Supplementary balance training fails to protect against fatigue-induced sway in professional volleyball players

Abstract

Background

Professional volleyball demands high-level neuromuscular control, yet frequent physical exhaustion during competition can compromise postural stability. While balance training is often utilized in athletic training programs, its specific efficacy in mitigating fatigue-induced deficits among elite athletes remains under-investigated. The aim of this study was to determine whether 12 weeks of supplementary balance training, integrated into a routine athletic schedule, improves fatigue-induced postural sway in professional female volleyball players.

Methods

Twenty-three professional female volleyball players from two professional teams participated in this non-randomized, controlled study. One team implemented supplementary balance training in addition to routine practice, whereas the other team continued their standard seasonal program. Eleven players were in the training team, and twelve were in the control team. Postural sway was assessed using the Overall Stability Index (OSI), Anterior-Posterior Stability Index (APSI), and Medial-Lateral Stability Index (MLSI) with the Biodex Balance Systems SD device. Measurements were taken at baseline and multiple time points (0, 10, 20, 30 min) after Bruce protocol, both before and after the 12-week intervention. Data were analyzed using a robust rank-based, non-parametric method and by estimating relative treatment effects (RTEs) with 95% confidence intervals (CIs).

Results

Post-intervention analysis revealed no significant group-by-time interaction for OSI (p = 0.303), APSI (p = 0.397), or MLSI (p = 0.113). These results indicate that the trajectory of balance loss and recovery following high-intensity exercise was statistically similar for both groups. While a significant main effect of time was observed (p < 0.001), confirming that high-intensity exercise impaired stability, the supplementary balance training did not provide a statistically superior resistance to fatigue compared to routine training alone. Consistent with the non-significant group-by-time interaction tests (p > 0.05 for all stability indices), the 95% CIs of the RTEs overlapped substantially at all post-exercise time points; however, inferential conclusions are based on the interaction test results.

Conclusions

Supplementary balance training did not provide a statistically significant advantage in mitigating fatigue-induced postural instability compared to routine training in this sample.

Introduction

Maintaining postural balance is a fundamental skill required for the execution of complex athletic movements [1]. Consequently, balance training (BT) has been extensively used not only for the prevention and rehabilitation of lower-extremity injuries but also for the enhancement of sport-specific motor skills [2]. Both recreational enthusiasts and elite athletes frequently incorporate specialized balance exercises into their regimens to optimize postural control [3].

A primary factor contributing to the decline in postural stability is the onset of focal or generalized muscular fatigue [4–6]. Research indicates that high-intensity aerobic exercise significantly increases postural oscillation during static stance [7]. While the impact of physical fatigue on static postural sway is widely documented, its specific relevance and magnitude within competitive sports settings remain a subject of ongoing debate [8]. For instance, longitudinal data from British football teams have shown that injuries occur predominantly in the final minutes of each half, suggesting a positive correlation between injury incidence and muscular fatigue [9].

It is well established that muscular fatigue in the lower extremities can impair the proprioceptive and kinesthetic feedback mechanisms of the joints [10]. Numerous studies have demonstrated that postural sway increases significantly following various fatigue protocols [11–14]. Although balance is easily maintained during daily activities, external perturbations (e.g., unexpected shifts, slippery surfaces) or internal stressors (e.g., muscular stiffness, musculoskeletal disorders, or fatigue) can severely disturb stability [14]. Previous interventions have shown that specific balance exercises can improve postural control in both static and dynamic conditions [15, 16].

Despite the established benefits of balance training in recreational settings, limited evidence exists regarding the protective efficacy of balance training when implemented as a supplemental educational module for professional female athletes who have reached a high-performance plateau. Furthermore, despite the growing number of intervention studies in volleyball athletes [17], evidence regarding balance performance under exercise-induced fatigue conditions remains limited. Therefore, the present study sought to determine whether an additional balance education regimen, performed beyond the standard seasonal routine, could significantly mitigate fatigue-induced postural disturbances and enhance recovery kinetics in this elite population. We hypothesized that 12 weeks of supplementary balance training (10 min/day, 5 days/week) would significantly attenuate fatigue-induced postural sway and accelerate recovery kinetics compared to routine training alone.

Methods

Participants and study design

The study was conducted in accordance with the Declaration of Helsinki, and formal ethical approval was obtained from the Ankara University School of Medicine Ethics Committee. Clinical trial number: not applicable. All athletes were fully informed about the study procedures and provided voluntary written informed consent prior to participation.

