University of Groningen Effects of lower extremity power training on gait biomechanics in old adults Beijersbergen, Chantal

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University of Groningen

Effects of lower extremity power training on gait biomechanics in old adults Beijersbergen, Chantal

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Beijersbergen, C. (2017). Effects of lower extremity power training on gait biomechanics in old adults: The Potsdam Gait Study (POGS). Rijksuniversiteit Groningen.

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Chapter 3

Effects of power training on mobility

and gaitbiomechanics in old adults

with moderate mobility disability:

protocol and design of the

Potsdam Gait Study (POGS)

Chantal Beijersbergen

Tibor Hortobágyi

Rainer Beurskens

Romana Lenzen-Großimlinghaus

Martijn Gäbler

Urs Granacher

Gerontology. 2016;62(6):597-603.

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Abstract

Background: Walking speed decreases in old age. Even though old adults regularly participate in exercise interventions, we do not know how the intervention-induced changes in physical abilities produce faster walking. The Potsdam Gait Study (POGS) will examine the effects of 10 weeks of power training and detraining on leg muscle power and, for the first time, on complete gait biomechanics, including joint kinematics, kinetics, and muscle activation in old adults with moderate mobility disability. Methods/ Design: POGS is a randomized controlled trial with two arms, each crossed over, without blinding. Arm 1 starts with a 10-week control period to assess the reliability of the tests and is then crossed over to complete 25–30 training sessions over 10 weeks. Arm 2 completes 25–30 exercise sessions over 10 weeks, followed by a 10-week follow-up (detraining) period. The exercise program is designed to improve lower extremity muscle power. Main outcome measures are: muscle power, gait speed, and gait biomechanics measured at baseline and after 10 weeks of training and 10 weeks of detraining. Discussion: It is expected that power training will increase leg muscle power measured by the weight lifted and by dynamometry, and these increased abilities become expressed in joint powers measured during gait. Such favorably modified powers will underlie the increase in step length, leading ultimately to a faster walking speed. POGS will increase our basic understanding of the biomechanical mechanisms of how power training improves gait speed in old adults with moderate levels of mobility disabilities.

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Chapter 3

3.1. INTRODUCTION

Habitual walking speed gradually decreases in old age [1]. Walking speed is a comprehensive indicator of the age-related changes in mobility, and age-referenced slow gait is a predictor of numerous adverse health conditions (for reviews, see ref. [2,3]). Recognizing its functional significance, some authors consider walking speed as the sixth vital sign [4]. Maintaining mobility, independence, and delaying the onset of mobility disability are universal health care priorities [5]. Unsurprisingly, large, randomized clinical trials are underway to identify the optimal exercise dose in an effort to slow the age-related walking speed loss [5–7]. Regardless of intervention type, there are only a limited number of studies that examined the biomechanical mechanisms of how exercise-increased physical abilities allow old adults to walk faster [8,9].

Age-related mechanical plasticity of gait reflects a redistribution of lower extremity joint power output. Specifically, there is a distal to proximal shift in muscle function resulting in reduced ankle and increased hip power output in old compared with young adults walking at the same speed [10]. It is suggested that this redistribution of joint power is a result of age-related changes in the physiological and biomechanical properties of the lower extremity muscles in general and of the plantar flexors in particular [11,12]. A variety of exercise interventions are effective in maintaining and even increasing these physiological and biomechanical properties, resulting in increased muscle strength and power [13,14], together with increases in gait velocity and related stride characteristics [15,16]. Even though high-velocity strength or power training is particularly effective in improving muscle power and physical performance, including walking speed [17,18], somewhat unexpectedly, there is only a poor correlation between intervention-induced leg power and increase in walking speed [8]. These data thus suggest that the mechanisms of how newly acquired physical abilities (i.e., improved leg power) become incorporated into the gait pattern and produce faster walking is unknown. That is, there is a need to determine the biomechanical mechanisms mediating the increases in gait speed produced by exercise interventions in old age.

