Learning a new balance task: The influence of prior motor practice on training adaptations

ABSTRACT

Prior motor experience is thought to aid in the acquisition of new skills. However, studies have shown that balance training does not promote learning of a subsequent balance task. These results stand in contrast to the learning-to-learn paradigm, which is well described for other tasks. We therefore tested if a coordinative affinity between tasks is needed to achieve a learning-to-learn for balance control. Three groups trained different motor tasks during training phase1 (coordination ladder (COOR); bipedal wobble board (2WB); single-leg wobble board (1WB)). During training phase2, all groups trained a tiltboard balance task. Task-specific and transfer effects were evaluated for phase1. A potential learning-to-learn effect was evaluated by comparing the acquisition rates from phase2 for the tiltboard task that was used for training and testing. The results indicate task-specific adaptations after phase1 for 1WB. In contrast, 2WB showed similar improvements than 1WB and COOR (effect sizes: −0.31 to −0.38) when tested on the wobble board with bipedal stance indicating no task-specific improvement for 2WB. For phase2, the linear regression analysis showed larger adaptations for 1WB and 2WB when compared to COOR. This effect implies some uncertainty due to overlapping confidence intervals. Task-specific adaptations after phase1 were found for 1WB but not 2WB. It is discussed that the difficulty of the training task could explain these contrasting results. During phase2, larger adaptations were found for both groups that trained balance tasks during phase1. Thus, despite some uncertainty, prior balance training appears to promote adaptations of a subsequently learned balance task.

Highlights

• Prior balance training augments the learning of a new balance task if the two tasks share certain coordinative features.

• The concept of “learning to learn” can probably be applied to postural control, although further studies are needed.

• Balance training results (partly) in task-specific adaptations with no immediate transfer to other (but unrelated) balance tasks.

KEYWORDS:

• Learning to learn
• balance
• postural control
• prior motor practice

Introduction

Postural control is the basis of almost all motor tasks needed for daily life. Numerous studies therefore investigated how postural control is organized and how balance training affects behavioural adaptations and underlying mechanisms. It is well accepted that balance training is an efficient means to promote stance stability and to reduce the incidence of falls (Sherrington et al., 2019). In addition, balance training has several positive side-effects such as reduced injury rates (Rivera, Winkelmann, Powden, & Games, 2017) or enhanced motor performance (Gruber & Gollhofer, 2004; Taube et al., 2007). Increasing the effectiveness of balance training might therefore have positive effects on health and performance.

There is a growing body of evidence showing highly task-specific training adaptations in response to balance training (Giboin, Gruber, & Kramer, 2015, 2019a, 2019b). These task-specific adaptations with minor or no transfer effects to non-trained tasks are also confirmed by a meta-analysis (Kummel, Kramer, Giboin, & Gruber, 2016). These data are therefore well in line with the specificity hypothesis that sees motor skills as highly specific, bearing only superficial resemblance to each other (Henry, 1968). However, it must be emphasized that the concept of task-specificity in the context of postural control is based on cross-sectional evidence (Drowatzky & Zuccato, 1967; Ringhof & Stein, 2018; Sanborn & Wyrick, 1969; Travis, 1945) and training studies with rather short intervention periods (Giboin et al., 2015, 2019a, 2019b). The idea of task-specificity stands also in contrast to the findings of meta-analyses reporting reduced fall incidences after long-term balance training (Sherrington et al., 2017; Sherrington et al., 2019). If balance training would indeed be highly task-specific with no transfer effects at all, then, as a logical consequence, falls could solely be prevented in the trained tasks (e.g. air cushions, therapy devices, etc.). As it is well known that falls do also occur during normal activities such as strolling, we consider this possibility very unlikely and therefore wanted to better understand possible adaptations as well as transfer effects in response to balance training.