This study employed a team-based (cluster) quasi-experimental design with two clusters (teams), utilizing non-randomized allocation and no blinding. Due to shared training environment, coaching strategies, and match exposure, players within the same team cannot be considered statistically independent. Therefore, the findings should be interpreted with caution, as the unit of allocation (team) differs from the unit of analysis (individual player). Twenty-three professional female volleyball players from two teams in the Turkish Women’s Second League participated in this study. One professional volleyball team implemented supplementary balance training in addition to their routine program, while the other team continued their standard seasonal training without additional intervention. Eleven players were in the supplementary balance-training team, and twelve were in the control team. Due to the limited number of professional female volleyball players available within a single team structure, all eligible athletes (n = 23) were recruited.

While no a priori sample size calculation was performed because the study aimed to include the entire accessible population of professional athletes within the participating teams, a post-hoc power analysis was conducted (G*Power 3.1). For a mixed-model design with a moderate effect size (f = 0.25), an alpha level of 0.05, and a power of 0.80, a total sample size of 20 participants was required. Thus, the final sample of 23 professional athletes was considered adequate to detect moderate effects under the assumptions of the specified statistical model. The physical characteristics of the participants are presented in Table 1.

Table 1.

Anthropometric parameters (M ± SD)

Anthropometric Parameters	Control Group	Training Group
Height (cm)	179.36 ± 8.5	180 ± 8.4
Age (Years)	20.4 ± 3.5	21.8 ± 3.7
Weight (kg)	67.18 ± 4.8	68 ± 8.9

Inclusion and exclusion criteria

To ensure the homogeneity of the sample, specific eligibility criteria were applied. No participants were involved in rehabilitation programs at the time of the study. All athletes had no history of ankle inversion injuries or other lower extremity pathologies, including fractures, sprains, or arthritis, in the 6 months prior to the study.

Standard training routine

Throughout the 12-week intervention, both groups maintained their regular seasonal training schedule. This routine consisted of six two-hour volleyball training sessions per week and one official match per week. Every training session commenced with a standardized 20-minute general warm-up protocol. While the control group followed only this standard routine, the experimental group received additional balance education as part of their program.

Experimental procedure and design

The study was conducted over three separate sessions. The first two sessions took place at the beginning of the season to establish baseline data, while the final session was performed after the 12-week intervention. All measurements were conducted on the athletes’ dominant leg, defined as the leg used for takeoff during a spike or serve. In volleyball, this is typically the leg opposite the hitting arm. This approach is supported by previous findings that indicate no significant difference in unilateral postural stability between the functionally dominant and non-dominant limbs in healthy young adults [18].

First session

Participants underwent a physical examination and were thoroughly briefed on the study protocol. To ensure data reliability, athletes were instructed to be well-rested and to maintain a fasting period of at least 3–4 h prior to testing.

Second session

Performed on the following day, involved baseline postural stability measurements followed by a high-intensity exercise protocol on a treadmill (Technogym Runrace 1200HC) using the Bruce protocol.

Third session

Carried out at the end of the 12th week, repeating the baseline and post-fatigue measurements.

Submaximal exercise-induced fatigue condition

A graded exercise test using the Bruce protocol was administered [19]. The test was terminated when participants reached 85% of their age-predicted maximum heart rate. This threshold was selected as a standardized endpoint; therefore, the protocol was submaximal by design and did not aim for true maximal exhaustion. All participants reached the predefined 85% HRmax threshold, and no tests were terminated due to volitional exhaustion. Heart rate was continuously monitored using a Polar device. Detailed objective fatigue markers such as blood lactate levels or ratings of perceived exertion (RPE) were not recorded. The protocol commenced on the treadmill at a speed of 2.74 km/h (1.7 mph) and a 10% incline.

Post-fatigue assessments

Immediately following the termination of the Bruce protocol (0 min) and at subsequent intervals of 10, 20, and 30 min, postural sway measurements were repeated. Athletes remained seated in a rested position between these measurement intervals.

Balance training (BT) program

Following the initial assessments, the experimental group (EG) participated in a 12-week supplementary balance training program, whereas the control group (CG) continued only their routine volleyball training.

