Age- and speed-related differences in harmonic ratios during walking

Gait & Posture 35 (2012) 272–276

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Age- and speed-related differences in harmonic ratios during walking K.A. Lowry a,*, N. Lokenvitz b,1, A.L. Smiley-Oyen c,2 a

Division of Geriatric Medicine, 3471 Fifth Avenue, Kaufmann Medical Bldg Suite 500, Pittsburgh, PA 15213, USA ChildServe, 5406 Merle Hay Rd., Johnston, IA 50131, USA c Dept. of Kinesiology, Iowa State University, 235 Forker Building, Ames, IA 50011, USA b

A R T I C L E I N F O

A B S T R A C T

Article history: Received 28 February 2011 Received in revised form 17 August 2011 Accepted 20 September 2011

Harmonic ratios (HRs), derived from trunk accelerations, measure smoothness of trunk motion during gait; higher ratios indicate greater smoothness. Previous research indicates that young adults optimize HRs at preferred pace, exhibiting reduced HRs at speeds faster and slower than preferred. Recent studies examining HRs and other trunk acceleration measures challenge this finding. The purpose of this study was to examine age-related differences in HRs across a range of self-selected overground walking speeds. Anteroposterior (AP), vertical (VT), and mediolateral (ML) HRs were examined in 13 young adults (ages 20–23), 13 healthy older adults (ages 60–69), and 13 healthy old-old adults (ages 80–86) while walking overground at very slow, slow, preferred, fast, and very fast speeds. Young and older adults exhibited similar HRs in all directions of motion across speeds, while old-old adults exhibited lower AP- and VTHRs. All groups exhibited reduced HRs at speeds slower than preferred. However, there were no differences in HRs between preferred and faster speeds, with the exception of reduced VT-HRs in the very fast condition for the older groups. The ML-HR was not different between groups, and varied less across speeds. Stride time variability exhibited inverse relations with, and independently contributed to, HRs across speeds; lower stride time variability was associated with greater smoothness of trunk motion. Older groups were not disproportionately affected by walking more slowly and smoothness of trunk motion did not show a clear pattern of optimization at preferred pace for any group. ß 2011 Elsevier B.V. All rights reserved.

Keywords: Gait Smoothness Accelerometry Elderly Lifespan

1. Introduction Trunk kinematics are sensitive to age-related differences in gait, even in healthy older adults walking at normal speeds [1,2]. One kinematic measure, the harmonic ratio (HR), is derived from anteroposterior (AP), vertical (VT) and mediolateral (ML) trunk accelerations, and quantifies smoothness of trunk motion while walking. Higher HRs correspond to greater smoothness and provide an indication of whole body balance during gait [3,4]. HRs have discriminated between older adults who have and have not fallen [5] and healthy older adults and individuals with Parkinson’s disease and peripheral neuropathy [6–8]. Typical AP and VT acceleration patterns have repeatable biphasic patterns that reflect the cyclical movement of the trunk during one stride, thus characterized by a peak frequency at 2 Hz coinciding with step frequency. Mediolateral accelerations, regulated by the stride frequency, exhibit more complex monophasic

* Corresponding author. Tel.: +1 412 864 2083; fax: +1 412 692 2370. E-mail addresses: [email protected] (K.A. Lowry), [email protected] (N. Lokenvitz), [email protected] (A.L. Smiley-Oyen). 1 Tel.: +1 515 727 0253. 2 Tel.: +1 515 294 8261; fax: +1 515 294 8740. 0966-6362/$ – see front matter ß 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.gaitpost.2011.09.019

patterns and are characterized by multiple low amplitude peaks between 1 and 11 Hz. [9,10] In healthy gait, frequency decomposition of AP and VT accelerations per stride yields a dominance of the second harmonic and subsequent even harmonics, as these represent acceleration patterns that are resolved within each stride. The even harmonics represent regular, in-phase accelerations, while the odd harmonics correspond to irregular, out-ofphase accelerations. Conversely, frequency decomposition of ML accelerations yields a dominance of the first harmonic and subsequent odd harmonics, with odd harmonics representing the regular, in-phase accelerations and the even harmonics representing the irregular out-of-phase accelerations stride. [3,11] Thus, HRs provide information regarding an individual’s ability to smoothly control trunk motion during gait. While age-related reductions in HRs have been shown during usual overground walking [1,12], less is known about gait under real world conditions. Home and community ambulation often requires speed changes to successfully adapt to the environment (negotiating furniture, obstacles, and crosswalks) or achieve a goal (answering the door, phone). Examining walking smoothness during the challenges of slow and fast speeds can offer insight into how healthy older adults control speed changes, and reveal conditions where they are most vulnerable.

