https://orcid.org/0000-0003-2205-0925
Figures
Abstract
The goals of this study were to determine if a single 30-minute session of practice walking on a treadmill mounted balance beam: 1) altered sacral marker movement kinematics during beam walking, and 2) affected measures of balance during treadmill walking and standing balance. Two groups of young, healthy human subjects practiced walking on a treadmill mounted balance beam for thirty minutes. One group trained with intermittent visual occlusions and the other group trained with unperturbed vision. We hypothesized that the subjects would show changes in sacrum movement kinematics after training and that there would be group differences due to larger improvements in beam walking performance by the visual occlusions group. We also investigated if there was any balance transfer from training on the beam to treadmill walking (margin of stability) and to standing static balance (center of pressure excursion). We found significant differences in sacral marker maximal velocity after training for both groups, but no significant differences between the two groups from training. There was limited evidence of balance transfer from beam-walking practice to gait margin of stability for treadmill walking and for single leg standing balance, but not for tandem stance balance. The number of step-offs while walking on a narrow beam had the largest change with training (partial η2 = 0.7), in accord with task specificity. Other balance metrics indicative of transfer had lower effect sizes (partial η2<0.5). Given the limited transfer across balance training tasks, future work should examine how intermittent visual occlusions during multi-task training improve real world functional outcomes.
Citation: Symeonidou E-R, Esposito NM, Reyes R-D, Ferris DP (2023) Practice walking on a treadmill-mounted balance beam modifies beam walking sacral movement and alters performance in other balance tasks. PLoS ONE 18(6): e0283310. https://doi.org/10.1371/journal.pone.0283310
Editor: Andrew Sawers, University of Illinois at Chicago, UNITED STATES
Received: January 27, 2023; Accepted: March 2, 2023; Published: June 15, 2023
Copyright: © 2023 Symeonidou et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The data has been made public under the following link: https://figshare.com/articles/dataset/Practice_walking_on_a_treadmill-mounted_balance_beam_modifies_beam_walking_sacral_movement_and_alters_performance_in_other_balance_tasks_/23258048.
Funding: DF, R01NS104772, NIH, https://app.dimensions.ai/details/grant/grant.7519576, The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Practicing a balance task that not only improves performance on that balance task but also transfers improvement to other balance tasks would be ideal for motor skill training and rehabilitation. Recent studies have mixed findings on the transfer of balance training. Many studies that have examined standing balance training have found little to no transfer of balance improvements to other balance tasks [1–4]. In contrast, some studies that have looked at walking balance have shown some limited transfer to other balance tasks [5, 6]. The likelihood of transfer seems to be highest if balance practice involves walking balance with self-generated transfers from single support to double support and does not have external physical perturbations disrupting balance.
One potential training method for challenging dynamic balance is walking on a narrow beam. Studies on humans walking on an overground balance beam have been useful for understanding motor skill expertise [7–9] and studying the effects of vision, reflex modulation, and loss of balance in walking [10–13]. Walking on a treadmill mounted balance beam has been used to test the effects of error augmentation and physical guidance on balance motor learning [14, 15] and can be combined with physical perturbations, visual perturbations, or immersive virtual reality environments [16–18].
Successful locomotion across a narrow beam requires humans to control movement of their center of mass (CoM) to prevent stepping off the beam. In normal gait, humans actively maintain medio-lateral balance by controlling foot placement for each step [19–21]. Walking on a narrow beam (e.g., 2.5 cm wide) does not allow humans to adjust foot placement in the way they do when walking overground. They do have to monitor the movement of their center of mass and repeatedly shift weight from the trailing limb to the leading limb, requiring active involvement from many brain areas including prefrontal, anterior cingulate, premotor, sensorimotor, and posterior parietal cortices [12, 17].
Brief intermittent visual perturbations during beam walking practice can improve subsequent beam walking performance [16, 22]. Peterson and colleagues [16] showed that intermittent visual rotations presented through a virtual reality headset during 30 minutes of beam walking practice led to a reduction in step-offs of 42% compared to a 9% reduction in step-offs when practicing without intermittent visual rotations. More recently, Symeonidou and Ferris [22] used liquid crystal lens glasses to train participants with brief intermittent visual occlusions during the same beam walking paradigm. The visual occlusions group showed a 78% reduction in step-offs on the same day after training and a 60% retention two weeks later. In comparison, the unperturbed vision group only showed a 20% reduction in step-offs on the same day and a 5% retention [22, 23]. These studies provide strong evidence that incorporating brief reoccurring visual perturbations when learning a difficult balance task can improve subsequent performance of the same balance task. The neural mechanisms responsible for the visual perturbation effect on balance improvement are not known, but it is possible that cross-modal synchronization of sensory information processing enhances training [24–28]. From previous studies on beam walking, it is not clear how the subjects altered their body movement patterns to achieve reductions in beam step-offs with practice.