In contrast to the above-mentioned task-specific balance adaptations, a recent meta-analysis differentiated between balance transfer tasks that were either “closely related to the training content” or “not related to the training content” (Donath, Roth, Zahner, & Faude, 2017). The authors showed moderate transfer effects for balance tasks that were closely related to the trained task. It is therefore reasonable to argue that balance tasks need to be related from a kinematic and dynamic perspective to allow for transfer effects. This idea is in line with the concept of coordinative affinity known from exercise science. This concept states that a coordinative affinity between motor tasks is a prerequisite for transfer effects between training and competition tasks (Muller, Benko, Raschner, & Schwameder, 2000). The concept also stipulates that a coordinative affinity between training and testing/competition tasks has the advantage of inducing favourable stimuli in the neuromuscular system that can be used to accomplish both tasks. The idea of this concept may also hold true in the context of postural control. In fact, there is evidence from learning studies supporting the idea of an enhanced adaptability with prior practice of similar tasks (Bejjanki et al., 2014; Braun, Mehring, & Wolpert, 2010; Brown & Kane, 1988; Kattner, Cochrane, Cox, Gorman, & Green, 2017; Langbein, Siebert, Nürnberg, & Manteuffel, 2007; Mulavara, Cohen, & Bloomberg, 2009; Seidler, 2004). Enhancing the learning rate when acquiring a novel task with prior learning experiences of relatively similar tasks is also called “learning to learn” (Harlow, 1949).

In contrast to the reported evidence from cognitive and classical motor learning tasks, two recent studies did not find a learning-to-learn effect for human postural control (Giboin, Gruber, & Kramer, 2019a, 2019b). The authors concluded that the task-specificity of balance training might hinder a learning-to-learn effect. However, in both studies, the influence of prior motor practice on acquisition rates in the new balance task was tested during a single training session. No previous study tested if prior balance training enhances training gains when the new balance task is trained during several training sessions. Furthermore, no previous study assessed if a coordinative affinity between balance tasks is needed when aiming for a learning-to-learn effect in the context of balance training.

Our aim was therefore to evaluate the effects of prior motor practice (training phase1) on training adaptations of a subsequently trained balance task (training phase2). We included three different groups that trained the identical tiltboard balance task during phase2 (study design in Figure 1). Because all groups trained the tiltboard task during phase2, we expected to find task-specific adaptations after phase 2 in all groups. However, we hypothesized to find differences in the magnitude of these adaptations depending on the group-specific task trained in phase1. The inclusion of the three different groups allowed us to evaluate if a coordinative affinity between motor tasks is a prerequisite of a learning-to-learn effect in the context of balance training. The tiltboard task trained during phase2 is a postural task in which participants are asked to keep the tiltboard in a horizontal position while standing on one leg. The tasks of phase1 showed either similar or different task requirements than the tiltboard task. The COOR group trained running and jumping movements using a coordination ladder, while the other two groups trained balance tasks. The 2WB group trained bipedal stance on the wobble board and 1WB trained unipedal stance on the same wobble board. As the 1WB group trained related tasks in both training phases (single leg stance on an unstable surface), we expected to find the largest training adaptations in this group during phase2. Because the training of COOR during phase1 had negligible emphasis on postural control, the smallest adaptations in response to phase2 were expected for COOR.

Figure 1. The general study design is shown in this figure. As can be seen, all participants performed an identical training regime during training phase 2, while the groups trained different tasks during training phase 1. All participants were tested in three different testing sessions to assess training gains in response to training phase 1 and training phase 2. (1WB: training with one leg on wobble board; 2WB: training with bipedal stance on wobble board; COOR: group that trained coordination tasks using the coordination ladder)

Methods

Participants and administration

Forty-five healthy adults, divided into 3 groups of 15 subjects each, participated in this study (1WB: 170 ± 9 cm, 65 ± 11 kg, 23 ± 4 years, 6 women; 2WB: 175 ± 9 cm, 71 ± 13 kg, 21 ± 2 years, 7 women; COOR: 173 ± 7 cm; 66 ± 11 kg; 22 ± 3 years, 7 women). This sample size was priorly justified according to previous findings (Giboin et al., 2015) showing large between-group effects after tiltboard training.

All participants provided written informed consent prior to study involvement. The study protocol was approved by the local ethics committee (ID 2018-01457) and was in line with the declaration of Helsinki. None of the volunteers reported any history of disorders or injuries that would have affected the testing or training. Three participants did not complete the whole study (see Figure 1) due to dissatisfaction with group allocation (n = 1), insufficient training sessions (only three sessions completed) after training period 1 (n = 1), or an injury that occurred during leisure time physical activity (n = 1).

