Human Movement Science 52 (2017) 191–196
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Exploring phase dependent functional gait variability Daniel Hamacher a,⇑, Dennis Hamacher b, Roy Müller a,c,d, Lutz Schega b, Astrid Zech a a
Institute of Sport Science, Friedrich Schiller University of Jena, Seidelstraße 20, Jena 07749 Germany Department Sport Science, Otto von Guericke University Magdeburg, Zschokkestraße 32, Magdeburg 39104 Germany Department of Neurology, Klinikum Bayreuth GmbH, Hohe Warte 8, Bayreuth 95445 Germany d Department of Orthopedic Surgery, Klinikum Bayreuth GmbH, Hohe Warte 8, Bayreuth 95445 Germany b c
a r t i c l e
i n f o
Article history: Received 13 October 2016 Revised 4 February 2017 Accepted 12 February 2017
Keywords: Gait Functional variability Minimum toe clearance Inertial sensor
a b s t r a c t Gait variability is frequently used to evaluate the sensorimotor system and elderly fallers compared to non-fallers exhibit an altered variability in gait parameters during unchanged conditions. While gait variability is often interpreted as movement error, it is also necessary to change the gait pattern in order to react to internal and external perturbations. This phenomenon has been described as functional variability and ensures the stability of gait motor control. The aim of the current study is to explore the functional variability in relation to the different phases of the gait cycle (phase-dependent gait variability). Kinematics of the foot, shank and thigh were registered with inertial sensors (MTw2, Xsens Technologies B.V) in 25 older participants (70 ± 6 years) during normal overground walking. Phase-dependent variability was defined as the standard deviation of the Euclidean norm of the angular velocity data. To assess differences with respect to the variability of different body segments (foot, shank, and thigh), the statistical parametric mapping method was applied. In normal walking, the variability of the time-continuous foot kinematics during parts of the swing phase was higher compared to the shank (9–14% of swing phase, p < 0.000) and to the thigh (3–43%, p < 0.000 and 92%, p = 0.024 of swing phase). Compared to the thigh, the shank kinematics was less variable at 62–64% (p = 0.013) of the swing phase. The magnitudes of the variability were comparable regarding all three body segments during mid swing. Furthermore, those magnitudes of variability were smallest during mid swing where the minimum toe clearance was identified. In conclusion, we found signs of phase-dependent functional variability particularly in the swing phase of gait. In fact, we found reduced variability in the time-continuous foot kinematics in mid swing during normal walking where also the minimum toe clearance event occurs. Ó 2017 Elsevier B.V. All rights reserved.
1. Introduction Gait analyses are widely used to determine movement characteristics associated with orthopedic and neurologic disorders (Whittle, 1996) and elderly fallers compared to non-fallers exhibit an altered variability in gait parameters during unchanged conditions (Hamacher, Singh, Van Dieën, Heller, & Taylor, 2011). Sensorimotor control mechanisms during walk⇑ Corresponding author. E-mail addresses:
[email protected] (D. Hamacher),
[email protected] (D. Hamacher),
[email protected] (R. Müller), lutz.
[email protected] (L. Schega),
[email protected] (A. Zech). http://dx.doi.org/10.1016/j.humov.2017.02.006 0167-9457/Ó 2017 Elsevier B.V. All rights reserved.
