Falls are a leading cause of injuries and mortality in older adults and the risk of falling increases with age (Rubenstein, 2006). Many falls in community-dwelling older adults occur during locomotion, particularly when postural stability is challenged by perturbations like slips and trips (Berg et al., 1997). Locomotor stability is generally realized through accurate positioning of the swing-foot relative to the center of mass (CoM) (Bruijn and van Dieën, 2018). In the frontal plane this requires active control by the central nervous system (Kuo, 2002; Bruijn and van Dieën, 2018) realized through activation of the swing limb hip musculature in response to states of the stance limb (Bruijn and van Dieën, 2018). However, increased neuromotor noise and associated motor variability (Kang and Dingwell, 2009) may negatively affects control of mediolateral (ML) foot placement and increases variability in ML placement of the foot (Bruijn and van Dieën, 2018), which may increase fall risk (Brach et al., 2005). Nonetheless, if an individual can compensate for increased variability, by channeling it into a subspace that does not affect performance, then the high variability would not be hazardous. The uncontrolled manifold (UCM) analysis provides a means to quantify the extent to which motor variability may or may not “be hazardous” (Latash et al., 2007).

The UCM analysis quantifies the extent to which all available degrees of freedom (DoF) that contribute to a task-relevant performance variable co-vary so as to stabilize (limit trial-to-trial variation in) that performance variable (Scholz and Schöner, 1999; Latash et al., 2007). The analysis decomposes variability in a set of elemental variables into two components: “good” variance that has no effect on the performance variable and “bad” variance that results into deviations of the performance variable. A positive synergy index, which quantifies the relative amount of “good” variance compared to “bad” variance, implies that the performance variable is stabilized by a synergy (Scholz and Schöner, 1999; Latash et al., 2007). Such stabilization allows secondary tasks that rely on the same set of elemental variables to be performed without affecting the primary task. It is generally thought that the effects of aging on motor coordination manifest as low amounts of “good variance” (Kapur et al., 2010). However, in response to challenging locomotor conditions such as uneven surface walking, healthy community-dwelling older adults are able to counteract perturbation-related increases in “bad” variability by channeling elemental variability into “good” variability (Eckardt and Rosenblatt, 2018). Similar findings have been reported for upper extremity tasks (Kapur et al., 2010). Thus, the aging human CNS possess the ability to harness motor flexibility, i.e., to increase the synergy index by increasing “good” variance through exploration of motor solution space. Given its’ importance, there is a need to understand whether this ability is trainable and if so, what exercises are optimal to train this ability.

With regard to the upper extremity, it has been demonstrated that non-repetitive tasks performed under conditions of manipulated stability can help promote large amounts of “good” variance (Shim et al., 2008; Wu and Latash, 2014). Similarly it has been suggested that exercise interventions which introduce tasks that promote, rather than limit, exploration of motor solutions may be particularly appropriate for promoting motor flexibility and the coordination patterns utilized to ambulate in complex environments (Rosenblatt et al., 2014). In turn, such exercises may help to reduce risk of falling and their introduction into fall prevention interventions would represents a considerable departure from traditional exercises, such as resistance training (RT) that targets muscle strength and power to achieve this goal (Benichou and Lord, 2016). Indeed, machine-based resistance training (S-MRT) does not seem suited to promote exploration of motor solutions due to restricted movements during exercise execution; a combination of balance and resistance training, i.e., “instability free-weight resistance training” (I-FRT), may be better suited to do this.

Instability free-weight RT is an exercise modality which involves tasks that specifically promote exploration of motor solutions by having participants engage in RT training while standing on destabilizing surfaces. The inherent instability during execution of the I-FRT results in greater overall muscle activation of the lower limbs due to the constant need for postural readjustment (Lawrence and Carlson, 2015). Indeed, the greater demands of I-FRT may explain why in our recent 10-week RCT I-FRT elicited similar increases in balance, power, and strength in older adults compared to S-MRT despite using half the training load (Eckardt, 2016). We hypothesized that inter-and intramuscular coordination may be the driving reason for increases in the respective outcomes. Nonetheless, strength, power, and balance are measures of performance that do not directly address changes in coordination such as those quantified by synergies within the UCM analysis. There is evidence, albeit limited, that RT can improve synergies; one study has evaluated the effects of finger RT on finger synergies, independence, force control and adaptations in multi-finger coordination (Shim et al., 2008). If RT does impact synergies, then RT focusing on the hip could be particularly beneficial with regards to improving kinematic synergies related to the mediolateral trajectory of the swing-foot during gait. Indeed, swing limb hip abductors activity is critical in modulating foot placement and is predicted by the relationship between the CoM and the stance limb (Rankin et al., 2014; Roden-Reynolds et al., 2015). On the other hand, strengthening may not significantly affect swing limb mechanics, which in part contribute to kinematic synergies related to foot placement. For example, increasing strength by 26% (i.e., control condition relative to a weaker nerve block condition) does not affect swing limb kinematics (Pohl et al., 2015). In fact, it is entirely possible that weaker older adults employ stronger synergies to compensate for weakness, as has been argued to occur during sit-to-stand tasks (Greve et al., 2013) such that hip strengthening could lead to a reduction in synergies. Thus, the extent to which RT, and particularly hip-specific RT, can impact motor coordination during locomotion remains unclear.

