- Email: [email protected]
Real-time feedback enhances forward propulsion during walking in old adults
Clinical Biomechanics 29 (2014) 68–74
Contents lists available at ScienceDirect
Clinical Biomechanics journal homepage: www.elsevier.com/locate/clinbiomech
Real-time feedback enhances forward propulsion during walking in old adults Jason R. Franz a,b,⁎, Michela Maletis a, Rodger Kram a a b
Department of Integrative Physiology, University of Colorado, Boulder, CO 80309-0354, United States Department of Mechanical Engineering, University of Wisconsin, Madison, WI 53706, United States
a r t i c l e
i n f o
Article history: Received 25 March 2013 Accepted 22 October 2013 Keywords: Elderly Ankle power Biofeedback Rehabilitation Intervention
a b s t r a c t Background: Reduced propulsive function during the push-off phase of walking plays a central role in the deterioration of walking ability with age. We used real-time propulsive feedback to test the hypothesis that old adults have an underutilized propulsive reserve available during walking. Methods: 8 old adults (mean [SD], age: 72.1 [3.9] years) and 11 young adults (age: 21.0 [1.5] years) participated. For our primary aim, old subjects walked: 1) normally, 2) with visual feedback of their peak propulsive ground reaction forces, and 3) with visual feedback of their medial gastrocnemius electromyographic activity during push-off. We asked those subjects to match a target set to 20% and 40% greater propulsive force or push-off muscle activity than normal walking. We tested young subjects walking normally only to provide reference ground reaction force values. Findings: Walking normally, old adults exerted 12.5% smaller peak propulsive forces than young adults (P b 0.01). However, old adults significantly increased their propulsive forces and push-off muscle activities when we provided propulsive feedback. Most notably, force feedback elicited propulsive forces that were equal to or 10.5% greater than those of young adults (+20% target, P = 0.87; +40% target, P = 0.02). With electromyographic feedback, old adults significantly increased their push-off muscle activities but without increasing their propulsive forces. Interpretation: Old adults with propulsive deficits have a considerable and underutilized propulsive reserve available during level walking. Further, real-time propulsive feedback represents a promising therapeutic strategy to improve the forward propulsion of old adults and thus maintain their walking ability and independence. © 2013 Elsevier Ltd. All rights reserved.
1. Introduction The prevalence of walking ability limitations among old adults is profound; 17%, 28% and 47% of people aged 65–74, 75–84, and 85+ years respectively report that walking difficulties interfere with their daily activities (U.S. Dept. of Health and Human Services, 2011). Compromised walking ability ultimately predicts health and survival in old adults (Studenski et al., 2011). Considerable research has demonstrated that a reduction in propulsion during the push-off phase of walking, even in otherwise healthy old adults, plays a central role in the deterioration of walking ability with age (Prince et al., 1997; Winter et al., 1990). Specifically, old adults exert smaller peak propulsive forces (Franz and Kram, 2013a; Prince et al., 1997), perform less trailing leg positive mechanical work (Franz and Kram, 2013a), and generate less ankle power (Cofre et al., 2011; DeVita and Hortobagyi, 2000; Franz and Kram, in press; Kerrigan et al., 1998; Prince et al., 1997; Winter et al., 1990) than young adults walking at the same
⁎ Corresponding author at: 2258 Mechanical Engineering Building, 1513 University Avenue, Madison, WI 53706, United States. E-mail address: [email protected] (J.R. Franz). 0268-0033/$ – see front matter © 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.clinbiomech.2013.10.018
speed. Eventually, the propulsive deficits of old adults may lead to slower walking speeds on the level, inability to walk uphill, and an associated loss of independence in the community. One might presume that sarcopenia and leg muscle weakness are responsible for these changes. Such thinking has understandably led to the widespread prescription of muscle strengthening programs for old adults. While these programs improve muscle strength and mitigate sarcopenia, strengthening alone generally fails to improve the walking ability of old adults (Alfieri et al., 2010; Berg and Lapp, 1998; Buchner et al., 1997; Geirsdottir et al., 2012; Hanson et al., 2009; Holviala et al., 2012; Kalapotharakos et al., 2005; Nelson et al., 2004; Nowalk et al., 2001). Those outcomes suggest that factors other than sarcopenia bring the propulsive deficits of older adults. Consistent with this idea, old adults who exhibit diminished forward propulsion when walking over level ground at a comfortable speed can both voluntarily walk faster (Hernandez et al., 2009; Ortega and Farley, 2007; Schmitz et al., 2009; Silder et al., 2008) and walk uphill (Franz and Kram, 2013a,b, in press). For example, we have shown that compared to level walking, old adults increase their peak propulsive GRFs, trailing leg positive mechanical work, and average ankle power generation during push-off by 69%, 115%, and 44% to walk uphill, respectively (Franz and Kram, 2013a, in press).
J.R. Franz et al. / Clinical Biomechanics 29 (2014) 68–74
Based on their ability to successfully walk uphill, we propose that old adults have a considerable and underutilized propulsive reserve during level walking. We sought to implement an approach to strategically tap into this reserve during level walking and improve the forward propulsion of old adults. Clinicians have long recognized that real-time feedback can facilitate a return to normal function in people following stroke (Binder et al., 1981; Colborne et al., 1993; Intiso et al., 1994), amputation (Isakov, 2007), and total joint replacement (Isakov, 2007; White and Lifeso, 2005). Can real-time feedback of propulsive effort during walking similarly facilitate enhanced forward propulsion in old adults? Answering this question could lead to a better understanding of the mechanism(s) underlying the loss of propulsion with age and the development of more effective rehabilitative and prehabilitative therapies. Some metrics of propulsion (e.g., ankle power generation) require complex calculations (Vaughan et al., 1999) that are impractical to implement using real-time feedback. In contrast, peak propulsive GRFs and ankle extensor (plantarflexor) muscle activities present relatively convenient and representative metrics of forward propulsion (Gottschall and Kram, 2003). In this study, we used real-time visual feedback to test the hypothesis that old adults can increase their peak propulsive GRFs and ankle extensor muscle activities during level walking. We also hypothesized that real-time feedback would elicit peak propulsive GRFs comparable to young adults. Forward propulsion during walking predictably decreases as people take progressively shorter steps at the same speed (Martin and Marsh, 1992) and, for many reasons, old adults typically choose shorter steps (Kerrigan et al., 1998; Winter et al., 1990). Thus, as a secondary aim, we used auditory cues from a metronome to encourage old adults to take slower, longer steps than normal and investigated the effect on increasing their forward propulsion.
2. Methods 2.1. Subjects Our subjects comprised 8 old adults (5 F/3 M, mean [SD], age: 72.1 [3.9] years, mass: 66.4 [11.8] kg, height: 1.71 [0.09] m) and 11 young adults (6 F/5 M, age: 21.0 [1.5] years, mass: 69.2 [6.8] kg, height: 1.75 [0.09] m) who were healthy and exercised regularly. All subjects reported regularly taking part in at least moderate intensity exercise (swimming, biking, and/or running) at least 3 times per week. Subjects completed a health questionnaire based on recommendations of the American College of Sports Medicine (2006) prior to participating. Exclusion criteria were: BMI ≥ 30, sedentary lifestyle, first degree family history of coronary artery disease, cigarette smoking, high blood pressure, high cholesterol, diabetes or prediabetes, orthopedic or neurological condition, or taking medication that causes dizziness. All subjects gave written informed consent as per the University of Colorado Institutional Review Board.
2.2. Instrumentation and visual feedback After preparing the skin with fine sandpaper and alcohol, we placed single differential electrodes with wireless preamplifiers (Noraxon USA Inc., Scottsdale, AZ, USA) over old subjects' right leg soleus and bilaterally over their medial gastrocnemius (MG), and sampled at 2000 Hz. We verified electrode positions and signal quality by visually inspecting the electromyographic (EMG) signals while subjects performed standing calf raises. A force platform (model ZBP-7124-6-4000, Advanced Mechanical Technology, Inc., Watertown, MA, USA) mounted under the right side of a custom dual-belt treadmill recorded the threedimensional ground reaction forces (GRF) for each subject's right leg at 1000 Hz (Franz and Kram, 2013a; Kram et al., 1998). To synchronize these data, we delayed the GRF signals to account for the 300 ms transmission delay inherent to the wireless EMG system. Fig. 1 depicts our experimental setup. A custom script written in Labview (National Instruments Corp., Austin, TX, USA) continuously processed and monitored the GRF and EMG signals. The script lowpass filtered the GRF signals (fourth-order Butterworth, 20 Hz cut-off) and demeaned, band-pass filtered (20–450 Hz), and full-wave rectified the EMG signals. Our feedback algorithm relied on the stance phase timing provided by the force platform mounted under the right treadmill belt. Accordingly, the script extracted the data from each right leg stance phase in real-time, based on a 20 N vertical GRF threshold. At the instant of each toe-off, the real-time algorithm calculated and stored: 1) the propulsive peak of the anterior–posterior (AP) GRF and 2) the mean “push-off” MG activity (i.e., that over the second half of stance) (Fig. 1). We chose MG activity for use in this algorithm based on evidence that the gastrocnemius muscle is considerably involved in forward propulsion (Francis et al., in press; Gottschall and Kram, 2003; Winter, 1983). However, we analyzed EMG data from both the MG and SOL due to their combined contribution to generating ankle power during push-off (Neptune et al., 2009). Finally, a computer monitor positioned in front of the treadmill displayed points each corresponding to a 2-stride average of those measurements (i.e., one dot appeared every two strides, scrolling from right to left) (Fig. 1). During pilot testing, we discovered that to preserve the ordinate scaling on the monitor across conditions, the algorithm needed to consider the magnitude, variability, and target percent increases of each measure. Thus, we normalized the scaling of each subject's feedback data on the monitor by setting the ordinate range from the minimum value during normal walking to the largest target plus half the range observed during normal walking.