In this study, we sought to identify changes in body movement patterns related to improved beam walking performance with practice, and if there was transfer of better dynamic balance from beam walking to other balance tasks. Specifically, we wanted to determine if a single 30-minute session of practice walking on a treadmill mounted balance beam: 1) altered sacral marker movement kinematics during beam walking, and 2) affected measures of balance during treadmill walking and standing balance. We chose sacral movement as the primary focus of our kinematic analysis for beam walking performance because it is a simple method and good approximation for estimating CoM motion during walking [29–32] and treadmill beam walking in particular [14, 15]. Whole body CoM estimation would be time-consuming and requires a whole-body marker set, limiting its applicability in real-life settings. We studied two groups of subjects, one with brief intermittent visual occlusions and one with unperturbed vision, walking on a treadmill mounted balance beam for 30 minutes. We expected the group with intermittent visual occlusions to improve more in beam walking performance than the unperturbed vision group. We hypothesized that sacral movement kinematics during beam walking would be altered after practice, and that the visual occlusions group would show greater changes from pre-test to post-test than the unperturbed vision group. As there is no clear evidence from the literature whether sacral movement would be increased or decreased with improved balance walking on a beam [14, 15, 33, 34], we did not a priori predict a direction of the change from pre-test to post-test. We also compared other metrics of balance during treadmill walking and standing balance to determine if there was any evidence of transfer from the balance beam practice to other balance measures.
Materials and methods
Participants
Twenty healthy, young participants (9 females, 11 males) took part in this study. They reported no neurological, orthopedic, or musculoskeletal conditions or lower limb surgeries. All participants were right-foot dominant. We assessed foot-dominance by asking them which foot they would use to kick a ball. Written, informed consent was obtained from all study participants and the study was approved by the University of Florida Institutional Review Board (IRB). The study was conducted according to the Declaration of Helsinki.
Experimental design
Beam training.
Participants performed a beam walking task with or without the presence of reoccurring intermittent visual occlusions (Fig 1). The balance beam walking task consisted of a 3 min pre-test trial30 minutes of training, and a 3-min minute post-test trial. One group trained with brief intermittent visual occlusions (4 females, 6 males, age = 24.3 ± 5.1) that were presented over liquid crystal lens glasses (Senaptec Strobe, Senaptec, Oregon, 148 USA) in a periodic fashion (1.5 s occlusion and 7.5 s of clear vision) and another group (5 females, 5 males, age = 25.2 ± 4.7) trained on the beam while wearing the glasses but without any visual occlusions. The treadmill-mounted beam was 2.5 cm high and 2.5 cm wide [12, 15, 16, 22] and participants walked on a constant speed of 0.22 m/s [15, 17, 22].
Participants performed the beam walking training on a treadmill mounted balance beam while wearing the occlusion glasses. They were strapped for safety in a harness attached to a body-weight support system that was modified to allow for freedom of movement in the mediolateral direction. A number of reflective markers were attached to the participant’s body for motion tracking purposes.
The visual occlusions were presented in a periodic manner using a Samsung Note 10 that was running Senpatec app (Senaptec, Oregon, USA). The occlusion cycle would start when participants stepped on the beam with both feet and ended when they stepped off. A random delay of up to 1 s was added to the start of the cycle to avoid any anticipatory effects. To start and stop the cycle we used TeamViewer and a custom Matlab script to access the screen of the phone over a desktop monitor. Every time participants stepped off the beam, they had to count 5 steps before stepping back on [15–17]. The pre-test and post-test of the balance beam walking task were performed with the glasses on but in the transparent mode for both groups. During the balance beam walking participants were able to take breaks between trials and had to take a 5-min break between the last training trial and the post-test trial.
Walking and static balance tasks.
Participants performed the walking and static balance tasks on a split-belt treadmill (Bertec, Ohio, USA) that recorded forces through embedded force plates with a sampling rate of 1000 Hz. Participants walked for 5 mins and performed a 30-sec tandem and single leg stance before (pre), and after (post) the beam walking task (Fig 2). The order of the tasks was counterbalanced across participants for the pre and post session. The split-belt treadmill was set at a constant speed of 0.5 m/s and participants were instructed to walk on the center of each belt in the anterior-posterior direction. For the single leg-stance task participants were instructed to stand on one belt of the treadmill and raise their left leg 10 cm off the ground. During the tandem stance task participants were instructed to stand on one belt with the right foot in front of the left foot, with the heel of the right foot touching the fingers of the left foot. For both static balance tasks, participants were asked to rest their arms at their waist, look straight, and keep this position for 30 sec.
Participants performed the standing and walking balance tasks before and after the balance beam training: walking, tandem stance, and single-leg stance in random order.
Data analysis
Beam kinematics.