Based on previously described concepts [8,18], the Potsdam Gait Study (POGS) is an altogether new approach to traditional intervention studies, reviewed previously [13,15–17]. Instead of examining the intervention effects on mobility behavior only, POGS examines a set of potential factors that can underlie the changes in mobility behavior. POGS focuses on gait velocity as a summary expression of mobility behavior. The chief causative factors include variables determined through comprehensive biomechanical gait analyses. A critical aspect of POGS is the ability to determine, for the first time, the associations between changes in mobility behavior and changes in underlying factors that can cause such gait modifications. After the intervention, we will be able to predict changes in gait speed from powers measured individually at the ankle, knee, and hip joint during gait. Such a prediction will allow us to characterize the hierarchical reorganization of the mechanical output caused by an intervention

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and pinpoint the mechanisms of adaptation in gait speed. POGS will also allow us to determine if, after withdrawing the exercise stimulus, changes in mobility occur in a temporally synchronized and causative manner with the changes in joint powers.

Taken together, POGS will examine the effects of 10 weeks of power training and detraining on leg muscle power and gait biomechanics, including joint kinematics, kinetics, and muscle activation, in old adults with moderate mobility disability. We hypothesize that power training will increase leg muscle power measured by the training weight lifted and by dynamometry, and these increased abilities will become expressed in joint powers measured during gait, causing changes in mobility. We expect that such favorable modifications in joint kinetics underlie the increases in step length, leading ultimately to a faster post-intervention walking speed. The POGS protocol is intended to provide a model for future intervention studies in an effort to shift away from pure behavioral examination of the intervention effects to a more mechanistic approach [19]. The specific study results will increase basic scientists’ and clinicians’ understanding on how an improved physical ability, i.e., leg power, contributes to increased gross motor function in the form of gait speed. This can help to further improve the effectiveness of exercise therapies designed to minimize old adults’ mobility disability, a universal public health goal.

3.2 METHODS/DESIGN 3.2.1. Study design

POGS is a randomized controlled trial with two arms, each crossed over, without blinding (Fig. 3.1), following the CONSORT guidelines. Participants will be randomized (www. randomizer. org) into two arms, stratified by gender, and based on the order in which they sign up for the initial baseline testing. Participants in arm 1 will start with a 10-week control period to determine the stability and reliability of the measures. The same participants will cross over to a 10-week power training program. Arm 2 will start with 10 weeks of power training, followed by a 10-week detraining phase. The study design, procedures, and informed consent were approved by the Ethics Committee of the University of Potsdam (reference number 40/2014), Potsdam, Germany, and will be conducted according to the ethical standards of the Helsinki Declaration. POGS has been registered with the Dutch Trial Register (Trial ID: NTR5151) on April 17, 2015.

3.2.2. Recruitment and screening process

Participants included in this study will be male and female old adults aged ≥ 65 years, with moderate levels of mobility disability. Participants are recruited from the local Potsdam (Germany) area using word of mouth and newspaper, radio, TV, and Internet advertisements and with assistance from a local geriatric clinic. Participant candidates can contact a research technician for information and sign up for an initial screening. The initial screening involves a standardized telephone interview concerning general health,

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Chapter 3

medical, and mobility status. Exclusion criteria are: inability to walk 10 m independently; knee or hip joint replacements less than 6 months before enrollment; uncontrolled cardiovascular disease or angina; neuromuscular disease; diagnosed Parkinson’s disease; multiple sclerosis; stroke; cancer therapy less than 3 months before enrollment; severe asthma or chronic bronchitis; diagnosed diabetes with neuropathy, poor and uncorrected vision, or current participation in any other type of exercise program. Participant candidates will be invited to the Biomechanics Laboratory at the University of Potsdam for further screening after they have passed the telephone interview.

