The Relationship between Personality and Postural Control in Young Adults—A Pilot Study

Abstract

Postural control is a term used to describe how the central nervous system regulates sensory information from other systems to produce adequate motor output to maintain a controlled, upright posture. Emotions (fear, anxiety) and thus personality type can affect the strategy of body control. This study aimed to evaluate the impact of personality on postural control. Thirty-three healthy individuals participated in this study. The big-five model was used to examine personality traits. Each participant performed four different standing tasks (one and both legs standing with eyes open (eo) and closed (ec): 2eo, 2ec, 1eo, 1ec). We showed that the dominant personality traits in the study group were extraversion and agreeableness. There were significantly low negative associations between nonlinear parameters and personality traits. A moderate correlation was noted for the 1eo trial between Openness and the Lyapunov exponent. In conclusion, nonlinear measures provide a possible link between personality and postural control. The relationships detected are weak. It shows that factors such as visual control and the size of the support area rather than personality will play a significant role in describing postural control.

1. Introduction

Postural control is a concept that combines information from all possible sources, but mainly from visual, vestibular, and somatosensory systems. Thus, it describes how the central nervous system regulates sensory information to produce adequate motor output to maintain a controlled upright posture. According to Horak [1], the choice of a particular postural strategy depends not only on the characteristics of the external postural displacement but also on the individual expectations, goals, and previous experiences.

The most common and simple technique used to quantify postural control in an upright stance is the assessment of the variability of the center of pressure (CoP). Among different methods for assessing CoP, the force plate is commonly used [2]. The CoP is a measurement of whole-body dynamics and represents the sum of various neuromusculoskeletal components acting at different joint levels [3]. Since the upright standing position is a complicated task, many authors have assessed it under the influence of various stimuli or manipulations. The most simple changes involve manipulating the size of the base of support (double-limb stance, tandem stance, or single-limb stance) [2,3] or sensory (eyes opened, eyes closed) [4]. More challenging tasks involve the dual-task concept [2,5]. It is also worth noting that many groups have been studied over the years. These include healthy children [6], young people [5], the elderly [7], and those with various dysfunctions [8].

The body and mind are highly linked and interact with each other [9]. According to Briñol et al. [10] and Dael et al. [11], postural control can adapt to or depend on emotional, psychological, and relational conditions. In the available literature, numerous papers describe how provoking specific emotions (mood states, fear, and anxiety) influences postural control [12,13]. Individuals exhibiting anxiety and fearful behaviors during testing are more unstable [13,14,15]. Bolmont et al. [16] showed that adverse changes in mood states may affect balance either through sensory organization or motor control. Brown et al. [17] highlighted that, disregarding age, postural control is more conservative as anxiety increases, but age does not affect how anxiety alters the regulation of postural control. Suffering from diseases that are connected with emotional negative load, such as asthma (panic-related risk factors associated with breathing problems) contribute to changes in postural control dynamics even in young people [18]. Roerdink et al. [19] showed that internal monitoring which depends on paying greater attention to somatic sensations is connected with a decrease in postural control automaticity, and increased center of pressure regularity. Individuals with a high level of anxiety sensitivity pay greater attention to somatic sensations and have more detailed interoception than people with low-anxiety sensitivity [20]. Phenomena, being aware of bodily states, may point to less continuous inter-segmental postural coordination patterns, a more reactive pattern of forces to maintain a stance. It could be the opposed to a more active, continuous strategy, that relies on smaller corrections during a more automatic stance. On the other hand, Azevedo et al. [21], Facchinetti et al. [22], and D’Attilio et al. [23] reported some reduced body sway in participants who were exposed to unpleasant pictures. Such reduced body sway has been interpreted as “freezing behavior” induced by a stress stimulus. At this point, it is worth mentioning that anxiety is one of the main manifestations of neuroticism. Thus, it can be assumed that the emotions assessed in the abovementioned works may be closely related to personality.

Personality assessment is a field of psychology that seeks to understand and measure individual personality differences that characterize people across time. There are many personality models in circulation [24]. The most popular is the big-five model. The five-factor model of personality (FFM) is a set of five broad traits (

Table 1. Big-five factor markers and their descriptions in base on [24].