Marker trajectories were cleaned using Motive software (Optitrack, Natural Point Inc, Corvallis, OR, USA) and processed with Visual3D (C-Motion, Germantown, MD). We low-pass filtered the marker data using a 6 Hz cut-off frequency and fourth-order Butterworth filter and determined on-beam phases using a manual threshold for the position of the toe, fifth metatarsal, and heel foot markers in the medio-lateral and vertical directions for each step. We used a manually adjusted, empirically determined threshold because the beam location varied slightly across subjects and sessions. A number of reflective markers (n = 28) were placed on the participants’ feet, knees, hips, sacrum, back, shoulder, neck, and head, to record motion capture data (Optitrack, Natural Point Inc, Corvallis, OR) sampled at 100 Hz for all tasks (beam-walking, treadmill walking, tandem stance, single-leg stance).
We calculated the sacrum kinematics when participants were on the beam in the medio-lateral, anterior-posterior, and vertical direction. We calculated the average standard deviation, as a surrogate for the CoM variability, during the pre- and post- trials for each participant and averaged the values within the two groups. To identify if the visual occlusions group employed faster corrective adjustments after the training compared to the unperturbed vision group, we looked at sacrum peak displacement and velocity for the pre- and post-trials. To do so, we calculated the highest absolute value of displacement and velocity of the sacrum marker for each trial and participant, and then averaged within groups. To investigate if participants adopted a more crouched position after training, we calculated the normalized sacrum-ankle distance for the pre- and post- trials. For each on-the-beam stance phase within each trial, we averaged the vertical component of the sacrum and ankle marker and found the distance between the two. This distance was normalized to the sacrum-ankle distance of the participant’s upright stance when off-the beam. Normalized sacrum-ankle distance values were averaged within trials and groups.
Walking margin of stability.
We calculated the dynamic margin of stability to see if there was any transfer of the gained balance benefit from the balance beam training to the treadmill walking [35]. We estimated CoM position using the average position of seven pelvic markers. The CoM was then used to find the extrapolated center of mass (XcoM), expressed as
(1)
Where x is the CoM position, is the CoM velocity, g is the gravitational constant, and l is the equivalent pendulum length, i.e., the mean distance from the leading foot’s fifth metatarsal marker (left or right) to the center of mass at every heel strike. The dynamic margin of stability (MOS) was then calculated as
(2)
Where BOS is the boundary of the base of support. Dynamic margin of stability and base of support were both measured separately in the anterior-posterior and medio-lateral directions. The base of support was defined by the position of the leading foot marker at heel strike. Dynamic margin of stability values were calculated at heel strike for each step in the pre- and post-walking test and averaged within the whole trail. Heel strikes were detected using the force plates positioned underneath the split-belt treadmill.
Static balance measures.
For the tandem stance and single-leg stance task we low-pass filtered the force plate data using a 12 Hz cut-off frequency and calculated several center of pressure (CoP) measures: i) average CoP sway excursion, ii) Standard Deviation of CoP sway excursion, ii) mean CoP velocity in the medio-lateral and anterior-posterior direction, and iv) area of CoP sway excursion during tandem stance and single-leg stance. For all data analysis we used custom Matlab scripts (The Mathworks Inc, Natick, MA, USA).
Statistical analysis
We had to exclude one participant from each group from the whole analysis due to missing force plate and marker data during the experiment (step-offs and on-beam sacral marker kinematics analysis, n = 9/group). We had to exclude an additional participant from each group from the walking and static balance tasks analysis due to missing force plate and marker data during some of the relevant trials (walking and static balance kinematics analysis, n = 8/group). We performed a repeated measures ANOVA with the intervention (visual occlusions, unperturbed vision) as the between subjects’ factor and the test-trial (pre-test, post-test) as the within subjects’ factor for the on-beam measures i) sacrum standard deviation ii) maximum sacrum displacement ii) maximum sacrum velocity in the medio-lateral, vertical, and anterior-posterior direction, and iii) ankle-sacrum ratio. The same statistical analysis with the same between and within subject factors was performed for the beam performance in step-offs/min. Similarly, we performed a repeated measures ANOVA with the intervention (visual occlusions, unperturbed vision) as the between subjects’ factor and the time point (pre, post) as the within subject’s factor for the i) average CoP sway excursion, ii) SD of CoP sway excursion, ii) mean CoP velocity in the medio-lateral and anterior-posterior direction, and iv) area of CoP sway excursion during tandem stance and single-leg stance. We performed the same statistical analysis with the same between and within subject factors for the i) mean margin of stability, ii) margin of stability variability in the medio-lateral and anterior-posterior direction during walking. Before deciding to use a parametric statistical test for the above measures, we checked the normal distribution of our data. Most of our data was normally distributed. Our primary and secondary outcome measure were step-offs/min and sacral marker standard deviation, which were both normally distributed. We expected limited to no changes in other balance metrics on the beam, and/or during standing and walking. Last, all significant results reported in the next section came from normally distributed data, therefore, violation of normality within some of our measures did not affect our results. We report Greenhouse-Geisser corrected values wherever the test of sphericity is violated.