The Mini-Mental State Examination (MMSE) [20] will determine the cognitive state, with a score of ≥ 27 needed for inclusion. The Short Physical Performance Battery (SPPB) will define the mobility disability status [21], and a score between 4 and 10 will pass inclusion. After inclusion, participants will sign an informed consent document and fill in the Freiburg Questionnaire of Physical Activity (FQoPA) to assess health-related physical activity at baseline [22]. The FQoPA has high test-retest reliability after 14 days and 6 months [22]. Participants are then invited for the 2-hour testing battery that will be repeated after 10 and 20 weeks. Participants will wear tight-fitting shorts and T-shirt, together with their own athletic shoes during testing. An octopolar tactile-electrode impedance meter (InBody 720; BioSpace, Seoul, Korea) is used to assess total body mass (kg), fat mass (kg), and skeletal muscle mass (kg). Body height (m) is measured and the

Figure 3.1. Design of the POGS.

Telephone interview and screening Randomization (n = 34)

10-week power training program 10-week control

period

10-week detraining period 10-week

power training program

Allocated to arm 1 (n = 17) Allocated to arm 2 (n = 17)

Baseline testing Baseline testing

Posttraining testing Detraining testing (n = 17) Control testing Posttraining testing (n = 17)

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body mass index (kg/m2_{) calculated.}

3.2.3. Lower extremity torque and power testing

Torque and power of the ankle and knee muscles will be measured using a dynamometer (Isomed 2000 ®; Hemau, Germany). We will test the right leg in the position recommended by the manufacturer. Warm-up consists of 10 submaximal isokinetic contractions. Ankle testing in the neutral joint position starts with three, 3- to 5-second-long maximal isometric plantar- and dorsiflexions separated by 30 s of rest. Isokinetic testing is performed at 20, 40, and 60°/s between 10° of dorsiflexion and 40° of plantarflexion. Participants are instructed to perform five consecutive maximal contractions for each velocity and will be thoroughly instructed to act as forcefully and as fast as possible without exhaling forcefully. Mechanical power is calculated as the product of peak torque and angular velocity. For isometric contractions, the knee joint is fixed at 45° of knee flexion. For isokinetic testing, subjects extend and flex the knee joint at 60, 120, and 180°/s between 80 and 20° of knee flexion. The order of the ankle and knee tests is systematically rotated between participants and is similar during testing at weeks 10 and 20.

3.2.4. Six-minute walk test

Subjects perform the 6-min walk test (6MWT) in a gymnasium with a parquet floor along the perimeter of a 12 × 9-meter area demarked with four cones. Instructions will be: ‘Walk at your normal pace for 6 min on the outside of the four cones. The test will determine the distance you cover in 6 min.’ Participants perform one trial. A technician times the 6 min with a stopwatch, measures the distance covered in meters, and computes the average gait velocity in m/s. The 6MWT is a valid and reliable test to quantify lower extremity functional limitations, and a change of 20 m is clinically meaningful [23]. The Optogait system (Microgate) will record about 300 steps for each participant during the 6MWT, providing data for the computation of stride length, stride time, cadence, and the corresponding coefficients of variation (SD/mean × 100). The Optogait system is a valid instrument for the assessment of spatiotemporal gait parameters (intraclass correlation coefficient ≥ 0.9) [24].

3.2.5. Stair climb power test

The stair climb power test measures functional power [25] and will be performed on a 12-stair flight (each stair height 16.5 cm). The instructions are: ‘The purpose of the test is to measure your ability to ascend/descend 12 steps of stairs as safely and fast as you can. The outcome of the test is the time in seconds you climb the stairs without any assistance. You may walk near the hand railing but, if possible, complete the climb without holding on to the hand rail.’ A technician will measure the time subjects ascend and descend separately to the nearest 0.01 s. Timing begins after the countdown

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‘ready-set-go’ on the word ‘go’ and stops when both of the participants’ feet reach the top step (ascent) or ground level (descent). Subjects perform two trials separated by 1 min of rest, and the faster of the two ascending or descending trials is used in the analysis. Subsequently, stair climb power is calculated as: force × velocity [(body mass × gravitational acceleration) × (vertical stair height/time)].The test-retest reliability of the stair test is excellent (R = 0.99) [26].