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ing can be validly assessed using the stride-to-stride variability of gait parameters. Extreme levels of gait variability seem to be indicators for pathologies with respect to the human sensorimotor system (Singh, Konig, Arampatzis, Heller, & Taylor, 2012). However, movement variability can be caused by different sources: 1) by a noisy sensorimotor control system (variability interpreted as movement error), 2) by the adaption to situational constraints or 3) by mechanisms initiated to compensate for prior movement deviations (e.g. to compensate for earlier movement error) (Loosch, 1999). Since the sensorimotor system is intrinsically noisy (Davids, Glazier, Araújo, & Bartlett, 2003), movement variability during unchanged conditions is sometimes interpreted to be an error in the sensorimotor control of human motion (e.g. Hamacher, Hollander, & Zech, 2016). Yet, individual cyclic repetitive movement patterns (e.g. gait) are reproducible with some degree of precision. Despite of the movement variability, those movements are executed robustly and stably in healthy individuals even in situation with small perturbations (Riley & Turvey, 2002). Gait stability (e.g. local dynamic stability) is interpreted as the system’s capacity to compensate or recover from small perturbations potentially arising from intrinsic (e.g. neuromuscular noise) or extrinsic sources (e.g. uneven ground) (Bruijn, Meijer, Beek, & Van Dieën, 2013). In order to compensate for perturbations, the sensorimotor system must adapt those perturbations which, again, results in movement variability (Muller, Tschiesche, & Blickhan, 2014). Due to the different explanatory approaches, the term ‘‘functional variability” has not been consistently used in the literature. Frequently, it is referred to as the adaptability to varying situational constraints in order to achieve a consistent performance (Barris, Farrow, & Davids, 2013, 2014). Bootsma and van Wieringen (1990) provided a more general definition. They propose the term ‘‘compensatory variability” in the cases where the variability in an execution variable (relevant variable that does not directly reflect the primary result parameters but does influence those) is compensated by the variability of another execution variable in order to achieve a stable result parameter (Bootsma & van Wieringen, 1990). Our definition of functional variability is an enhanced version of the definition of Barris et al. (2014). We define functional variability as the variability which is not simply movement error but which evolves 1) due to adaptions to situational constraints or 2) due to the compensation for deviations in different movement parameters (points 2 and 3 according to Loosch, 1999). As an approach to identify functional movement variability, we will use the Bootsmas et al. (Bootsma & van Wieringen, 1990) definition: In cases where the functional variability exists, the variability of the task-relevant result parameter is rather small when comparing it to the variability of the execution variables (Müller & Loosch, 1999; Winter, 1984). The occurrence of functional variability has already been investigated in e.g. acyclic sports techniques as for instance, in trained athletes performing sprint starts (Bradshaw, Maulder, & Keogh, 2007) and athletic pistol shooting (Scholz, Schöner, & Latash, 2000). It also has been reported that (functional) movement variability increases with enlarged expertise in handball players regarding their throw movements (Schorer, Baker, Fath, & Jaitner, 2007). However, another study did not find an altered variability with increasing expertise in the basketball free-throw (Button, MacLeod, Sanders, & Coleman, 2003). Furthermore, Koenig, Tamres, and Mann (1994) reported that in golfers the variability of the ground reaction force increased during the back swing and during parts of the downswing but it was particularly reduced during the impact phase (Koenig et al., 1994) indicating phase dependent functional variability. While most studies focused on acyclic sports techniques, there are only few that analyzed functional variability in gait. Winter (1984) analyzed force moment variability in the sagittal plane of the lower extremity. The sum of the force moment variances of each joint was higher than the support moment which indicates that there was a ‘cancellation of joint moments’. This was interpreted as a ‘fine motor tuning’ to ‘correct minor deviations’ (Winter, 1984, p. 60) and can, thus, be described as functional variability. However, this work only analyzed averaged and non-phase-dependent force moment variability of the lower extremities. Other studies analyzed gait variability and the adaptability of human locomotion by looking at timediscrete parameters (mostly variability of stride time, stride length, speed and minimum toe clearance) (BohnsackMcLagan, Cusumano, & Dingwell, 2016). One reason why functional variability was rarely examined in cyclic movements might be the fact that the task-relevant result parameters are not that obvious. However, since functional variability is required to form a stable system (stable gait pattern), analyzing the role of functional variability in gait is important to understand gait variability which is also frequently assessed with respect to pathological gait. Thus, the aim of the current study was to explore whether phase-dependent functional variability can be verified in human gait. To analyze functional variability, the task-relevant parameter must be analyzed. For human walking, it was supposed that the minimum toe-ground distance (minimum toe clearance) might be a task-relevant parameter during swing phase (Hamacher, Hamacher, Herold, & Schega, 2016; Hamacher, Hamacher, & Schega, 2014a). Obviously, our task-relevant parameter is indirectly affected by the kinematics of shank and thigh. Analogues to the approach of Winter (1984), Müller and Loosch (1999) and Bootsma and van Wieringen (1990), functional variability manifests as a relatively low variability of the task-relevant parameter (foot kinematics during minimum toe clearance) compared to the variability of parameters in the execution space (kinematics of shank and thigh). Thus, if there is either no increase or even a decrease in the variability of the foot compared to the shank or thigh kinematics, we consider this as a sign of functional variability. Due to the above mentioned facts, we hypothesized that during minimum toe clearance the variability of the foot kinematics is lower or equal compared to the variability of the kinematics of shank or thigh. Another focus of gait control is on the stabilization of the body, and especially the pelvis during the stance phase (Perry, 2010). Regarding the stance phase, the current study also explores the time-continuous variability of the lower extremities. Since an altered gait variability is associated with increased fall risk (Hamacher et al., 2011), functional variability is relevant in older adults and we, thus, will test the hypotheses in elderly.
