Research Paper 2015

. . . Published ahead of Print

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Copyright © 2015 American College of Sports Medicine

The Relationships between Age and Running Biomechanics

Paul DeVita

1, Rebecca E. Fellin

2, Joseph F. Seay

2, Edward Ip

3,

Nicole Stavro4, and Stephen P. Messier

4

1Department of Kinesiology, East Carolina University, Greenville, NC;

2Military Performance

Division, United States Army Research Institute of Environmental Medicine, Natick, MA;

3Department of Biostatistical Sciences and Department of Social Sciences & Health Policy,

Wake Forest School of Medicine Winston Salem, NC; 4

Department of Health and Exercise

Science, Wake Forest University, Winston-Salem, NC

Accepted for Publication: 24 July 2015

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The Relationships between Age and Running Biomechanics

Paul DeVita1, Rebecca E. Fellin

2, Joseph F. Seay

2, Edward Ip

3, Nicole Stavro

4, and

Stephen P. Messier4

1Department of Kinesiology, East Carolina University, Greenville, NC;

2Military Performance

Division, United States Army Research Institute of Environmental Medicine, Natick, MA;

3Department of Biostatistical Sciences and Department of Social Sciences & Health Policy,

Wake Forest School of Medicine Winston Salem, NC; 4

Department of Health and Exercise

Science, Wake Forest University, Winston-Salem, NC

Running title: Age and Running Biomechanics

Corresponding Author:

Paul DeVita, Ph.D.

332 Ward Sports Medicine Building

Department of Kinesiology

East Carolina University

Greenville, NC 27858

email: [email protected]

This study was sponsored by grant W81XWH-10-1-0455, USAMRAA (U.S. Army). Research

supported in part by an appointment to the Postgraduate Research Participation Program (REF)

funded by U.S. Army Research Institute of Environmental Medicine and administered by Oak

Ridge Institute for Science and Engineering. The authors report no conflict of interest. The

results of the present study do not constitute endorsement by the American College of Sports

Medicine.

Medicine & Science in Sports & Exercise, Publish Ahead of PrintDOI: 10.1249/MSS.0000000000000744

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Copyright © 2015 by the American College of Sports Medicine. Unauthorized reproduction of this article is prohibited.

Abstract:

Running has high injury rates, especially among older runners. Most aging literature compares

young vs old runners without accounting for the progression of biomechanics throughout the

lifespan. We used age as a continuous variable to investigate the continuum of age-related gait

adaptations in running along with determining the chronology and rate of these adaptations.

PURPOSE: Identify the relationships among age and selected running biomechanics throughout

the range of 18 to 60 years. METHODS: Experienced (n = 110), healthy runners (54% male)

provided informed consent and ran at their training pace while motion and force data were

captured. Kinematics, ground reaction forces (GRFs) and lower limb joint torques and powers

were correlated with age using Pearson product-moment correlations and linear regression.

RESULTS: Running velocity was inversely related to age (r = -0.27, p = 0.005) due to decreased

stride length (r = -0.25, p = 0.008) but not rate. Peak vertical GRF (r = -0.23, p = 0.016) and peak

horizontal propulsive GRF decreased with age (r = -0.38, p < 0.0001). Peak ankle torque (r=-

0.32, p = 0.0007), and peak negative (r = 0.34, p = 0.0003) and positive (r = -0.37, p < 0.0001)

ankle power decreased with age. Age-based regression equations and per year reductions in all

variables significantly related to age are reported. CONCLUSIONS: Data support prior work

showing lower GRFs, stride length and velocity in old runners. Results are novel in showing the

rate of decline in running biomechanics on a per-year basis and that mechanical reductions at the

ankle but not hip or knee were correlated with age confirming previous observation of

biomechanical plasticity with age showing reduced ankle but not hip function in gait.

Key words: older runners, joint torque, power, aging, gait

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Introduction

Running is a popular physical activity with an estimated 30 million people having run at

least 50 days per year in the United States in 2012 and 2013 (40). Running regularly has many

health benefits that protect against disability and early mortality, while also prolonging

disability-free lives (4,10,46). Specifically, adherence to a physically active lifestyle that

includes running into middle and old age (e.g. > 70 years) reduces age-related attenuation of

oxidative capacity, bone mineral density, functioning motor units, and cardiovascular health

compared to a sedentary lifestyle (23,24,26,36,37,41,44). These benefits lead to reduced risk for

coronary heart disease (41) and reduced musculoskeletal pain (2) in older runners compared to

sedentary, older adults.