3.2.6. Biomechanical gait analysis

Subjects walk over an AMTI force platform (Watertown, Mass., USA) embedded in the center of a 6.5 × 1.5-meter level walkway to measure ground reaction forces and moments of force at 1 kHz and a gain of 4,000. A total of 32 reflective markers will be placed on the pelvic and bilateral lower extremity body segments (foot, shank, and thigh), and 3D kinematics are captured at 100 Hz using a 9 infrared camera system (Vicon, Denver, Colo., USA). Kinematic data are filtered with a fourth-order low-pass Butterworth filter at 6 Hz, and ground reaction forces are filtered with a second-order low-pass Butterworth filter at 45 Hz. A 3D inverse-dynamics gait analysis will be performed using Visual 3D (C-Motion Inc., Rockville, Md., USA) to compute 3D joint angular positions and velocities, and joint torques and powers at the hip, knee, and ankle of the right leg during one stride [10,27]. Kinematic and kinetic data are calculated using custom Matlab scripts (version 2015b; The Mathworks, Natick, Mass., USA). Participants will walk under three conditions: (1) a habitual pace by instructing the participants as follows: ‘Walk at a habitual pace as if you were walking down the street to go to the store’; (2) a safely fast walking speed: ‘Walk safely as fast as you can but do not run’, and (3) a fixed speed of 1.25 ± 0.6 m/s. An optoelectronic system (MicroGait, Bolzano, Italy) is used to directly measure gait speed, and verbal feedback about the speed is provided. Participants will practice on the walkway to ensure that they step on the force platform with the right foot without altering their walking pattern. Five successful trials will be collected per condition (a total of 15 trials per participant). All gait variables are statistically analyzed based on the average of the five gait trials per condition for each participant.

3.2.7. Muscle activity

Surface electromyography (EMG) electrodes (Noraxon USA, Inc., Scottsdale, Ariz., USA) will be placed on the vastus lateralis, biceps femoris, tibialis anterior, gastrocnemius medialis, and soleus of the right leg to record muscle activity captured at 1 kHz during both the dynamometry tests and gait analysis. The root mean square (RMS) of the EMG data will be obtained using a 20-ms smoothing window. Mean and peak RMS EMG activity during stance phase will be determined and expressed as a percent of maximal RMS EMG activity determined during the isometric trials. Coactivity at the knee and ankle joint will be computed as the quotient of the agonist and antagonist RMS EMG

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activity. Similar to the gait variables, EMG variables will be statistically analyzed based on the average of the five gait trials per condition.

3.2.8. Interventions

The power training program consists of 30 sessions administered over 10 weeks. A minimum adherence rate of 80% will be used for a participant’s data to be included in the analysis. Each session starts with 3–5 min of warm-up, consisting of a menu (riding a stationary bike for 3–5 min, stretching exercises, floor exercises). Subjects will perform the following main exercises using Cybex Eagle NC line weight lifting equipment (Cybex International, Inc., Medway, Mass., USA): (1) knee extension, (2) knee flexion, (3) supine leg press, and (4) ankle press. The ankle press exercises are performed on a supine leg press with the hips and knees extended.

Participants will be tested for their three repetition maximum (3-RM) at an accuracy of 0.5 kg during the first training session. The 3-RM is the maximum load participants are able to lift three times throughout the full range of motion while maintaining the correct technique. The 3-RM test is repeated biweekly to control training intensity and measure exercise progression. As recommended for power training by the ACSM [28], participants will perform three sets of 6–10 repetitions at 40–60% of the most recent 3-RM with the intention to move the weights rapidly and explosively during the concentric phase. As recommended previously, we will include about 10 s of rest between repetitions [29]. Participants exercise using bilateral movements and under the supervision of two persons.

Participants are explicitly instructed to refrain from involvement in any other type of exercise program and to maintain their level of physical activity as it was before enrollment to the study for both the 10-week control and detraining periods.

