The Effects of Perturbation Intensities on Backward Slip-Falls Induced by a Split-belt Treadmill

doi:10.21203/rs.3.rs-4677876/v1

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The Effects of Perturbation Intensities on Backward Slip-Falls Induced by a Split-belt Treadmill

https://doi.org/10.21203/rs.3.rs-4677876/v1

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Fall-inducing systems have two critical applications. One is to obtain the biomechanical features of falling, and the other is to systematically train individuals and reduce the risk of falling. While the former application necessitates the actual occurrence of falls, the latter does not require fall-inducing perturbations to be excessively intense. This study investigated the effects of perturbation intensities (a combination of speeds and durations) on the number of falls, fall rates, and maximum loading forces resulting from backward slips induced by a split-belt treadmill. Twenty-four healthy young adults (12 males and 12 females), naïve to the purpose of the study, completed 16 randomized trials, including 12 perturbation trials and 4 false trials. The forces between a safety harness and a rail were used to identify falls and non-falls and to assess the maximum loading force during falls. Although the number of falls, fall rates, and maximum loading force significantly increase as the slipping speed increases for both durations, the relative risk analysis shows that fall risk significantly increases as the slipping speed increases regardless of the duration. These findings can contribute to establishing design criteria for controlled perturbations in studies on the biomechanics of falls and fall prevention training.

Health sciences/Risk factors

Physical sciences/Engineering/Biomedical engineering

Biological sciences/Neuroscience/Motor control

Across all age groups, the consequences of falls affect quality of life and everyday activities¹^–³, or result in fractures, concussions, and even death⁴^–⁶. Although various causes can result in falls, more than 50% of falls are caused by slips, and more than 25% are caused by trips in all age groups⁷^–⁹. When a slip occurs, falling backward is particularly risky since it often results in serious injuries such as traumatic brain injury, back injury, and limb fracture¹⁰^–¹².

Although the risk of falling is affected by several physical, neurological, metabolic, and environmental factors, the ability to rapidly restore the body’s stability after a gait perturbation (e.g., slipping, tripping, or stumbling) is definitely critical to preventing falls⁸^,¹³^,¹⁴. Mechanical obstacles, moveable platforms, pulling cables, and slippery contaminants (e.g., oil) have been the most common methods for generating gait perturbations (i.e., slips and trips) that could lead to a fall¹⁵^–¹⁹. These methods were primarily used to explore the biomechanical mechanisms leading to a fall. Additionally, multiple studies have demonstrated that perturbation-based training, which involves training the body’s compensatory responses following repeated exposure to slips and trips, is more task-specific than conventional balance and strength exercises for preventing falls²⁰^–²³. Two systematic reviews indicated that individuals can substantially improve their body responses after multiple exposures to unexpected slips and trips²⁴^,²⁵. These findings underscore the potential benefits of using gait perturbations to better understand the body’s post-fall responses or motor adaptations after fall prevention training.

However, the most common methods employing mechanical obstacles, moveable platforms, pulling cables, or slippery contaminants lack the ability to quantitatively control perturbations, making them unsuitable for practical fall-inducing and fall prevention training when perturbations need to be systematically designed and administered. For example, the pre-located obstacles or contaminants may allow the participants to anticipate the upcoming perturbation, which can possibly assist them in preventing falls and reduce the potential training effects resulting from unpredictability. Alternatively, instrumented treadmill-based systems equipped with force plates can address the issue of uncontrollable or predictable gait perturbations²⁶^–³³; treadmill belt can be precisely controlled to induce perturbation with selected parameters (e.g., speed, acceleration, duration, etc.) at any arbitrary moment. Reproducing perturbation experiments or training sessions under consistent treadmill settings can ensure reliable outcomes, and the settings can be modified and adjusted in a controlled manner to provide proper perturbation for the various applications.

Previous overground walking studies have demonstrated that a perturbed foot’s slipping dynamics, such as speed, duration, and distance affect the likelihood of falling³⁴^–³⁷. Therefore, there have been attempts to evaluate various perturbation intensities using a single-belt instrumented treadmill. Using feet in a standing position and three peak belt speeds, one research group found more falls at 1.8 m/s compared to 0.9 or 1.2 m/s³⁸. However, these findings may not be generalizable to dynamic contexts (i.e., falls caused by different slipping speeds during walking). Other studies have investigated whether perturbation-based training using different slipping accelerations, durations, and distances modulated by a single-belt instrumented treadmill could lead to fewer overground falls¹⁶^,¹⁸^,³⁹^–⁴¹. Although these studies have found that the training group had fewer overground falls than the control group without training, they have not quantitatively examined fall rates as a function of different slipping accelerations, durations, and distances. More importantly, the previous studies used a single-belt instrumented treadmill, which caused both feet to slip simultaneously¹⁶^,¹⁸^,³⁹^–⁴², rather than a split-belt instrumented treadmill.