2.3. Protocol Subjects first walked normally for 5 min at 1.25 m/s on the dualbelt, force-measuring treadmill to allow their movement patterns to stabilize. We collected GRF (old and young subjects) and EMG data
Real-time data extraction
MG EMG
69
Computer Monitor
Mean Push-off EMG OR Peak Propulsive Force
Dual-Belt, Force-Measuring Treadmill
AP GRF Target (+20% or +40% Normal) Real-time data
Fig. 1. Experimental setup and real-time analysis for visual feedback trials. In different trials, we provided real-time feedback of the peak positive anterior–posterior ground reaction force (AP GRF) and the mean push-off medial gastrocnemius (MG) muscle activity.
J.R. Franz et al. / Clinical Biomechanics 29 (2014) 68–74
(old subjects only) during the final 30 s of this 5 min trial (Normal 1). We tested young subjects walking normally only to provide reference ground reaction force values and thus this concluded the young subjects' session. For old subjects, we immediately used data from the Normal 1 trial to calculate targets for the first pair of visual feedback trials — force or EMG, depending on the randomized trial order. Prior to starting the visual feedback trials, we described the gait cycle to each subject and emphasized the late stance phase during which the ankle extends and the muscles of the leg generate a propulsive force on the ground. We then instructed each subject to more vigorously extend their ankles and push their legs backwards during late stance without excessively vaulting themselves vertically. For each visual feedback trial, we asked subjects to match a target line set to 20% and 40% greater than their normal walking for 2 min (force: F20 and F40 or EMG: EMG20 and EMG40). We selected those targets based on our previous studies in which old adults walked with 21% smaller peak propulsive forces, 21% smaller push-off MG activity, and 26% smaller peak ankle power generation than young adults (Franz and Kram, 2013a,b, in press). To determine whether subjects could maintain a more vigorous push-off after removing visual feedback, we then turned the monitor off and asked subjects to maintain that same exaggerated push-off for 1 min. During pilot testing, we discovered that some people retained a more vigorous push-off for several minutes even after returning to normal walking. To help wash out any lingering effects, subjects sat for 5 min following the first pair of visual feedback trials. Subjects then walked normally on the treadmill for 1 min (Normal 2) from which we calculated targets for the alternate pair of visual feedback trials. In other trials, in order to promote longer steps, we asked subjects to walk for 1 min while matching their steps to the beat of an audio metronome set to step frequencies 10% and 20% slower than normal walking (SF10 and SF20, respectively). We then turned the metronome off and asked subjects to maintain that same step frequency for 1 min.
force and EMG feedback symmetrically by comparing the average increase from normal in right and left MG activity using paired-samples t-tests (mean, 12% asymmetry, P N 0.16). A repeated measures ANOVA tested for significant main effects of condition using a P b 0.05 criterion. When a significant main effect was found, we performed post-hoc pairwise comparisons to determine which conditions differed statistically. Finally, independent samples t-tests compared old and young adult peak propulsive GRFs. 3. Results Walking normally, old adults exerted 12.5% smaller peak propulsive GRFs than young adults (P b 0.01) (Fig. 2A). However, when we provided real-time propulsive feedback, old adults significantly increased their propulsive GRFs and push-off muscle activities. Force feedback elicited propulsive GRFs that were either equal to or 10.5% greater than those of young adults walking normally (F20, P = 0.87; F40, P = 0.02) (Fig. 2A). Those conditions also elicited significant increases in pushoff muscle activity (Fig. 2B). Figs. 3 and 4 show the stance phase GRF and EMG profiles, respectively. Force feedback also elicited greater mechanical power generation to propel the body's CoM forward during the push-off phase of walking and to raise the CoM vertically during single support (Fig. 5). With EMG feedback, old adults significantly increased their push-off muscle activities but without increasing their propulsive GRFs (Fig. 2B).
A
2.4. Data analysis
28
Force Feedback EMG Feedback Stride Frequency Cueing
† ** *
26 ** *
*
24
Young Adults †
22
†
†
†
20 18
0
Normalized Push-off EMG (MG, % Normal Stride)
20
SF
10
SF
Force Feedback EMG Feedback Stride Frequency Cueing *
40
300
G
B
EM 20
G
EM
0
F4
0 F2
al
m
or
N
*
** *
250 *
200
*
150
0 20
10
SF
SF
40 G EM 0 2 G
EM
0 F4
0 F2
al
m or
N
A custom MATLAB (Mathworks, Inc., Natick, MA, USA) script processed all data based on analysis techniques identical to those implemented in the real-time algorithm and summarized earlier in this paper. The script calculated stride-averaged values over the final minute of each normal walking trial, the second minute of each visual feedback trial, and the final 30 s of each auditory cueing trial. Starting with the first stride after we removed visual feedback or auditory cueing, we also calculated stride-by-stride values of peak propulsive force, pushoff MG activity, and stride time during the final minute of the F20/F40, EMG20/EMG40, and SF10/SF20 trials, respectively. We report average values for each stride up to the fewest number of strides taken by a subject during this minute (approximately 40). After the data were collected, we used the individual limbs method (ILM) to calculate the instantaneous rates of mechanical work performed by the legs for each condition from the stride-averaged GRF data (Donelan et al., 2002; Franz and Kram, 2013a). We first calculated the velocity of the body's center of mass (CoM) by integrating the GRF signals with respect to time (Cavagna, 1975). We then calculated mechanical work rates over an average stride as the dot product of the three-dimensional right leg GRF and center of mass velocity. We first evaluated the distribution of all measurements using a Shapiro–Wilk's test. All outcome measures were normally distributed except for mean push-off MG activity during trials with visual EMG feedback (P b 0.01 for EMG20 and EMG 40). A paired-samples t-test confirmed that our primary outcome measures (peak propulsive GRF and mean push-off MG activity) did not differ between Normal 1 and Normal 2 (P = 0.109 and P = 0.083, respectively). Because subjects completed Normal 2 after their first round of visual feedback, we used Normal 1 as our baseline condition for all comparisons to avoid any possible confounding factors. We also confirmed that subjects responded to
30
Peak Propulsive GRF (% BW)
70
Fig. 2. Mean [SE] peak propulsive force (A) and push-off muscle activity (B) for old adults walking with visual force and EMG feedback and auditory stride frequency cueing. Horizontal dashed line in A represents the peak propulsive force of young adults walking normally. Single asterisks (*) indicate significantly different from normal walking. Double asterisks (**) indicate 20% and 40% targets significantly different from 10% and 20% targets, respectively. Crosses (†) indicate significantly different from young adults (P b 0.05).
J.R. Franz et al. / Clinical Biomechanics 29 (2014) 68–74
A AP GRF (%BW)
F40 F20 Normal
20
C
EMG Feedback
Stride Frequency Cueing SF20 SF10 Normal
EMG40 EMG20 Normal
*
*
0 −20
*
*
−40 150
Vertical GRF (%BW)
B
Force Feedback
40
71
* *
*
*
100
50
0 0
0.2
0.4
0.6
0.8
0
0.2
0.4
time (s)
0.6
0.8
0
0.2
time (s)
0.4
0.6
0.8
time (s)
Fig. 3. Mean stance phase ground reaction force (GRF) profiles for old adults walking with visual force and EMG feedback and auditory stride frequency cueing. Asterisks (*) indicate significantly different from normal walking (P b 0.05).
Likely because both muscles are involved in forward propulsion, feedback of medial gastrocnemius activity elicited similar increases in soleus muscle activity during push-off (Fig. 4). Old adults generally increased their propulsive function with force and EMG feedback but they did not quite reach the 20% and 40% targets. On average, old subjects increased their propulsive GRF by 15% and 26% for F20 and F40 and their push-off MG activity by 19% and 30% for EMG20 and EMG40, respectively. Moreover, subjects could maintain this more vigorous push-off for at least 40 strides after we removed force (Fig. 6A) or EMG (Fig. 6B) feedback. Only for F40 did subjects show a significant decrease in stride-by-stride propulsive forces after we removed visual feedback (P = 0.03).