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Several studies examining the relationship between HRs and walking speed in young adults found an inverted U-shaped response for all directions of motion; HRs were highest at preferred speed, and reduced at speeds faster and slower than preferred [11,13]. Alternatively, others found AP- and VT-HRs were highest at self-selected fast speeds in young men [14], while another study in young and older women walking at preferred and fast speeds found reductions in VT- and ML-HRs, with no change in AP-HR [12]. While these disparate findings may be due to gender, differences in walking speeds and/or methodology, overall the relationships among age, smoothness, and walking speed remain unclear. To date, no study has compared HRs between young and older adults across a wide range of walking speeds. The primary purpose of this study was to examine smoothness of trunk motion in healthy young adults (YA), healthy 60-year-olds (OA), and healthy 80-year-olds (OOA) during overground walking at self-selected speeds ranging from very slow to very fast. We hypothesized that (1) compared to YAs, OAs would exhibit reduced HRs only at the extreme very fast and very slow speeds, and (2) OOAs would exhibit reduced HRs in general, with disproportionate reductions in HRs at very slow and very fast speeds. Additionally, we examined the contribution of spatiotemporal parameters to HRs across speeds.

zero-crossings in the first-order derivative of the filtered data. Each stride was classified as consecutive maximum deceleration points (heel strike to heel strike of the same foot). The ‘true’ heel strike points were then found using a localized search about each point for the maximum deceleration point in the original data. The HR was then determined per stride.

2. Methods

3. Results

2.3.2. Spatiotemporal variables Spatiotemporal variables were: average speed (m/s), using a stop watch, the time to walk the middle 12.5 m; stride time (s), number of samples in the acceleration signal between consecutive heel strikes of the same foot  5 ms/1000 ms; and the coefficient of variation (COV, standard deviation/mean  100) to quantify the variability of stride time. 2.4. Data reduction and analysis The first trial was considered practice; values were averaged across trials 2 and 3. Prior to calculation of means, trials were visually inspected to determine if the program correctly selected strides. To avoid acceleration and deceleration effects, the first 2 and last 2 strides were removed from the acceleration data. All data were checked for normality, and non-significant Kolmogorov–Smirnov statistics and low skewness values were confirmed for each variable. Six separate Group (3)  Condition (5 self-selected speeds) repeated measures ANOVAs were conducted for the AP-, VT- and ML-HRs, walking speed, stride time, and stride time variability. Main effects were interpreted using Bonferroni-corrected pairwise comparisons, interactions were followed up with between group one-way ANOVAs. Alpha level was set at 0.008 (0.05/6). Normalizing the data for leg length did not alter the results, thus the original data are presented. All statistical analyses were performed using PASW 18.