3.2.9. Outcome measures

The primary outcome measures are gait speed at habitual and maximal pace measured during the biomechanical gait analysis. Secondary outcomes are ankle and knee power measured during dynamometry testing, selected joint kinematics, kinetics, and EMG variables during waking at habitual, maximal, and fixed speeds, and functional performance measured with the 6MWT and stair climb power test.

3.2.10. Statistical analysis and sample size

We performed an a priori power analysis to determine the sample size necessary to attain statistically significant exercise effects on gait speed. A recent meta-analysis assessing the effects of exercise on gait speed in old adults showed average improvements of 0.1 m/s (± 0.12) with large effect sizes (0.84) [16]. We were unable to calculate effect sizes for the various kinetic variables selected for this study, since there are no such data available. To avoid underestimation of the sample size, we decided to use a small effect size of 0.2

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for the power analysis. To achieve 80% power (type II error of 0.20) with an effect size of 0.2, a type I error of 5% and considering a dropout rate of 20%, 17 participants per arm will be needed. A 2 × 3 repeated measures analysis of variance (ANOVA) will be used to determine main effects of group (arm 1 vs. arm 2) and time (baseline, at 10 weeks, and at 20 weeks), and group × time interactions. Post hoc tests include Bonferroni-adjusted α and will be used for statistically significant group and time differences. Effect sizes are determined by calculating Cohen’s d, a measure that defines whether a difference is of practical concern. Cohen’s d values are classified based on a model for strength training research and as follows: 0.00 ≤ d ≤ 0.34 indicates trivial, 0.35 ≤ d ≤ 0.79 indicates small, 0.80 ≤ d ≤ 0.1.49 indicates moderate, and d ≥ 1.5 indicates large effects [30]. All data will be analyzed using SPSS 22.0 (SPSS Inc., Chicago, Ill., USA). An α level of 0.05 is set a priori.

3.3. DISCUSSION

Walking is an essential activity of daily living, enabling individuals to change location freely. Starting at age 60, there are unfavorable changes in neuromuscular and skeletal functions that combine into a characteristic and clearly recognizable slowing of habitual walking speed by as much as 16% per decade [3,31,32]. Habitual walking speed is important for independent functioning in daily life [33], and a decrease in walking speed as small as 0.1 m/s has been linked to difficulties in performing instrumented activities of daily living [34]. The growing number of old adults worldwide together with an increase in life expectancy due to improved medical care call for the implementation of interventions that can reduce or delay the onset of functional and mobility loss and ultimately help contain health care costs.

Exercise, when embedded as a systematic form of physical activity into a weekly routine, can increase old adults’ habitual gait speed by up to 53% [15,18]. A recent meta-analysis categorized 42 randomized clinical trials into three exercise intervention types, as a preventative measure, and compared their efficacy with respect to increasing healthy old adults’ gait speed [16]. The analysis revealed that resistance (0.09 m/s), coordination (0.08 m/s), and multimodal (0.05 m/s) exercise training can all increase gait speed functionally and statistically significantly. The improvements in fast gait speed were remarkably consistent and intervention independent (0.12 m/s), confirming the idea that systematic exercise is an effective method for maintaining healthy old adults’ gait speed. The common element these studies share is the examination of mobility behavioral outcomes only, without considering the underlying biomechanical factors causing the improvements in gait speed. We discuss how POGS will enrich the existing but incomplete and inconsistent data concerning the mechanisms of gait adaptations in response to a specific weight-lifting exercise program.

Persch et al. [35] examined the effects of a 12-week strength training program on the measures of muscle strength, stride characteristics, and joint kinematics in healthy

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old adults. Hip, knee, and ankle strength increased, stride characteristics improved, and participants showed more hip, knee, and ankle flexion during heel strike. There were low associations between different stride characteristics and strength gains at the hip (r 2 = 0.17, P < 0.05), knee (r 2 = 0.44, P < 0.05), and ankle (r 2 = 0.30, P < 0.01). In addition, the strength gains and changes in gait speed did not correlate. These findings suggest that knee extensor strength is the most important predictor of improvements in stride length and cadence. Although the results indirectly suggest that increase in muscle strength enabled participants to walk with improved joint kinematics and kinetics, no biomechanical adaptations during walking were directly measured.