Normalized SOL Activity (% Normal Stride)
Normalized MG Activity (% Normal Stride)
A
*
F40 F20 Normal
500 400
B
Force Feedback
Auditory stride frequency cueing elicited smaller increases in propulsive GRFs and push-off muscle activities in old adults than walking with feedback of those measures directly (Fig. 2). Old subjects walked with 8% (P b 0.01) and 23% (P b 0.01) slower, longer steps than normal for SF10 and SF20, respectively. Similarly, step times were 6% (P = 0.01) and 15% (P b 0.01) slower than normal for F20 and F40, respectively. 4. Discussion Our findings reveal that old adults with propulsive deficits (compared to young adults) actually have a considerable and underutilized
C
EMG Feedback EMG40 EMG20 Normal
Stride Frequency Cueing SF20 SF10 Normal
*
*
300 200 100 0 500
*
400
*
*
300 200 100 0 0
0.2
0.4
time (s)
0.6
0.8
0
0.2
0.4
time (s)
0.6
0.8
0
0.2
0.4
0.6
0.8
time (s)
Fig. 4. Mean stance phase medial gastrocnemius (MG) and soleus (SOL) activity for old adults walking with visual force and EMG feedback and auditory stride frequency cueing. Asterisks (*) indicate significantly greater than normal walking (P b 0.05).
72
J.R. Franz et al. / Clinical Biomechanics 29 (2014) 68–74
Vertical Contribution to Mechanical Power (W/kg)
AP Contribution to Mechanical Power (W/kg)
A
B
Force Feedback
2
F40 F20 Normal
1
C
EMG Feedback
Stride Frequency Cueing
EMG40 EMG20 Normal
*
SF20 SF10 Normal
*
0 −1 *
−2 2
* *
1 0 −1 −2 0
0.2
0.4
0.6
0.8
0
0.2
0.4
time (s)
0.6
0.8
0
0.2
time (s)
0.4
0.6
0.8
time (s)
Fig. 5. Mean stance phase instantaneous mechanical power generation for old adults walking with visual force and EMG feedback and auditory stride frequency cueing. Asterisks (*) indicate significantly greater than normal walking (P b 0.05).
propulsive reserve available during level walking. Further, real-time visual feedback of propulsive effort can effectively call upon this reserve. As hypothesized, the old adults in this study were able to significantly and
AP GRF (% Normal)
A
160
Normalized MG Activity (% Normal) Stride Time (% Normal)
C
F40
F20
140
120
100
B
dramatically increase their peak propulsive GRFs and ankle extensor muscle activities when we provided real-time force and EMG feedback compared to normal walking. Moreover, and also as hypothesized, real-time
y = 0.14 x + 115.45 *
160
y = -0.24 x + 129.91 *
EMG40
EMG20
140
120
100
y = 0.12 x + 111.99
160
y = -0.13 x + 121.10
SF10
SF20
140
120
100
y = -0.02 x + 109.50 0
10
20
stride (#)
30
40
y = -0.02 x + 122.75 0
10
20
30
40
stride (#)
Fig. 6. Post-feedback retention. Mean stride-by-stride values of peak propulsive force, push-off medial gastrocnemius activity, and stride time during the minute after we removed visual feedback or auditory cueing of those measures. Horizontal dashed line indicates target percent increase. Asterisks (*) indicate a linear regression slope significantly different from zero (P b 0.05).
J.R. Franz et al. / Clinical Biomechanics 29 (2014) 68–74
force feedback in old adults elicited peak propulsive GRFs that were equal to and even greater than those of young adults. Consistent with the speculation of some earlier studies (DeVita and Hortobagyi, 2000; Schmitz et al., 2009), we have systematically shown that old adults with propulsive deficits are not explicitly limited in their capacity to increase forward propulsion during level walking. To the contrary, our data imply that old adults have a reserve of at least 26% for exerting greater propulsive GRFs and 49% for increasing ankle extensor muscle recruitment. Remarkably, healthy and active old adults can walk with as vigorous a push-off as young adults, but do not naturally do so. Why don't old adults better utilize their propulsive capacity during level walking? One possibility is that the disproportionate recruitment of hip vs. ankle muscles commonly reported in old adults (Cofre et al., 2011; DeVita and Hortobagyi, 2000; Franz and Kram, 2013b; Schmitz et al., 2009) inherently reduces propulsion during push-off. For example, we previously found that to help initiate leg swing, old adults produce 63% larger peak hip flexor moments than young adults during push-off (Franz and Kram, in press). Larger peak hip flexor moments in old adults may act to reduce the force they exert on the ground during push-off and thus their ankle power generation and forward propulsion. We have also proposed that changes in neural control with age may lead to this preferential recruitment of proximal vs. distal muscles (Franz and Kram, 2013b). Although we did not measure hip muscle activity or estimate joint kinetics in this study, real-time feedback of propulsive effort may supplant the distal to proximal redistribution of muscle recruitment, thereby enabling old adults to increase their forward propulsion. Another possibility is that a trade-off exists in which increasing forward propulsion during push-off compromises other features of gait in old adults (Huang and Ahmed, 2011). For example, old adults with trailing leg propulsive deficits consequently exhibit more conserved (i.e., “smoother”) patterns of CoM velocity fluctuations than young adults (Franz and Kram, 2013a). If this smoother pattern of CoM velocity change promotes improved balance, old adults may prioritize it over a more vigorous push-off and perhaps even despite an increase in metabolic cost (Franz and Kram, 2013a). We tested three different approaches to increase propulsion in old adults: force feedback, EMG feedback, and auditory stride frequency cueing. Real-time force and EMG feedback more effectively elicited a more vigorous push-off than using a metronome to encourage old adults to take slower, longer steps. We doubt that the specific sensory modality (visual vs. auditory) used is responsible for these differences. Rather, we suspect that propulsive force and/or EMG feedback are more effective than stride frequency cueing because they are more specific. Which is more effective at improving the forward propulsion of old adults, force or EMG feedback? First, note that the target 20% and 40% increases for propulsive force do not correspond to equal increases in push-off EMG. Indeed, based on our findings and subjects' verbal comments, force feedback required a considerably more vigorous push-off than the same target percent increase in medial gastrocnemius activity using EMG feedback. Not surprisingly, asking subjects to push-off more vigorously by increasing MG activity also elicited similar increases in SOL activity. However, while EMG feedback did elicit significantly greater push-off muscle activities in old adults, not even the 40% target increase in EMG elicited a significant increase in peak propulsive forces or mechanical power generation. Thus, we infer that real-time propulsive force feedback more directly and effectively encourages old adults to utilize their propulsive reserve. To our knowledge, this study represents the first attempt to use realtime feedback to encourage old adults to increase their forward propulsion during walking. Future studies are needed to determine if longer term propulsive feedback training has beneficial results. We showed here that old adults can maintain a more vigorous push-off for at least 40 strides after we removed visual feedback. For how long after propulsive feedback training do old adults retain this effect, and does the effect transfer to overground walking? Generally, people tend to adopt a
73
combination of step length, width, and frequency at a given walking speed that minimizes metabolic cost (Donelan et al., 2001; Umberger and Martin, 2007). Our old adult subjects may have consumed oxygen at a faster rate while walking in the novel way elicited by propulsive feedback compared to walking normally. Alternatively, the characteristic propulsive mechanics of old adults may explain why they consume ~20% more metabolic energy than young adults walking normally. Thus, it is possible that propulsive feedback training could both improve forward propulsion in old adults and reduce their metabolic cost. Finally, sarcopenia and leg muscle weakness may more explicitly limit propulsive GRFs and ankle power generation in sedentary and/or frail old adults. While resistance training in old adults can improve their muscle strength and mitigate sarcopenia, strengthening alone generally fails to improve their walking ability (Alfieri et al., 2010; Berg and Lapp, 1998; Buchner et al., 1997; Geirsdottir et al., 2012; Hanson et al., 2009; Holviala et al., 2012; Kalapotharakos et al., 2005; Nelson et al., 2004; Nowalk et al., 2001). Thus, frail and/or sedentary old adults may benefit most from leg muscle strengthening to increase their propulsive capacity combined with propulsive feedback training to encourage neural utilization of that capacity. There are several limitations of this study. First, because of the preliminary nature of this study, subjects only completed a single session and walked with propulsive feedback for 2 min. The old subjects in this study were also active and fit. Although we did not measure leg muscle strength, it is notable that even these very active subjects exhibited propulsive deficits during walking compared to young adults. However, we cannot conclude that more sedentary and/or frail old adults have a propulsive reserve available during level walking. Moreover, the etiology and functional consequences of the biomechanical and physiological changes brought by advancing age vary considerably among old adults. Thus, to apply more generally, future research must consider the extent to which visual, vestibular, muscular, and/or neural changes in old adults influence their response to propulsive feedback. Because only the right belt of our dual-belt treadmill records GRFs, we based our real-time feedback algorithm on measurements taking from the right leg only. Subjects were not made aware of this limitation and we placed EMG electrodes over subjects' right and left legs. Although not statistically significant, subjects increased their right leg MG activity on average ~12% more than their left leg MG activity in response to propulsive force and EMG feedback. 5. Conclusions In summary, we have shown that apparently healthy old adults with subclinical propulsive deficits have a considerable and underutilized propulsive reserve available during level walking and that real-time propulsive feedback can effectively tap into this reserve. Eventually, the propulsive deficits of old adults may restrict their walking ability and thus their independence in the community. Real-time propulsive feedback represents a promising strategy to maintain and/or improve the forward propulsion of old adults and thus maintain their walking ability and independence. Acknowledgments We thank Dr. Alena Grabowski from the Department of Veterans Affairs Eastern Colorado Healthcare System for use of the electromyography equipment. This work was supported by a Dissertation Completion Fellowship from the University of Colorado Graduate School. References Alfieri, F.M., Riberto, M., Gatz, L.S., Ribeiro, C.P., Lopes, J.A., Battistella, L.R., 2010. Functional mobility and balance in community-dwelling elderly submitted to multisensory versus strength exercises. Clin. Interv. Aging 5, 181–185.