2.1. Participants Thirteen healthy young, old, and old-old adults participated in this study. YAs Young adults were university students. Older adults were recruited from the local community. Reasons for exclusion included neurological diagnoses, history of head trauma, significant heart disease, significant musculoskeletal impairments, or symptoms such as recent fracture or joint replacement, severe chronic pain, peripheral neuropathy, cognitive decline, and use of a walking device. All participants signed an approved written consent form, and were recruited and tested according to institutional review board procedures. 2.2. Instrumentation and procedures A triaxial accelerometer (Crossbow CXLO2LF3, range 2 g) mounted to a plastic base plate on a gait belt and secured and aligned with the third lumbar vertebrae [3,15] measured trunk accelerations in AP, VT, and ML directions. The accelerometer was calibrated on a flat surface, and once positioned on the lower trunk, was leveled prior to each trial. Data were sampled at 200 Hz using a portable data logger (Crossbow AD2000 Ready DAQ) worn in a small backpack. Participants, wearing their own comfortable walking shoes walked at 5 speeds based on the following instructions: (1) walk very slowly as if in an art gallery, (2) walk slower than normal, as if there were ample time, (3) walk at preferred, usual speed, (4) walk faster than normal but not maximal speed, and (5) walk as fast as is safe without running. These cues were the same as in previous studies [11,13]. Participants did not fixate on a target, but were told to ‘look ahead’ and avoid looking around the laboratory. Two markings were made to designate the middle 12.5 m of the walkway for determination of walking speed. Three consecutive trials were performed in each condition. Participants were first asked to walk at their preferred pace followed by slow, very slow, fast and very fast. This order was chosen so that preferred pace would not be contaminated by the other conditions.

3.1. Subject characteristics and self-selected speeds Subject characteristics and gait speeds for each condition are reported in Table 1. The ANOVA for speed revealed a Group  Condition interaction (p = 0.004). Follow up analyses revealed group differences only in the fast (p = 0.02) and very fast (p = 0.012) conditions, where OOAs walked more slowly than YAs. Within a group, pairwise comparisons revealed that all speed conditions were different from one another, indicating the cues were effective in achieving the desired speed differences. 3.2. Harmonic ratios Data for all variables for each speed condition are presented in Table 2. The ML-HR differed only by Condition (p < 0.001). Pairwise comparisons revealed that ML-HR values in the preferred, fast, and very fast conditions were not different, but were higher than the very slow and slow conditions; the Group effect did not reach significance (p = 0.047). The VT-HR analysis revealed a Group  Condition interaction (p = 0.005). The OOAs exhibited lower VT-HRs compared to OAs and YAs in the fast condition (p = 0.005), and lower values than YAs in the very fast condition (p = 0.001). The AP-HR analysis revealed main effects for both Group (p < 0.001) and Condition (p < 0.001). The OOAs exhibited

2.3. Gait variables 2.3.1. Harmonic ratios The primary dependent variables were AP-, VT-, and ML-HRs. Full descriptions of HR theory and determination are reported elsewhere [7,13]. The AP- and VTHRs, calculated by dividing the summed amplitude of the first 20 even harmonics by the summed amplitudes of the first 20 odd harmonics, should be high if the even harmonics dominate the pattern and odd harmonics are small, which is expected in a healthy gait pattern. Conversely, ML accelerations exhibit a monophasic pattern during one stride, thus the first and subsequent odd harmonics are dominant and the HR is calculated from a ratio of the odd harmonics divided by even harmonics [11]. Harmonic analysis was applied to all acceleration data using custom Visual Basic software using National Instruments Measurement StudioTM 6.0 libraries. A lowpass second-order Butterworth filter with a cutoff frequency of 21 Hz was applied to the raw acceleration data prior to stride segmentation. Stride segmentation was determined by identifying local maximum deceleration points in the vertical axis. Maximum deceleration candidate points were determined from negative-going

Table 1 Means (SD) of subject characteristics and self-selected gait speeds.

n, # women Age (yrs) Leg length (m) Mass (kg) Gait speed (m/s) Very slow Slow Preferred Fast Very fast *

Young adults

Old adults

Old-Old adults

13, 9 22.13 (0.9), 20–23 0.90 (0.04) 69 (12.9)

13, 10 66.34 (2.6), 60–69 0.93 (0.06) 77.6 (13.4)

13, 9 82.47 (2.2), 80–86 0.88 (0.08) 67.38 (10)

0.56 0.93 1.33 1.69 2.23

0.64 1.01 1.34 1.60 2.10

0.59 0.96 1.28 1.49 1.92

(0.18) (0.19) (0.25) (0.26)* (0.26)*

(0.17) (0.11) (0.13) (0.10) (0.28)

Two groups are different from one another at p < 0.05.