Results
Both groups experienced significantly fewer step-offs after training (F (1, 16) = 31.4, p < .001, partial η2 = .66). The visual occlusions group showed a four-fold reduction in step-offs (F (1, 16) = 14.9, p < .001, partial η2 = .48, t(8) = 4.9, p = .001) while the unperturbed vision group showed 23% improvement between pre-test and post-test (t(8) = 2.9, p = .02) (Fig 3). Both effect sizes (partial η2) of the test-trial (~0.7) and visual occlusion training (0.5) on step-offs were large (Table 1).
Mean margin of stability and margin of stability variability for the visual occlusions and unperturbed vision group before (pre) and after the training (post). There was a significant increase of margin of stability in the anterior-posterior direction between pre and post-test (F(1, 14) = 5.11, p = .04, partial η2 = .27), but no main effect of training type (visual occlusions vs unperturbed vision: F(1, 14) = .3, p = .59, partial η2 = .021). There were no significant differences in margin of stability in the medio-lateral direction between pre and post-test (F(1, 14) = 1.12, p = .31, partial η2 = .08) or training type (F(1, 14) = .4, p = .54, partial η2 = .03). Similarly, there were no significant differences in margin of stability variability between test-trials (anterior-posterior: F(1, 14) = 1.1, p = .32, partial η2 = .07; medio-lateral: F(1, 14) = 1.1, p = .31, partial η2 = .08) or training types (anterior-posterior: F(1, 14) = 7.5, p = .4, partial η2 = .05; medio-lateral: F(1, 14) = .4, p = .54, partial η2 = .03) in both directions.
Mean and standard deviation (SD) of on-beam step-offs/min, sacrum standard deviation, maximum sacral displacement, and maximum sacral velocity in the medio-lateral (ML), vertical (Vert.), and anterior-posterior (AP) direction, as well as normalized ankle-sacrum distance before (Pre) and after (Post) the balance beam training for 9 participants/group. All kinematic measures are in cm or cm/s except for the step-offs/min and the normalized ankle-sacral distance. Significant p-values are denoted in bold with an asterisk (*).
Sacral movement variability, as measured by sacral marker standard deviation, did not show any clear changes from beam walking practice. Participants of both groups showed no differences in sacral marker standard deviation between test-trials in the medio-lateral (F(1, 16) = 3.3, p = .092, partial η2 = .17), vertical (F(1, 16) = .69, p = .42, partial η2 = .04), and anterior-posterior direction(F(1, 16) = 4.2, p = .06, partial η2 = .2) (Table 1). The training type had also no effect on sacral marker standard deviation (medio-lateral: F(1, 16) = 1.8, p = .2, partial η2 = .10; vertical: F(1, 16) = 1.1, p = .31, partial η2 = .07; vertical: F (1, 16) = .2, p = .18, partial η2 = .11) (Table 1).
Subjects did show significant increases in maximal sacral vertical velocity after training compared to before training. There were significant main effects of test-trial (F(1,16) = 4.9, p = .04, partial η2 = .24) and training type (F(1,16) = 8.7, p = .009, partial η2 = .35) on maximal sacral vertical velocity. There was also a significant training type effect on mediolateral maximal sacral velocity (F(1,16) = 5.1, p = .04, partial η2 = .24), as the intermittent visual occlusion group had a decrease and the unperturbed vision group has a very small increase (Table 1, S1 Fig).
Participants adopted a more crouched posture in beam walking after practice. There was a reduction in ankle-sacrum distance for both groups after training (F(1,16) = 15.8, p = .001, partial η2 = .5), but there was not a statistical difference between groups (Table 1, S2 Fig).
For the treadmill walking data, there was only one indication of changes in dynamic balance. The margin of stability in the anterior-posterior direction was larger in the post-test compared to the pre-test for both groups (F (1, 14) = 5.11, p = .04, partial η2 = .27) (Fig 3). There was no difference between training type. There were no significant differences in margin of stability in the medio-lateral direction between test-trials or type of training. We did not detect any significant differences in margin of stability variability between test-trials or training in any direction.
In the standing measures of balance, we found two indicators of training transfer. In the single leg stance task, there was a significant reduction in medio-lateral (F(1, 14) = 14, p = .002, partial η2 = .50) and anterior-posterior (F (1, 14) = 8.8, p = .01, partial η2 = .38) center of pressure velocity (Table 2). We did not find any differences between groups (i.e., main effect of training type) for the single leg stance task. There were no significant differences between test-trials or training type in center of pressure kinematics in the tandem leg stance task (Table 3, S3 Fig).