General study design and intervention

This study was designed as a three-armed randomized controlled trial. Volunteers were randomly assigned to one of three groups. A person who was not involved in the measurements and trainings performed the randomization using lot-drawing. After the PRE test, all groups took part in training phase1 that lasted for two weeks. During phase1, one group trained a balance wobble board task (Kübler Sport; 37*9 cm; radius of sphere 13.4 cm) while standing on the right leg (group 1WB), whereas another group trained with the identical device while standing on both legs (group 2WB). A third group trained running and jumping tasks using the coordination ladder (group COOR). The COOR group served as active control group performing motor tasks that involved muscles of the lower limb. However, this group trained demanding tasks to increase running and jumping performance, whereas improving balance was not a primary goal. During phase1, all groups performed the identical number of training sessions (n = 6) and trials (each session: 4 blocks, 5 trials per block, 20 s per repetition;20 s rest between trials; 3 min rest between blocks). After phase1, all participants were tested in a MID test to assess potential training gains from PRE to MID. After MID, all participants accomplished training phase2 where all groups trained a tiltboard task. The participants were asked to stand on the tiltboard (right leg) and to keep it in a horizontal position (dimensions of the board: 35.0*14.2 cm; maximum deviation from horizontal position: 17°). The number of sessions and trials were identical to phase1. After phase2, all groups were tested in a POST test. The same tests were performed PRE, MID, and POST. During balance training and balance assessments, participants were always instructed to (a) focus on a target on the wall, (b) remain as still as possible, (c) place the hands on the hips. We asked volunteers to refrain from any changes in their leisure time physical activity during the study period.

Procedures

All tests were accomplished in accordance with the standard operating procedures established in our laboratory. We assessed three trials with a duration of 20 s for each condition. One minute of rest was given between trials and three minutes of rest between conditions. To reduce the influence of diurnal variation, all three testings were performed at an intra-individually similar time of day.

Balance performance was tested in three different conditions: (i) right leg stance on wobble board, (ii) bipedal stance on wobble board, and (iii) right leg stance on tiltboard. We assessed balance performance in conditions (i)–(ii) with a force plate (Kistler, Type 9286BA, Winterthur, Switzerland) recording data at 120 Hz. We placed the wobble board on the force plate in these two conditions. The path length of the centre of pressure was analysed as an indicator for stance stability. The tiltboard task was assessed using a motion capture system working at 120 Hz (Vicon Nexus, 8 Vero cameras). Four reflective markers were attached at the corners of the tiltboard for assessing the angle of the board in relation to the ground. The tiltboard performance was evaluated by calculating the time the tiltboard was within a range ±5° of the horizontal position (Giboin et al., 2015). The raw data was processed and analysed using Matlab (R2018b, Mathworks, Natick, MA, USA). For each condition, the mean of the three trials was calculated and used for statistical analysis. The chronological order of the conditions was randomized between participants but was kept identical individually for PRE, MID and POST measurements.

Statistical analysis

For the comparison of adaptations between groups, we calculated different linear regression analyses. For training phase1, one linear regression analysis was calculated for each testing task (wobble board single-leg stance, wobble board bipedal stance, tiltboard). While the PRE values served as covariate, the MID values served as dependent variable and group allocation was used to differentiate between groups. Adaptations in tiltboard performance in response to training phase2 were calculated using a linear regression analysis with performance values from MID serving as covariate, POST values as dependent variable while group allocation was used as between-subject variable. Additionally, pairwise comparisons were calculated for training adaptations in response to training phase1 (PRE to MID) but also for training phase2 (MID to POST). As suggested for intervention studies (Hecksteden, Faude, Meyer, & Donath, 2018), change scores are also reported for training adaptations.

Intervention effects are described and illustrated with estimation statistics by means of estimationstats.com (Ho, Tumkaya, Aryal, Choi, & Claridge-Chang, 2018). The focus of this data analysis is on the magnitude (e.g. effect size) as well as exactness (e.g. confidence intervals) of an effect and less on statistical significance testing (Claridge-Chang & Assam, 2016). We decided to not discuss P-values, because statistical peers have shown that discussing results with confidence intervals and/or effect sizes allow a more complete, honest, and useful interpretation of statistical findings (Amrhein, Greenland, & McShane, 2019; Claridge-Chang & Assam, 2016; Greenland et al., 2016; Ho et al., 2018). The relevance of the pairwise adaptations is therefore discussed using Cohen’s d effect sizes (ES). The effect sizes are discussed in terms of negligible (<0.2), small (0.2–0.5), moderate (0.5–0.8) and large (≥0.8) effects (Cohen, 1988). Results are reported as effect sizes with 95% confidence interval and two-sided permutation t-tests.