McGibbon et al. [36] examined the effects of a 6-week strength or functional training program on muscle strength and gait biomechanics in old adults with osteoarthritis and at least one functional limitation. This study is the closest to POGS, revealing some insights into the biomechanical mechanisms of gait adaptation in response to exercise training. The functional strength group improved gait speed by increasing work generated by hip muscles. In contrast, the functional group improved gait function through the generation of more mechanical work by the ankle and knee extensor muscles. Of note, although both interventions were effective in increasing gait speed [35,36], the underlying mechanisms that enabled the participants to walk faster most likely differed between groups, a proposition also addressed by a recent review [16]. The intervention-independent effects further emphasize the need to investigate the mechanisms of gait adaptations caused by exercise interventions so that exercise prescriptions can be more effectively individualized.

In contrast to the biomechanical analyses [35,36], other studies tried to identify the mechanisms of intervention- induced gait adaptations by determining the association between gains in muscle strength and gait speed [14,19,35,37]. For example, Uematsu et al. [19] showed that hip extensors and ankle plantarflexors became the only significant predictors of healthy old adults’ habitual and maximal gait speed following lower extremity power training, providing behavioral but not biomechanically mechanistic evidence for the observed gait adaptations. Indeed, such observations may be the exceptions rather than the rule, because increases in muscle strength and power, when computed across studies, tend to correlate poorly with increases in step length and gait velocity in old adults [8].

We can only speculate concerning the neural mechanisms underlying training-induced changes in joint kinetics because, to the best of our knowledge, there are no studies that reported on the changes in muscle activation during gait as measured by surface EMG in old adults. One potential mechanism is that increases in agonist muscle activation underlie the increase in joint powers measured during gait [38]. Another possibility is a reduction in antagonist muscle coactivation [39]. A third potential mechanism could be the change in timing of muscle activation so that the positive joint power is generated at the time that optimizes the total power output across the ankle, knee, and hip joints.

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Again, these mechanisms are all speculative due to a complete lack of data on the effects of exercise training on neural activation of locomotor muscles in old adults during gait.

POGS proposes a shift from conventional and behavioral outcome measures to more sophisticated biomechanical analyses that examine joint kinematics and kinetics before and after training interventions. Although biomechanical assessments are more time consuming and the analysis is more complex compared with the conventional functional tests, there is a need to identify the final common pathway through which exercise-evoked increases in physical ability enable old adults to walk faster. Our explicit expectation is that improved physical abilities become incorporated into the mechanics of gait, providing evidence for a causative relationship between the intervention-altered gait kinematics and kinetics and gait speed. A lack of exercise-increased gait speed will still allow us to gain insights into how, if at all, the increased physical ability results in an adaptation of positive power generation among the hip, knee, and ankle joints. That is, it is conceivable that the global outcome as quantified by gait speed does not change, but there is a reconfiguration of how the individual joint powers are generated and contribute to the total output. For example, it may be that generation of positive power at the ankle increases, but decreases at the hip, resulting in no net change in power generation and gait speed. Future studies could further increase our understanding of the mechanisms of how interventions improve gait speed by expanding the biomechanical gait analysis with an analysis of gait energetics, muscle-tendon function, gait variability, musculoskeletal modeling, and muscle synergies.

In summary, exercise interventions are beneficial to maintain and improve old adults’ gait speed measured on a level surface. To date, the underlying mechanisms of how exercise-induced increases in physical capacity become incorporated into the gait pattern and produce faster walking in old adults are not well understood. POGS will provide insights into the biomechanical mechanisms of how power training improves gait speed in old adults with moderate levels of mobility disabilities. The results will increase our basic understanding of how exercise-evoked improvements in physical ability lead to an improvement in walking speed, which can be used by clinicians to create specifically tailored training programs.

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