Factor Label	Descriptions	High Score	Low Score
Factor I— Extraversion	Trait that describes a person’s assertiveness, emotional expression, and comfort levels in social situations.	Thrives on socializing with others; Likes to start conversations; Finds it easy to make new friends; Enjoys being the center of attention.	Feels exhausted after socializing; Prefers being alone; Dislikes making small talk or starting conversations; Dislikes being the center of attention.
Factor II— Neuroticism (Emotional Stability)	Refers to a person’s ability to remain stable and balanced.	Gets upset more easily; Appears to always be stressed; Struggles to bounce back after troubles in life.	Emotionally stable and resilient; Deals well with stress; Rarely feels sad, moody, or depressed.
Factor III— Agreeableness	Trait that describes a person’s overall kindness, affection levels, trust, and sense of altruism.	Kind and compassionate toward others; Feels empathy and concern for other people; Prefers to cooperate and be helpful.	Does not care about other people’s feelings or problems; Takes little interest in others; Prefers to be competitive and stubborn.
Factor IV— Conscientiousness	Trait that describes a person’s ability to self-discipline and self-control.	Does not give in to impulses; Finishes important tasks on time; Enjoys adhering to a schedule; Is on time when meeting others.	Dislikes structure and schedules; Messy and less detail-oriented; Fails to stick to a schedule; Is always late when meeting others.
Factor V— Openness (Intellect/ Imagination)	Trait that describes a person’s preference for imagination, artistic, and intellectual activities.	More creative or intellectual in focus; Embraces trying new things or visiting new places; Abstract ideas come more easily.	Avoids change or new ideas; Does not enjoy new things or visiting new places; Has trouble with abstract or theoretical concepts.

)—extraversion, agreeableness, conscientiousness, neuroticism (sometimes named by its polar opposite, emotional stability), and openness to experience (sometimes named intellect) [25].

To date, few studies [26,27,28] have monitored personality traits (extraversion, introversion) concerning sports performance. One of the few consistent findings is that athletes are usually more extroverted and emotionally stable than non-athletes [27,28]. There is also evidence of a relationship between posture and personality. Guimond and Massrieh [29] established a correlative relationship between flat back and sway-back postures with introverted personalities.

In the literature to date, only Wojciechowska-Maszkowska et al. [30] and Rusnakova [31] have explored the relationships between body balance control and the big-five personality traits in the sample of football players and combat soldiers, respectively. Wojciechowska-Maszkowska et al. [30] showed that CoP variability was significantly greater in very conscientious soccer players in the anterior–posterior direction with eyes closed, while less conscientious athletes showed greater postural sway in the anterior–posterior plane during the eyes-open condition. Rusnakova [31] indicated that facets of neuroticism and conscientiousness personality traits are important factors in determining a soldier’s postural stability performance, while soldiers of combat troops who were emotionally stable and conscientious reported better postural stability performance. On the other hand, soldiers displaying higher levels of aspects of neuroticism reported a lower performance in postural stability. Another study presented that higher neuroticism was related to increased sways in a dual-task trial during the weather prediction task [32]. In the present study, we believe that the nonlinear measures that have begun to be used in recent years to assess postural control may be somehow related to personality. Despite the abovementioned attempts to link personality and postural control, nonlinear methods were not used in any papers.

Nonlinear measures provide us with information about the regularity and complexity of the system [33]. In contrast, linear measures, such as CoP path length, can quantify the body’s amount of motion while performing a specific task. Among different nonlinear methods for CoP signal evaluation, the most commonly used are sample entropy, fractal dimension, and the Lyapunov exponent.

One of the tools used to identify chaos is the presence of the positive Lyapunov exponent (LyE). Human dynamic stability characterized by LyE measures the resistance of the human locomotor control system to perturbations. A higher value of LyE indicates the ability of the postural control system to respond more flexibly and faster to different stimuli [3]. Sample entropy describes the irregularity of the CoP signal. High values of sample entropy indicate that the system is healthy, responds appropriately to disturbances, and has a high degree of automaticity [2]. The fractal dimension describes the complexity of biological signals. Higher values of the fractal dimension indicate greater adaptability of the postural control system. This study aimed to explore the relationships between postural control assessed by nonlinear measures and the five-factor model of personality. We ask the question of whether personality traits can determine postural control in healthy young adults. We hypothesized that individuals with different personalities may have different postural control under typical measurement conditions.