The additional balance education was implemented five days a week, excluding match and rest days. These sessions lasted 10 min and were integrated into the warm-up phase of routine training. Each exercise was performed for 3 × 30 s, resulting in approximately 9 min of active training, with the remaining time used for brief transitions between exercises. The exercise content remained consistent throughout the 12-week intervention period, and no structured progression in difficulty (e.g., surface instability, visual conditions, or external perturbations) was implemented. This decision was made to isolate the effect of a standardized, low-volume supplementary balance stimulus in an already highly trained population, and to prevent an increased risk of overuse injuries. The program utilized wobble boards pivoting 360 degrees and BOSU balls to provide varied unstable surfaces. A wobble board is a hemisphere or smaller spherical cap (or an approximately similar shape) whose flat side is attached at the center of the board’s underside. (Fig. 1). This allows the board to pivot in all directions during a single ride: forward–backward, left–right, and anywhere in between, i.e., toward 360 degrees. A Bosu ball consists of an inflated rubber hemisphere attached to a rigid platform. The device is often used for balance training. All sessions were performed under supervision as part of routine team training. All players in the training group completed 100% of the prescribed sessions according to coach records. No injuries or adverse events related to the balance training program were reported. Each session included six distinct exercises, as detailed in Table 2.

Fig. 1.

Balance training

Table 2.

Balance training exercises

Exercises	Time*
One leg standing balance exercise on the bosu ball	3x30
Two legs standing balance exercise on the bosu ball	3x30
One leg squat balance exercise on the bosu ball	3x30
One leg lateral toe touches balance exercise on the bosu ball	3x30
One leg standing balance exercise on the wobble board	3x30
Two legs standing balance exercise on the wobble board	3x30

*Time in seconds

Measurement of postural stability

Postural sway was quantified using the Biodex Balance System SD (Biodex Medical Systems, Shirley, NY, USA) with “Athletic Single Leg Test” protocol (Fig. 2). The reliability of this system has been previously validated [20].

Fig. 2.

Balance measurements

Test protocol

Familiarization

Subjects were given practice trials to become familiar with the moving platform and to determine optimal foot positioning.

The platform’s instability was set to level 5.

All measurements were performed with athletes wearing non-skid footwear, as recommended by the device manufacturer.

Data collection

Once the platform was released, subjects were required to maintain their balance on the dominant leg for three trials of 20 s each.

Participants were instructed to keep the platform as level as possible without touching the side supports.

The device automatically calculated the Anteroposterior Stability Index (APSI), Mediolateral Stability Index (MLSI), and Overall Stability Index (OSI). Lower scores in these indices represent superior postural control and smaller deviations from the center of gravity.

Statistical analysis

Due to the non-normal distribution of the data (confirmed by Shapiro-Wilk’s test, p < 0.05), non-parametric tests were employed for hypothesis testing. Continuous variables were summarized using medians and minimum-maximum ranges. To analyze longitudinal data from repeated observations in both groups, the robust, rank-based, nonparametric method proposed by Brunner and Puri [21] was used. Although players within the same team share training and environmental exposures, the analysis treated individual players as independent observations. The reported p-values and confidence intervals do not account for potential within-team clustering. The F1-LD-F1 design was implemented to evaluate three primary null hypotheses: (1) no main effect of time, (2) no main effect of group, and (3) no group-by-time interaction effect. Relative Treatment Effects (RTEs) were calculated as descriptive point estimators. An RTE represents the probability that a randomly selected observation from a specific group or time point is larger than a randomly selected observation from the entire dataset, regardless of group or time [22]. Under the null hypothesis, all groups and time points are expected to have an RTE of 0.50. Post hoc descriptive comparisons were performed using 95% confidence intervals (CIs) for RTEs. Overlap of CIs was considered a descriptive observation consistent with non-significant interaction effects, but formal statistical inference was based on the group-by-time interaction p-values from the nparLD model. Statistical significance was set at p < 0.05. All analyses were performed using R version 4.2.2 (R Development Core Team). The ‘nparLD’ package (version 2.1) was used for non-parametric longitudinal data analysis [23]. The RTEs and their corresponding 95% CIs are visualized in Figs. 3, 4 and 5.

Fig. 3.

Overall stability index (Tr =Training, Ex = Exercise, m: Minute, Total N = 23: CG N = 12, TG N = 11)

Fig. 4.