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2. Methods 2.1. Subjects For the current study, 25 older participants (12 males, 13 females, age: 70 ± 6 years, weight: 77 ± 13 kg, height: 1.68 ± 0.08 m) were recruited. Inclusion criteria were an age of at least 60 years and the ability to walk continuously for at least 10 min. Exclusion criteria were any self-reported motor-functional impairments that could affect gait. All participants provided their written informed consent after they were briefed about the research protocol, which complied with the principles of the Declaration of Helsinki and was approved by the local ethical committee (No. FSV 16/01). 2.2. Testing procedure Wireless inertial motion trackers (MTw2, Xsens Technologies B.V., Enschede, The Netherlands, range of measurement of angular velocity: ±1200 deg/s, sampling rate: 100 Hz) were attached on the dominant leg to the subjects’ forefeet with tape and to the tibia (medial and distal to the tibia tuberositas) as well as on the iliotibial tract at the middle of the thigh with elastic straps. The dominant leg was identified by asking the subjects which foot they would choose to shoot a ball into a goal. To familiarize to the test setting all participants walked back and forth on a 22-m track for approximately three minutes which also improves reliability (Hamacher, Hamacher, Krowicki, & Schega, 2016). Among another condition (we also recorded dual-task-walking which was not analyzed), the kinematic gait data was recorded in random order in normal overground walking where subjects were asked to walk normally and continuously on the 22-m track for three minutes with their preferred pace. 2.3. Data analysis Gait data of the first and last bout as well as the first and last 2.5 m before and after each turning has been excluded from the subsequent analysis. Gait parameter calculation (minimum toe clearance) has been conducted using an evaluated algorithm (Hamacher, Hamacher, Taylor, Singh, & Schega, 2014): Hereto, heel strikes and toe-off events were identified based on local minima of the angular velocity of the foot in the sagittal plane. For all subjects, the data of the first 80 strides, that have not been excluded, have been analyzed. The phase-dependent variability was calculated such as already presented for the evaluation of trunk movements while walking (Hamacher, Hamacher, & Schega, 2014b) for swing phase and stance phase, separately. Thereto, the Euclidian norm of the three-dimensional angular velocity data of each body segment was calculated. Thereafter, the data were time normalised to 100 data points for each swing phase and also for each stance phase. To calculate variability, the standard deviation for each person, each body segment and each time normalised data point were analyzed. Usually, the mean of the standard deviations for all 100 time-normalised data points was used to get one single measure per segment and condition. Since our aim was to analyze the phase specific variability, we omitted this step. We further defined the gait phases according to Perry’s model (Perry, 2010): Here, the swing phase is structured into initial swing (62–75% of complete gait cycle or 0–34% of swing phase), mid swing (75–87% of complete gait cycle or 34–66% of swing phase) and terminal swing (87–100% of complete gait cycle or 66–100% of swing phase). 2.4. Statistics To assess differences comparing the body segments (foot vs. shank vs. thigh), the statistical parametric mapping method was applied using an open-source software package (spm1D, version 0.3) (Pataky, 2012). Differences for each time normalised data point were tested using paired t-tests. Thereafter, the random field theory was used to account for spatiotemporal correlations and to avoid the problem of multiple testing. As a result, time intervals that differ significantly (one p-value for a significant time interval) are presented. For all statistical analyses, a significance level of a = 5% was applied. 3. Results The data of two participants were omitted since an inertial sensor which was fixed to the shank or the thigh loosened slightly during walking. Hereafter, we will only report differences regarding the variabilities of the different body segments if those were statistically significant. The minimum toe clearance was identified at 55% (SD = 4%) of the swing phase. The variability of the time-continuous foot kinematics during swing phase was higher compared to the shank (9–14% of swing phase, p < 0.000, Fig. 1A) and to the thigh (3–43%, p < 0.000 and 92%, p = 0.024 of swing phase). Furthermore, the variability of the shank was more pronounced than in the thigh (5–39%, p < 0.000, Fig. 1A). At 99% (p = 0.009) and 97–99% (p = 0.009) of the swing phase, the foot kinematics was even less variable compared to shank and thigh kinematics, respectively. Furthermore, compared to the thigh, the shank kinematics was less variable at 62–64% of the swing phase.
