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# Age differences in adaptation of medial-lateral gait parameters during split-belt treadmill walking

## Introduction

The upright human body is biomechanically unstable and requires active control by the nervous system to stabilize. We do not have a comprehensive understanding of how humans control balance during walking, or how this control changes with age. The ability to dynamically adapt locomotion is critical for maintaining balance. In the event that one slips or trips, a quick adjustment must be made in order to maintain balance1,2. New locomotor patterns must also be adopted to safely navigate along a narrow path3 or on uneven terrain4. Furthermore, we know older adults struggle with the ability to maintain balance5 and have difficulties adapting new motor patterns6. Here we investigate whether older and younger adults adapt differently to a split belt treadmill paradigm, which comprises walking on two belts moving at differing speeds. Our particular emphasis is on the medial-lateral (ML) control of balance.

The balance problem is generally understood to require more active control in the ML direction17,18,19, implied by modeling and experimental work showing differences in the source of the variability of foot placement between the anterior-posterior (AP) and ML directions. This supports a growing consensus that step length and cadence are separately controlled from ML gait parameters such as ankle roll (active modulation of center of pressure (CoP) under the stance foot) and step width. But the step timing and duration of the single and double stance periods also affect balance. These timing parameters are shared between the AP and ML directions, so the AP changes enforced by the split belt treadmill paradigm represent a challenge to balance in the ML direction. We hypothesize that gait parameters specific to ML balance control are also modulated throughout the split belt treadmill protocol, but at different rates than the commonly assessed spatiotemporal variables.

Roper et al found modifications of ML ground reaction forces during adaptation in healthy young adults14, but these modifications seemed to coincide with the ground reaction forces dictated by the speed of the foot; thus it is not clear whether these changes were adaptive or simply reflect gait speed changes. Buurke et al sought to investigate whether ML gait parameters, including the margin of stability, are altered in the split belt treadmill paradigm15. Margin of stability is a measure of dynamic stability that takes into account the center of mass (CoM) position and velocity relative to the base of support (CoP)20. The authors found that the CoP and the margin of stability are changed over many steps when participants are adjusting to the split condition, but these variables did not exhibit classic aftereffects (i.e. a performance change when the belts are returned to the same speeds that is in the opposite direction of what occurred during the split condition), indicating that these changes may have been more strategic rather than adaptive per se. Instead, the margin of stability appeared to be closely related to the amount of time spent in stance21, at least in young healthy adults.

## Methods

### Participants

Thirty-seven young adults (19 female, $$22.4 \pm 3.78$$ years) and 28 older adults (12 female, $$72.5 \pm 5.26$$ years) participated in this study. Handrails were installed on the sides of the treadmill to prevent falls, if necessary, while performing the experiment. No harness was used for this experiment. Due to force plate data corruption and/or marker occlusions we excluded six younger and seven older adults’ data from analysis, leaving 31 younger adults and 21 older adults. This experiment was approved by the University of Florida Institutional Review Board. All participants provided written informed consent to participate.

Exclusion criteria included: History of any neurologic condition, e.g., stroke, Parkinson’s disease, seizures, or a concussion in the last six months. Psychiatric condition, e.g., active depression or bipolar disorder. As part of the larger study, subjects were also screened for magnetic resonance imaging (MRI) and transcranial magnetic stimulation eligibility22. We also excluded those who self-reported smoking, consuming more than two alcoholic drinks per day on average or a history of treatment for alcoholism, as participants also underwent GABA MR spectroscopy. We excluded those with any contraindications for these devices, e.g., implanted metal, claustrophobia, or pregnancy. We also excluded individuals who reported taking medications which are contraindications for these devices within the previous 30 days23. Such medications included those commonly used to treat psychiatric conditions e.g., depression, bipolar disorder, anxiety, attention-deficit/hyperactivity disorder, and schizophrenia, pain, and seizures, as well as some antiviral and chemotherapy medications.