Anterior/posterior stability index (Tr =Training, Ex = Exercise, m: Minute, Total N = 23: CG N = 12, TG N = 11)

Fig. 5.

Medial/lateral stability index (Tr =Training, Ex = Exercise, m: Minute, Total N = 23: CG N = 12, TG N = 11)

Results

Comparison of postural stability at baseline and follow-up

At baseline, no significant differences were observed between the Supplementary Balance Training (SBT) group and the control group (CG) in postural stability. Following the 12-week intervention, baseline values for OSI, APSI, and MLSI did not differ significantly between the two groups based on the non-significant group effect. The overlapping 95% confidence intervals were consistent with this finding.

Effects of high-intensity exercise on postural sway

The high-intensity exercise protocol induced a significant increase in postural sway across all.

measured indices. A highly significant main effect of time was detected for OSI (p < 0.001), APSI (p < 0.001), and MLSI (p < 0.001). High-intensity exercise significantly impaired stability in all athletes, regardless of their group assignment.

Efficacy of supplementary balance training

The primary analysis revealed no significant group-by-time interaction for any of the stability indices.

OSI: After adjusting for repeated measures (though not accounting for team-level clustering), the group-by-time interaction was non-significant (p = 0.303). As shown in Fig. 3, the RTE trajectories for both groups followed a nearly identical pattern of post-exercise impairment and subsequent recovery, with only minimal separation between groups across the recovery period, suggesting a negligible practical effect of the supplementary balance-training program despite numerical differences between groups.

For APSI and MLSI, the interaction effects were non-significant (p = 0.397 and p = 0.113). Post-hoc examination of the 95% CIs for RTEs (Figs. 3, 4 and 5) showed that the CIs for the SBT and control groups overlapped to a large extent at all post-fatigue time points (0, 10, 20, and 30 min). This descriptive finding is consistent with the non-significant group-by-time interaction p-values reported in Table 3 (OSI: p = 0.303; APSI: p = 0.397; MLSI: p = 0.113). Therefore, the formal statistical analysis based on the interaction tests indicates that supplementary balance training did not provide a statistically superior protective effect against exercise-induced postural sway compared to routine training alone. Furthermore, the limited separation of the RTE trajectories and the substantial overlap of the corresponding confidence intervals suggest that any practical effect of the intervention was likely small. Although the SBT group showed slightly lower median sway values at certain intervals, these differences did not reach statistical significance, indicating that the additional training load did not produce a statistically significant effect on the recovery kinetics of postural stability in this elite population. Table 4 presents the median (minimum-maximum) values for APSI, MLSI, and OSI at all time points for both groups, before and after the intervention.

Table 3.

Non-parametric longitudinal model results (Brunner-Puri method) for Overall Stability Index (OSI), Anterior-Posterior Stability Index (APSI), and Medial-Lateral Stability Index (MLSI). Primary inference is based on the group × time interaction effects

Outcome	Effect	Test statistic (Wald-type)	df	p-value
OSI	Group	1.9905	1.0000	0.729
	Time	5.4315	3.0115	< 0.001
	Group × Time	1.0585	3.0115	0.303
APSI	Group	2.0890	1.0000	0.777
	Time	4.1625	2.9704	< 0.001
	Group × Time	1.0817	2.9704	0.397
MLSI	Group	1.4554	1.0000	0.458
	Time	5.0870	3.5939	< 0.001
	Group × Time	1.4841	3.5939	0.113

Wald-type statistics are reported as generated by the nparLD package in R. Degrees of freedom (df) reflect the numerator df for each effect

Table 4.