Indeed, a split-belt instrumented treadmill may be more effective than a single-belt instrumented treadmill in accurately replicating real-life slip conditions, which are often asymmetric and involve the complex dynamics of a slipping foot, such as variations in speed, acceleration, duration, and distance. Nevertheless, prior studies using a split-belt instrumented treadmill have primarily employed a uniform speed and/or acceleration, and have focused on biomechanical and physiological responses and learning effects after induced perturbations such as trips and slips²⁶^,²⁷^,³¹^–³³^,⁴³ Consequently, a gap in these previous studies exists in our knowledge of how different perturbation intensities, modulated by a split-belt instrumented treadmill, affect backward slip falls (i.e., foot slipping forward).

In this study, to systematically analyze the effects of perturbation intensities on backward slip falls, we refined the previous version of our fall-inducing system and exploited the controllable and unpredictable gait perturbation induced by the split-belt treadmill. We selected three speeds and two durations based on the results of previous overground and treadmill walking studies, identified falls and non-falls based on conventional harness load cell criteria, and evaluated the number of falls, fall rates, and maximum loading forces during falls as a function of the six perturbation intensities (i.e., the combination of three speeds and two durations).

A. Slip-Inducing System and Perturbation Intensities

The slip-inducing system’s hardware and software were refined and implemented based on our previous fall-inducing developments²⁶^,²⁷^,³²^,³³. Fig.1 shows the hardware’s components, consisting of (1) an instrumented split-belt treadmill equipped with two force plates beneath each belt (Bertec Corporation, Columbus, OH, USA) and (2) a load cell (LCCD-250, Omega Engineering Inc., CT, USA) attached between a safety harness and a stationary walking rail.

The custom software was developed and implemented using C++ based on our previous studies²⁶^,²⁷^,³²^,³³. Fig.2 shows the custom software’s flowchart. In real time, the custom software sampled the vertical ground reaction forces (GRFs), speeds of the treadmill’s belts, and loading forces from a load cell at a rate of 100 Hz. The raw GRFs were low-pass filtered with a 10 Hz cut-off frequency to eliminate high-frequency noise²⁶^,²⁷^,³²^,³³. Using filtered GRFs, a gait recognition algorithm detected heel strike and toe-off events based on a pre-specified threshold (i.e., 2% of GRFs normalized to each participant’s weight)⁴⁴. Slip perturbation occurred at a randomly selected foot (either left or right foot) and step using one of six perturbation intensities.

The six perturbation intensities were determined by combining three speeds and two durations. These parameters were chosen considering the possible effects of participant’s preferred walking speed (PWS) on recovery from slips⁴⁵ and the range of foot speed and duration during overground slips³⁴^,³⁵^,³⁷. In particular, previous overground studies have shown that individuals can successfully recover from induced falls when the foot’s slipping speed is less than or equivalent to their PWS, whereas falls occur when they slip at a speed that approximately reaches 2×PWS³⁴^,³⁵^,³⁷. Therefore, we chose three speeds of 0.5×PWS (slow), 1×PWS (moderate), and 2×PWS (fast).

Previous studies have indicated that the compensatory limb (i.e., non-perturbed limb) reacts within approximately 300 ms after a slip, regardless of whether the recovery is successful or a fall occurs²⁶^,⁴⁵. This finding implies that falls are likely to occur under two scenarios. First, even with a relatively rapid response time (i.e., ≤ 300 ms), a fast slipping speed can cause an extended slipping distance, leading to a fall. Second, even at a relatively slow or moderate slipping speed, a longer slipping duration can increase the distance and likelihood of falling. Considering these, we chose two distinct durations of 300 ms and 500 ms.

Although previous overground studies have identified recovery or falls based on individual parameters (e.g., slipping speed, duration, or distance)³⁴^–³⁷, they have not investigated the combined effects of these parameters. In this study, we examined six perturbation intensities, combining three speeds (0.5×PWS, 1×PWS, and 2×PWS) and two durations (300 ms and 500 ms). For all perturbation intensities, one belt for the selected foot was accelerated anteriorly, causing backward loss of balance.

The acceleration of one belt was automatically calculated and applied based on the three speeds (i.e., 0.5×PWS, 1×PWS, and 2×PWS). We used different values of acceleration to achieve the intended belt speed at the heel strike of the selected foot, even with the non-zero response time of the system and the short time window between the toe-off and heel strike. Resultantly, the average accelerations were 4.54 m/s² for 0.5×PWS, 5.87 m/s² for 1×PWS, and 7.75 m/s² for 2×PWS, as confirmed by the recorded speed profiles.

Fig.3 shows a representative belt speed profile. Considering the response time of the treadmill’s control system and motor characteristics when abruptly changing the direction of the belt’s speed, we accelerated the belt providing the slip perturbation immediately after the perturbed foot’s toe-off just before the perturbation step, and the perturbation persisted for 300 ms or 500 ms after the perturbed foot’s heel strike. After the perturbation, the two belts stopped when the loading force recorded by the load cell exceeded 30% of body weight⁴⁶, which indicated a fall; otherwise, the belt providing the slip perturbation returned to pre-perturbation speed, which indicated a non-fall. For data analysis, the custom software stored GRFs, belts’ speeds, loading forces at a rate of 100 Hz, fall and non-fall events in a binary manner (1 or 0), foot selection (left or right), perturbed step (number), and perturbation intensity (from 1 to 6).