74
J.R. Franz et al. / Clinical Biomechanics 29 (2014) 68–74
American College of Sports Medicine, 2006. Participation, Health Screening and Risk Stratification, Guidelines for Exercise Testing and Prescription. Lippincott Williams & Wilkins, Philadelphia 19–35. Berg, W.P., Lapp, B.A., 1998. The effect of a practical resistance training intervention on mobility in independent, community-dwelling older adults. J. Aging Phys. Act. 6. Binder, S.A., Moll, C.B., Wolf, S.L., 1981. Evaluation of electromyographic biofeedback as an adjunct to therapeutic exercise in treating the lower extremities of hemiplegic patients. Phys. Ther. 61, 886–893. Buchner, D.M., Cress, M.E., de Lateur, B.J., Esselman, P.C., Margherita, A.J., Price, R., et al., 1997. The effect of strength and endurance training on gait, balance, fall risk, and health services use in community-living older adults. J. Gerontol. A Biol. Sci. Med. Sci. 52, M218–M224. Cavagna, G.A., 1975. Force platforms as ergometers. J. Appl. Physiol. 39, 174–179. Cofre, L.E., Lythgo, N., Morgan, D., Galea, M.P., 2011. Aging modifies joint power and work when gait speeds are matched. Gait Posture 33, 484–489. Colborne, G.R., Olney, S.J., Griffin, M.P., 1993. Feedback of ankle joint angle and soleus electromyography in the rehabilitation of hemiplegic gait. Arch. Phys. Med. Rehabil. 74, 1100–1106. DeVita, P., Hortobagyi, T., 2000. Age causes a redistribution of joint torques and powers during gait. J. Appl. Physiol. 88, 1804–1811. Donelan, J.M., Kram, R., Kuo, A.D., 2001. Mechanical and metabolic determinants of the preferred step width in human walking. Proc. Biol. Sci. 268, 1985–1992. Donelan, J.M., Kram, R., Kuo, A.D., 2002. Simultaneous positive and negative external mechanical work in human walking. J. Biomech. 35, 117–124. Francis, C.A., Lens, A.L., Lenhart, R.L., Thelen, D.G., 2013. The modulation of forward propulsion, vertical support, and center of pressure by the plantarflexors during human walking. Gait Posture 38 (4), 993–997. Franz, J.R., Kram, R., 2013a. Advanced age affects the individual leg mechanics of level, uphill, and downhill walking. J. Biomech. 46, 535–540. Franz, J.R., Kram, R., 2013b. How does age affect leg muscle activity/coactivity during uphill and downhill walking? Gait Posture 37, 378–384. Franz, J.R., Kram, R., 2013. Advanced age and the mechanics of uphill walking: a joint-level, inverse dynamic analysis. Gait Posture (in press). Geirsdottir, O.G., Arnarson, A., Briem, K., Ramel, A., Tomasson, K., Jonsson, P.V., et al., 2012. Physical function predicts improvement in quality of life in elderly Icelanders after 12 weeks of resistance exercise. J. Nutr. Health Aging 16, 62–66. Gottschall, J.S., Kram, R., 2003. Energy cost and muscular activity required for propulsion during walking. J. Appl. Physiol. 94, 1766–1772. Hanson, E.D., Srivatsan, S.R., Agrawal, S., Menon, K.S., Delmonico, M.J., Wang, M.Q., et al., 2009. Effects of strength training on physical function: influence of power, strength, and body composition. J. Strength Cond. Res. 23, 2627–2637. Hernandez, A., Silder, A., Heiderscheit, B.C., Thelen, D.G., 2009. Effect of age on center of mass motion during human walking. Gait Posture 30, 217–222. Holviala, J., Kraemer, W.J., Sillanpaa, E., Karppinen, H., Avela, J., Kauhanen, A., et al., 2012. Effects of strength, endurance and combined training on muscle strength, walking speed and dynamic balance in aging men. Eur. J. Appl. Physiol. 112, 1335–1347.
Huang, H.J., Ahmed, A.A., 2011. Tradeoff between stability and maneuverability during whole-body movements. PLoS One 6, e21815. Intiso, D., Santilli, V., Grasso, M.G., Rossi, R., Caruso, I., 1994. Rehabilitation of walking with electromyographic biofeedback in foot-drop after stroke. Stroke 25, 1189–1192. Isakov, E., 2007. Gait rehabilitation: a new biofeedback device for monitoring and enhancing weight-bearing over the affected lower limb. Eura Medicophys. 43, 21–26. Kalapotharakos, V.I., Michalopoulos, M., Tokmakidis, S.P., Godolias, G., Gourgoulis, V., 2005. Effects of a heavy and a moderate resistance training on functional performance in older adults. J. Strength Cond. Res. 19, 652–657. Kerrigan, D.C., Todd, M.K., Della Croce, U., Lipsitz, L.A., Collins, J.J., 1998. Biomechanical gait alterations independent of speed in the healthy elderly: evidence for specific limiting impairments. Arch. Phys. Med. Rehabil. 79, 317–322. Kram, R., Griffin, T.M., Donelan, J.M., Chang, Y.H., 1998. Force treadmill for measuring vertical and horizontal ground reaction forces. J. Appl. Physiol. 85, 764–769. Martin, P.E., Marsh, A.P., 1992. Step length and frequency effects on ground reaction forces during walking. J. Biomech. 25, 1237–1239. Nelson, M.E., Layne, J.E., Bernstein, M.J., Nuernberger, A., Castaneda, C., Kaliton, D., et al., 2004. The effects of multidimensional home-based exercise on functional performance in elderly people. J. Gerontol. A Biol. Sci. Med. Sci. 59, 154–160. Neptune, R.R., Clark, D.J., Kautz, S.A., 2009. Modular control of human walking: a simulation study. J. Biomech. 42, 1282–1287. Nowalk, M.P., Prendergast, J.M., Bayles, C.M., D'Amico, F.J., Colvin, G.C., 2001. A randomized trial of exercise programs among older individuals living in two long-term care facilities: the FallsFREE program. J. Am. Geriatr. Soc. 49, 859–865. Ortega, J.D., Farley, C.T., 2007. Individual limb work does not explain the greater metabolic cost of walking in elderly adults. J. Appl. Physiol. 102, 2266–2273. Prince, F., Corriveau, H., Hebert, R., Winter, D.A., 1997. Gait in the elderly. Gait Posture 5, 128–135. Schmitz, A., Silder, A., Heiderscheit, B., Mahoney, J., Thelen, D.G., 2009. Differences in lower-extremity muscular activation during walking between healthy older and young adults. J. Electromyogr. Kinesiol. 19, 1085–1091. Silder, A., Heiderscheit, B., Thelen, D.G., 2008. Active and passive contributions to joint kinetics during walking in older adults. J. Biomech. 41, 1520–1527. Studenski, S., Perera, S., Patel, K., Rosano, C., Faulkner, K., Inzitari, M., et al., 2011. Gait speed and survival in older adults. J. Am. Med. Assoc. 305, 50–58. U.S. Dept. of Health and Human Services, 2011. A Profile of Older Americans. Government Printing Office, Washington. Umberger, B.R., Martin, P.E., 2007. Mechanical power and efficiency of level walking with different stride rates. J. Exp. Biol. 210, 3255–3265. Vaughan, C.L., Davis, B.L., O'Connor, J.C., 1999. Dynamics of Human Gait. Human Kinetics, Champaign, IL. White, S.C., Lifeso, R.M., 2005. Altering asymmetric limb loading after hip arthroplasty using real-time dynamic feedback when walking. Arch. Phys. Med. Rehabil. 86, 1958–1963. Winter, D.A., 1983. Energy generation and absorption at the ankle and knee during fast, natural, and slow cadences. Clin. Orthop. Relat. Res. 147–154. Winter, D.A., Patla, A.E., Frank, J.S., Walt, S.E., 1990. Biomechanical walking pattern changes in the fit and healthy elderly. Phys. Ther. 70, 340–347.