Prior to enrollment, we screened participants for suspected cognitive impairment over the phone using the Telephone Interview for Cognitive Status24. We excluded those who scored < 21 of 39 points; this is equivalent to scoring < 25 points on the Mini-Mental State Exam (MMSE) and indicates probable cognitive impairment. At the first testing session, participants were re-screened for cognitive impairment using the Montreal Cognitive Assessment25; we excluded those who scored < 23 of 30 points26. None of the subjects had walked on a split-belt treadmill prior to participation in this study.

Participants walked on an instrumented split-belt treadmill (Bertec Inc., Columbus, Ohio, USA) with embedded force plates that captured kinetic data at 1200 Hz. Sixteen passive reflective markers were placed according to the Vicon Plug-in-Gait (Vicon, Oxford, UK) lower body marker system27. Kinematic data were collected at 120 Hz using 8 cameras surrounding the treadmill. Of those participants that are included in this analysis, kinematic data were collected with a Vicon Nexus (Vicon, Oxford, UK) motion capture system for 37 participants (22 young and 15 old) and a Qualisys Track Manager (Qualisys, Göteborg, Sweden) motion capture system for 15 participants (9 young and 6 old) due to construction.

We first administered a treadmill warm-up lasting 5 minutes at participants’ self-selected speed to allow them to accommodate to walking on the treadmill. For the remainder of the walking trials, the belts moved at a fixed speed for all participants. We then stopped the treadmill before beginning the baseline walking trials, consisting of a baseline slow (0.7 m s−1), baseline fast (1.4 m s−1), and baseline slow (0.7 m s−1) walking trial, each lasting two minutes. These trials acclimated the participants to the slow and fast walking speeds. Then we began the split trial, where the left belt was moving at the fast speed, and the right belt was moving at the slow speed, lasting 10 minutes28. The split trial was followed by an after adaptation trial, where both belts were fixed at the slow speed to identify any aftereffects. Participants also performed a readaptation and a washout trial. For this analysis, we did not include the second baseline slow, readaptation, or washout trials. Table 1 provides the condition names, durations, and belt speeds for each trial. The green box indicates the trials used in the present analyses. For all trials, the treadmill acceleration was set to 0.5 m s−2.

### Data management and organization

Kinematic data were low pass filtered with a 4th order Butterworth filter at a cut-off frequency of 10 Hz. Small gaps in the marker data of up to 100 ms length from occlusions were filled using cubic splines. Time points with occlusions greater than 100 ms were excluded from further analysis. Due to limited marker supply, we used the middle of the posterior hip markers as a proxy for the body CoM position29. We refer to this as the center of mass (CoM) throughout the manuscript.

We identified heel-strikes as the local maxima of forward progression of the heel marker, and push-off events local maxima of backward progression of the toe marker. We visually inspected the result of this automatic identification and applied manual corrections in the rare cases where events were misidentified. We have used this method of gait event detection successfully in the past and very rarely requires manual adjustment30.

All data between heel strikes were normalized to 100 time points. We subtracted the baseline slow mean from all data, including baseline slow, for every subject.

Throughout the manuscript and in the figures we refer to the individual feet by the speed with which they moved during the split condition. Specifically, the left foot was on the belt moving at 1.4 m s−1 during split, therefore we refer to this foot as fast. The right foot was on the belt moving at 0.7 m s−1 during split, so we refer to this foot as slow.

### Quantifying outcome variables

Analyzing the adaptation of our gait parameters on every step requires that each parameter is summarized by a single value. All of the data here are represented as a change in response from the average of the baseline slow steps ($$\Delta$$). We use the following definitions to quantify the use of the gait parameters on every step:

Spatial gait parameters

• Step Length: Distance from the trailing foot to the leading foot heel marker at the leading foot heel-strike in AP direction.

• Step Width: Distance from the trailing foot heel marker to the leading foot heel marker at the leading foot heel-strike in ML direction.

Temporal gait parameters

• Single Stance Time: Time between the swing foot pushoff and heel-strike.

• Double Stance Time Time between the stance foot heel-strike and the swing foot pushoff.

CoM referenced gait parameters (ML balance parameters)

• CoM: ML position of the middle of the posterior hip markers at heel strike of the swing foot.