Median (minimum-maximum) values for APSI, MLSI, and OSI at all time points

		Pre-Intervention Baseline	Pre-Intervention Post-Fatigue 0 min	Pre-Intervention Post-Fatigue 10 min	Pre-Intervention Post-Fatigue 20 min	Pre-Intervention Post-Fatigue 30 min	Post-Intervention Baseline	Post-Intervention Post-Fatigue 0 min	Post-Intervention Post-Fatigue 10 min	Post-Intervention Post-Fatigue 20 min	Post-Intervention Post-Fatigue 30 min
APSI	Control	1.6 (0.6–2.3)	2.0 (1.0-2.7)	1.5 (0.9–1.9)	1.4 (0.6-2.0)	1.3 (0.6–2.3)	1.2 (0.4–2.2)	1.2 (0.6-2.0)	1.1 (0.6–1.8)	1.2 (0.6–2.1)	1.4 (0.7–2.1)
	Training Group	1.2 (0.8–2.5)	1.3 (0.6–2.3)	1.1 (0.6–1.7)	1.1 (0.6–1.7)	0.9 (0.7–1.7)	1.0 (0.7–1.8)	1.1 (0.7–1.6)	1.1 (0.7–1.3)	1.2 (0.8–1.9)	0.9 (0.7–1.4)
	Total	1.3 (0.6–2.5)	1.5 (0.6–2.7)	1.2 (0.6–1.9)	1.2 (0.6-2.0)	0.9 (0.6–2.3)	1.0 (0.4–2.2)	1.1 (0.6-2.0)	1.1 (0.6–1.8)	1.2 (0.6–2.1)	1.2 (0.7–2.1)
MLSI	Control	1.5 (0.9–2.7)	1.7 (0.9–3.4)	1.2 (0.8-2.0)	1.1 (0.7–2.7)	1.1 (0.7–2.1)	1.0 (0.7–2.6)	1.4 (0.7–2.4)	1.0 (0.7–3.6)	0.9 (0.6–2.1)	1.1 (0.6–1.9)
	Training Group	0.9 (0.6–2.7)	1.3 (0.6–2.6)	1.2 (0.8-2.0)	1.1 (0.6–2.7)	1.0 (0.6–1.4)	1.0 (0.7–1.7)	1.1 (0.8-2.0)	0.9 (0.7–1.5)	1.0 (0.5–1.5)	1.0 (0.7–1.6)
	Total	1.2 (0.6–2.7)	1.4 (0.6–3.4)	1.2 (0.8-2.0)	1.1 (0.6–2.7)	1.1 (0.6–2.1)	1.0 (0.7–2.6)	1.2 (0.7–2.4)	1.0 (0.7–3.6)	1.0 (0.5–2.1)	1.0 (0.6–1.9)
OSI	Control	2.3 (1.1–3.5)	3.2 (1.4–4.2)	2.0 (1.3-3.0)	1.9 (1.1–3.4)	1.9 (1.0-3.4)	1.5 (0.9–3.7)	2.0 (1.1–3.4)	1.6 (0.9–4.2)	1.6 (1.0-2.8)	2.0 (1.0-3.1)
	Training Group	1.7 (1.1-4.0)	2.0 (1.1–3.8)	1.7 (1.1–2.4)	1.6 (1.0-2.5)	1.4 (1.0-2.2)	1.6 (1.0-2.4)	1.6 (1.1–2.6)	1.6 (1.1–2.1)	1.6 (1.2–2.7)	1.4 (1.0-2.2)
	Total	2.0 (1.1-4.0)	2.2 (1.1–4.2)	1.8 (1.1-3.0)	1.7 (1.0-3.4)	1.5 (1.0-3.4)	1.6 (0.9–3.7)	2.0 (1.1–3.4)	1.6 (0.9–4.2)	1.6 (1.0-2.8)	1.7 (1.0-3.1)

The RTE trajectories and their 95% CIs are visualized in Figs. 3, 4 and 5.

Discussion

The present study evaluated whether a 12-week supplementary balance training program could mitigate exercise-induced postural sway in professional female volleyball players. However, the findings indicate that the intervention did not provide a statistically significant advantage over routine seasonal training, thus the initial hypothesis was not confirmed. The efficacy of balance training in improving postural stability has been well-documented across diverse populations, ranging from sedentary individuals to recreational athletes [24, 25]. Furthermore, many researchers advocate integrating proprioceptive drills to mitigate the detrimental effects of exhaustion [26]. Multidirectional balance-board training programs have been shown to reduce postural sway in various experimental settings [27]. However, the present findings suggest that, in professional female volleyball players, a 12-week supplementary balance-training program does not provide a statistically significant advantage against exercise-induced sway compared with routine seasonal training alone.

The high-intensity Bruce protocol induced a clear and consistent impairment of postural stability across all athletes, which is consistent with previous literature reporting fatigue-related balance deficits [4, 12]. This effect is likely mediated by acute neuromuscular and sensory disturbances, including altered proprioceptive input, accumulation of metabolites, and impairment of somatosensory processing [28]. Our findings align with this framework, as the Bruce protocol induced significant postural sway in all athletes, regardless of group assignment. The lack of significant divergence between the supplementary training and control groups may be explained by a ceiling effect in this elite population, alongside the lack of progression in the intervention and an absence of fatigue-specific or sport-specific balance stimuli [4, 15, 29].