B. Participants

Twenty-four healthy young adults (12 females and 12 males; age: 23.25 ± 2.47 years; height: 170.17 ± 6.78 cm; weight:

65.82 ± 10.50 kg) participated. Potential participants were excluded if they had musculoskeletal dysfunctions, peripheral sensory diseases, neurological disorders, or surgical histories that impaired balance or gait performance. The recruited participants were right-footed, as identified by their self-reported foot dominance, and naïve to the purpose of the study. All participants read and signed the informed consent form for both study participation and publication of identifying information and images in an online open-access publication. The Seoul National University Institutional Review Boards approved the study protocol (IRB No. 2307/001-001), which was in accordance with the Helsinki Declaration.

C. Experimental Protocols

There were no practice trials with the slip perturbation. All participants received no information about slip intensity, perturbation foot, perturbation onset, and recovery. Before an experiment session began, each participant’s PWS was determined by the conventional protocol⁴⁷^,⁴⁸. Then, each participant wore noise-cancelling headphones (QC45, Boss, USA) to mask the noise from the treadmill motors, which might possibly enable the participant to anticipate the onset of the perturbation. Each participant also wore safety harness (Fig.1). To minimize the impact of the wearing safety harness and ensure the validity of the load cell criteria, the vertical hip height in a seated position while wearing the safety harness was set to 37% of the participant’s height⁴⁶.

The experiment session included 16 randomized trials, consisting of 12 perturbation trials (six perturbation intensities applied to both the left and right foot) and four false trials without any perturbation applied to either foot. The false trials were added to reduce any potential learning effects²⁷^,³². During 12 perturbation trials, the slip perturbation was randomly applied between the 26th and 50th steps to minimize the possibility of anticipating a slip occurrence.

During all the experimental trials, each participant was instructed to walk as normally as possible while keeping their gaze fixed on an “X” mark positioned 3 m ahead at eye level and not to hold onto the safety harness. White noise was played through the noise-cancelling headphones to remove ambient noise, such as belt running sounds. If a participant fell according to the load cell criteria (i.e., 30% of body weight⁴⁶), both belts were immediately stopped for safety, and the trial was terminated. Otherwise, a participant continued walking for 20 steps at the pre-perturbation speed (i.e., PWS) after the perturbation, to ensure enough steps for successful recovery and a return to normal walking²⁶^,²⁷^,³²^,³³. Each trial lasted less than 90 s. Consecutive trials were separated by 30 s of rest.

D. Data and Statistical Analyses

The outcome measures (number of falls and non-falls and rates for the 12 trials, and maximal loading force for perturbation trials involving falls) were analyzed using SPSS (IBM Corp., Armonk, NY, USA). The preliminary statistical analysis for fall occurrence (i.e., fall (1) or non-fall (0)) and maximum loading forces indicated no significant effects of gender, perturbation foot (i.e., left or right), or perturbation trials (i.e., learning). Therefore, chi-square tests were performed to evaluate the association between fall occurrence and six perturbation intensities. Associations between fall occurrence and perturbation intensities were also assessed using odds ratios (OR) with 95% confidence intervals (CI) and relative risk (RR). After confirming the normal distributions of the maximum loading forces using the Shapiro-Wilk test, a two-way analysis of variance (ANOVA) was performed to assess the main effects of the three speeds and two durations and their interactions. Post hoc analysis using the Šídák method was performed to determine the factors influencing the main and interaction effects on the maximum loading forces. The significance levels were set at a 2-sided p < 0.05.

Number of Non-Falls and Falls and Their Rates

The mean and standard deviation of the PWS across all participants were 1.00 and 0.11 m/s. Table 1 reports the number of falls and non-falls and their rates as a function of the three speeds and two durations. The overall fall rate was 76%, with a tendency for the fall rate to increase as speed and duration increased.

Table 1

Number of Falls and Non-Falls and Their Rates as a Function of the Three Speeds and Two Durations
Speed	Duration	Fall (n)	Non-Fall (n)	Total (n)	Fall Rate (%)
0.5×PWS	300 ms	25	23	48	52.08
0.5×PWS	500 ms	27	21	48	56.25
1×PWS	300 ms	38	10	48	79.17
1×PWS	500 ms	42	6	48	87.50
2×PWS	300 ms	40	8	48	83.33
2×PWS	500 ms	47	1	48	97.92
Total		219	69	288	76.04

Table 2 reports the results of the chi-square analysis for the three speeds and two durations. The main effects of the three speeds on fall occurrence were significant for both durations, but the main effects of the two durations on fall occurrence were significant only for the 2×PWS and insignificant for the 0.5×PWS and 1×PWS. Crammer’s V values for the main effects of the three speeds and two durations showing statistical significance exceeded 0.25, indicating a medium and large effect size⁴⁹.