2. Materials and Methods

2.1. Participants and Procedures

A total of 33 healthy young adults (19 women and 14 men) participated in this study. All persons were students of the physiotherapy department of the Józef Piłsudski University of Physical Education in Warsaw, Poland. Means and standard deviations characterizing those subjects were as follows: age—21.94 ± 1.64 years old; height—173.85 ± 6.56 cm; body mass—67.45 ± 10.89 kg. The inclusion criteria comprised: (1) no balance problems (due to neurological or heart diseases, vertigo, etc.); (2) no current musculoskeletal complaints; (3) no psychological treatment. All 33 individuals expressed interest and provided their informed consent to participate in the study. The study had previously been approved by the university’s institutional review board (no. SKE01-09/2020). The study was conducted according to the ethical guidelines and principles of the Declaration of Helsinki.

Study participants underwent four balance measurements in the following order: standing barefoot on both feet with eyes open (2eo), standing on both feet with eyes closed (2ec), one leg standing with eyes open (1eo), one leg standing with eyes closed (1ec). Participants stood barefoot on the platform with arms held alongside, and each attempt lasted 30 s. All persons stood on the non-dominant (left) limb [34]. There was a 1 min break between each trial. Data collection began after participants stated that they felt stable. A trial was discarded and then repeated if (1) the non-tested limb made contact with the force platform or the stance limb; (2) the participant hopped or took a step with the stance limb; (3) the participant lifted a forefoot or heel. Center of pressure (CoP) trajectories in the anterior–posterior (AP) and mediolateral (ML) directions were measured using the AMTI AccuSway force platform (Advanced Mechanical Technology Inc., Watertown, MA, USA) integrated with Balance Clinic software at a sampling rate of 100Hz.

Before the measurement on the plate, in a separate room, personality was assessed in each subject using the IPIP-NEO-FFI-50 test (International Personality Item Pool NEO-Five Inventory 50) [35]. The validation and adaptation of IPIP-NEO-FFI-50 was performed by polish science workers Strus, Cieciuch, and Rowiński [24]. They are authors of polish adaptation. It is worth adding that IPIP-NEO-FFI-50 is the shorter version of the IPIP questionnaire [35]. Personality traits were assessed using standard “paper and pencil” questionnaires consisting of 50 items [24,35], 10 for each of the 5 dimensions of adult personality. The subjects responded to statements on a 5-point Likert scale ((1) Very Inaccurate, (2) Moderately Inaccurate, (3) Neither Accurate nor Inaccurate, (4) Moderately Accurate, or (5) Very Accurate) (Table S1). These were assessing the big-five personality model—extraversion, neuroticism, agreeableness, conscientiousness, and openness to experience—for each of the scales (consists of 10 statements). The psychologist calculated the scores from the IPIP big-five factor markers. In addition, to check and verify the validity of the calculations, each person’s test was uploaded to the open-source Psychometrics Project website (https://openpsychometrics.org/tests/IPIP-BFFM/1.php, accessed on 10 March 2022), where the scores were given automatically.

2.2. Nonlinear Parameters Calculation

The study used the linear parameter of CoP path length and three nonlinear measures, sample entropy (SampEn), fractal dimension (FD), and Lyapunov exponent (LyE), to assess CoP dynamics. CoP time series data were exported from the AMTI plate, and nonlinear coefficients were counted, using MATLAB software (MathWorks, Natick, MA, USA), separately for mediolateral (ML) and anterior–posterior (AP) directions, according to the rules described below. The data for the 30 s had 3000 points in each direction.