• $$\int$$CoP-CoM: Displacement between the CoM and the CoP from the slow baseline steps, integrated over the single stance phase.

• Step-CoM: Displacement of the swing foot heel marker from the CoM at heel-strike.

We then quantified the symmetry between feet (slow and fast). Symmetry is defined as $$\Delta$$fast$$\Delta$$slow. Following Finely et al.7, we quantified the rate of adaptation based on the number of steps it took for a variable to reach plateau. The plateau of the symmetry was defined as the average value during the last 50 steps, and the threshold for reaching plateau was defined as the step when the next 9 consecutive steps remained within 2 standard deviations of the plateau7. We refer to this value as the number of steps to plateau. We calculated the plateau for each participant and all gait parameters for the split. We also calculated the magnitude of symmetry change influenced by the perturbation (split) and aftereffect (after) which is defined as the magnitude of the asymmetry at the time of plateau.

### Statistical analysis

We investigate whether older adults adapt differently to a split belt treadmill paradigm compared to younger adults, with an emphasis on the ML control of balance. We ran statistics to test for two primary questions. (1) Do the gait parameters exhibit age differences in the plateau and magnitude of perturbation effects during the split, reflecting different strategies? (2) Do the gait parameters that are associated with the control of balance exhibit age differences in steps to plateau during the split condition and magnitude of aftereffects, compared to traditional parameters, reflecting separate neural control.

To test our hypotheses that aging affects how the gait parameters change throughout the split belt treadmill paradigm, we used lme431 within R (version 3.5.1;32) to perform linear mixed effects analyses. We fit three linear mixed models to analyze how the gait parameters were modulated in the split and after conditions, and whether these modulations differed by age group and gait parameter. Group (younger vs older) and gait parameters were treated as fixed effects, and participant intercepts were random effects. Equation (1) provides the lmer model testing for age and gait parameter differences in steps to plateau during the split condition,

\begin{aligned} \texttt {Split\_Plateau} \sim \texttt {group} \cdot \texttt {gait\_parameters} + \texttt {1|Subject}, \end{aligned}
(1)

Equation (2) provides the lmer model testing for age and gait parameter differences in magnitude of plateau,

\begin{aligned} \texttt {Split\_Plateau\_Magnitude} \sim \texttt {group} \cdot \texttt {gait\_parameters} + \texttt {1|Subject}, \end{aligned}
(2)

and Eq. (3) provides the lmer model testing for gait parameter differences in magnitude of aftereffects (first 10 steps in after condition),

\begin{aligned} \texttt {After\_Magnitude} \sim \texttt {group} \cdot \texttt {gait\_parameters} + \texttt {1|Subject}. \end{aligned}
(3)

For each model we performed an ANOVA using Satterthwaite’s method33 implemented in the R-package lmerTest34 to obtain parameter-specific p values. We then performed posthoc pairwise analyses for each model by calculating the least squares means and estimating the 95% confidence intervals, using a Kenward–Roger approximation35 implemented in the R-package emmeans (version 1.4.1;36). We converted the $$\Delta$$ values into percent change from the average of the slow condition to enable pairwise-comparisons between gait parameters.

### Ethics approval and consent to participate

Subjects provided informed verbal and written consent to participate. Written informed consent was obtained from the individual for the publication of any potentially identifiable images or data included in this article. The experiment was approved by the University of Florida Institutional Review Board (IRB ID: IRB201801417). All methods were carried out in accordance with relevant guidelines and regulations.

### Consent for publication

All authors provided approval for publication.

## Results

All participants included in this analysis completed the treadmill walking trials without falling or tripping. In general, all participants adjusted multiple gait parameters to accommodate the different treadmill belt speeds.

In Fig. 1 we observe step length increases from slow to fast baseline conditions. During split, step length is larger when the fast foot is in stance compared to the slow foot stance, but becomes more symmetric over time. During after the step length exhibits an aftereffect opposite to the modulation observed during split. Step width drops slightly from slow to fast baseline conditions. Step width increases initially in both the split and after conditions. Both groups generally show similar trends of adaptation throughout the split and after conditions.