(0.12) (0.15) (0.12) (0.15)* (0.18)*

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274 Table 2 Means (SD) of harmonic ratios for each speed condition. Very slow ML-HR 1.96 (0.43) YA OA 2.09 (0.35) OOA 1.89 (0.44) VT-HR YA 2.19 (0.41) OA 2.47 (0.48) OOA 2.19 (0.49) AP-HR YA 2.59 (0.71) OA 2.86 (0.63) OOA 2.33 (0.55) Stride time (s) YA 1.94 (0.66) 1.71 (0.29) OA OOA 1.59 (0.177) COV stride time (%) YA 4.82 (2.21) OA 4.79 (1.97) OOA 5.67 (2.03)

Slow

Preferred

Fast

Very fast

2.05 (0.38) 2.26 (0.40) 1.93 (0.40)

2.34 (0.49) 2.58 (0.52) 2.13 (0.43)

2.43 (0.25) 2.75 (0.57) 2.23 (0.60)

2.55 (0.47) 2.59 (0.65) 2.35 (0.61)

2.91 (0.73) 3.27 (0.79) 2.66 (0.57)

3.74 (0.54) 3.65 (0.40) 3.13 (0.83)

4.09 (0.61) 3.94 (0.59) 3.12 (0.99)**

3.97 (0.51)* 3.41 (0.91) 2.79 (0.73)*

3.25 (0.70) 3.44 (0.60) 2.60 (0.75)**

3.85 (0.81) 3.80 (0.47) 3.00 (0.75)**

3.93 (0.54) 3.90 (0.59) 3.03 (0.83)**

3.80 (0.51) 3.79 (0.83) 3.00 (0.82)**

1.32 (0.19) 1.24 (0.09) 1.21 (0.13)

1.09 (0.10) 1.05 (0.07) 1.02 (0.09)

0.99 (0.08) 0.97 (0.06) 0.95 (0.08)

0.85 (0.06) 0.82 (0.08) 0.79 (0.08)

4.36 (1.99) 3.60 (1.51) 4.46 (1.16)

2.48 (.80) 2.38 (0.72) 2.94 (1.24)

1.94 (0.69)* 2.08 (0.76) 2.91 (1.55)*

1.66 (0.74)* 2.28 (1.40) 3.51 (1.76)*

ML-HR, mediolateral harmonic ratio; VT-HR, vertical harmonic ratio; AP-HR, anterior–posterior harmonic ration; YA, young adults; OA, older adults (60-yearolds); OOA, old-old adults (80-year-olds). * Two groups are different from one another at p < 0.05. ** Group is different from other two groups at p < 0.05, based on one-way ANOVAs.

lower AP-HRs compared to both YA and OA groups. Pairwise comparisons for Condition revealed that values in the preferred, fast, and very fast conditions were not significantly different, and were higher than for the very slow and slow conditions. In order to establish that group differences in HRs were not due to group differences in walking speed in the fast and very fast conditions, we conducted a series of post-hoc univariate analyses for both conditions using the HRs as the dependent variables, group as the fixed factor, and fast or very fast walking speed as the covariate. These analyses revealed that group differences for both the VT- and AP-HRs remained after accounting for gait speed, and post-hoc tests for group revealed the same pattern of results as the repeated measures ANOVAs. 3.3. Stride time and COV stride time The analysis for stride time revealed only a Condition effect (p < 0.001). Pairwise comparisons revealed all conditions were different from one another, with stride time decreasing as speed increased. Group effect did not reach significance (p = 0.032), though YAs tended to have longer stride times, particularly at the slower speeds. The analysis for COV stride time revealed significant effects for both Group (p = 0.003) and Condition (p < 0.001). Follow up analyses for group revealed OOAs had higher stride time variability than the other two groups. Pairwise comparisons for Condition revealed higher variability in very slow and slow conditions, while preferred, fast and very fast were not different from one another. Though the Group  Condition interaction was not significant (p = 0.136), OOAs exhibited a more distinct Ushaped response with increased variability in the very fast condition. 3.4. Relationships among variables A series of exploratory stepwise multiple regressions were used to examine prediction of HRs at very slow, preferred, and very fast speeds (Table 3). The predictor variables in the model were age, walking speed, and COV stride time. Overall, the adjusted R2 values showed that the ML-HR had the least amount of variance accounted for, speed consistently explained a significant amount