2.2.1. Sample Entropy (SampEn)

The sample entropy was mathematically computed as follows:

(1)

From a vector $\mathit{X}={\mathit{x}}_{\mathbf{1}},{\mathit{x}}_{\mathbf{2}},\dots ,{\mathit{x}}_{\mathit{N}}$, two sequences of m consecutive points—${\mathit{x}}_{\mathit{m}}\left(\mathit{i}\right)={\mathit{x}}_{\mathbf{1}},{\mathit{x}}_{\mathbf{2}},\dots ,{\mathit{x}}_{\mathit{i}+\mathit{m}-\mathbf{1}}$ and ${\mathit{x}}_{\mathit{m}}\left(\mathit{j}\right)={\mathit{x}}_{\mathbf{1}},{\mathit{x}}_{\mathbf{2}},\dots ,{\mathit{x}}_{\mathit{j}+\mathit{m}-\mathbf{1}}$ $\mathit{i},\mathit{j}\in \left[\mathbf{1},\mathit{N}-\mathit{m}\right],\mathit{i}\ne \mathit{j}$—were selected to compute the maximum distance and compared to tolerance, r, for repeated sequence counting, according to:

$$\mathit{d}\left[{\mathit{X}}_{\mathit{m}}\left(\mathit{i}\right),{\mathit{X}}_{\mathit{m}}\left(\mathit{j}\right)\right]=\mathit{m}\mathit{a}\mathit{x}\left[\left|{\mathit{x}}_{\mathit{i}+\mathit{k}},{\mathit{x}}_{\mathit{j}\mathit{+}\mathit{k}}\right|\right]\le r, \left(\mathit{k}\in \left[0,\mathit{m}-\mathbf{1}\right], \mathit{r}\ge \mathbf{0}\right),$$

where the tolerance r is equal to 0.1∼0.2 × SD and SD is the standard deviation of X_N [36].

(2)

${\mathit{B}}^{\mathit{m}}\left(\mathit{r}\right)$is the average amount of ${\mathit{B}}_{\mathit{i}}^{\mathit{m}}\left(\mathit{r}\right)$for $\mathit{i}\in \left[\mathbf{1},\mathit{N}-\mathit{m}\right]$ and ${\mathit{B}}^{\mathit{m}+\mathbf{1}}\left(\mathit{r}\right)$is the average of m + 1 consecutive points; thus, sample entropy can be computed as follows:

$$\mathit{SampEn}\left(\mathit{N},\mathit{m},\mathit{r}\right)=-\mathit{l}\mathit{n}\left[\frac{{\mathit{B}}^{\mathit{m}+\mathbf{1}}\left(\mathit{r}\right)}{{\mathit{B}}^{\mathit{m}}\left(\mathit{r}\right)}\right].$$

In the case of this paper, the SampEn was calculated using MATLAB codes obtained from the Physionet tool [37]. For calculating this measure, the “default” parameter values m = 2 and r = 0.2 × (standard deviation of the data) were applied.

2.2.2. Fractal Dimension (FD)

The FD was calculated using Higuchi’s algorithm [38]. Higuchi’s algorithm calculates the FD directly from the time series. Reconstruction of the attractor phase space is not necessary; therefore, this algorithm is simpler and faster than other classical measures derived from chaos theory. Moreover, it can be applied to short time series [38]. Higuchi’s algorithm can be described as follows:

(1)

For one dimensional time series: $\mathit{X}=\mathit{x}\left[\mathbf{1}\right],\mathit{x}\left[\mathbf{2}\right],\dots ,\mathit{x}\left[\mathit{N}\right]$, a new k time series can be formed as follows:

$${\mathit{X}}_{\mathit{k}}^{\mathit{m}}=\mathit{x}\left[\mathit{m}\right],\mathit{x}\left[\mathit{m}+\mathit{k}\right],\mathit{x}\left[\mathit{m}+\mathbf{2}\mathit{k}\right],\dots ,\mathit{x}\left[\mathit{m}+\mathit{i}\mathit{n}\mathit{t}\left(\frac{\mathit{N}-\mathit{m}}{\mathit{k}}\right)·\mathit{k}\right],$$

where k and m are integers, $\mathit{int}\left(\frac{\mathit{N}-\mathit{m}}{\mathit{k}}\right)$is the integral part of $\left(\frac{\mathit{N}-\mathit{m}}{\mathit{k}}\right)$, k indicates the discrete time interval between points, whereas m = 1, 2, …, k.