Table 2

Statistical Analysis Results of Fall Occurrence for The Three Speeds and Two Durations (DF: Degrees of Freedom; *: Statistical Significance)
Effect	Layer	DF	Pearson chi-square	p-value	Crammer’s V
Speed	300 ms	2	13.57	0.001*	0.307
Speed	500 ms	2	3.221	< 0.0001*	0.447
Duration	0.5×PWS	1	0.17	0.682	0.042
	1×PWS	1	1.20	0.273	0.112
	2×PWS	1	6.00	0.014*	0.250

Figure 4 shows the fall rate as a function of the three speeds and two durations, including the statistical significance results of chi-square tests. For the 300 ms duration, the results of statistical analyses showed that the 0.5×PWS had significantly lower fall rates than the 1×PWS (p = 0.045) and the 2×PWS (p = 0.010). The OR values with 95% CI were 3.50 (95% CI: 1.43, 8.58; RR: 1.52) for the 1×PWS versus the 0.5×PWS and 4.60 (95% CI: 1.78, 11.86; RR: 1.60) for the 2×PWS versus the 0.5×PWS, respectively.

For the 500 ms duration, the results of statistical analyses showed that 0.5×PWS had significantly lower fall rates than the 1×PWS (p = 0.010) and the 2×PWS (p < 0.0001), and the 1×PWS had significantly lower fall rates than the 2×PWS. The OR values with 95% CI were 5.44 (95% CI: 1.95, 15.22; RR: 1.56) for the 1×PWS versus the 0.5×PWS, 36.56 (95% CI: 4.65,

287.15; RR: 1.74) for the 2×PWS versus the 0.5×PWS, and 6.71 (95% CI: 0.78, 58.08; RR: 1.12) for the 2×PWS versus the 1×PWS, respectively.

For the three speeds, the 300 ms had significantly lower fall rates than the 500 ms only for the 2×PWS. The OR value with 95% CI was 9.40 (95% CI: 1.23, 78.41; RR: 1.18) for the 500 ms versus 300 ms.

Maximum Loading Forces Due to Falls

Table 3 reports the statistical analysis results of the maximum loading forces as a function of the three speeds and two durations for 219 perturbation trials involving falls reported in Table 1. A two-way ANOVA revealed significant main effects of the three speeds but no significant main effects of the two durations or interaction effects on the maximum loading forces.

Table 3

Statistical Analysis Results of The Maximum Loading Forces for The Three Speeds and Two Durations (DF: Degrees of Freedom; *: Statistical Significance)
Effects	DF	F value	p-value
Speed	2	20.90	< 0.0001*
Duration	1	0.04	0.843
Speed × Duration	2	0.81	0.447

Figure 5 shows the maximum loading forces as a function of the three speeds and two durations, including the statistical significance of post hoc multiple comparisons. For the 300 ms duration, the 2×PWS resulted in significantly higher maximum loading forces than the 0.5×PWS (p = 0.001). For the 500 ms duration, the 0.5×PWS resulted in significantly lower maximum loading forces than the 1×PWS (p = 0.002) and the 2×PWS (p < 0.0001).

The substantial expenses associated with falls underscore the need to design effective training programs for fall prevention and comprehensively understand the biomechanics of falls. One potentially effective strategy to improve individuals’ ability to restore balance after perturbation and reduce the risk of falling is to repetitively expose them to unexpected gait perturbations in a well-controlled training setting²⁴^,²⁵. To implement this scheme, it is critical to design and apply gait perturbations with proper intensities. Likewise, to reveal the biomechanics of falls comprehensively, it is imperative to perform a well-designed experiment that includes perturbations with sufficient intensity. One critical obstacle to achieving these goals is a lack of our knowledge about the adequacy of the perturbations; there is uncertainty over the “proper” or “sufficient” levels of the perturbation intensity.

In this study, to contribute to filling this research gap, we investigated the effects of six different perturbation intensities on the fall occurrences and rates following slip perturbations by exploiting a split-belt treadmill system which can apply perturbation in a precisely controlled manner. We also quantified the maximum loading forces resulting from falls. The results showed a significant increase in the rate of falls as the speed of the induced slip increased regardless of the duration, and a higher occurrence of falls with a slip duration of 500 ms compared to 300 ms, when the slipping speed was twice the PWS. Maximum loading forces increased as the slipping speed increased, whereas the slip duration had no significant effect regardless of the slip speed.