Mean and standard deviation (SD) single-leg stance and tandem stance medio-lateral (ML) and anterior posterior (AP) center of pressure (CoP) sway excursion, standard deviation (SD) of CoP sway excursion, CoP velocity, and CoP sway area before (Pre) and after (Post) the balance beam training for 8 participants. Values are in cm, cm/s, or cm2. Significant p-values are denoted in bold with an asterisk.
Mean and standard deviation (SD) tandem stance medio-lateral (ML) and anterior posterior (AP) center of pressure (CoP) sway excursion, standard deviation (SD) of CoP sway excursion, CoP velocity, and CoP sway area before (Pre) and after (Post) the balance beam training for 8 participants. Values are in cm, cm/s, or cm2. No significant differences were observed.
Discussion
Both groups showed a significant reduction in step-offs after the beam walking training. The visual occlusions training led to a higher reduction of step-offs (75%) than the unperturbed vision training (23%). Both within subjects (test trial) and between subjects (training type) effects sizes were large (η2 = ~0.7 and η2 = 0.5, respectively). Thirty minutes of treadmill mounted beam walking practice had a large effect on motor performance, and the intermittent visual occlusions had a large effect on boosting the improvement.
The neuromechanism responsible for visual occlusion training improvement could come from multiple pathways. Previous studies have suggested that perturbing one sensory modality can enhance sensitivity and processing of other available sensory information [36–38]. Decreasing surface support during balance can downregulate somatosensory processing and increase reliance on visual input [39, 40]. However, vision is inherently coupled with processing delays and not optimal to inform postural adjustments during a challenging balance task [41]. Expert athletes and gymnasts tend to rely less on vision for postural control compared to novices in a wide range of sports and activities [42–44]. Robertson and colleagues (1994), observed that experts walking on a balance beam relied more on proprioceptive and vestibular information compared to visual feedback [11]. The above studies suggest that perturbing vision by inducing re-occurring intermittent visual occlusions could have increased reliance on other sensory information (vestibular and/or proprioceptive). Alternatively, the visual occlusion could have induced neural cross-modal synchronization, a phenomenon that occurs when different sensory electrocortical oscillations synchronize due to an external stimulus [26, 45, 46]. Cross-modal synchronization within proprioceptive, vestibular, and visual areas, could have enhanced sensory processing and multisensory integration related to the balance task [24, 25, 47]. Future studies could examine these possibilities using electroencephalography.
There were not many sacral movement parameters that changed with practice. The maximum sacral mediolateral velocity decreased with training in the visual occlusions group and the maximum sacral vertical velocity showed a change with training for both groups. The decrease in sacral mediolateral velocity could reflect better balance control [33, 34]. The increase in maximum sacral vertical velocity could be an indicator of more confident stepping patterns, returning to the vertical oscillations of the CoM that are characteristic to normal human walking [48, 49]. We did not find changes in sacral marker standard deviation in any direction. There are no consistent findings in literature regarding sacral marker variability of increased balance control during beam walking. Domingo and colleagues [14, 15] showed that increased sacral variability in the mediolateral direction during beam training correlated with better performance on the beam after training. However, no differences in mediolateral sacrum variability were observed before and after the training. Subjects did walk with a slightly shorter effective limb length after training, about 1 cm less distance between the sacral marker and ankle marker. The lack of change in other sacral parameters during beam walking with practice suggest there was not a clear, consistent strategy in sacral movement dynamics that the subjects adopted to reduce beam step-offs.
We found limited transfer of balance beam performance gains to treadmill walking margin of stability and single-leg stance center of pressure velocity. These measures had lower effect sizes (partial η2<0.5) compared to step-offs off the beam (partial η2>0.5), supporting the task specificity of balance training principle [2]. Kümmel and colleagues (2016), reviewed the transfer effects of six balance training protocols in healthy participants and found limited or no balance gain transfers on non-trained tasks. However, the meta-analysis only included two studies of walking balance as the trained task (slackline walking) that consisted of multiple sessions over several weeks [50, 51], while our study only consisted of a 30-minute training session. More recent studies looked at transfer effects of repeated slackline training sessions on other static and dynamic balance tasks and found similar results [1, 5]. The above studies didn’t indicate any specific transfer effects between feed-forward and reactive balance tasks either. Future studies need to test transfer effects of a balance beam training protocol consisting of multiple training sessions on different balance tasks, including static, dynamic, and reactive balance tasks to investigate if beam walking could be used to improve balance in multiple motor tasks.