(2)

The length of each new time series can be defined as follows:

$$\mathit{L}\left(\mathit{m},\mathit{k}\right)=\left({{\displaystyle \sum }}_{\mathit{i}=\mathbf{1}}^{\mathit{int}\left(\frac{\mathit{N}-\mathit{m}}{\mathit{k}}\right)}\left|\mathit{x}\left[\mathit{m}+\mathit{i}\mathit{k}\right]-\mathit{x}\left[\mathit{m}+\left(\mathit{i}-\mathbf{1}\right)\mathit{k}\right]\right|\right)·\left(\frac{\mathit{N}-\mathbf{1}}{\mathit{int}\left(\frac{\mathit{N}-\mathit{m}}{\mathit{k}}\right){\mathit{k}}^{\mathit{2}}}\right),$$

where N is length of the original time series X.

(3)

The length of the curve for the time interval k is defined as the average of the k values L(m, k), for m = 1, 2, …, k:

$$\mathit{L}\left(\mathit{k}\right)=\frac{\mathbf{1}}{\mathit{k}}{\displaystyle \sum }_{\mathit{m}=\mathbf{1}}^{\mathit{k}}\mathit{L}\left(\mathit{m},\mathit{k}\right).$$

Finally, when L(k) is plotted against 1/k on a double logarithmic scale, with k = 1, 2, …, k_max, the data should fall on a straight line, with a slope equal to the FD of X. Thus, Higuchi’s FD is defined as the slope of the line that fits the pairs $\left(\mathit{l}\mathit{n}\left[\mathit{L}\left(\mathit{k}\right), \mathit{l}\mathit{n}\left(\frac{\mathbf{1}}{\mathit{k}}\right)\right]\right)$ in a least-squares sense. In order to choose an appropriate value of the parameter k_max, Higuchi’s FD values were plotted against a range of k_max. The point at which the FD plateaus was considered a saturation point, and that k_max value should be selected [39]. A value of k_max = 100 was chosen for our study.

2.2.3. The Lyapunov Exponent (LyE)

The LyE was computed using an algorithm that estimates the dominant Lyapunov exponent of a 1D timeseries by monitoring orbital divergence. The algorithm was distributed for many years by Wolf et al. [40] in Fortran and C language. In this case, the same code was adopted into MatLab [41].

2.3. Statistical Analysis

Statistical analyses were performed using PQStat v.1.8.2 (PQStat Software, Poznań, Poland), with the significant p-value set at 0.05. All coefficients (scores for five personality traits, nonlinear and linear parameters) were tested for normal distribution, using the Shapiro–Wilk test. The effect sizes were calculated as Cohen’s d (0.0–0.2—trivial; 0.2–0.6—moderate; 0.6–1.2—large; >1.2—very large) [42]. The application of Friedman’s ANOVA with the Dunn–Bonferroni test showed which personality is dominant in the study group (extroversion and agreeableness). Then, the medians were calculated for each of the five personality traits. For a given feature, values higher than the median were marked as high. Based on these findings, the study group was divided into two groups (H, D). Group H consisted of individuals characterized by high extroversion and agreeableness. Group D consisted of the remaining participants.

Next, the nonparametric Mann–Whitney U test with Z statistics was used to compare whether there was a difference between the two distinguished personality groups for all parameters. The Kruskal–Wallis ANOVA and post hoc Dunn test with Bonferroni correction was applied to examine differences between the trials (2eo, 2ec, 1eo, and 1ec) for all coefficients.

The last step was to find the correlation between the five personality traits and the results of the nonlinear and CoP path length parameters calculated for each of the four standing trials (2eo, 2ec, 1eo, 1ec). The Spearman rank correlation was applied here. Our interpretations of the size of a correlation coefficient is shown in

Table 2. Interpreting the size of a correlation coefficient.

