Keywords

1 Introduction

The practice of physical activities in adolescents is related to greater social interaction, less risk of diseases with aging, better musculoskeletal development among other benefits [1, 2]. However, how exercise affects segmental thigh-leg coordination in young people remains unclear.

Walking is a cyclical movement that is repeated in several and different patterns for each individual, in which coordination between different segments is of importance. Recent literature estimates that five [3], ten [4] to fifteen gait cycles [5] are the minimum number of gait cycles needed to calculate a reliable coordinative variability. However, it is commonly agreed that less than five cycles are a small number and the reported values cannot be representative of the true variability of an individual or group. For more reliable results, this work used the entire time series collected, a total of 25 gait cycles for everyone.

According to studies the ideal analysis is in the sagittal plane, because this is the most demanding plane presenting expressive extension and flexion excursions in the joint that connects the lower limb segments, analysis of sagittal plane can clearly show the phase and anti-phase relationships be- tween segments [6,7,8].

The aim of this study was to estimate the coordination and coordination variability between the Thigh-Leg segments of two groups of young people (sedentary and active), while walking on a treadmill at different speeds, using the previously reported modified Vector Coding (VC) technique [7]. As the practice of physical actives can contribute to a lower risk of injuries, we hypothesized that (1) young active have greater coordinative variability compared to the other group, (2) and the instants of support would be of greater concern, with smaller values for sedentary group.

2 Materials and Methods

2.1 Subjects

Thirty young adults, 15 sedentary and 15 actives participated in the study. Young adults were classified as active if they practice physical activity at least three times a week, one hour a day.

2.2 Protocol

For data collection, 16 retro-reflective markers were fixed at specific anatomical points according to Vicon’s lower limb plug-in-gait model (Vicon, Oxford Metrics, Oxford, UK). A 3D capture system containing 10 infrared cameras operating at 100 Hz was used. Data were filtered using a low pass, zero- lag, fourth order, Butterworth filter with a cut-off frequency of 8 Hz. Kinematic data were exported as text file and ana- lyzed with a custom MatLab code (R2018a, MathWorks, Na- tick, MA).

The preferred walking speed (PWS) on the treadmill was determined according to a previously reported protocol [9]. A four-minute walk on the treadmill was allowed for familiarization and immediately followed by a two-minute rest. After the rest period participants performed three walks of 1 min each, in the PWS, 120% of the PWS and 80% of the PWS, in randomized order.

As already stated above the Thigh-Leg segment was analyzed for 25 strides, normalized to 100 points each, for each one-minute walking period. The segmental angles were calculated in relation to the global coordinate system of the la- boratory. Then the coupling angles were calculated using the previously reported modified Vector Coding technique [7], in four phases of the gait cycle: first double support, single support, second double support and swing phase. The coupling angles represent the coordination patterns and the standard deviation of the coupling angle at each instant of the gait cycle represents the coordination variability.

2.3 Statistical Analysis

The repeated measures analysis of variance (ANOVA) with mixed design was used to compare the two groups, the main effect of speed and the effect of interaction between groups and speed, followed by a post hoc test with Bonferroni correction in the cases where the main or interaction effect was significant. Statistical analysis was performed using SPSS software, version 23 (SPSS Inc., Chicago, IL, USA), with a significance level set at α < 0.05.

Figure 1 show a typical example of the coupling angle (γmean) and coupling angle variability (CAV) for the Thigh- Leg segment, for the two conditions that showed differences.

Fig. 1
figure 1

Coupling angle variability (CAV) and coupling angle (γmean) for the Thigh-Leg segments at 120 and 80% of preferred walking speed (PWS)

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3 Results

The segments Thigh-Leg had rotation in the same direction, being, therefore, in-phase. The statistical results for each phase are shown in the Table 1.

Table 1 Coordination variability of thigh-Leg pair
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Regarding Table 1, comparing the two groups, there were no significant differences for Groups or for Groups versus Speeds. However, there were differences between 120% of PWS and 80% of PWS (p = 0.0016) for speed (bold in table), with greater variability to 120% of PWS.

Even though it is not the focus of the article, in Fig. 1, the values of the coordination of this segment are represented, which is being analysed as a way of seeing how its variability behaves in relation to its coordination.

4 Discussion

The groups presented similar results, showing that the level of physical activity of the active’s group was not enough to produce significant changes in Thigh-Leg coordination during walking. The first two hypotheses were discarded the results only support the second hypothesis: significant differences were found in the single support phase and were sensitive to the speed.

With respect to speed, similar results were previously re- ported for young and older adults [10, 11], besides people tend to have more difficulties walking at a lower speed than at a higher speeds in relation to PWS itself [12]. Furthermore, all differences were observed in stance phase. This suggests that Thigh-Leg segments coordination occurs during foot contact and subsequent body weight loading on unilateral lower limb. This may lead to different patterns of overuse in stance phase at different walking speeds, as it has been reported that altered segment coordination is indicative or marker of overuse injury [13], through the shift of stress to tissues not adapted for repetitive loading [3]. The different segment coordination between walking speeds can be a result of a change in amplitude or relative timing of adjacent segment.

5 Conclusions

There was no significant main effect of activity, and no significant interaction effect between groups and speeds. However, significant main effect of speed was observed during single support phase. These preliminary results suggest that Thigh and Leg coordination variability during single support phase are speed dependent showing that, although these segments are in phase irrespective to walking speed, their coordination variability differs. Changes in walking speed produce changes in motion amplitude or relative tim- ing of the analyzed segments that, in turn, alter the coordination variability during single support phase. However, this result was not sensitive to the level of activity, probably because the balance control during single support phase, where human body behaves as an inverted pendulum, is not affected by level of activity [14]. Future studies can investigate more closely this link between the level of physical activities and speed for this segment, in order to develop practices of physical exercises that add more to the health of young people as well as improving the quality of gait.