There were several limitations to the study that should be considered for future work. The sample size was relatively small. The number of subjects had been chosen based on the expected effect size of the improvements in step-offs. Indeed, we had a large effect size for the training type (~0.5) and for the pre- to post-training (~0.7) in regard to the primary performance metric (beam step-offs). The effect sizes for the sacral kinematic measures that were significantly different after beam practice were equal to or less than 0.35. Future studies that want to determine if there were small changes in sacral or CoM movement dynamics that we did not detect would ned to recruit a larger sample to account for the smaller effect sizes. Another limitation of our study was that we only looked for differences in sacral movement patterns and transfer to other balance tasks after one 30-minute training session. It is possible that many sessions of training over days or weeks would have a larger effect on sacral movement dynamics and transfer to other balance tasks. We chose the time of training for this study based on the expected large effect size from 30 minutes of practice. Symeonidou and Ferris [22, 23] showed that 30 minutes of training would have a large effect on beam walking balance performance and that the visual perturbation training would increase the effect considerably. Future studies should extend the training time across days and weeks, to see if there is greater transfer to a wider range of balance tasks and to balance impaired patient populations. Determining if individuals of olde age or with balance disorders perform similarly as the young, healthy subjects in this study would be valuable for future clinical interventions.
Conclusion
Our results support the benefits of treadmill mounted beam walking and intermittent visual occlusions as means to improve dynamic balance in healthy, young humans. There were few indicators of changes in sacral movement dynamics after training that may have been responsible for the large improvements in beam walking performance after half an hour of practice. Balance performance during single leg stance and treadmill walking showed some limited transfer effects after beam walking practice, but the overall transfer was not large. Future studies examining the effects of intermittent visual occlusions using longer training interventions with multiple motor tasks on balance-challenged individuals are warranted.
Supporting information
S1 Fig. Sacral marker maximum velocity.
Medio-lateral and vertical sacral marker maximum velocity for the visual occlusions (red) and unperturbed vision group (blue) before (Pre) and after (Post) the training. In the medio-lateral direction there was a significant main effect of training type (F(1,16) = 5.1, p = .04, partial η2 = .24). The visual occlusions group also showed a decline in maximum sacrum velocity between pre- and post-test. In the vertical direction we saw a main effect of training type (F(1,16) = 8.7, p = .009, partial η2 = .35) and a main effect of test-trial (F(1,16) = 4.9, p = .04, partial η2 = .24). Error bars represent standard error of the mean.
https://doi.org/10.1371/journal.pone.0283310.s001
(TIF)
S2 Fig. Ankle-sacrum distance.
Normalized ankle-sacrum distance for the visual occlusions (red) and unperturbed vision group (blue) before (Pre) and after (Post) the training. Both groups showed a significant reduction of ankle-sacrum distance between pre- and post-test (F(1,16) = 15.8, p = .001, partial η2 = .5), meaning both adapted a more crouched position after training. The visual occlusions group showed ~0.7 cm reduction, while the unperturbed vision group showed a larger deduction of ~1.7 cm. Error bars represent standard error of the mean.
https://doi.org/10.1371/journal.pone.0283310.s002
(TIF)
S3 Fig. Single-leg stance center of pressure velocity.
Mean mediolateral and anterior-posterior center of pressure (CoP) velocity during the single-leg stance task. Both groups showed a decrease in CoP velocity in the medio-lateral and anterior-posterior direction. There was a significant main effect of test-trial in the medio-lateral (F(1, 14) = 14, p = .002, partial η2 = .50) and anterior-posterior direction (F (1, 14) = 8.8, p = .01, partial η2 = .38). Error bars represent standard error of the mean.
https://doi.org/10.1371/journal.pone.0283310.s003
(TIF)
Acknowledgments
We would like to thank Evdokia Ptitsyna and Lourdes Bernandez for their assistance in data collection as well as Roehl Reyes and Crosman Cruz for their assistance in data analysis.