Our results are consistent with those of previous studies on overground slips, which established a correlation between falls and the speed, duration, and distance of the slipping foot¹⁹^,³⁴^,³⁵^,³⁷. In previous overground studies¹⁹^,³⁴^–³⁷^,⁵⁰, the average slipping speed of a perturbed foot that mostly resulted in recovery was 1.43 ± 0.55 m/s, which was lower than or comparable to the participants’ PWS (1.32 ± 0.22 m/s). However, the slipping speed that primarily caused falls was 2.21 ± 0.53 m/s, which was comparable to 2×PWS (1.34 ± 0.16 m/s). Additionally, the slipping distance of a perturbed foot ranged approximately 10.19 cm to 47.07 cm for recovery and 30.82 cm to 56.44 cm for falling, with some overlap between the two ranges. Furthermore, the slipping duration of a perturbed foot was nearly identical, being 0.30 ± 0.17 s for recovery and 0.29 ± 0.14 s for falling. However, it is reasonable to infer that a slow slipping speed and shorter distance likely resulted in stable recovery steps, whereas a faster slipping speed and longer distance led to unstable steps and falls. Taken together, these previous findings support the selection of three speeds and two durations assessed in this study. More importantly, this study demonstrates that adjusting speed and duration can effectively alter slipping distances, thereby potentially influencing the likelihood of falls. The observed slipping distances for each perturbation intensity were: 15 cm (range: 13.3 cm to 16.7 cm) for 0.5×PWS at 300 ms, 30 cm (range: 26.7 cm to 33.3 cm) for 1×PWS at 300 ms, 60 cm (range: 53.4 cm to 66.6 cm) for 2×PWS at 300 ms, 25 cm (range: 22.5 cm to 27.8 cm) for 0.5×PWS at 500 ms, 50 cm (range: 44.5 cm to 55.5 cm) for 1×PWS at 500 ms, and 100 cm (range: 89.0 cm to 111.0 cm) for 2×PWS at 500 ms. Therefore, the six perturbation intensities (i.e., the combination of three speeds and two durations) identified and assessed in this study could be appropriate for investigating falls or training recovery responses on split-belt instrumented treadmills, which contrasts with the outcomes of previous studies that have mainly focused on compensatory responses or learning effects³¹^–³³^,⁴²^,⁴³^,⁵¹.

The results of the RR analysis showed that fall risk significantly increased as slipping speed increased, ranging from 1.12

7/11

to 1.78 times regardless of slipping duration. Only the 2×PWS showed a significant increase in fall risk when the duration increased from 300 ms to 500 ms, which we attribute to a large difference in slipping distances between the duration of 300 ms and 500 ms (i.e., 60 cm vs. 100 cm, compared to 15 cm vs. 25 cm for the 0.5×PWS, and 30 cm vs. 50 cm for the 1×PWS).

Consistent with the results of this study’s fall rate analysis for the three speeds and two durations, the maximum loading force significantly increased as the slipping speed increased in the trials involving falls as shown in Fig.5. The average maximum loading forces ranged from 42.96% to 50.72% of the participant’s body weight. The slip duration had no significant effect on the maximum loading forces for 0.5×PWS, 1×PWS, and 2×PWS as shown in Fig.5.

The limitations of this study include a lack of kinematic measures, participant homogeneity (i.e., healthy young adults), and the absence of speed less than 0.5×PWS and durations shorter than 300 ms. Future research integrating a kinematic measurement system, such as a full-body motion capture system, with our slip-inducing system will allow us to comprehensively evaluate whole-body kinematics after slip disturbances with varying intensities. We also plan to include populations at high risk of falling (e.g., older adults, people with neurological conditions, etc.) and investigate the effects of speeds less than 0.5×PWS and durations shorter than 300 ms on falls and non-falls.

This is the first study to systematically assess the effect of perturbation intensities on backward slip falls induced by a split-belt treadmill. We investigated the effects of six perturbation intensities combining the three speeds and two durations on falling, and found a positive correlation between the speed of the induced slip perturbation and the fall rates regardless of the two durations. The maximum loading forces during falls also showed a positive correlation with the increase in slipping speed but were not affected by the durations.

Our findings can contribute to developing the design criteria for gait perturbations that can be used either to investigate the biomechanics of falls by applying “sufficient” fall-inducing perturbations or to train individuals by exposing them to “sub-fall-inducing” perturbations. We specifically revealed that perturbation speed and the resulting distance are critical factors in inducing falls. The technological approaches, experimental procedures, and results of this study could be used as guidelines for future fall-related studies.

Data availability

All data sets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.

Acknowledgements

The authors thank Hyeonhee Jung, Yoojin Choi, Jeongin Moon, Hyunji Kim, Eunsik Choi, Taegyun Park, Seongwon Hwang, Jangwhan Ahn, Ilseung Park, Jungho Lee, Junyeon Hwang, Hong Min, Chaelynne Kim, Seungeun Lee, Jeongbin Park, and Chaeyoung Rhee for their data collection assistance.

Author contributions statement

J.A. and B.L. conceived the study and supervised data collection, C.L. and B.L. developed the system, C.L., J.A., and

B.L. designed the experimental protocols, interpreted the results, and drafted the manuscript, C.L. and B.L. performed data collection, data analysis, and statistical analysis, J.A. and B.L. acquired the research funding, and all authors reviewed and revised the manuscript.

