reported [2], [9]. Collins and De Luca [2] reported the Intra-class Correlation Coefficients (ICC) on participants (n = 10) that completed 30 trials at 30 s of length with eyes open. ICC values were calculated on three scores; one from the first 10 trials, one from the second 10 trials, and one from the final 10 trials. They indicated that this system of summing over multiple trials to calculate a single score was employed because studies that investigate diffusion type processes typically use either a relatively long time series or a relatively large number of smaller time series. In their study, the reliability of diffusion coefficients, scaling exponents, and critical time and distance values were all found to be fair to excellent, ranging from 0.46 to 0.92. The lone exception was for the critical time value in the medial–lateral direction, which produced poor reliability (r = 0.04). Schiffman et al. [9] calculated ICC values, but only for the scaling exponent. These researchers examined the effect of repeated testing after a 7-day period. They reported fair to excellent reliability (r = 0.49–0.84) on all values except for the medial–lateral long-term values (r = 0.18). Both of these studies utilized Classical Test Theory [16] when investigating the reliability of the SDA. This approach yields a single overall measure of reliability and does not provide any information about the source of variability.
Generalizability Theory is a statistical technique intended to provide researchers with the tools to investigate measurement design and reliability. Cronbach et al. originally introduced the theory to address the weaknesses of Classical Test Theory [17]. In G-Theory, and for that matter Classical Test Theory, observed scores are composed of the true score (T) and an error component (E). In Classical Test Theory, E is singular and undifferentiated. In other words anything that contributes to the difference between the observed score and the true score is lumped into this single term. The strength of G-Theory is partially due its ability to untangle this error term. Potential sources of variance, or facets, are identified and investigated individually through a series of analysis of variance (ANOVA) procedures. Specifically G-Theory permits us to identify potential sources of variance, manipulate those sources, and design a measurement protocol that satisfies our established level of reliability. The purpose of this study was to use the tools of G-Theory to investigate the effect of the number of trials and length of these trials on the reliability of SDA.
Section snippets
Participants and procedure
Fifteen healthy college-aged individuals from a large midwestern university (7 male, 8 female; age: 19.9 ± 1.3 years, height: 1.69 ± 0.04 m, weight: 72.2 ± 12.5 kg) completed this study1. (This sample size provides similar variances as previous reliability assessments [2], suggesting that the sample is representative of the study group.) The University's Institutional Review Board for
Results
Descriptive results for SDA parameters in the anterior–posterior, medial–lateral, and radial direction, based on 10 eyes open (EO) or eyes closed (EC) trials—each sampled for 30 s, 60 s, or 90 s intervals, are summarized in Table 1, Table 2, Table 3. The descriptive results of this study are comparable with results of previous studies [12], [19]. Detailed G-study results for each SDA measure are available via electronic addendum, but are summarized below.
Discussion
Force platform analysis of quiet standing offers a non-invasive, low-impact option to investigate postural control. There is relatively little consistency in methodology employed and measurements chosen for COP analysis when using a force platform [1]. SDA offers information on the dynamic nature of the COP profile and can be used as a supplement to the summary COP measurements. There is little information about the reliability of this measurement technique in terms of the optimal number and
Conflicts of interest
None of the authors has a potential conflict of interest (e.g., consultancies, stock ownership, equity interests, patent-licensing arrangements) related to the manuscript or the work it describes.
References (19)
- et al.
Generalizability of center of pressure measures of quiet standing
Gait Posture
(2007) - et al.
Degradation of postural control system as a consequence of Parkinson's disease and ageing
Neurosci Lett
(2005) - et al.
Aging, muscle activity, and balance control: physiologic changes associated with balance impairment
Gait Posture
(2003) - et al.
Predicting the dynamic postural control response from quiet-stance behavior in elderly adults
J Biomech
(2003) - et al.
The assessment of body sway and the choice of the stability parameter(s)
Gait Posture
(2005) - et al.
Effects of carried weight on random motion and traditional measures of postural sway
Appl Ergon
(2006) - et al.
An improved technique for the extraction of stochastic parameters from stabilograms
Gait Posture
(2000) - et al.
Recurrence quantification analysis of postural fluctuations
Gait Posture
(1999) - et al.
Open-loop and closed-loop control of posture: a random-walk analysis of center-of-pressure trajectories
Exp Brain Res
(1993)
Cited by (25)
Evaluation of stabilometry descriptors for human balance function classification using diagnostic and statokinesigram data
2023, Biomedical Signal Processing and ControlOpen- and closed-loop responses of joint mechanisms in perturbed stance under visual and cognitive interference
2018, Biomedical Signal Processing and ControlCitation Excerpt :The visual condition in this experiment caused no effect on the Ds values. In contrast, there was a great consensus on the deteriorating effects of the vision removal on the standing stability in terms of the open-loop diffusion coefficient [25,27,9,53]. The difference between these results may be due to the different test conditions (unperturbed vs. perturbed stance).
Can measures of limb loading and dynamic stability during the squat maneuver provide an index of early functional recovery after unilateral total hip Arthroplasty?
2014, Archives of Physical Medicine and RehabilitationCitation Excerpt :The displacement of the center of pressure beneath each foot was analyzed separately. RMS values in both the mediolateral and anteroposterior directions were calculated as measures of dynamic stability.16,17 The R statistics programming languaged was used for all statistical procedures.
The reliability of a rambling-trembling analysis of center of pressure measures
2013, Gait and PostureSampling duration effects on centre of pressure descriptive measures
2011, Gait and PostureCitation Excerpt :The results (see Supplementary Figure) clearly demonstrate that the same effect of increased sampling duration can be observed independent of the direction in which the signal was analyzed, suggesting that fatigue has little effect on our overall findings. While the reliability of a DM calculated from a shorter duration sample may improve by averaging over multiple trials [6–9], our results clearly demonstrate that measures derived from the average of 10 × 60 s segments remain significantly different from measures derived from the entire unsegmented 600 s sample. This result is not surprising considering that each measure from the time domain, or frequency domain (using a variable frequency window), will consistently fail to capture the largest amplitude, low frequency components of a COP signal, ensuring that the average of these individual trials will consistently under- or over-estimate the true value of the COP signal, respectively.