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Fig. 1. Variability of angular velocity data in the swing phase (A) and stance phase (B) of gait for the body segments foot, shank and thigh in normal overground walking. Differences in variability comparing the body segments were assessed using the statistical parameter mapping method (SPM). In the lower part of the figure from top to bottom the test-statistics (SPM{t}) comparing the variability of foot vs. shank, foot vs. thigh and shank vs. thigh are plotted.
In the stance phase, the variability of the foot kinematics was reduced compared to the shank (0–2%, p = 0.019 and 15– 48%, p < 0.000 of stance phase) and thigh (0–1%, p = 0.002; 6–54%, p < 0.000; and 57–63%, p < 0.000 of swing phase) but increased during 74–98% (vs. shank, p < 0.000) and 76–98% (vs. thigh, p < 0.000) of the stance phase (Fig. 1B). The thigh vs. shank was less variable at 8–9% (p = 0.019) at 11% (p = 0.019) and 87–93% (p < 0.000) of the stance phase. 4. Discussion The objective of the current study was to investigate if phase-dependent functional variability does exist in human gait kinematics. Our approach was to analyze time-continuous variability of the foot, shank and thigh during normal overground walking. The most import results were that 1) particularly in the first third of the swing phase, the variability of the foot kinematics was higher than in the shank which again was higher compared to the variability of the thigh, 2) in the second third of the swing phase, where the minimum toe clearance was identified, the magnitudes of the variability were similar regarding all three body segments and those were smallest and
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3) during single limb support (mid stance and terminal stance [19–81% of stance phase]), the variability of the thigh and shank was similar. Therefore, our data is partly in accordance with our hypothesis one: the variability of foot kinematics is equal during mid swing compared to the variability of the shank and thigh. Since the variability of our task-relevant parameter (foot kinematics at minimum toe clearance) is equal to the variability of the parameters in the execution space (thigh and shank kinematics), this is a sign of functional variability. In mid stance and terminal stance, the variability of the thigh and shank did not differ significantly. In the initial swing phase, the foot detaches from ground and moves forward (Perry, 2010). Here, decreased variability might not be beneficial since it does not stand in contrast with a task-relevant parameter (minimum toe clearance). This might be the reason why there is higher variability especially within the foot and shank kinematics compared to the mid swing phase. In this initial swing phase, the variability might mostly be a result of an uncontrolled error-prone sensorimotor control of gait with some (partly) independent movement variability. This kind of variability would accumulate from proximal parts of the body to distal parts of the body. Notably, the variability of the thigh is low. In the mid swing phase, the variability of all three body segments is minimal and of comparable magnitudes. Here, the specific variability of shank and thigh seem to cancel out each other which results in an overall lower foot variability. Otherwise (if variability was independent) the error should accumulate from proximal to distal alike as in the initial swing. Probably, a reason for the occurrence of functional variability, which leads to a minimal variability in the foot kinematics in mid swing, is that there is the minimum foot clearance event. In the older community, 21% of all falls (not only in gait) are attributed to tripping in this phase (Robinovitch et al., 2013). To prevent tripping, minimum toe clearance might, thus, be controlled with high(er) priority and constitutes a primary task-relevant parameter (Hamacher et al., 2016; Hamacher et al., 2014a). The increasing variability, which we observed in the terminal swing, might stem from two possible factors. First, we assume that minimum toe clearance is a task-relevant parameter and that at the moment of the minimum toe clearance event, the risk of tripping is highest (Hamacher, Hamacher, Herold et al., 2016; Hamacher et al., 2014a). We, therefore, speculate that there might be no urgent need to precisely control the foot in the first half of the terminal swing since the critical phase for tripping (the minimum toe clearance) already