Additional information

Research was supported in part by the Brain Pool Program funded by the Ministry of Science and ICT through the National Research Foundation of Korea (2002H1D3A2A01096469), the grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare (No. HK23C0071), Industrial Technology Innovation Program (No. 20007058; (development of safe and comfortable human augmentation hybrid robot suit), and Industrial Strategic Technology (No. 20018157) funded by the Ministry of Trade, Industry & Energy (MOTIE, Korea), the National Research Foundation of Korea grant funded by the Korean Government (MSIT) (No. RS-2023-00208052), and the grants supported by Ministry of Culture, Sports and Tourism (RS-2024-00396700).

K. A. Hartholt, E. F. van Beeck, S. Polinder, N. van der Velde, E. M. van Lieshout, M. J. Panneman, T. J. van der Cammen, and P. Patka, “Societal consequences of falls in the older population: injuries, healthcare costs, and long-term reduced quality of life,” J Trauma, vol. 71, no. 3, pp. 748-753, Sep., 2011.
N. Salari, N. Darvishi, M. Ahmadipanah, S. Shohaimi, and M. Mohammadi, “Global prevalence of falls in the older adults: a comprehensive systematic review and meta-analysis,” J Orthop Surg Res, vol. 17, no. 1, p. 334, Jun. 28, 2022.
WHO. (2012) Falls. Available: http://www.who.int/news-room/fact-sheets/detail/falls
D. Oliver, J. B. Connelly, C. R. Victor, F. E. Shaw, A. Whitehead, Y. Genc, A. Vanoli, F. C. Martin, and M. A. Gosney, “Strategies to prevent falls and fractures in hospitals and care homes and effect of cognitive impairment: systematic review and meta-analyses,” Bmj, vol. 334, no. 7584, p. 82, Jan. 13, 2007.
E. Burns and R. Kakara, “Deaths from falls among persons aged >65 years - United States, 2007-2016,” MMWR Morb Mortal Wkly Rep, vol. 67, no. 18, pp. 509-514, May. 11, 2018.
S. K. Verma, J. L. Willetts, H. L. Corns, H. R. Marucci-Wellman, D. A. Lombardi, and T. K. Courtney, “Falls and fall-related injuries among community-dwelling adults in the United States,” PLoS One, vol. 11, no. 3, p. e0150939, Mar. 15, 2016.
W. P. Berg, H. M. Alessio, E. M. Mills, and C. Tong, “Circumstances and consequences of falls in independent community- dwelling older adults,” Age Ageing, vol. 26, no. 4, pp. 261-268, Jul., 1997.
M. J. Heijnen and S. Rietdyk, “Falls in young adults: Perceived causes and environmental factors assessed with a daily online survey,” Hum Mov Sci, vol. 46, pp. 86-95, Apr 2016.
T. K. Courtney, G. S. Sorock, D. P. Manning, J. W. Collins, and M. A. Holbein-Jenny, “Occupational slip, trip, and fall-related injuries–can the contribution of slipperiness be isolated?,” Ergonomics, vol. 44, no. 13, pp. 1118-1137, Oct. 20 2001.
H. Amandus, J. Bell, H. Tiesman, and E. Biddle, “The epidemiology of slips, trips, and falls in a helicopter manufacturing plant,” Hum Factors, vol. 54, no. 3, pp. 387-95, Jun 2012.
T. K. Courtney, G. S. Sorock, D. P. Manning, J. W. Collins, and M. A. Holbein-Jenny, “Occupational slip, trip, and fall-related injuries–can the contribution of slipperiness be isolated?,” Ergonomics, vol. 44, no. 13, pp. 1118-37, Oct 20 2001.
J. A. Stevens, J. E. Mahoney, and H. Ehrenreich, “Circumstances and outcomes of falls among high risk community- dwelling older adults,” Inj Epidemiol, vol. 1, no. 5, Mar 20 2014.
L. A. Talbot, R. J. Musiol, E. K. Witham, and E. J. Metter, “Falls in young, middle-aged and older community dwelling adults: perceived cause, environmental factors and injury,” BMC Public Health, vol. 5, p. 86, Aug. 18, 2005.
P. Raina, N. Sohel, M. Oremus, H. Shannon, P. Mony, R. Kumar, W. Li, Y. Wang, X. Wang, K. Yusoff, R. Yusuf, R. Iqbal, A. Szuba, A. Oguz, A. Rosengren, A. Kruger, J. Chifamba, N. Mohammadifard, E. A. Darwish, G. Dagenais, R. Diaz, A. Avezum, P. Lopez-Jaramillo, P. Seron, S. Rangarajan, K. Teo, and S. Yusuf, “Assessing global risk factors for non-fatal injuries from road traffic accidents and falls in adults aged 35-70 years in 17 countries: a cross-sectional analysis of the Prospective Urban Rural Epidemiological (PURE) study,” Inj Prev, vol. 22, no. 2, pp. 92-98, Apr., 2016.
T. Bhatt, E. Wang, and Y.-C. Pai, “Retention of adaptive control over varying intervals: prevention of slip-induced backward balance loss during gait,” Journal of Neurophysiology, vol. 95, no. 5, pp. 2913-2922, 2006.