of variance in HRs in the very slow condition, and COV stride time explained a significant amount of variance in AP- and VT-HRs across speeds. 4. Discussion We examined the impact of walking speed on age-related differences in smoothness of trunk motion. We found that YA and OA performed similarly across speeds while OOAs exhibited reduced smoothness, particularly in the AP direction. We did not find that HRs were optimized around one’s preferred pace. Consistent with previous research using HRs [11,13], all groups exhibited reduced smoothness in all directions of motion at speeds slower than preferred. This finding is consistent with other studies that found altered coordination dynamics and muscle activations at slower speeds. Trunk and pelvis coordination patterns were variable between 0.5 and 1.0 m/s (our very slow and slow speed ranges), and less variable in normal speed ranges from 1.0 to 1.3 m/s [16]. Slow speeds also result in reduced EMG magnitudes, increased co-activation of hip and knee muscles, and increased variability of EMG patterns [17–19]. Altered coordination dynamics and EMG patterns may contribute to irregular trunk accelerations and reduced smoothness of trunk motion at slower speeds. In contrast to our hypothesis, we did not find disproportionate loss of smoothness of trunk motion at very slow speeds in either older group, as all groups were similarly unsmooth in all directions of motion. This response may be floor effect where, as discussed above, smoothness drops considerably as gait dynamics break down. Interestingly, the very slow self-selected speed was similar among groups (also similar to Latt et al. [13]), suggesting that 0.50 may represent a cut-off speed where healthy individuals feel they are walking as slow as possible while still progressing forward and maintaining global equilibrium. In contrast to previous studies [11,13], we found that all groups maintained smoothness at faster paces, with the exception that both older groups exhibited reduced VT smoothness in the very fast condition. In support of our findings, measures of signal regularity and repeatability derived from trunk accelerations in young adults during walking were shown to be less repeatable and regular at slow speeds compared to preferred, but similar between preferred and fast walking [20]. Interlimb coordination between extremities improved with increasing speed in young adults [21], and for a prosthetic group [22]. These authors argued that walking at faster speeds is accompanied by a reduction in stride length and frequency combinations (i.e., as speed increases both parameters increase), and it is this reduction in variation that improves coordination. Additionally, variability of EMG patterns decreased with increased speed [18]. Decreased variability of coordination and EMG patterns may result in more consistent, regular trunk accelerations, possibly accounting for sustained smoothness at faster speeds. In general, 80-year-olds exhibited reduced AP and VT smoothness across speeds. Age-related alterations in limb dynamics may underlie these reductions in smoothness. Previous research found healthy OAs exhibited support moments similar to YAs, but this was accomplished by a redistribution of joint torques and powers where OAs performed more work at the hip and less at the ankle than YAs [23]. A re-distribution towards proximal segments may result in irregular trunk accelerations and reduced smoothness. Interestingly, while OOAs maintained AP smoothness at faster speeds, they exhibited a loss of VT smoothness during very fast walking, a pattern also seen in OAs. At faster speeds OAs further increased hip extension moments with no similar increase at the ankle [24]. If altered limb dynamics with greater proximal hip work does account for reduced smoothness, it is unclear why there

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Table 3 Linear regression model summary for prediction of HRs. Condition

Independent variables

Very slow

Age Very slow speed COV stride time Adjusted R2 model Age Preferred speed COV stride time Adjusted R2 model Age Very fast speed COV stride time Adjusted R2 model

Preferred

Very fast

AP-HR

VT-HR

ML-HR

b (p value)

b (p value)

b (p value)

NS 0.295 (0.020) 0.586 (<0.0001) 0.573 (<0.0001) 0.298 (0.042) NS 0.409 (0.007) 0.248 (0.002) NS NS 0.531 (0.001) 0.263 (0.001)

NS 0.515 (<0.0001) 0.395 (0.002) 0.595 (<0.0001) NS 0.350 (0.029) NS 0.099 (0.029) 0.328 (0.027) NS 0.430 (0.005) 0.391 (<0.0001)