Contents lists available at ScienceDirect
Clinical Biomechanics journal homepage: www.elsevier.com/locate/clinbiomech
Real-time feedback enhances forward propulsion during walking in old adults Jason R. Franz a,b,⁎, Michela Maletis a, Rodger Kram a a b
Department of Integrative Physiology, University of Colorado, Boulder, CO 80309-0354, United States Department of Mechanical Engineering, University of Wisconsin, Madison, WI 53706, United States
a r t i c l e
i n f o
Article history: Received 25 March 2013 Accepted 22 October 2013 Keywords: Elderly Ankle power Biofeedback Rehabilitation Intervention
a b s t r a c t Background: Reduced propulsive function during the push-off phase of walking plays a central role in the deterioration of walking ability with age. We used real-time propulsive feedback to test the hypothesis that old adults have an underutilized propulsive reserve available during walking. Methods: 8 old adults (mean [SD], age: 72.1 [3.9] years) and 11 young adults (age: 21.0 [1.5] years) participated. For our primary aim, old subjects walked: 1) normally, 2) with visual feedback of their peak propulsive ground reaction forces, and 3) with visual feedback of their medial gastrocnemius electromyographic activity during push-off. We asked those subjects to match a target set to 20% and 40% greater propulsive force or push-off muscle activity than normal walking. We tested young subjects walking normally only to provide reference ground reaction force values. Findings: Walking normally, old adults exerted 12.5% smaller peak propulsive forces than young adults (P b 0.01). However, old adults significantly increased their propulsive forces and push-off muscle activities when we provided propulsive feedback. Most notably, force feedback elicited propulsive forces that were equal to or 10.5% greater than those of young adults (+20% target, P = 0.87; +40% target, P = 0.02). With electromyographic feedback, old adults significantly increased their push-off muscle activities but without increasing their propulsive forces. Interpretation: Old adults with propulsive deficits have a considerable and underutilized propulsive reserve available during level walking. Further, real-time propulsive feedback represents a promising therapeutic strategy to improve the forward propulsion of old adults and thus maintain their walking ability and independence. © 2013 Elsevier Ltd. All rights reserved.
1. Introduction The prevalence of walking ability limitations among old adults is profound; 17%, 28% and 47% of people aged 65–74, 75–84, and 85+ years respectively report that walking difficulties interfere with their daily activities (U.S. Dept. of Health and Human Services, 2011). Compromised walking ability ultimately predicts health and survival in old adults (Studenski et al., 2011). Considerable research has demonstrated that a reduction in propulsion during the push-off phase of walking, even in otherwise healthy old adults, plays a central role in the deterioration of walking ability with age (Prince et al., 1997; Winter et al., 1990). Specifically, old adults exert smaller peak propulsive forces (Franz and Kram, 2013a; Prince et al., 1997), perform less trailing leg positive mechanical work (Franz and Kram, 2013a), and generate less ankle power (Cofre et al., 2011; DeVita and Hortobagyi, 2000; Franz and Kram, in press; Kerrigan et al., 1998; Prince et al., 1997; Winter et al., 1990) than young adults walking at the same
⁎ Corresponding author at: 2258 Mechanical Engineering Building, 1513 University Avenue, Madison, WI 53706, United States. E-mail address: [email protected] (J.R. Franz). 0268-0033/$ – see front matter © 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.clinbiomech.2013.10.018
speed. Eventually, the propulsive deficits of old adults may lead to slower walking speeds on the level, inability to walk uphill, and an associated loss of independence in the community. One might presume that sarcopenia and leg muscle weakness are responsible for these changes. Such thinking has understandably led to the widespread prescription of muscle strengthening programs for old adults. While these programs improve muscle strength and mitigate sarcopenia, strengthening alone generally fails to improve the walking ability of old adults (Alfieri et al., 2010; Berg and Lapp, 1998; Buchner et al., 1997; Geirsdottir et al., 2012; Hanson et al., 2009; Holviala et al., 2012; Kalapotharakos et al., 2005; Nelson et al., 2004; Nowalk et al., 2001). Those outcomes suggest that factors other than sarcopenia bring the propulsive deficits of older adults. Consistent with this idea, old adults who exhibit diminished forward propulsion when walking over level ground at a comfortable speed can both voluntarily walk faster (Hernandez et al., 2009; Ortega and Farley, 2007; Schmitz et al., 2009; Silder et al., 2008) and walk uphill (Franz and Kram, 2013a,b, in press). For example, we have shown that compared to level walking, old adults increase their peak propulsive GRFs, trailing leg positive mechanical work, and average ankle power generation during push-off by 69%, 115%, and 44% to walk uphill, respectively (Franz and Kram, 2013a, in press).
J.R. Franz et al. / Clinical Biomechanics 29 (2014) 68–74
Based on their ability to successfully walk uphill, we propose that old adults have a considerable and underutilized propulsive reserve during level walking. We sought to implement an approach to strategically tap into this reserve during level walking and improve the forward propulsion of old adults. Clinicians have long recognized that real-time feedback can facilitate a return to normal function in people following stroke (Binder et al., 1981; Colborne et al., 1993; Intiso et al., 1994), amputation (Isakov, 2007), and total joint replacement (Isakov, 2007; White and Lifeso, 2005). Can real-time feedback of propulsive effort during walking similarly facilitate enhanced forward propulsion in old adults? Answering this question could lead to a better understanding of the mechanism(s) underlying the loss of propulsion with age and the development of more effective rehabilitative and prehabilitative therapies. Some metrics of propulsion (e.g., ankle power generation) require complex calculations (Vaughan et al., 1999) that are impractical to implement using real-time feedback. In contrast, peak propulsive GRFs and ankle extensor (plantarflexor) muscle activities present relatively convenient and representative metrics of forward propulsion (Gottschall and Kram, 2003). In this study, we used real-time visual feedback to test the hypothesis that old adults can increase their peak propulsive GRFs and ankle extensor muscle activities during level walking. We also hypothesized that real-time feedback would elicit peak propulsive GRFs comparable to young adults. Forward propulsion during walking predictably decreases as people take progressively shorter steps at the same speed (Martin and Marsh, 1992) and, for many reasons, old adults typically choose shorter steps (Kerrigan et al., 1998; Winter et al., 1990). Thus, as a secondary aim, we used auditory cues from a metronome to encourage old adults to take slower, longer steps than normal and investigated the effect on increasing their forward propulsion.
2. Methods 2.1. Subjects Our subjects comprised 8 old adults (5 F/3 M, mean [SD], age: 72.1 [3.9] years, mass: 66.4 [11.8] kg, height: 1.71 [0.09] m) and 11 young adults (6 F/5 M, age: 21.0 [1.5] years, mass: 69.2 [6.8] kg, height: 1.75 [0.09] m) who were healthy and exercised regularly. All subjects reported regularly taking part in at least moderate intensity exercise (swimming, biking, and/or running) at least 3 times per week. Subjects completed a health questionnaire based on recommendations of the American College of Sports Medicine (2006) prior to participating. Exclusion criteria were: BMI ≥ 30, sedentary lifestyle, first degree family history of coronary artery disease, cigarette smoking, high blood pressure, high cholesterol, diabetes or prediabetes, orthopedic or neurological condition, or taking medication that causes dizziness. All subjects gave written informed consent as per the University of Colorado Institutional Review Board.
2.2. Instrumentation and visual feedback After preparing the skin with fine sandpaper and alcohol, we placed single differential electrodes with wireless preamplifiers (Noraxon USA Inc., Scottsdale, AZ, USA) over old subjects' right leg soleus and bilaterally over their medial gastrocnemius (MG), and sampled at 2000 Hz. We verified electrode positions and signal quality by visually inspecting the electromyographic (EMG) signals while subjects performed standing calf raises. A force platform (model ZBP-7124-6-4000, Advanced Mechanical Technology, Inc., Watertown, MA, USA) mounted under the right side of a custom dual-belt treadmill recorded the threedimensional ground reaction forces (GRF) for each subject's right leg at 1000 Hz (Franz and Kram, 2013a; Kram et al., 1998). To synchronize these data, we delayed the GRF signals to account for the 300 ms transmission delay inherent to the wireless EMG system. Fig. 1 depicts our experimental setup. A custom script written in Labview (National Instruments Corp., Austin, TX, USA) continuously processed and monitored the GRF and EMG signals. The script lowpass filtered the GRF signals (fourth-order Butterworth, 20 Hz cut-off) and demeaned, band-pass filtered (20–450 Hz), and full-wave rectified the EMG signals. Our feedback algorithm relied on the stance phase timing provided by the force platform mounted under the right treadmill belt. Accordingly, the script extracted the data from each right leg stance phase in real-time, based on a 20 N vertical GRF threshold. At the instant of each toe-off, the real-time algorithm calculated and stored: 1) the propulsive peak of the anterior–posterior (AP) GRF and 2) the mean “push-off” MG activity (i.e., that over the second half of stance) (Fig. 1). We chose MG activity for use in this algorithm based on evidence that the gastrocnemius muscle is considerably involved in forward propulsion (Francis et al., in press; Gottschall and Kram, 2003; Winter, 1983). However, we analyzed EMG data from both the MG and SOL due to their combined contribution to generating ankle power during push-off (Neptune et al., 2009). Finally, a computer monitor positioned in front of the treadmill displayed points each corresponding to a 2-stride average of those measurements (i.e., one dot appeared every two strides, scrolling from right to left) (Fig. 1). During pilot testing, we discovered that to preserve the ordinate scaling on the monitor across conditions, the algorithm needed to consider the magnitude, variability, and target percent increases of each measure. Thus, we normalized the scaling of each subject's feedback data on the monitor by setting the ordinate range from the minimum value during normal walking to the largest target plus half the range observed during normal walking.