J. D. Lurie, A. B. Zagaria, D. M. Pidgeon, J. L. Forman, and K. F. Spratt, “Pilot comparative effectiveness study of surface perturbation treadmill training to prevent falls in older adults,” BMC geriatrics, vol. 13, pp. 1-8, 2013.
Y.-C. Pai, F. Yang, T. Bhatt, and E. Wang, “Learning from laboratory-induced falling: long-term motor retention among older adults,” Age, vol. 36, pp. 1367-1376, 2014.
F. Yang, T. Bhatt, and Y.-C. Pai, “Generalization of treadmill-slip training to prevent a fall following a sudden (novel) slip in over-ground walking,” Journal of biomechanics, vol. 46, no. 1, pp. 63-69, 2013.
R. Cham and M. S. Redfern, “Heel contact dynamics during slip events on level and inclined surfaces,” Saf Sci, vol. 40, no. 7, pp. 559-576, Oct. 1, 2002.
M. D. Grabiner, J. R. Crenshaw, C. P. Hurt, N. J. Rosenblatt, and K. L. Troy, “Exercise-based fall prevention: can you be a bit more specific?,” Exerc Sport Sci Rev, vol. 42, no. 4, pp. 161-168, Oct., 2014.
M. D. Grabiner, S. Donovan, M. L. Bareither, J. R. Marone, K. Hamstra-Wright, S. Gatts, and K. L. Troy, “Trunk kinematics and fall risk of older adults: translating biomechanical results to the clinic,” J Electromyogr Kinesiol, vol. 18, no. 2, pp. 197-204, Apr., 2008.
U. Granacher, T. Muehlbauer, L. Zahner, A. Gollhofer, and R. W. Kressig, “Comparison of traditional and recent approaches in the promotion of balance and strength in older adults,” Sports Med, vol. 41, no. 5, pp. 377-400, May. 1, 2011.
L. I. E. Oddsson, P. Boissy, and I. Melzer, “How to improve gait and balance function in elderly individuals—compliance with principles of training,” Eur Rev Aging Phys Act, vol. 4, no. 1, pp. 15-23, Apr. 1, 2007.
C. McCrum, M. H. G. Gerards, K. Karamanidis, W. Zijlstra, and K. Meijer, “A systematic review of gait perturbation paradigms for improving reactive stepping responses and falls risk among healthy older adults,” Eur Rev Aging Phys Act, vol. 14, p. 3, Mar. 2, 2017.
Z. Taylor, G. S. Walsh, H. Hawkins, M. Inacio, and P. Esser, “Perturbations during gait: A systematic review of methodologies and outcomes,” Sensors (Basel), vol. 22, no. 15, Aug. 8, 2022.
B. C. Lee, C. S. Kim, and K. H. Seo, “The body’s compensatory responses to unpredictable trip and slip perturbations Induced by a programmable split-belt treadmill,” IEEE Trans Neural Syst Rehabil Eng, vol. 27, no. 7, pp. 1389-1396, Jul., 2019.
B. C. Lee, B. J. Martin, T. A. Thrasher, and C. S. Layne, “The effect of vibrotactile cuing on recovery strategies from a treadmill-induced trip,” IEEE Trans Neural Syst Rehabil Eng, vol. 25, no. 3, pp. 235-243, Mar., 2017.
T. M. Owings, M. J. Pavol, and M. D. Grabiner, “Mechanisms of failed recovery following postural perturbations on a motorized treadmill mimic those associated with an actual forward trip,” Clin Biomech (Bristol, Avon), vol. 16, no. 9, pp. 813-819, Nov., 2001.
A. Shapiro and I. Melzer, “Balance perturbation system to improve balance compensatory responses during walking in old persons,” J Neuroeng Rehabil, vol. 7, no. 1, p. 32, Jul. 15, 2010.
H. Shimada, S. Obuchi, T. Furuna, and T. Suzuki, “New intervention program for preventing falls among frail elderly people: the effects of perturbed walking exercise using a bilateral separated treadmill,” Am J Phys Med Rehabil, vol. 83, no. 7, pp. 493-499, Jul., 2004.
D. Yoo, K. H. Seo, and B. C. Lee, “The effect of the most common gait perturbations on the compensatory limb’s ankle, knee, and hip moments during the first stepping response,” Gait Posture, vol. 71, pp. 98-104, Jun., 2019.
B. C. Lee, J. Choi, J. Ahn, and B. J. Martin, “The different contributions of the eight prefrontal cortex subregions to reactive responses after unpredictable slip perturbations and vibrotactile cueing,” Front Hum Neurosci, vol. 17, p. 1236065, 2023.
B. C. Lee, J. Choi, and B. J. Martin, “Roles of the prefrontal cortex in learning to time the onset of pre-existing motor programs,” PLoS One, vol. 15, no. 11, p. e0241562, 2020.
L. J. Allin, M. A. Nussbaum, and M. L. Madigan, “Feet kinematics upon slipping discriminate between recoveries and three types of slip-induced falls,” Ergonomics, vol. 61, no. 6, pp. 866-876, Jun., 2018.