NS 0.465 (0.003) NS 0.195 (0.003) NS NS 0.345 (0.032) 0.095 (0.032) NS NS NS –

Dependent variables: ML-HR, mediolateral harmonic ratio; VT-HR, vertical harmonic ratio; AP-HR, anteroposterior harmonic ratio. Independent variables in the model: age, speed, stride time variability.

was not a similar reduction in AP smoothness during very fast walking. Smoothness of trunk motion in the ML direction exhibited a different pattern of results; there was less change across speeds, and though OOAs exhibited lower average values, they were not statistically different from YAs at any speed. Bauby and Kuo [25] proposed that ML dynamics require active control, involving brainstem and higher centers for sensory integration and feedback for continual adjustment of lateral foot placement. We speculate that while altered limb dynamics impacted AP and VT smoothness, active feedback processes were sufficiently intact in both older groups that ML smoothness was maintained. Regression analyses indicated that speed was a significant contributor to HRs in the very slow condition, and across speed conditions stride time variability was a significant contributor to AP- and VT-HRs. Higher stride time variability was associated with reduced smoothness of trunk motion. This finding is consistent with the presence of higher stride time variability in pathology [26] and as a predictor of fall risk. [27] It is not known whether increased stride time variability is causal to reduced smoothness of trunk motion or vice versa, or whether this relationship is indirect through the relation of both to another mechanism, such as muscle activations. One limitation was the small sample sizes, as well as an inability to examine gender differences because of too few men per group. We also did not randomize conditions; it is feasible the OOAs were more fatigued in the final fast and very fast conditions, which could have contributed to their lower HRs. However, ample rest time was provided. The older adults were healthy, thus these results and implications cannot be generalized to all communitydwelling older adults. As this study focused solely on smoothness, we were only able to speculate about the mechanisms underlying the pattern of results. A future research direction would be to examine kinematic and kinetic data together with HRs and other trunk acceleration-derived measures, to better understand how the system controls global body motion during walking, and which measures are most sensitive to age-related changes in gait.

gait patterns of high and low risk fallers [5], no study has investigated the ability of HRs to predict future fall risk. (2) All groups exhibited reduced smoothness at speeds slower than preferred. Smoothness values should be interpreted with caution when examining individuals who walk at slower speeds, or if using HRs to examine the effect of a dual task, as it is expected that given a certain cognitive load, people will walk more slowly. (3) Smoothness was not clearly optimized at preferred speed; rather, all groups maintained smoothness in the fast condition. These data indicate that healthy older adults are able to control upper body motion at fast speeds, at least for level, unobstructed walking. As preferred walking speed declines with age [28], and slower preferred gait speeds have been consistently associated with poorer outcomes [29,30], preventing or slowing down this decline is important. For healthy older adults, training at faster speeds, while promoting cardiovascular health, may also result in improved ability to adapt to environmental challenges and help to slow or prevent the decline in preferred speed. (4) Higher stride time variability was associated with reduced smoothness of trunk motion, adding to the body of knowledge that stride time variability is an informative clinical indicator of gait control. Acknowledgements We thank Dr. Douglas Bonett for his statistical advice, Carolyn Siskoff DPT, Lisa Stefl DPT, and all the students who assisted with data collection, coding, and processing. Finally, we thank all the participants in this study. These data were presented at the annual meeting of the International Society for Posture & Gait Research, 2007. This study was supported by the APTA Section on Geriatrics Adopt-A-Doc award, and by training grant T32 AG021885 from the National Institutes of Health. Conflict of interest statement The authors of this manuscript have no conflicts of interest. References

5. Conclusion (1) YAs and OAs performed similarly across speeds, while OOAs exhibited reduced smoothness of trunk motion across speeds. This finding indicates that examining 60-year-olds separately from 80year-olds is important, and that inclusion of older adult participants into one large group, even if they are healthy, may mask or misrepresent age-related changes in motor performance. The functional meaning of reduced walking smoothness in OOAs is not known. While smoothness has effectively discriminated the

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