References
- 1. Giboin LS, Gruber M, Kramer A. Three months of slackline training elicit only task-specific improvements in balance performance. PLoS One. 2018 Nov 26;13(11):e0207542. pmid:30475850
- 2. Kümmel J, Kramer A, Giboin LS, Gruber M. Specificity of balance training in healthy individuals: a systematic review and meta-analysis. Sports Medicine. 2016 Sep;46(9):1261–71 pmid:26993132
- 3. Gerards MH, McCrum C, Mansfield A, Meijer K. Perturbation‐based balance training for falls reduction among older adults: Current evidence and implications for clinical practice. Geriatrics & gerontology international. 2017 Dec;17(12):2294–303. pmid:28621015
- 4. McCrum C, Gerards MH, Karamanidis K, Zijlstra W, Meijer K. A systematic review of gait perturbation paradigms for improving reactive stepping responses and falls risk among healthy older adults. European review of aging and physical activity. 2017 Dec;14(1):1–1. pmid:28270866
- 5. Donath L, Roth R, Zahner L, Faude O. Slackline training (balancing over narrow nylon ribbons) and balance performance: a meta-analytical review. Sports Medicine. 2017 Jun;47(6):1075–86. pmid:27704483
- 6. Giboin LS, Gruber M, Kramer A. Task-specificity of balance training. Human movement science. 2015 Dec 1;44:22–31. pmid:26298214
- 7. Robertson S, Elliott D. Specificity of learning and dynamic balance. Research Quarterly for Exercise and Sport. 1996 Mar 1;67(1):69–75. pmid:8735996
- 8. Sawers A, Ting LH. Beam walking can detect differences in walking balance proficiency across a range of sensorimotor abilities. Gait & posture. 2015 Feb 1;41(2):619–23. pmid:25648493
- 9. Sawers A, Hafner B. Validation of the narrowing beam walking test in lower limb prosthesis users. Archives of physical medicine and rehabilitation. 2018 Aug 1;99(8):1491–8 pmid:29653108
- 10. Llewellyn M, Yang JF, Prochazka A. Human H-reflexes are smaller in difficult beam walking than in normal treadmill walking. Experimental brain research. 1990 Dec;83(1):22–8. pmid:2073943
- 11. Robertson S, Collins J, Elliott D, Starkes J. The influence of skill and intermittent vision on dynamic balance. Journal of Motor Behavior. 1994 Dec 1;26(4):333–9 pmid:12719190
- 12. Sipp AR, Gwin JT, Makeig S, Ferris DP. Loss of balance during balance beam walking elicits a multifocal theta band electrocortical response. Journal of neurophysiology. 2013 Nov 1;110(9):2050–60. pmid:23926037
- 13. Robertson S, Elliott D. The influence of skill in gymnastics and vision on dynamic balance. International Journal of Sport Psychology. 1996;27(4):361–8.
- 14. Domingo A, Ferris DP. The effects of error augmentation on learning to walk on a narrow balance beam. Experimental brain research. 2010 Oct;206(4):359–70. pmid:20853102
- 15. Domingo A, Ferris DP. Effects of physical guidance on short-term learning of walking on a narrow beam. Gait & posture. 2009 Nov 1;30(4):464–8. pmid:19674900
- 16. Peterson SM, Rios E, Ferris DP. Transient visual perturbations boost short-term balance learning in virtual reality by modulating electrocortical activity. Journal of neurophysiology. 2018 Oct 1;120(4):1998–2010. pmid:30044183
- 17. Peterson SM, Ferris DP. Differentiation in theta and beta electrocortical activity between visual and physical perturbations to walking and standing balance. eneuro. 2018 Jul;5(4). pmid:30105299
- 18. Peterson SM, Furuichi E, Ferris DP. Effects of virtual reality high heights exposure during beam-walking on physiological stress and cognitive loading. PloS one. 2018 Jul 6;13(7):e0200306. pmid:29979750
- 19. Arvin M, van Dieën JH, Bruijn SM. Effects of constrained trunk movement on frontal plane gait kinematics. Journal of biomechanics. 2016 Sep 6;49(13):3085–9. pmid:27492418
- 20. Bruijn SM, Van Dieën JH. Control of human gait stability through foot placement. Journal of The Royal Society Interface. 2018 Jun 30;15(143):20170816. pmid:29875279
- 21. Bauby CE, Kuo AD. Active control of lateral balance in human walking. Journal of biomechanics. 2000 Nov 1;33(11):1433–40. pmid:10940402
- 22. Symeonidou ER, Ferris DP. Intermittent Visual Occlusions Increase Balance Training Effectiveness. Frontiers in Human Neuroscience. 2022:258. pmid:35547194
- 23.
Symeonidou ER, Ferris DP. Training with Brief Visual Occlusions Improves Balance Control in Treadmill Beam Walking. In 9th International Symposium on Adaptive Motion of Animals and Machines (AMAM 2019) 2019.
- 24. Lakatos P, Karmos G, Mehta AD, Ulbert I, Schroeder CE. Entrainment of neuronal oscillations as a mechanism of attentional selection. science. 2008 Apr 4;320(5872):110–3. pmid:18388295
- 25. Schroeder CE, Lakatos P. Low-frequency neuronal oscillations as instruments of sensory selection. Trends in neurosciences. 2009 Jan 1;32(1):9–18. pmid:19012975
- 26. Thorne JD, De Vos M, Viola FC, Debener S. Cross-modal phase reset predicts auditory task performance in humans. Journal of Neuroscience. 2011 Mar 9;31(10):3853–61. pmid:21389240
- 27. Fell J, Axmacher N. The role of phase synchronization in memory processes. Nature reviews neuroscience. 2011 Feb;12(2):105–18. pmid:21248789
- 28. Axmacher N, Mormann F, Fernández G, Elger CE, Fell J. Memory formation by neuronal synchronization. Brain research reviews. 2006 Aug 30;52(1):170–82. pmid:16545463
- 29. Yang F, Pai YC. Can sacral marker approximate center of mass during gait and slip-fall recovery among community-dwelling older adults?. Journal of biomechanics. 2014 Dec 18;47(16):3807–12. pmid:25468302
- 30. Gard SA, Miff SC, Kuo AD. Comparison of kinematic and kinetic methods for computing the vertical motion of the body center of mass during walking. Human movement science. 2004 Apr 1;22(6):597–610. pmid:15063043
- 31. Gordon KE, Ferris DP, Kuo AD. Metabolic and mechanical energy costs of reducing vertical center of mass movement during gait. Archives of physical medicine and rehabilitation. 2009 Jan 1;90(1):136–44. pmid:19154840
- 32. Thirunarayan MA, Kerrigan DC, Rabuffetti M, Della Croce U, Saini M. Comparison of three methods for estimating vertical displacement of center of mass during level walking in patients. Gait & Posture. 1996 Oct 1;4(4):306–14.