2.3. Protocol Subjects first walked normally for 5 min at 1.25 m/s on the dualbelt, force-measuring treadmill to allow their movement patterns to stabilize. We collected GRF (old and young subjects) and EMG data
Real-time data extraction
MG EMG
69
Computer Monitor
Mean Push-off EMG OR Peak Propulsive Force
Dual-Belt, Force-Measuring Treadmill
AP GRF Target (+20% or +40% Normal) Real-time data
Fig. 1. Experimental setup and real-time analysis for visual feedback trials. In different trials, we provided real-time feedback of the peak positive anterior–posterior ground reaction force (AP GRF) and the mean push-off medial gastrocnemius (MG) muscle activity.
J.R. Franz et al. / Clinical Biomechanics 29 (2014) 68–74
(old subjects only) during the final 30 s of this 5 min trial (Normal 1). We tested young subjects walking normally only to provide reference ground reaction force values and thus this concluded the young subjects' session. For old subjects, we immediately used data from the Normal 1 trial to calculate targets for the first pair of visual feedback trials — force or EMG, depending on the randomized trial order. Prior to starting the visual feedback trials, we described the gait cycle to each subject and emphasized the late stance phase during which the ankle extends and the muscles of the leg generate a propulsive force on the ground. We then instructed each subject to more vigorously extend their ankles and push their legs backwards during late stance without excessively vaulting themselves vertically. For each visual feedback trial, we asked subjects to match a target line set to 20% and 40% greater than their normal walking for 2 min (force: F20 and F40 or EMG: EMG20 and EMG40). We selected those targets based on our previous studies in which old adults walked with 21% smaller peak propulsive forces, 21% smaller push-off MG activity, and 26% smaller peak ankle power generation than young adults (Franz and Kram, 2013a,b, in press). To determine whether subjects could maintain a more vigorous push-off after removing visual feedback, we then turned the monitor off and asked subjects to maintain that same exaggerated push-off for 1 min. During pilot testing, we discovered that some people retained a more vigorous push-off for several minutes even after returning to normal walking. To help wash out any lingering effects, subjects sat for 5 min following the first pair of visual feedback trials. Subjects then walked normally on the treadmill for 1 min (Normal 2) from which we calculated targets for the alternate pair of visual feedback trials. In other trials, in order to promote longer steps, we asked subjects to walk for 1 min while matching their steps to the beat of an audio metronome set to step frequencies 10% and 20% slower than normal walking (SF10 and SF20, respectively). We then turned the metronome off and asked subjects to maintain that same step frequency for 1 min.
force and EMG feedback symmetrically by comparing the average increase from normal in right and left MG activity using paired-samples t-tests (mean, 12% asymmetry, P N 0.16). A repeated measures ANOVA tested for significant main effects of condition using a P b 0.05 criterion. When a significant main effect was found, we performed post-hoc pairwise comparisons to determine which conditions differed statistically. Finally, independent samples t-tests compared old and young adult peak propulsive GRFs. 3. Results Walking normally, old adults exerted 12.5% smaller peak propulsive GRFs than young adults (P b 0.01) (Fig. 2A). However, when we provided real-time propulsive feedback, old adults significantly increased their propulsive GRFs and push-off muscle activities. Force feedback elicited propulsive GRFs that were either equal to or 10.5% greater than those of young adults walking normally (F20, P = 0.87; F40, P = 0.02) (Fig. 2A). Those conditions also elicited significant increases in pushoff muscle activity (Fig. 2B). Figs. 3 and 4 show the stance phase GRF and EMG profiles, respectively. Force feedback also elicited greater mechanical power generation to propel the body's CoM forward during the push-off phase of walking and to raise the CoM vertically during single support (Fig. 5). With EMG feedback, old adults significantly increased their push-off muscle activities but without increasing their propulsive GRFs (Fig. 2B).
A
2.4. Data analysis
28
Force Feedback EMG Feedback Stride Frequency Cueing
† ** *
26 ** *
*
24
Young Adults †
22
†
†
†
20 18
0
Normalized Push-off EMG (MG, % Normal Stride)
20
SF
10
SF
Force Feedback EMG Feedback Stride Frequency Cueing *
40
300
G
B
EM 20
G
EM
0
F4
0 F2
al
m
or
N
*
** *
250 *
200
*
150
0 20
10
SF
SF
40 G EM 0 2 G
EM
0 F4
0 F2
al
m or
N
A custom MATLAB (Mathworks, Inc., Natick, MA, USA) script processed all data based on analysis techniques identical to those implemented in the real-time algorithm and summarized earlier in this paper. The script calculated stride-averaged values over the final minute of each normal walking trial, the second minute of each visual feedback trial, and the final 30 s of each auditory cueing trial. Starting with the first stride after we removed visual feedback or auditory cueing, we also calculated stride-by-stride values of peak propulsive force, pushoff MG activity, and stride time during the final minute of the F20/F40, EMG20/EMG40, and SF10/SF20 trials, respectively. We report average values for each stride up to the fewest number of strides taken by a subject during this minute (approximately 40). After the data were collected, we used the individual limbs method (ILM) to calculate the instantaneous rates of mechanical work performed by the legs for each condition from the stride-averaged GRF data (Donelan et al., 2002; Franz and Kram, 2013a). We first calculated the velocity of the body's center of mass (CoM) by integrating the GRF signals with respect to time (Cavagna, 1975). We then calculated mechanical work rates over an average stride as the dot product of the three-dimensional right leg GRF and center of mass velocity. We first evaluated the distribution of all measurements using a Shapiro–Wilk's test. All outcome measures were normally distributed except for mean push-off MG activity during trials with visual EMG feedback (P b 0.01 for EMG20 and EMG 40). A paired-samples t-test confirmed that our primary outcome measures (peak propulsive GRF and mean push-off MG activity) did not differ between Normal 1 and Normal 2 (P = 0.109 and P = 0.083, respectively). Because subjects completed Normal 2 after their first round of visual feedback, we used Normal 1 as our baseline condition for all comparisons to avoid any possible confounding factors. We also confirmed that subjects responded to
30
Peak Propulsive GRF (% BW)
70
Fig. 2. Mean [SE] peak propulsive force (A) and push-off muscle activity (B) for old adults walking with visual force and EMG feedback and auditory stride frequency cueing. Horizontal dashed line in A represents the peak propulsive force of young adults walking normally. Single asterisks (*) indicate significantly different from normal walking. Double asterisks (**) indicate 20% and 40% targets significantly different from 10% and 20% targets, respectively. Crosses (†) indicate significantly different from young adults (P b 0.05).
J.R. Franz et al. / Clinical Biomechanics 29 (2014) 68–74
A AP GRF (%BW)
F40 F20 Normal
20
C
EMG Feedback
Stride Frequency Cueing SF20 SF10 Normal
EMG40 EMG20 Normal
*
*
0 −20
*
*
−40 150
Vertical GRF (%BW)
B
Force Feedback
40
71
* *
*
*
100
50
0 0
0.2
0.4
0.6
0.8
0
0.2
0.4
time (s)
0.6
0.8
0
0.2
time (s)
0.4
0.6
0.8
time (s)
Fig. 3. Mean stance phase ground reaction force (GRF) profiles for old adults walking with visual force and EMG feedback and auditory stride frequency cueing. Asterisks (*) indicate significantly different from normal walking (P b 0.05).
Likely because both muscles are involved in forward propulsion, feedback of medial gastrocnemius activity elicited similar increases in soleus muscle activity during push-off (Fig. 4). Old adults generally increased their propulsive function with force and EMG feedback but they did not quite reach the 20% and 40% targets. On average, old subjects increased their propulsive GRF by 15% and 26% for F20 and F40 and their push-off MG activity by 19% and 30% for EMG20 and EMG40, respectively. Moreover, subjects could maintain this more vigorous push-off for at least 40 strides after we removed force (Fig. 6A) or EMG (Fig. 6B) feedback. Only for F40 did subjects show a significant decrease in stride-by-stride propulsive forces after we removed visual feedback (P = 0.03).