R. A. Brady, M. J. Pavol, T. M. Owings, and M. D. Grabiner, “Foot displacement but not velocity predicts the outcome of a slip induced in young subjects while walking,” J Biomech, vol. 33, no. 7, pp. 803-808, Jul., 2000.
A. Sawers, Y. C. Pai, T. Bhatt, and L. H. Ting, “Neuromuscular responses differ between slip-induced falls and recoveries in older adults,” J Neurophysiol, vol. 117, no. 2, pp. 509-522, Feb. 1, 2017.
K. L. Troy, S. J. Donovan, J. R. Marone, M. L. Bareither, and M. D. Grabiner, “Modifiable performance domain risk-factors associated with slip-related falls,” Gait Posture, vol. 28, no. 3, pp. 461-465, Oct., 2008.
S. Shin, “Quantifying effects of perturbation intensity on slip outcome in young adults,” Master of Science, Department of Kinesiology and Health, Georgia State University, 2023.
A. Lee, T. Bhatt, X. Liu, Y. Wang, and Y. C. Pai, “Can higher training practice dosage with treadmill slip-perturbation necessarily reduce risk of falls following overground slip?,” Gait Posture, vol. 61, pp. 387-392, Mar., 2018.
X. Liu, T. Bhatt, and Y. C. Pai, “Intensity and generalization of treadmill slip training: High or low, progressive increase or decrease?,” J Biomech, vol. 49, no. 2, pp. 135-40, Jan. 25, 2016.
Y. Wang, T. Bhatt, X. Liu, S. Wang, A. Lee, E. Wang, and Y. C. Pai, “Can treadmill-slip perturbation training reduce immediate risk of over-ground-slip induced fall among community-dwelling older adults?,” J Biomech, vol. 84, pp. 58-66, Feb. 14, 2019.
P. H. Sessoms, M. Wyatt, M. Grabiner, J. D. Collins, T. Kingsbury, N. Thesing, and K. Kaufman, “Method for evoking a trip-like response using a treadmill-based perturbation during locomotion,” J Biomech, vol. 47, no. 1, pp. 277-280, Jan. 3, 2014.
H. Debelle, C. Harkness-Armstrong, K. Hadwin, C. N. Maganaris, and T. D. O’Brien, “Recovery from a forward falling slip: Measurement of dynamic stability and strength requirements using a split-belt instrumented treadmill,” Front Sports Act Living, vol. 2, p. 82, 2020.
J. Perry, Gait analysis: normal and pathological function, 2nd ed. New Jersey: Slack Incorporated, 2010.
T. Bhatt, J. D. Wening, and Y. C. Pai, “Influence of gait speed on stability: recovery from anterior slips and compensatory stepping,” Gait Posture, vol. 21, no. 2, pp. 146-156, Feb., 2005.
F. Yang and Y. C. Pai, “Automatic recognition of falls in gait-slip training: Harness load cell based criteria,” J Biomech, vol. 44, no. 12, pp. 2243-2249, Aug. 11, 2011.
P. Pathak, J. Moon, S. G. Roh, C. Roh, Y. Shim, and J. Ahn, “Application of vibration to the soles reduces minimum toe clearance variability during walking,” PLoS One, vol. 17, no. 1, p. e0261732, 2022.
C. Meyer, T. Killeen, C. S. Easthope, A. Curt, M. Bolliger, M. Linnebank, B. Zörner, and L. Filli, “Familiarization with treadmill walking: How much is enough?,” Scientific Reports, vol. 9, no. 1, p. 5232, 2019/03/26 2019.
H. Akoglu, “User’s guide to correlation coefficients,” Turk J Emerg Med, vol. 18, no. 3, pp. 91-93, Sep., 2018.
K. L. Troy and M. D. Grabiner, “Recovery responses to surrogate slipping tasks differ from responses to actual slips,” Gait & posture, vol. 24, no. 4, pp. 441-447, 2006.
F. Yang, T. Bhatt, and Y. C. Pai, “Generalization of treadmill-slip training to prevent a fall following a sudden (novel) slip in over-ground walking,” J Biomech, vol. 46, no. 1, pp. 63-69, Jan. 4, 2013.
P. R. Golyski, E. Vazquez, J. K. Leestma, and G. S. Sawicki, “Onset timing of treadmill belt perturbations influences stability during walking,” J Biomech, vol. 130, p. 110800, Jan., 2022.

No competing interests reported.

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The Effects of Perturbation Intensities on Backward Slip-Falls Induced by a Split-belt Treadmill

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Abstract

Figures

Introduction

Methods

A. Slip-Inducing System and Perturbation Intensities

B. Participants

C. Experimental Protocols

D. Data and Statistical Analyses

Results

Number of Non-Falls and Falls and Their Rates

Maximum Loading Forces Due to Falls

Discussion

Conclusion

Declarations

Data availability

Acknowledgements

Author contributions statement

Additional information

References

Additional Declarations

Status:

Version 1