Results

The means, standard deviations and change scores for all groups and all measurement points can be found in the supplementary material.

Training adaptations from PRE to MID

Wobble board with bipedal stance: The performance data in PRE and MID assessments are displayed in Figure 2(A). These data show for the 2WB group – the group that trained the same task during phase1 – a reduced sway path with a small effect size (Cohen’s d = −0.38 [95% CI: −0.75, −0.03]; P = .04). The other groups who did not train this task during phase1 showed comparable small reductions (1WB: Cohen’s d = −0.33 [95% CI: −0.88, 0.05]; P = .21; COOR: Cohen’s d = −0.31 [95% CI: 0.66, 0.02]; P = .09). The linear regression analysis revealed no differences between 1WB and 2WB (standardized estimate 0.18 [95% CI: −0.39, 0.75]; P = .53) but also COOR and 2WB (standardized estimate 0.20 [95% CI: −0.36, 0.77]; P = .47). The comparison of COOR-1WB did not reveal any differences between groups (standardized estimate 0.02 [95% CI: −0.54, 0.59]; P = .93).

Figure 2. This Cumming estimation plot shows the sway path data assessed during PRE and MID measurements in the bipedal stance condition (A) but also the single-leg stance condition (B) on the wobble board. In the upper parts (A) and (B), the raw data are shown for all participants. Paired samples are connected by a line. In the lower part of (A) and (B), the paired Cohen's d for changes from PRE to MID are depicted as dots with 95% confidence intervals. (1WB: group that trained on the wobble board in single-leg stance; 2WB: group that trained on the wobble board using a bipedal stance; COOR: group that trained with the coordination ladder)

Wobble board with participants standing on the right leg: Performance changes from PRE to MID for all groups are displayed in Figure 2(B). While the 1WB group showed large improvements in this task (Cohen’s d = −1.07 [95% CI: −1.66, −0.55]; P = .003), the performance changes in 2WB (Cohen’s d = −0.27 [95% CI: −0.74, 0.31]; P = .26) and COOR (Cohen’s d = −0.43 [95% CI: −0.96, 0.02]; P = .11) were small. The linear regression analysis showed differences when comparing 2WB-1WB (standardized estimate 0.75 [95% CI: 0.08, 1.41]; P = .03), and COOR-1WB (standardized estimate 0.61 [95% CI: −0.05, 1.28]; P = .07). However, the confidence interval for the comparison COOR-1WB indicates some uncertainty with regard to the effect. The comparison of COOR-2WB (standardized estimate −0.13 [95% CI: −0.78, 0.52]; P = .68) did not reveal different training adaptations.

Tiltboard with participants standing on the right leg: All three groups showed altered performances in the untrained tiltboard task from PRE to MID (see Figure 3). While the 1WB and 2WB groups showed small mean changes (1WB: Cohen’s d = 0.20 [95% CI: −0.03, 0.47]; P = .08; 2WB: Cohen’s d = 0.25 [95% CI: −0.14, 0.90]; P = .20), the COOR group showed negligible adaptations (Cohen’s d = 0.04 [95% CI: −0.32, 0.52]; P = .83). However, the linear regression showed no differences when comparing 1WB-COOR (standardized estimate 0.12 [95% CI: −0.35, 0.60] P = .61), 2WB-COOR (standardized estimate 0.18 [95% CI: −0.30, 0.65] P = .46), and 2WB-1WB (standardized estimate 0.05 [95% CI: −0.42, 0.53] P = .82).