Figure 2 shows single and double stance time decreases from slow to fast baseline conditions. During the split condition, the two stance phases are modulated differently. The single stance time generally hovers around the stance time observed during the respective baseline condition (i.e. slow stance time during split was similar to that during slow baseline). The double stance time does not follow this same behavior. During split, the double stance time was longer when the fast stance foot was leading, but eventually the double stance phase becomes symmetric. During after, the double stance time exhibited similar aftereffects as step length, with a reversal in the difference between the two feet (i.e. double stance was longer when slow stance foot is leading).

Figure 3 depicts CoM referenced gait parameters that inform us about ML balance. We observed dramatic age differences in changes in the CoM variables in the split condition. During the baseline conditions, the two groups exhibit roughly similar behavior for these CoM referenced variables. The CoM shifted further away from the stance foot in the fast compared to the slow condition. Similarly, the $$\int$$CoP-CoM during the fast condition also shifted away from the stance foot relative to the slow condition. The step placement relative to the CoM shifted more towards the stance foot from slow to fast baseline conditions, mostly due to the shift of the CoM away from the stance foot. The behavior during the split condition is quite different for the younger compared to older adults. The younger adults quickly adopted an asymmetry in all three variables. During the split condition, the younger adults’ CoM referenced variables hover around the values that were observed during the respective baseline conditions for each stance foot. The older adults did not exhibit a similar, sustained asymmetry, during the split condition for the CoM referenced variables.

Figure 4 displays the adaptation trajectories for the symmetry scores of the spatial gait parameters. The triangles indicate the individual steps to plateau (x-axis) and the associated magnitude (y-axis). The squares represent the group average. There was a large asymmetry in step length that reduces throughout the split condition and then reverses during the after condition, for both age groups. During the split condition, the step length rate of adaptation was significantly different between the younger and older adults, with younger adults taking longer to reach a plateau (95% confidence intervals do not overlap, Table 3, Fig. 5). The magnitude of plateau did not significantly differ between the two groups for step length (Table 5, Fig. 6). Step width did not exhibit significant asymmetry during split for either age group (Table 5, Fig. 6).

Figure 7 displays the adaptation trajectories for the symmetry scores of the temporal gait parameters. There was a rather constant asymmetry for single stance time for both groups during the split condition. The single stance time rate of adaptation differed between the groups, with older adults taking longer to reach a plateau (95% CIs do not overlap, Table 3, Fig. 5). Double stance time exhibits a similar asymmetry to that observed in step length, with an eventual convergence to symmetry during the split condition. Neither steps to plateau nor the magnitude of plateau were significantly different between age groups for double stance time (Tables 35, Figs. 56).

Figure 8 displays the adaptation trajectories for the symmetry scores of the CoM gait parameters. We observed significant asymmetry in the CoM referenced variables, at least for the younger adults, during the split condition. The rate of adaptation during split is not significantly different between groups for any CoM referenced variables (Tables 23, Fig. 5). However, there was a significant difference between groups for the magnitude of plateau for Step-CoM and CoM-CoP, where the younger adults had greater asymmetry in both of these parameters (95% CIs do not overlap, Tables 45, Fig. 6).

Tables 67, and Fig. 9 display the ANOVA and Posthoc comparison results for testing the differences in magnitude of aftereffects (% $$\Delta$$ in first 10 steps of after condition). Step length, double stance time, CoM-CoP are the only variables that had significant aftereffects for both groups, and Step-CoM showed an aftereffect for the older adults (95% CI did not overlap with zero). Relative to the change observed during split, the aftereffects were in the opposite direction for all of these variables, with the exception of CoM-CoP for the older adults.

## Discussion

Our goal was to analyze multiple gait parameters that span both the AP and ML directions during split belt treadmill adaptation in young and older adults to determine whether they adapt differently. Previous split belt treadmill studies have shown that older adults generally adapt less, more slowly, and show reduced aftereffects relative to younger adults28,37,38. Many of these studies have not included ML gait parameters in their analysis, though. Examining ML balance control is necessary to gain a comprehensive understanding of how humans adapt to the split belt treadmill, and how this process differs by age.