Size of Correlation (Range)	Interpretation
(0.90, 1.00); (−0.90, −1.00)	Very high positive (negative correlation)
(0.70, 0.90); (−0.70, −0.90)	High positive (negative) correlation
(0.50, 0.70); (−0.50, −0.70)	Moderate positive (negative) correlation
(0.30, 0.50); (−0.30, −0.50)	Low positive (negative) correlation
(0, 0.30); (0, −0.30)	Negligible correlation

[43].

3. Results

3.1. Big-Five Personality Group Characteristics

The Shapiro–Wilk test showed that the results for the extraversion, neuroticism (emotional stability), and agreeableness traits did not have a normal distribution. The application of Friedman’s ANOVA (F(4, 128) = 19.92, p = 0.0001, d = 0.58) with the Dunn–Bonferroni test revealed that the trait scores for extraversion were significantly higher compared with conscientiousness (p = 0.0239) and intellect (p = 0.0008). In addition, agreeableness trait scores were also significantly higher than for the intellect trait (p = 0.0239) (Figure 1).

This result allows us to conclude that extraversion and agreeableness are the dominant personality traits in the study group. Individuals with a high personality component had scores above the median. According to these outcomes, the study group was divided into two groups (H, D). Group H included individuals characterized by high extraversion and agreeableness. There were 15 such individuals (age—21.67 ± 1.72 years old; height—173.2 ± 5.98 cm; body mass—66 ± 8.69 kg). Group D (N = 18; age—22.17 ± 1.58 years old; height—174.39 ± 7.13 cm; body mass—68.67 ± 12.56 kg) consisted of the remaining participants. The use of the U-Mann test showed no differences between the anthropometric characteristics of the groups.

3.2. Effects of Personality and Trial on Nonlinear Parameter Results

Starting the analysis with the classical application of the U Mann–Whitney test, we noted that there are no differences between the personality groups (H vs. D) (effect size: d = 0.58) for the studied parameters within each trial (2eo, 2ec, 1eo, and 1ec).

When comparing the results of nonlinear parameters and CoP path length between trials, we obtained statistically significant differences for all parameters (SampEn_ML: H (3, N = 132) = 28.49, p = 0.0001; SampEn_AP: H (3, N = 132) = 17.16, p = 0.0007; FD_ML: H (3, N = 132) = 45.07 p = 0.0001; LyE_ML: H (3, N = 132) = 55.04, p = 0.0001; LyE_AP: H (3, N = 132) =36.91, p = 0.0001; CoP path length: H (3, N = 132) =28.15, p =0.0001) except FD_AP. After applying Dunn’s post hoc test with Bonferroni correction, the values of all the abovementioned parameters were significantly (p = 0.0001) higher for the 1ec trial compared to those obtained during bipedal standing with and without visual inspection (2eo and 2ec) (Figure 2). Additionally, LyE values in both directions (AP and ML) in the 1ec trial were significantly (p = 0.0001) higher than those for the 1eo (Figure 2D,E). The parameters SampEn_ML, FD_ML, and CoP path length had significantly higher values in the 1eo trial compared with those noted for the 2eo and 2ec (Figure 2A,C,F). For LyE_ML, significant differences were reported only between 1eo and 2ec (Figure 2D).

3.3. Correlations of Nonlinear within Trials Coefficients with Personality Scores

Within each trial (2eo, 1eo, 2ec, 1ec), Spearman correlations of nonlinear coefficients with the scores obtained for each personality trait were conducted (

Table 3. The significant Spearman correlations of personality traits scores with nonlinear parameter values within trials (p ≤ 0.05).

Trial	Correlations	Interpretation
2eo	Agreeableness and SampEn_ML (r = −0.37)	Low negative association
	Openness and SampEn_AP (r = −0.44)	Low negative association
2ec	Neuroticism and FD_ML (r = 0.47)	Low positive association
	Conscientiousness and FD_ML (r = −0.34)	Low negative association
	Agreeableness and LyE_ML (r = −0.41)	Low negative association
1eo	Neuroticism and FD_ML (r = −0.50)	Moderate negative association
	Openness and LyE_AP (r = 0.53)	Moderate positive association
	Openness and LyE_ML (r = 0.59)	Moderate positive association
1ec	Openness and LyE_AP (r = 0.37)	Low positive association

).