Normalized SOL Activity (% Normal Stride)
Normalized MG Activity (% Normal Stride)
A
*
F40 F20 Normal
500 400
B
Force Feedback
Auditory stride frequency cueing elicited smaller increases in propulsive GRFs and push-off muscle activities in old adults than walking with feedback of those measures directly (Fig. 2). Old subjects walked with 8% (P b 0.01) and 23% (P b 0.01) slower, longer steps than normal for SF10 and SF20, respectively. Similarly, step times were 6% (P = 0.01) and 15% (P b 0.01) slower than normal for F20 and F40, respectively. 4. Discussion Our findings reveal that old adults with propulsive deficits (compared to young adults) actually have a considerable and underutilized
C
EMG Feedback EMG40 EMG20 Normal
Stride Frequency Cueing SF20 SF10 Normal
*
*
300 200 100 0 500
*
400
*
*
300 200 100 0 0
0.2
0.4
time (s)
0.6
0.8
0
0.2
0.4
time (s)
0.6
0.8
0
0.2
0.4
0.6
0.8
time (s)
Fig. 4. Mean stance phase medial gastrocnemius (MG) and soleus (SOL) activity for old adults walking with visual force and EMG feedback and auditory stride frequency cueing. Asterisks (*) indicate significantly greater than normal walking (P b 0.05).
72
J.R. Franz et al. / Clinical Biomechanics 29 (2014) 68–74
Vertical Contribution to Mechanical Power (W/kg)
AP Contribution to Mechanical Power (W/kg)
A
B
Force Feedback
2
F40 F20 Normal
1
C
EMG Feedback
Stride Frequency Cueing
EMG40 EMG20 Normal
*
SF20 SF10 Normal
*
0 −1 *
−2 2
* *
1 0 −1 −2 0
0.2
0.4
0.6
0.8
0
0.2
0.4
time (s)
0.6
0.8
0
0.2
time (s)
0.4
0.6
0.8
time (s)
Fig. 5. Mean stance phase instantaneous mechanical power generation for old adults walking with visual force and EMG feedback and auditory stride frequency cueing. Asterisks (*) indicate significantly greater than normal walking (P b 0.05).
propulsive reserve available during level walking. Further, real-time visual feedback of propulsive effort can effectively call upon this reserve. As hypothesized, the old adults in this study were able to significantly and
AP GRF (% Normal)
A
160
Normalized MG Activity (% Normal) Stride Time (% Normal)
C
F40
F20
140
120
100
B
dramatically increase their peak propulsive GRFs and ankle extensor muscle activities when we provided real-time force and EMG feedback compared to normal walking. Moreover, and also as hypothesized, real-time
y = 0.14 x + 115.45 *
160
y = -0.24 x + 129.91 *
EMG40
EMG20
140
120
100
y = 0.12 x + 111.99
160
y = -0.13 x + 121.10
SF10
SF20
140
120
100
y = -0.02 x + 109.50 0
10
20
stride (#)
30
40
y = -0.02 x + 122.75 0
10
20
30
40
stride (#)
Fig. 6. Post-feedback retention. Mean stride-by-stride values of peak propulsive force, push-off medial gastrocnemius activity, and stride time during the minute after we removed visual feedback or auditory cueing of those measures. Horizontal dashed line indicates target percent increase. Asterisks (*) indicate a linear regression slope significantly different from zero (P b 0.05).
J.R. Franz et al. / Clinical Biomechanics 29 (2014) 68–74
force feedback in old adults elicited peak propulsive GRFs that were equal to and even greater than those of young adults. Consistent with the speculation of some earlier studies (DeVita and Hortobagyi, 2000; Schmitz et al., 2009), we have systematically shown that old adults with propulsive deficits are not explicitly limited in their capacity to increase forward propulsion during level walking. To the contrary, our data imply that old adults have a reserve of at least 26% for exerting greater propulsive GRFs and 49% for increasing ankle extensor muscle recruitment. Remarkably, healthy and active old adults can walk with as vigorous a push-off as young adults, but do not naturally do so. Why don't old adults better utilize their propulsive capacity during level walking? One possibility is that the disproportionate recruitment of hip vs. ankle muscles commonly reported in old adults (Cofre et al., 2011; DeVita and Hortobagyi, 2000; Franz and Kram, 2013b; Schmitz et al., 2009) inherently reduces propulsion during push-off. For example, we previously found that to help initiate leg swing, old adults produce 63% larger peak hip flexor moments than young adults during push-off (Franz and Kram, in press). Larger peak hip flexor moments in old adults may act to reduce the force they exert on the ground during push-off and thus their ankle power generation and forward propulsion. We have also proposed that changes in neural control with age may lead to this preferential recruitment of proximal vs. distal muscles (Franz and Kram, 2013b). Although we did not measure hip muscle activity or estimate joint kinetics in this study, real-time feedback of propulsive effort may supplant the distal to proximal redistribution of muscle recruitment, thereby enabling old adults to increase their forward propulsion. Another possibility is that a trade-off exists in which increasing forward propulsion during push-off compromises other features of gait in old adults (Huang and Ahmed, 2011). For example, old adults with trailing leg propulsive deficits consequently exhibit more conserved (i.e., “smoother”) patterns of CoM velocity fluctuations than young adults (Franz and Kram, 2013a). If this smoother pattern of CoM velocity change promotes improved balance, old adults may prioritize it over a more vigorous push-off and perhaps even despite an increase in metabolic cost (Franz and Kram, 2013a). We tested three different approaches to increase propulsion in old adults: force feedback, EMG feedback, and auditory stride frequency cueing. Real-time force and EMG feedback more effectively elicited a more vigorous push-off than using a metronome to encourage old adults to take slower, longer steps. We doubt that the specific sensory modality (visual vs. auditory) used is responsible for these differences. Rather, we suspect that propulsive force and/or EMG feedback are more effective than stride frequency cueing because they are more specific. Which is more effective at improving the forward propulsion of old adults, force or EMG feedback? First, note that the target 20% and 40% increases for propulsive force do not correspond to equal increases in push-off EMG. Indeed, based on our findings and subjects' verbal comments, force feedback required a considerably more vigorous push-off than the same target percent increase in medial gastrocnemius activity using EMG feedback. Not surprisingly, asking subjects to push-off more vigorously by increasing MG activity also elicited similar increases in SOL activity. However, while EMG feedback did elicit significantly greater push-off muscle activities in old adults, not even the 40% target increase in EMG elicited a significant increase in peak propulsive forces or mechanical power generation. Thus, we infer that real-time propulsive force feedback more directly and effectively encourages old adults to utilize their propulsive reserve. To our knowledge, this study represents the first attempt to use realtime feedback to encourage old adults to increase their forward propulsion during walking. Future studies are needed to determine if longer term propulsive feedback training has beneficial results. We showed here that old adults can maintain a more vigorous push-off for at least 40 strides after we removed visual feedback. For how long after propulsive feedback training do old adults retain this effect, and does the effect transfer to overground walking? Generally, people tend to adopt a
73
combination of step length, width, and frequency at a given walking speed that minimizes metabolic cost (Donelan et al., 2001; Umberger and Martin, 2007). Our old adult subjects may have consumed oxygen at a faster rate while walking in the novel way elicited by propulsive feedback compared to walking normally. Alternatively, the characteristic propulsive mechanics of old adults may explain why they consume ~20% more metabolic energy than young adults walking normally. Thus, it is possible that propulsive feedback training could both improve forward propulsion in old adults and reduce their metabolic cost. Finally, sarcopenia and leg muscle weakness may more explicitly limit propulsive GRFs and ankle power generation in sedentary and/or frail old adults. While resistance training in old adults can improve their muscle strength and mitigate sarcopenia, strengthening alone generally fails to improve their walking ability (Alfieri et al., 2010; Berg and Lapp, 1998; Buchner et al., 1997; Geirsdottir et al., 2012; Hanson et al., 2009; Holviala et al., 2012; Kalapotharakos et al., 2005; Nelson et al., 2004; Nowalk et al., 2001). Thus, frail and/or sedentary old adults may benefit most from leg muscle strengthening to increase their propulsive capacity combined with propulsive feedback training to encourage neural utilization of that capacity. There are several limitations of this study. First, because of the preliminary nature of this study, subjects only completed a single session and walked with propulsive feedback for 2 min. The old subjects in this study were also active and fit. Although we did not measure leg muscle strength, it is notable that even these very active subjects exhibited propulsive deficits during walking compared to young adults. However, we cannot conclude that more sedentary and/or frail old adults have a propulsive reserve available during level walking. Moreover, the etiology and functional consequences of the biomechanical and physiological changes brought by advancing age vary considerably among old adults. Thus, to apply more generally, future research must consider the extent to which visual, vestibular, muscular, and/or neural changes in old adults influence their response to propulsive feedback. Because only the right belt of our dual-belt treadmill records GRFs, we based our real-time feedback algorithm on measurements taking from the right leg only. Subjects were not made aware of this limitation and we placed EMG electrodes over subjects' right and left legs. Although not statistically significant, subjects increased their right leg MG activity on average ~12% more than their left leg MG activity in response to propulsive force and EMG feedback. 5. Conclusions In summary, we have shown that apparently healthy old adults with subclinical propulsive deficits have a considerable and underutilized propulsive reserve available during level walking and that real-time propulsive feedback can effectively tap into this reserve. Eventually, the propulsive deficits of old adults may restrict their walking ability and thus their independence in the community. Real-time propulsive feedback represents a promising strategy to maintain and/or improve the forward propulsion of old adults and thus maintain their walking ability and independence. Acknowledgments We thank Dr. Alena Grabowski from the Department of Veterans Affairs Eastern Colorado Healthcare System for use of the electromyography equipment. This work was supported by a Dissertation Completion Fellowship from the University of Colorado Graduate School. References Alfieri, F.M., Riberto, M., Gatz, L.S., Ribeiro, C.P., Lopes, J.A., Battistella, L.R., 2010. Functional mobility and balance in community-dwelling elderly submitted to multisensory versus strength exercises. Clin. Interv. Aging 5, 181–185.