Here we quantified multiple gait parameters that have been used to study balance during walking. Figs. 12, and 3 reveal that most of the gait parameters are altered throughout the split belt treadmill protocol, including those that are thought to contribute to ML balance. The most commonly analyzed gait parameters (step length and stance times) show clear effects of the split belt treadmill protocol, including aftereffects and group differences. However, we found that the older adults reached the plateau for their step length more quickly than younger adults. We also see some notable changes in the other parameters, including an initial modification of step width. Step width increases slightly during the split and after conditions in both age groups, likely to increase stability. The CoM referenced variables exhibit the largest difference between groups in regards to magnitude of the perturbation (split condition).

The older participants, in contrast, do not use this strategy to compensate for the difference in gravitational acceleration due to asymmetric stance time. Another way to generate lateral acceleration is to actively push against the ground to move the body back towards the slow leg in order to keep it in the center of the treadmill. Such an active muscle force would most likely be generated using the ankle roll mechanism, shifting the CoP in the direction of the fast leg39,40. Using muscle force to generate these corrections requires substantial metabolic energy, enough to move the whole body mass. Shifting the foot placement to passively exploit gravitational forces, in comparison, requires minimal metabolic energy, since only the mass of the leg is moved by active muscle forces. We suggest that this solution of actively moving the whole body is adopted, on average, by the older participants in our study, even though it is metabolically more expensive.

To elucidate the different coordination patterns between the step location and the center of mass in the medial-lateral direction, we adapted a previously developed model of walking control in the frontal plane to include split-belt walking. We provide an overview of the model here and refer the reader to41 for details. The model consists of a single-link inverted pendulum with a mass-less leg that is instantaneously switched to a new contact point when a step is taken after a fixed time20. We used this model to reproduce the coordination pattern between body center and foot placement in the frontal plane observed in split-belt walking in the younger and older adult groups. We used the base model with only foot placement control to generate walking trajectories similar to tied-belt walking in both the slow and fast speed baseline conditions. We set the step duration to the experimentally observed average values of 0.68 s in the slow and 0.50 s in the fast condition and used constrained gradient descent optimization implemented in the MATLAB function fmincon to find parameters that generate the experimentally observed average step width of 0.145 m in the slow and 0.135 m in the fast condition. The optimization successfully found constant offset parameters b = 0.013 m in the slow and b = 0.021 m in the fast condition. The resulting trajectories are shown in Figure 10, with slow in blue and fast in red. To model split-belt walking, we set the step duration to the experimentally observed averages of 0.51 s for slow steps and 0.61 s for fast steps. We added an additional constraint to the optimization, requiring that the average lateral position remains constant over time, i.e. that the body stays in the center of the treadmill, and used the average step width of 0.145 m as the optimization goal. For the younger adults, we used separate offset control parameters for each leg. The optimization successfully found parameters $$b_\text{fast}$$ = 0.023 m and $$b_\text{slow}$$ = 0.015 m. The resulting trajectories, shown in orange in Fig. 10, show that the CoM is shifted away from the fast, left leg and towards the slow, right leg, reproducing the asymmetry observed in the young participants. For the older adults, we used a single constant offset parameter, resulting in symmetric foot placement relative to the CoM, as in tied-belt walking. The optimization was not successful in this case, confirming that additional force is required to compensate for the asymmetric gravitational acceleration, as described above. We then added a compensating force in the form of a constant-rate CoP shift. With this additional mode of lateral control, the optimization successfully found parameters for constant offset $$b = 0.019$$ m and CoP shift rate $$c = 0.019$$ m s−1. The resulting trajectories, shown in purple in Fig. 10, show symmetric foot placement relative to the CoM, as observed in the older adults.