4. Discussion

In the present study, subjects underwent four typical tests to assess postural stability. The trials included a combination of the width of the base of support (double- and single-leg stance) and visual control (eyes open and closed). Additionally, the big-five test (International Personality Item Pool NEO-Five Inventory 50) was used to assess personality.

Postural fluctuations can be measured using different techniques [3]. These include linear and nonlinear measures. The most common linear parameters are path length, sway area, and sway velocity. The higher the values of the above measures, the more the person sways [5]. Recently, a growing body of papers has argued that postural stability is achieved through the interaction of many different systems. These interactions can occur both within a single sense and between senses (for example, visual and vestibular [2,3,5]). Thus, the resulting data may be inherently nonlinear. Consequently, they are best studied using analyses based on nonlinear dynamics approaches. This study aimed to explore the relationships between postural control assessed by nonlinear measures and the five-factor model of personality. Given that the functioning of the nervous system may be related to emotions [44,45], we asked whether personality could be a determinant of postural control. Moreover, we hypothesized that individuals with different personalities may have different postural control under typical measurement conditions.

In this study, we showed that the dominant personality traits in the study group were extraversion and agreeableness. It seems that such a result correctly characterizes the study group, which comprised physiotherapy students. Individuals with high levels of extraversion and agreeableness are characterized by, among others, compassion towards others, empathy, and caring towards other people (Table 1). This is the kind of personality a physiotherapist should have. This result is in line with the paper of Bartram [46]. The author showed that a higher level of economic performance, higher levels of educational enrollment, life expectancy, and GDP are associated with increased variability in personality across countries. It was associated with higher average levels of extraversion, emotional stability, and agreeableness.

In our study, despite the division of the individuals into two groups differing in terms of personality, it was not possible to find differences between the parameters assessing postural control in individual trials (2eo, 2ec, 1eo, and 1ec). Thus, it is reasonable to conclude that personality is not a significant determinant of postural control and the hypothesis in the study group was disproved. It is worth noting that the tasks to which individuals were subjected were not intended to induce stress and thus affect the neurotic part of the personality. However, it is worth noting that the selected tests are the most commonly used in the clinic.

Going deeper, we tried to find associations between the results of nonlinear parameters counted for successive trials (2eo, 2ec, 1eo, and 1ec) and personality traits among all studied individuals. We have shown that in the 2eo, there is a low negative association between sample entropy with agreeableness and openness. Such a result can be explained by the fact that the greater the regularity and complexity of a signal, and the amount of attention given to the performance of a given task (high sample entropy), the lower is the dominance of agreeableness and openness. Standing on two legs with eyes closed (2ec) resulted in a low positive association of fractal dimension (calculated in ML direction) with neuroticism and a low negative association with conscientiousness. According to Kedziorek and Blazkiewicz [3], higher FD values indicate a more complex and irregular signals over time. Thus, in this trial, an increase in the complexity of the CoP signal may be slightly associated with individuals with a higher neuroticism component and lower conscientiousness. Furthermore, this test showed a low negative association of the Lyapunov coefficient (calculated in the ML direction) with agreeableness. It is worth noting that higher LyE values indicate the ability to respond more quickly to destabilizing stimuli and better balance control [47]. This result may suggest that individuals who display a high LyE in the lateral–medial direction during 2ec have a low agreeableness component. On the other hand, the performance of the 1ec test causes the openness component to increase with the increase in the LyE value (low positive association).