The purpose of the current study was to quantify how different RT modalities affect kinematic synergies related to the mediolateral trajectory of the swing-foot during normal and perturbed gait (walking across an uneven surface with and without additional asymmetric loading that promote additional imbalance). In addition to evaluating the effect of I-FRT and standard S-MRT on kinematic synergies, we also evaluated a highly specific adductor/abductor resistance training (S-MRT_HIP) to better understand the extent to which hip strength affects kinematic synergies related to foot placement. We hypothesized first that kinematic synergies (i.e., synergy index) would stay invariant across groups from pre- to post testing during normal walking, given that normal walking is a habitual task. Second, we hypothesized that only the I-FRT group would increase the kinematic synergy index during perturbed gait (in absence of prior literature, we assumed the null for S-MRT_HIP). Third, we hypothesized that in I-FRT the increase of the kinematic synergy index would result from an increase in “good” variance and a decrease in “bad” variance due to improved co-variation of lower-extremities based a previous study (Wu and Latash, 2014).

The individual results are deposited as complete dataset in the Supplementary Material SI. The machine-based stable resistance training (S-MRT) group had an average attendance of 94%, 95% for the free-weight instability resistance training (I-FRT) group, and 95 % for the machine-based stable adductor/abductor resistance training (S-MRT_HIP) group. Gait speed increased from pre- to post-testing for conditions (Figure 3): ES [t(66) = 3.74, p < 0.001, d_unb = 0.45; 95%-CI (0.21, 0.71); BF₁₀ = 61.67], US [t(66) = 3.89, p = 0.001, d_unb = 0.41; 95%-CI (0.16, 0.67); BF₁₀ = 21.84], and US_ISB [t(66) = 2.31, p = 0.024, d_unb = 0.29; 95%-CI (0.04, 0.53); BF₁₀ = 1.61]. Based on our a priori criteria for non-parametric testing, we were able to use parametric test for all variables. All outcomes are summarized in Figure 4, 5.

The purpose of this study was to quantify the effect of different RT modalities on kinematic synergies (derived using UCM analysis) related to the ML trajectory of the swing-foot during normal and perturbed gait (walking across an uneven surface with and without additional weight/imbalance). Our first hypothesis, that the kinematic synergy for the ES condition would remain invariant for all groups after the intervention was supported. Our second hypothesis that the kinematic synergy during both unstable conditions would increase after training for the F-IRT group only was also supported. We found decisive evidence for an increased magnitude of the synergy index in the US and in the US_ISB condition. Consistent with our third hypothesis these stronger synergies were due to reduced “bad” variance for I-FRT while walking across the more challenging conditions; i.e., US and US_ISB. All groups were able to rely on motor flexibility (proportional increase of “good” variability in relation to “bad” variability) to maintain a kinematic synergy at post-testing and across conditions. To the best of our knowledge, this is the first study investigating the use of the UCM approach to quantify (instability) resistance training induced changes within multi-segmental lower-extremity kinematic synergies to stabilize an important performance variable (i.e., ML swing-foot trajectory).

Previous research using the UCM analysis to investigate practice effects on coordination suggested a two-stage process of adaption in multi-segmental coordination (Wu and Latash, 2014). In the first stage “bad” variability drops due to optimized performance control, while “good” variability hardly changes. The second stage is characterized by a reduction in “good” variability, while “bad” variability remains constant. This can be explained by practice-induced optimized control over elemental features, other than the explicit performance variable (Wu and Latash, 2014). However, there are scenarios where a practice-induced increase of V_UCM can be found. An increase of V_UCM suggests a more robust and flexible system which can exploit an abundance of motor solutions, especially when being challenged (Wu and Latash, 2014; Latash and Huang, 2015). Our results certainly support the first stage, given the drop of V_ORT during both US conditions and the accompanying increase in the kinematic synergy. The fact that we observed an increase rather than a drop in V_UCM during the US conditions may be explained by increased gait speed. Given that gait speed increased following the intervention and that variability increases with speed, particularly at faster-than-preferred speed (Chien et al., 2015), we expected an increase particularly in performance destabilizing “bad” variability (Rosenblatt et al., 2014, 2015; Chien et al., 2015). With regard to the specific variance components, increases in movement speed have previously been associated with increased V_ORT (Chien et al., 2015; Rosenblatt et al., 2015) which is observed in the two machine-based groups. To counter the increased V_ORT these groups rely on motor flexibility and increase V_UCM as well, which is consistent with our previous cross-sectional study in which older adults compensated for challenged stability during walking by increasing V_UCM (Eckardt and Rosenblatt, 2018). The fact that I-FRT decreased V_ORT while walking across both US conditions despite increased gait speed is therefore noteworthy and consistent with the second stage of motor learning (Wu and Latash, 2014). The concurrent increase in V_UCM for this group highlights the fact that I-FRT specifically improved coordination, not to compensate for an increase in V_ORT but as means to develop coordination among elements to allow flexible performance and avoid reliance on a unique solution (Wu and Latash, 2014; Latash and Huang, 2015).