The model results support our hypothesis that the asymmetry adopted by the younger adults is beneficial by limiting the amount of active forces applied to maintain the CoM within the base of support. Given that the CoM acceleration is proportional to the distance between the CoP and the CoM20, the shortened stance time decreases the amount of time for gravitational forces to act on the CoM. The younger adults appear to shift the CoM to one side in order to create the same total absolute acceleration under both feet. This potentially comes at a cost of stability, decreasing the margin of stability on one side, but may contribute to enhanced metabolic efficiency. The older adults may sacrifice metabolic efficiency in order to maintain symmetry in their balance parameters. Recent work has investigated whether asymmetry in the AP direction is metabolically inefficient13,42, with mounting evidence suggesting asymmetry is more efficient when one leg is constrained43, but to our knowledge metabolic cost of asymmetry in the ML direction has not been studied. However, it is conceivable that the current findings are in line with recent work suggesting step time asymmetry is optimized to reduce metabolic expenditure44. Future work will be needed in order to determine whether the lack of asymmetry in the ML direction is metabolically inefficient for older adults, and which specific muscles are being used to counteract the constraints. A hip strategy may be used to keep the older adults balance parameters more symmetric, which would also lead to increased metabolic demands45. We believe the current results suggest the younger adults are more able to fine-tune their body configuration compared to the older adult group to achieve the same solution with less effort.

The magnitude and direction of aftereffects in the after condition indicates whether a gait parameter change was a temporary, strategic adjustment or a longer adaptation. That is, the presence of aftereffects is typically interpreted as reflecting adaptive processes, whereas a lack of aftereffects suggests that a change in performance in response to a perturbation was more of a transient strategy46. The carryover and opposite effect of motor adaptations into the after condition is the hallmark sign of sensorimotor adaptation47,48. Here we use the magnitude of aftereffects to confirm whether the gait parameter changes during split are adaptive changes or instead reflect transient strategies. Four gait parameters showed significant aftereffects, step length, double stance time, $$\int$$CoP-CoM, and Step-CoM. The step width increase is not detected by our symmetry measure because it increases symmetrically. Nevertheless, step width increases in both split and after suggest a quick strategic effect to deal with the split belt treadmill paradigm. It is often thought that increasing step width increases stability40,49, so increasing step width at the beginning of each perturbation condition (split and after) will increase stability enabling focus on the actual problem (feet moving at different speeds while in stance phase). On the other hand, step length and double stance time show significant aftereffects in the opposite direction of that observed during split for both age groups. The Step-CoM and $$\int$$CoP-CoM show aftereffects in the opposite direction only for the younger adults. This suggests a motor adaption occurred during split, and required readjustment when the belts were again moving at the same speed (after). Further, the rate of adaptation (steps to plateau) may be informative for determining the type of strategy employed. Step width adapts rather quickly, relative to step length and double stance time, for both groups. Therefore, we suggest that a shorter plateau reflects a strategic response for that gait parameter. However, the CoM referenced variables showed unique patterns. For example, the step-CoM showed a large asymmetry during the split condition, but relatively quick adaptation, followed by an aftereffect in the opposite direction of that shown in split. Therefore, it is not clear what learning procedure may be governing this balance control parameter.

## Data availability

Data will be made available upon request. Data processing code will be made available upon request. Modeling code is available on github (https://github.com/hendrikreimann/SplitBeltWalker).

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## Funding

During completion of this work KH was supported by a National Science Foundation Graduate Research Fellowship (Grants DGE-1315138; DGE-1842473), National Institute of Neurological Disorders and Stroke training grant T32-NS082128, and National Institute on Aging fellowship 1F99AG068440. TF, CH, and RS were funded by the National Institute of Health (U01AG061389). HR was funded by the National Science Foundation (NSF CRCNS 1822568) and the Parkinson’s Foundation (PF-JFA-2036).

## Author information

Authors

### Contributions

K.H. collected the data. T.F. and K.H. processed the data. H.R. created the model. T.F., K.H., H.R., J.C., C.H., R.S. interpreted the data and the model and wrote the manuscript.

### Corresponding author

Correspondence to Tyler Fettrow.

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### Competing interests

The authors declare no competing interests.

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Fettrow, T., Hupfeld, K., Reimann, H. et al. Age differences in adaptation of medial-lateral gait parameters during split-belt treadmill walking. Sci Rep 11, 21148 (2021). https://doi.org/10.1038/s41598-021-00515-z

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• DOI: https://doi.org/10.1038/s41598-021-00515-z