Professional volleyball players likely possess a high level of baseline neuromuscular adaptation due to long-term exposure to sport-specific demands, which may limit the potential for additional improvements. In this context, the low-volume supplementary balance program may have functioned as a subthreshold stimulus, insufficient to elicit measurable changes beyond those already achieved through routine training. Furthermore, it is possible that the standardized nature of the intervention, performed under non-fatigued conditions, did not specifically target fatigue-related neuromuscular adaptations [30]. A possible explanation is that high-intensity exercise may act as a dominant physiological stressor that overrides subtle neuromuscular gains from a supplementary training program in this elite population. However, this was not directly examined in the present study. Therefore, these findings should be interpreted with caution.

Limitations and strengths

The present study has several strengths, most notably its focus on an elite population of professional female volleyball players under exercise-induced fatigue conditions and its use of a robust, rank-based, nonparametric statistical framework (Brunner-Puri method), which ensures high reliability despite a small sample size. Furthermore, the inclusion of multiple post-fatigue measurement intervals (0, 10, 20, and 30 min) provides a detailed map of postural stability recovery kinetics.

However, an important limitation is the cluster-based design, in which only two teams were included. Players within the same team are likely to share similar training loads, coaching strategies, and environmental exposures, which may introduce team-level confounding. Additionally, the lack of independence between observations and the small number of clusters limit the strength of causal inferences.

The fatigue protocol was terminated at a standardized threshold of 85% of age-predicted HRmax for all participants; therefore, the protocol was submaximal and did not reflect true maximal exhaustion. No additional markers of fatigue (e.g., lactate, RPE) were recorded; therefore, fatigue magnitude may not have been uniform across participants.

The small sample size may have limited the statistical power to detect small but meaningful differences.

Furthermore, the lack of detailed baseline data regarding playing positions, professional experience, and comprehensive injury history, beyond ankle inversions, prevents a more granular assessment of group comparability.

The focus was restricted to the dominant leg, which might not fully capture the bilateral postural demands of a symmetrical sport like volleyball.

Additionally, the 10-minute supplementary balance training might have been insufficient to elicit a significant protective effect in athletes already at a high-performance plateau. Future research could benefit from incorporating bilateral assessments and exploring higher-intensity or fatigue-state-specific balance protocols to further investigate the boundaries of neuromuscular adaptation in elite sports.

Conclusions

In this sample of professional female volleyball players, 12 weeks of supplementary balance training did not significantly alter the trajectory of exercise-induced postural sway compared to routine training alone. One possible explanation, which remains hypothetical, is that professional-level fatigue may act as a dominant physiological stressor that overrides subtle neuromuscular gains from additional balance exercises. Alternatively, a ceiling effect in baseline postural control among elite athletes may limit the marginal benefits of supplementary training. However, these mechanistic interpretations were not directly tested in the present study. Accordingly, no specific training recommendations (e.g., prioritizing recovery over additional balance training) can be derived from these findings alone. Given the cluster-based design and limited number of teams, these findings should be considered exploratory and hypothesis-generating; therefore, causal inferences remain limited and the results should be interpreted cautiously. Furthermore, because the analysis treated individual players as independent despite team-level allocation, the reported p-values should be interpreted as descriptive of this specific sample rather than as definitive tests of population-level effects.

Abbreviations

OSI

Overall Stability Index

APSI

Anterior-Posterior Stability Index

MLSI

Medial-Lateral Stability Index

RTE

Relative Treatment Effect

CI

Confidence Interval

SBT

Supplementary Balance Training

CG

Control Group

Authors’ contributions

MMC, GB, and İD developed the concept and design of the study, collected data, performed the statistical analyses, and wrote the first draft of the article. GB, CA and İD contributed to data collection and critically reviewed the article. AMZ supervised the study design, interpreted the data, and approved the final version of the article. All authors have read and approved the final version of the article.

Funding

No funding was received for this study.

Data availability

Availability of data and materials: The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Declarations

Ethics approval and consent to participate

Ankara University Faculty of Medicine ethics committee approved this study and all participants signed the informed consent form. The study was conducted in accordance with the Declaration of Helsinki.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.