All the above correlations were at low levels. Nevertheless, stronger positive associations of the Lyapunov exponent with openness appeared during the one-leg standing test with eyes open (1eo). Thus, as openness increases, the body’s response to destabilizing stimuli may increase faster. In the same trial, the increased values of FD in the ML direction were accompanied by a moderate decrease in neuroticism. From a clinical perspective, individuals with high neuroticism scores should be better managed. They tend to become upset quickly, appear constantly stressed, and cope inappropriately with psychological stress [24]. It is well known that fear and anxiety affect postural control [12]. Individuals with anxiety disorders, phobias have changes in balance control compared with healthy controls [48]. People suffering from depression resulted in increased postural stability (greater ellipse area indicating worse postural control) in working memory tasks (dual-task) [49]. To test how emotions influence postural control, researchers manipulated fear and anxiety in individuals using a different height of the surface (height-induced postural threat) [50], showing pictures known to elicit negative emotional responses [51]. In addition, studies on social evaluative threat have demonstrated the influence of emotion on postural control. The evaluative social threat was due to the evaluative audience or social comparison. Doumas et al. [52] showed that stress induced using a combination of arithmetic tasks and social evaluative threat leads to systematic changes in postural control. The introduction of social evaluative threat caused a reduction in postural sway and increased reaction time relative to the time pressure condition. Significant increases in frequency and amplitude were observed during a two-legged stance without vision control while being assessed by an evaluator [53]. Kuznetsov [18] investigated whether young adults with asthma have impaired balance, and whether this impairment is related to altered musculoskeletal function and/or psychological characteristics. Participants with asthma showed a different postural control strategy in the absence of any obvious balance impairment. They were characterized by more regular dynamics (lower values of sample entropy in anterior–posterior direction) compared with controls. In summary, this examined group had greater anxiety sensitivity to physical concerns and that was associated with more regular center of pressure dynamics. Rusnakova [31] examined combat soldiers using personality traits and postural control measurements. The results showed that self-conscious soldiers had poorer postural stability than those who were self-disciplined, deliberate, and possess high achievement needs. Founded correlations resulted that a conscientious personality is also more emotionally stable.

Moving on to the comparison of postural control between trials, it is worth noting that the results do not differ from those reported in other papers [3]. The values of all the linear and nonlinear parameters were significantly higher for the 1ec trial compared with those obtained during bipedal standing with and without visual control (2eo and 2ec) and higher concerning the 1eo trial. This result suggests that the CoP signal during 1ec was more complex (high FD) and irregular in time (high SampEn), but also that individuals were able to better control their balance (high LyE). Observing the effect of visual control within both bipedal (2eo vs. 2ec) and one-leg standing (1eo vs. 1ec), it can be seen that the linear and nonlinear parameter values did not change significantly. This suggests that, for the study group, visual control itself was not a significant factor in affecting postural control. The only significant change was noted for the one-leg standing trial, for the Lyapunov exponent, which increased during the trial without visual control. This result is in line with the study of Raffalt et al. [54] and Ghofrani, Olyaei, Talebian, Bagheri, and Malmir [33]. More differences were noted when the base of support was reduced (1eo vs. 2eo and 1ec vs. 2ec). It has been shown that the reduction in the base of support without visual control causes a significant increase in the values of all parameters. On the other hand, reducing the base of support with eyes open increases only the values of CoP path length, SampEn_ML, and FD_ML. Therefore, it can be concluded that the reduction in the support surface area is an important factor influencing the control of body posture.

In summary, nonlinear measures provide a possible link between personality and postural control. The relationships detected are weak. It shows that factors such as visual control and the size of the support area will play a significant role in describing postural control. The paper is not without its limitations. The major problem seems to be the number of individuals, which affects the inability to divide into the same groups in terms of personality. Having such study groups, additional, more challenging trials could be created. New trials could potentiate certain personality traits (fear). Another limitation was the use of only one personality questionnaire. Multiple tools should be used to assess fear and anxiety, stress resistance, pathological personality traits, motivation, or mental health models. Using more difficult postural tasks, increasing the group of participants, and testing new questionnaires should provide more conclusive results. Furthermore, subsequent studies are suggested to examine groups varying in age (young, old) and environment/background (athletes, people working in business, scientists, etc.).

5. Conclusions

According to current knowledge and the literature, the relationships between personality traits and postural control is not clear yet. These findings promise to provide an important rule for understanding the neuromechanics that control healthy balance. This work highlights the need to recognize the potential contributions of psychological and physiological factors toward balance. Supporting individuals at increased risk for falls often involves influencing factors that change rapidly and are fully or partially corrected. Therefore, starting to influence selected personality traits can be effective prevention. The results of the present experiment, although moderately, indicate the influence of extraversion and agreeableness on postural stability. The use of relaxation techniques could help develop appropriate defense mechanisms in the psychological field.