Figure 3. This Cumming estimation plot shows the time in balance in the tiltboard task for the PRE and MID assessments. The raw data as well as paired samples are shown for all participants in the upper part of the figure. In the lower part of the figure, the paired Cohen's d for training adaptations occuring from PRE to MID are illustrated as dots with 95% confidence intervals. (1WB: group that trained on the wobble board in single-leg stance; 2WB: group that trained on the wobble board using a bipedal stance; COOR: group that trained with the coordination ladder)

Training adaptations from MID to POST

All groups showed an improved performance in the tiltboard task after phase2 (Figure 4). Large effect sizes were found in 1WB (Cohen’s d = 1.09 [95% CI: 0.64, 1.62]; P < .001) and 2WB (Cohen’s d = 1.13 [95% CI: 0.57, 1.74]; P < .001), while COOR showed moderate adaptations (Cohen’s d = 0.71 [95% CI: 0.34, 1.16]; P < .001). The linear regression showed that 1WB (estimate of +0.54 s) and 2WB (estimate of +0.64 s) showed larger adaptations than COOR. The standardized estimate indicates that 1WB (stand. estimate = 0.27 [95% CI: −0.20, 1.28]; P = .25) but also 2WB (stand. estimate = 0.56 [95% CI: −0.26, 0.78]; P = .17) had larger improvements than COOR. However, the confidence intervals of these pairwise comparisons include 0, indicating that these effects are associated with uncertainty. The comparison of performance changes did not show a difference between 1WB and 2WB (stand. estimate = 0.05 [95% CI: −0.62, 0.50]; P = .83).

Figure 4. This Cumming estimation plot shows the adaptations in response to training phase 2. The performance changes from MID to POST assessments are depicted as raw-data in the upper part of the figure. The effect sizes for the paired comparisons (POST minus MID) are shown in the upper part of the figure. The Cohen's d value are illustrated as dots with 95% confidence intervals. (1WB: group that trained on the wobble board in single-leg stance; 2WB: group that trained on the wobble board using a bipedal stance; COOR: group that trained with the coordination ladder)

Discussion

This study assessed training adaptations in three different groups. All participants trained the identical balance task during training phase2, while the three groups trained different tasks in a preceding phase1. The data show that prior balance experience promotes balance gains in a subsequent training phase. Consistent with previous studies, our data suggest – despite some uncertainty – that motor tasks of two successive training phases should share certain coordinative features (e.g. balance control) to allow more pronounced learning rates in phase2. The present results support the idea of a learning-to-learn effect for postural control, but further studies are needed to address remaining ambiguities.

Adaptations in response to training phase1

Henry (1968) postulated that characteristics of motor skills are specific hindering or minimizing transfer effects to other tasks. This task-specificity has also been reviewed for balance interventions (Kummel et al., 2016). Task-specificity means that behavioural adaptations in response to balance training are specific to the trained task making any transfer to other (untrained) balance tasks unlikely. The adaptations in response to training phase1 of 1WB are therefore well in line with the task-specificity principle of balance training (Giboin, Gruber, & Kramer, 2019b). The 1WB group outperformed the other groups when tested in the task that was trained during training phase1. Based on these results and the adaptations found in the tiltboard task after training phase1, we conclude that adaptations in response to balance training are task-specific in response to a short-term balance training.

However, the results found for 2WB stand in contrast to the task-specificity principle. The data analysis revealed small task-specific improvements in 2WB, but no increased adaptations compared to the other groups that trained different tasks but showed similar small improvements when balancing on the wobble board using bipedal stance. The absence of any enhanced adaptation in 2WB is difficult to explain and we can only speculate about possible reasons. One explanation is that training difficulties during phase1 were not identical for 1WB and 2WB, because both groups trained on the identical wobble board. The obtained data from PRE revealed enhanced sway paths for the wobble board task when participants balanced on one leg when compared to bipedal stance. Therefore, it is reasonable to assume that task-difficulty was lower for 2WB when compared to 1WB in phase 1. This assumption is supported by previous studies showing that enhancing the difficulty of postural tasks is associated with longer sway paths (Donath, Kurz, Roth, Zahner, & Faude, 2016). The lower task complexity during training might in turn have led to a ceiling effect that also prevented larger improvements in 2WB compared to both other groups. However, since we have not recorded the learning rates during training, any conclusion about a ceiling effect remains speculative.