- 33. Huber ME, Chiovetto E, Giese M, Sternad D. Rigid soles improve balance in beam walking, but improvements do not persist with bare feet. Scientific reports. 2020 May 6;10(1):1–7.
- 34. Chiovetto E, Huber ME, Sternad D, Giese MA. Low-dimensional organization of angular momentum during walking on a narrow beam. Scientific reports. 2018 Jan 8;8(1):1–4.
- 35. Young PM, Wilken JM, Dingwell JB. Dynamic margins of stability during human walking in destabilizing environments. Journal of biomechanics. 2012 Apr 5;45(6):1053–9. pmid:22326059
- 36. Assländer L, Peterka RJ. Sensory reweighting dynamics in human postural control. Journal of neurophysiology. 2014 May 1;111(9):1852–64. pmid:24501263
- 37. Assländer L, Peterka RJ. Sensory reweighting dynamics following removal and addition of visual and proprioceptive cues. Journal of neurophysiology. 2016 Aug 1;116(2):272–85. pmid:27075544
- 38. Peterka RJ. Sensorimotor integration in human postural control. Journal of neurophysiology. 2002 Sep 1;88(3):1097–118. pmid:12205132
- 39. Song K, Rhodes E, Wikstrom EA. Balance training does not alter reliance on visual information during static stance in those with chronic ankle instability: a systematic review with meta-analysis. Sports Medicine. 2018 Apr;48(4):893–905. pmid:29288435
- 40. Lubetzky-Vilnai A, McCoy SW, Price R, Ciol MA. Young adults largely depend on vision for postural control when standing on a BOSU ball but not on foam. The Journal of Strength & Conditioning Research. 2015 Oct 1;29(10):2907–18.
- 41. Stepan G. Delay effects in the human sensory system during balancing. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences. 2009 Mar 28;367(1891):1195–212. pmid:19218159
- 42. Nagy E, Toth K, Janositz G, Kovacs G, Feher-Kiss A, Angyan L, et al. Postural control in athletes participating in an ironman triathlon. European journal of applied physiology. 2004 Aug;92(4):407–13.
- 43. Croix G, Chollet D, Thouvarecq R. Effect of expertise level on the perceptual characteristics of gymnasts. The Journal of Strength & Conditioning Research. 2010 Jun 1;24(6):1458–63. pmid:20453683
- 44. Paillard TH, Noé F. Effect of expertise and visual contribution on postural control in soccer. Scandinavian journal of medicine & science in sports. 2006 Oct;16(5):345–8. pmid:16978254
- 45. Bauer AK, Debener S, Nobre AC. Synchronisation of neural oscillations and cross-modal influences. Trends in cognitive sciences. 2020 Jun 1;24(6):481–95. pmid:32317142
- 46. Voloh B, Womelsdorf T. A role of phase-resetting in coordinating large scale neural networks during attention and goal-directed behavior. Frontiers in systems neuroscience. 2016 Mar 8;10:18. pmid:27013986
- 47. Lakatos P, Chen CM, O’Connell MN, Mills A, Schroeder CE. Neuronal oscillations and multisensory interaction in primary auditory cortex. Neuron. 2007 Jan 18;53(2):279–92. pmid:17224408
- 48. Inman VT, Eberhart HD. The major determinants in normal and pathological gait. JBJS. 1953 Jul 1;35(3):543–58. pmid:13069544
- 49. Kuo AD, Donelan JM. Dynamic principles of gait and their clinical implications. Physical therapy. 2010 Feb 1;90(2):157–74. pmid:20023002
- 50. Donath L, Roth R, Zahner L, Faude O. Slackline training and neuromuscular performance in seniors: A randomized controlled trial. Scandinavian journal of medicine & science in sports. 2016 Mar;26(3):275–83. pmid:25756231
- 51. Donath L, Roth R, Rueegge A, Groppa M, Zahner L, Faude O. Effects of slackline training on balance, jump performance & muscle activity in young children. International journal of sports medicine. 2013 Dec;34(12):1093–8.