74
J.R. Franz et al. / Clinical Biomechanics 29 (2014) 68–74
American College of Sports Medicine, 2006. Participation, Health Screening and Risk Stratification, Guidelines for Exercise Testing and Prescription. Lippincott Williams & Wilkins, Philadelphia 19–35. Berg, W.P., Lapp, B.A., 1998. The effect of a practical resistance training intervention on mobility in independent, community-dwelling older adults. J. Aging Phys. Act. 6. Binder, S.A., Moll, C.B., Wolf, S.L., 1981. Evaluation of electromyographic biofeedback as an adjunct to therapeutic exercise in treating the lower extremities of hemiplegic patients. Phys. Ther. 61, 886–893. Buchner, D.M., Cress, M.E., de Lateur, B.J., Esselman, P.C., Margherita, A.J., Price, R., et al., 1997. The effect of strength and endurance training on gait, balance, fall risk, and health services use in community-living older adults. J. Gerontol. A Biol. Sci. Med. Sci. 52, M218–M224. Cavagna, G.A., 1975. Force platforms as ergometers. J. Appl. Physiol. 39, 174–179. Cofre, L.E., Lythgo, N., Morgan, D., Galea, M.P., 2011. Aging modifies joint power and work when gait speeds are matched. Gait Posture 33, 484–489. Colborne, G.R., Olney, S.J., Griffin, M.P., 1993. Feedback of ankle joint angle and soleus electromyography in the rehabilitation of hemiplegic gait. Arch. Phys. Med. Rehabil. 74, 1100–1106. DeVita, P., Hortobagyi, T., 2000. Age causes a redistribution of joint torques and powers during gait. J. Appl. Physiol. 88, 1804–1811. Donelan, J.M., Kram, R., Kuo, A.D., 2001. Mechanical and metabolic determinants of the preferred step width in human walking. Proc. Biol. Sci. 268, 1985–1992. Donelan, J.M., Kram, R., Kuo, A.D., 2002. Simultaneous positive and negative external mechanical work in human walking. J. Biomech. 35, 117–124. Francis, C.A., Lens, A.L., Lenhart, R.L., Thelen, D.G., 2013. The modulation of forward propulsion, vertical support, and center of pressure by the plantarflexors during human walking. Gait Posture 38 (4), 993–997. Franz, J.R., Kram, R., 2013a. Advanced age affects the individual leg mechanics of level, uphill, and downhill walking. J. Biomech. 46, 535–540. Franz, J.R., Kram, R., 2013b. How does age affect leg muscle activity/coactivity during uphill and downhill walking? Gait Posture 37, 378–384. Franz, J.R., Kram, R., 2013. Advanced age and the mechanics of uphill walking: a joint-level, inverse dynamic analysis. Gait Posture (in press). Geirsdottir, O.G., Arnarson, A., Briem, K., Ramel, A., Tomasson, K., Jonsson, P.V., et al., 2012. Physical function predicts improvement in quality of life in elderly Icelanders after 12 weeks of resistance exercise. J. Nutr. Health Aging 16, 62–66. Gottschall, J.S., Kram, R., 2003. Energy cost and muscular activity required for propulsion during walking. J. Appl. Physiol. 94, 1766–1772. Hanson, E.D., Srivatsan, S.R., Agrawal, S., Menon, K.S., Delmonico, M.J., Wang, M.Q., et al., 2009. Effects of strength training on physical function: influence of power, strength, and body composition. J. Strength Cond. Res. 23, 2627–2637. Hernandez, A., Silder, A., Heiderscheit, B.C., Thelen, D.G., 2009. Effect of age on center of mass motion during human walking. Gait Posture 30, 217–222. Holviala, J., Kraemer, W.J., Sillanpaa, E., Karppinen, H., Avela, J., Kauhanen, A., et al., 2012. Effects of strength, endurance and combined training on muscle strength, walking speed and dynamic balance in aging men. Eur. J. Appl. Physiol. 112, 1335–1347.
Huang, H.J., Ahmed, A.A., 2011. Tradeoff between stability and maneuverability during whole-body movements. PLoS One 6, e21815. Intiso, D., Santilli, V., Grasso, M.G., Rossi, R., Caruso, I., 1994. Rehabilitation of walking with electromyographic biofeedback in foot-drop after stroke. Stroke 25, 1189–1192. Isakov, E., 2007. Gait rehabilitation: a new biofeedback device for monitoring and enhancing weight-bearing over the affected lower limb. Eura Medicophys. 43, 21–26. Kalapotharakos, V.I., Michalopoulos, M., Tokmakidis, S.P., Godolias, G., Gourgoulis, V., 2005. Effects of a heavy and a moderate resistance training on functional performance in older adults. J. Strength Cond. Res. 19, 652–657. Kerrigan, D.C., Todd, M.K., Della Croce, U., Lipsitz, L.A., Collins, J.J., 1998. Biomechanical gait alterations independent of speed in the healthy elderly: evidence for specific limiting impairments. Arch. Phys. Med. Rehabil. 79, 317–322. Kram, R., Griffin, T.M., Donelan, J.M., Chang, Y.H., 1998. Force treadmill for measuring vertical and horizontal ground reaction forces. J. Appl. Physiol. 85, 764–769. Martin, P.E., Marsh, A.P., 1992. Step length and frequency effects on ground reaction forces during walking. J. Biomech. 25, 1237–1239. Nelson, M.E., Layne, J.E., Bernstein, M.J., Nuernberger, A., Castaneda, C., Kaliton, D., et al., 2004. The effects of multidimensional home-based exercise on functional performance in elderly people. J. Gerontol. A Biol. Sci. Med. Sci. 59, 154–160. Neptune, R.R., Clark, D.J., Kautz, S.A., 2009. Modular control of human walking: a simulation study. J. Biomech. 42, 1282–1287. Nowalk, M.P., Prendergast, J.M., Bayles, C.M., D'Amico, F.J., Colvin, G.C., 2001. A randomized trial of exercise programs among older individuals living in two long-term care facilities: the FallsFREE program. J. Am. Geriatr. Soc. 49, 859–865. Ortega, J.D., Farley, C.T., 2007. Individual limb work does not explain the greater metabolic cost of walking in elderly adults. J. Appl. Physiol. 102, 2266–2273. Prince, F., Corriveau, H., Hebert, R., Winter, D.A., 1997. Gait in the elderly. Gait Posture 5, 128–135. Schmitz, A., Silder, A., Heiderscheit, B., Mahoney, J., Thelen, D.G., 2009. Differences in lower-extremity muscular activation during walking between healthy older and young adults. J. Electromyogr. Kinesiol. 19, 1085–1091. Silder, A., Heiderscheit, B., Thelen, D.G., 2008. Active and passive contributions to joint kinetics during walking in older adults. J. Biomech. 41, 1520–1527. Studenski, S., Perera, S., Patel, K., Rosano, C., Faulkner, K., Inzitari, M., et al., 2011. Gait speed and survival in older adults. J. Am. Med. Assoc. 305, 50–58. U.S. Dept. of Health and Human Services, 2011. A Profile of Older Americans. Government Printing Office, Washington. Umberger, B.R., Martin, P.E., 2007. Mechanical power and efficiency of level walking with different stride rates. J. Exp. Biol. 210, 3255–3265. Vaughan, C.L., Davis, B.L., O'Connor, J.C., 1999. Dynamics of Human Gait. Human Kinetics, Champaign, IL. White, S.C., Lifeso, R.M., 2005. Altering asymmetric limb loading after hip arthroplasty using real-time dynamic feedback when walking. Arch. Phys. Med. Rehabil. 86, 1958–1963. Winter, D.A., 1983. Energy generation and absorption at the ankle and knee during fast, natural, and slow cadences. Clin. Orthop. Relat. Res. 147–154. Winter, D.A., Patla, A.E., Frank, J.S., Walt, S.E., 1990. Biomechanical walking pattern changes in the fit and healthy elderly. Phys. Ther. 70, 340–347.