Adaptations in response to training phase2

There is good evidence showing that prior experience helps to cope with a new task when these two tasks are related (Bejjanki et al., 2014; Braun et al., 2010; Brown & Kane, 1988; Kattner et al., 2017; Seidler, 2004). In contrast to these studies investigating rather simple tasks such as arm movements, a learning-to-learn effect was not found in two recent whole body balance training studies (Giboin et al., 2019a, 2019b). The authors argued after the first study that the duration of training phase1 was possibly too short for inducing a learning-to-learn effect (Giboin et al., 2019a). The same authors therefore designed a second study (Giboin et al., 2019b), in which they tested whether six weeks of balance training with seven different devices is capable to induce faster learning rates in a subsequent one-session balance training. The design of the second study was based on two ideas: (i) it is known that using a variety of tasks induces structural learning (Braun, Aertsen, Wolpert, & Mehring, 2009) and (ii) using a broader range of movements can lead to a better generalization (Berniker, Mirzaei, & Kording, 2014). Nevertheless, Giboin et al. (2019b) were not able to find an improved learning rate in the second training phase. A possible reason for the absence of facilitated learning in the study of Giboin et al. (2019b) could be the different requirements of tasks from phase1 and phase2 (e.g. one-leg stance vs. two-leg stance, hip strategy vs. ankle strategy). In line with a previous overview, we argue that a coordinative affinity between tasks has the advantage of inducing favourable stimuli in the neuromuscular system that allow better performance under similar conditions (Muller et al., 2000). Thus, the coordinative affinity might have been too weak in the previous balance studies for inducing a learning-to-learn effect.

For this reason, three groups were included in our study. The 1WB group trained primarily muscles surrounding the ankle during one-leg stance. The coordinative affinity with the tiltboard task is therefore deemed high. A medium coordinative affinity was assumed for the 2WB group (i.e. ankle strategy but bipedal stance) and hardly any coordinative affinity was considered for COOR (i.e. no emphasis on postural control). The COOR group served therefore as control group, because this group trained the same tiltboard task than 1WB and 2WB during phase2 but was the only group that trained no balance tasks during phase1. As expected, the COOR group showed a moderately improved tiltboard performance after phase2. When comparing these adaptations to the ones found for 1WB and 2WB, enhanced training gains compared to COOR during phase2 were found. The confidence intervals of the standardized estimates, however, are large and overlap zero indicating some amount of uncertainty in the observed effects. Despite this uncertainty, the data suggest that training a balance task promotes adaptations in a subsequent balance training. However, it must be emphasized that the (assumed) affinities of the balance tasks did not differently affect learning rates in phase 2. This result is contrary to our hypothesis, as we expected the largest adaptations after phase 2 in 1WB, but our data revealed similar adaptations for groups 1WB and 2WB. Unfortunately, this effect cannot be explained by the data obtained, and we can only speculate on possible reasons. One could argue that the coordinative affinities of balance tasks were not as similar/different as hypothesized. Another possible reason is that the duration of phase 2 was either too short/long to detect differences between groups. Future studies will therefore need to shed on this issue (see limitations).

It is important to mention that task-specific improvements after phase 1 were not enhanced for 2WB when compared to the other groups. Since the data do not show enhanced learning for 2WB in phase 1, it seems inappropriate to speak about a “learning-to-learn” effect. It is therefore reasonable to argue that practicing postural tasks helps to better learn new but related balance tasks in a subsequent training phase.

Limitations

Despite great efforts in creating a sound study design, the present study has certain limitations. For example, we did not measure the task-specific performance gains in COOR. We do therefore not know if this group showed any adaptations in the trained tasks after phase1. In addition, we cannot test for transfer effects from balance training to the tasks the COOR group has trained. However, these limitations do not have an influence on our primary outcome – a possible learning-to-learn effect in balance training. Nevertheless, the similar adaptations of 1WB and 2WB in response to training phase2 are contrary to our hypothesis and we can solely speculate about possible reasons. We assumed different levels of coordinative affinity but we cannot provide any evidence for our assumption. We therefore propose for future studies to evaluate the coordinative affinity between tasks. This could be done by capturing whole body movements using a motion capture system and applying a principal component analysis. Based on such an analysis, the influence of coordinative affinity on learning-to-learn could be further investigated. We would also like to mention that our sample consisted exclusively of young, healthy, and active adults, which makes generalizations to other populations impossible.

Conclusions and practical implications

In conclusion, we observed that previous balance training led to slightly better learning in a subsequently trained balance task when compared to a group without prior balance training. Although this effect is associated with some uncertainty, the present results suggest that practicing related balance tasks increases the rate of learning in a subsequent training phase. This observation is of importance in the development of effective fall prevention programmes and suggests that the inclusion of many different balance tasks has the highest potential for preventing falls.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.