Several prior studies support the idea that resistance training incorporating modalities that promote exploration of movement space may positively affect motor flexibility. For example, Hamed et al. (2018) showed that while both I-FRT (or exercise dynamic stability under unstable conditions as they call it) and S-MRT improved muscle strength and balance recovery in simulated forward falls, only I-FRT increased standing balance abilities. The authors attributed the effects of I-FRT on the fact that the RT is performed under continuously destabilizing conditions that require continuous processing and integration of sensory afferent information to attain an appropriate motor response (Hamed et al., 2018), which is critical to control of dynamic postural stability under the unstable conditions. Exercising under unpredictable/instable conditions requires motor output to remain flexible enough to produce appropriate responses to continuously changing input. Indeed, recent investigations on adaptive mechanisms during uneven surface walking and running shows a widening of neuromuscular synergies (EMG) to provide robust and flexible motor solutions to compensate for perturbations (Santuz et al., 2018). Interventions forcing the CNS to explore an abundant number of motor solutions can elicit robust and more flexible neuromuscular synergies (EMG) (Oliveira et al., 2017).

In addition to increasing kinematic synergies, we found that I-FRT resulted in an increase in leg extension strength, which is in contrast to a previous RCT by our group (Eckardt, 2016). Indeed, the improvements for I-FRT in this study are larger than the prior RCT (Cohen’s d: 0.50 vs. 0.65). A different protocol to estimate training load may explain this difference. In our prior study, the 1-RM was calculated on the respective instability device whereas the current study calculated the training load on even surface. However, it may be difficult to elicit a true 1-RM on instability devices. In fact, the 1-RM for I-FRT was slightly higher in this study compared to the prior one (8 ± 4 kg) which may elicit the present effect and the subsequent interaction effect time x group.

It was somewhat unexpected that S-MRT_HIP did not improve adductor/abductor strength, which would be predicted based on principles of specificity (Buckthorpe et al., 2015; Contreras et al., 2016). We assume that the hand-held dynamometry test hip strength did not isolate hip adductors/abductors such that the other groups (i.e., S-MRT and I-FRT) could have attained similar testing values by compensating for weaker adductor/abductors with activation of other muscles to coordinate hip movement. Regardless of the reason, the fact that hip adductor/abductor strength did not improve with S-MRT_HIP, or any of the other modalities, but that changes in the kinematic synergy were seen in I-FRT suggests that the neuromuscular strategies contributing to mediolateral foot placement and in turn synergistic behavior of lower-extremities may depend more on sensorimotor integration during dynamic (i.e., instable) situations than on force producing capabilities of the hip. Indeed both, S-MRT and S-MRT_HIP are quite stationary modalities, at least with regarding to the resistance components which were the primary training components; during resistive exercise execution there is little or no unpredicted movement and minimal counter-rotation of segments relative to the CoM (Hamed et al., 2018). Future work should consider individual effects – e.g., are kinematic synergies different between responders (those who increase adductor/adductor strength) and non-responders or do kinematic synergies scale with strength – to better understand the relationship between hip strength and kinematic synergies during gait.

Because our participants were healthy older adults (mean TUG time at pre-testing: 8.1 s), generalizability to frail older people needs to be established. In addition, the extent to which I-FRT can strengthen synergies (kinematic or otherwise) related to other performance variables has yet to be determined.

Given that the I-FRT group practiced walking across the US, it is not possible to entirely disentangle the impact of repeated US walking from instability RT on kinematic synergies. However, given that S-MRT practiced walking with weights and S-MRT_HIP practiced resisted walking yet demonstrated an increase rather than a reduction in V_ORT, it appears that RT training alone, in absence of instability, cannot explain the changes observed in the I-FRT group. In addition, the fact that we observed similar strength changes but differences in motor flexibility between I-FRT and S-MRT suggests that the context in which strength changes occur is critical to promoting motor flexibility.

The high inter-subject variability (see Figure 4, 5) may indicate that there are individual strategies to stabilize the kinematic synergy. Future research should try to identify such subject specific strategies.

The purpose of this RCT was to quantify how different resistance training modalities affect kinematic synergies related to the mediolateral trajectory of the swing-foot during normal and perturbed gait. For all groups the kinematic synergy during normal gait (ES condition) was unaffected by RT. However, I-FRT demonstrated significant increases in the kinematic synergy on the uneven surface which was achieved by reducing motor noise (V_ORT) and therefore stabilizing the ML trajectory of the swing foot. To our knowledge, this is the first time the UCM approach was used to quantify resistance training induced effects on locomotor stability.

All datasets analyzed for this study are included in the Supplementary Material SI.

NE analyzed the data, wrote the manuscript, and contributed to the conception and design of the study. NR contributed to the study design, assisted with the data analysis and interpretation, and critical review of the manuscripts. Both authors read and approved the final version of the manuscript.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.