successfully has happened. Allowing more variability would permit a more error-prone (but not so resources-consuming) movement control. Since prior to, during and after the heel contact, the variability of the foot (which is an end-point of the kinematic chain where variabilities of other segments add up) is lower than the variabilities of shank or thigh kinematics, we argue to have observed functional variability. At this point of time, the foot might probably be prepared for the collision provoked by the heel contact and the following loading response. Second, we speculate that a medium degree of (controlled) variability might be tolerable prior to and during heel contact since at the heel contact event, the ankle joint exhibits a high degree of intrinsic stability even in unintentional malalignment (Konradsen & Voigt, 2002). As such, the system can take advantage of the protective aspect of the (tolerated) variability, because it scatters the exact position where forces act on the body structures which possibly prevents overuse injuries. This theory is also underpinned by occupational health research, where movement variability in repetitive work is believed to reduce the risk of developing musculoskeletal disorders (for a review see Srinivasan & Mathiassen, 2012). Since during the stance phase the foot is in contact with the ground, the lower variability of the foot kinematics compared to shank or thigh was expected. However, we suggest not to interpret this phenomenon as a sign of functional variability. Contrarily, no differences were found in mid stance and terminal stance when comparing thigh vs. shank kinematics. If the variability was error related, it would increase from distal to proximal during stance phase. Here, functional variability might try to stabilize the pelvis leading to a reduction in the variability of the thigh kinematics which is in mid stance partially (non-significantly) lower than the thigh kinematics. Taken together, the results indicate that there is considerable phase dependent functional variability particularly in the gait phase where the minimum toe clearance event also does occur. This has also been shown for acyclic sports techniques. In golfers, for instance, the variability of ground reaction forces were minimal during hitting the ball but were increased prior and afterwards of that event (Koenig et al., 1994). This might indicate that functional variability evolves, if needed, to stabilize the task-relevant parameter. 5. Conclusions In conclusion, we found signs of phase-dependent functional variability particularly in the swing phase of gait. Interestingly, the time-continuous variability could be structured into 3 phases: In initial swing, where the variability of foot kinematics might not be relevant, variability is not prevented and, thus, relatively high. In the mid swing phase (where minimum toe clearance was identified), the variability of all three body segments is minimal and of comparable magnitudes. Here, variabilities of shank and thigh seem to cancel each other out which results in an overall lower foot kinematics variability. In terminal swing, increasing variability was registered. This might be due to a more error-prone movement control or as preparation for the heel-contact to vary the force acting on the body structures during weight loading which possibly prevents overuse injuries. Finally, functional variability emerged during mid stance and terminal stance probably to stabilize the pelvis trajectories.
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In fact, we found reduced variability in the time-continuous foot kinematics in mid swing, mid stance and terminal stance. The results concerning the swing phase corroborate the speculations about minimum toe clearance being a taskrelevant gait parameter. Formatting of funding sources This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Acknowledgements We would like to thank Professor Dr. R. Blickhan for his critical comments on the manuscript. References Barris, S., Farrow, D., & Davids, K. (2013). Do the kinematics of a baulked take-off in springboard diving differ from those of a completed dive. Journal of Sports Sciences, 31(3), 305–313. http://dx.doi.org/10.1080/02640414.2012.733018. Barris, S., Farrow, D., & Davids, K. (2014). Increasing functional variability in the preparatory phase of the takeoff improves elite springboard diving performance. 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