Although running provides numerous health benefits, it is also associated with a high rate

of overuse injuries, with an annual incidence rate as high as 79% (13). These injuries include

achilles tendinopathy and fasciopathy, plantar fasciitis, iliotibial band friction syndrome, and

medial tibial stress syndrome, however the most prevalent injury site is the knee, with anterior

knee pain reported more often than other injuries (30,34,43,45). Running injuries limit

participation in both running and other physical activities that may adversely affect an

individual’s health status. Additionally, age is associated with an increased risk of developing a

running-related overuse injury, especially over the age of 45 years (31,35) presumably due to

reduced muscle strength, flexibility, and altered gait biomechanics (12). Older compared to

younger runners more often have multiple injuries and soft-tissue-type injuries to the calf,

Achilles tendon, and hamstrings (31). Thus, older runners whose health status may be reduced

due to the natural history of aging, may face increased health risks associated with running

injuries and reduced physical activity.

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It is well supported that older runners use a variety of biomechanical adaptions compared

to younger runners. Older runners select a lower preferred training pace due to a shorter stride

length despite a higher stride rate (3,6,11). While running at their self-selected training pace,

older runners use less knee range of motion and exert lower vertical and anteroposterior ground

reaction forces and impulses (3). While running at the identical pace of 2.7 m/s, older compared

to younger runners again used reduced range of motion at the knee but also at the hip and ankle

and produced less positive work at the knee and ankle (12). These adaptations produced lower

maximum vertical and anterior propulsive ground reaction forces in the older runners but did not

reduce the maximum posterior braking force. These gait adaptations were attributed to declines

in maximal and submaximal cardiorespiratory characteristics and declines in muscle strength and

power (19,38), possibly selective weakness in plantar flexors (12), and for maximal speed

running, decreased Type II muscle fiber area and maximal and rapid force-generating capacity of

the lower limb muscles (21), and musculotendon mechanical properties (19).

Since most age-related literature on biomechanical gait adaptations treats age as a

dichotomous variable comparing distinct young and old populations (e.g. ref 11, mean ages

young and old adults were 31 and 69 years), few studies have taken a developmental approach to

aging. To our knowledge, Korhonen et al. (21) and Hamilton et al. (15) remain the only studies

in which biomechanical adaptations were investigated in adults ranging in age from 17 to 82

years (21, n = 77) and 30-94 years (15, n = 162). These studies were limited however to maximal

running speed. Korhonen et al. (21) showed substantial declines in stride length (4.3m to 3.2m)

and maximal running speed (9.8m/s to 6.5m/s) from youngest to oldest runners. Stride rate was

also attenuated, but to a lesser extent (2.2Hz to 2.1Hz). These changes were associated with a

linear decline in the braking posterior ground reaction force (GRF), but a quadratic decline in

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propulsive anterior GRF that increased in rate of change with age. Hamilton et al. (15) showed

similar results with declines in stride length (4.4m to 2.8m), maximal running speed (8.9m/s to

4.9 m/s), and range of motion in the knee joints (122º to 95º) from youngest to oldest runners.

These kinematic and external kinetic adaptations were associated with an age-related decline in

muscle thickness, Type II fiber area and maximal and rapid force-generating capacity of the

lower limb muscles.

Korhonen et al. (21) and Hamilton et al. (15) provide important information not only on

which characteristics change with age but also when they change, and their data represents an

important contribution to the biomechanics of maximal human performance. Most adults of all

ages, however, do not engage in maximal effort physical activity but at low to moderate efforts

to maintain and improve health, functional capacity, and quality of life. We therefore propose

that a cross-sectional design with age as a continuous variable investigating running

biomechanics at self-selected training paces is necessary to provide an ecologically valuable data

set on age-related biomechanical gait adaptations during running. We contend that such a data

set will provide the basis for determining which adaptations occur through the continuum of

adult aging and the chronological history and rate of these adaptations on a year-by-year basis.

The purpose of this study was to investigate the age-related adaptations in the biomechanics of

running throughout the age range of 18 to 60 years. The results of this study can have

implications for developing training programs and performance equipment for the aging runner

and ultimately may lead to reduced injury in older runners.

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Methods

Participants

Runners who were injury-free for at least 6 months and with current training unaffected

by any previous injury (n = 59 males; n = 51 females, mean age: 41.8±8.8 years; mean mass:

70.9±12.8 kg; mean BMI: 23.4±2.8 kg/m2) were recruited through advertisements on the internet

and in newspapers and by brochures posted at local running stores. Inclusion criteria were age

18 to 60 years and weekly mileage of 5 miles or greater for the past 6 months. Exclusion criteria

were chronic diseases or orthopedic conditions which could influence running biomechanics (i.e.

arthritis, osteoporosis, coronary disease, cancer, ACL injury, reconstructive joint surgery or

replacement, acute musculoskeletal injury that affects running, overuse running injury during the

past 6 mos) and pregnancy. We note however that the youngest and oldest participants meeting

all criteria and entered into the study were 23 and 59 years old. Mean running experience for all

participants was 11.3±9.4 years and mean weekly running distance was 33.8±22.0 miles.

Additionally, subject characteristics by 10-year age bins showed the youngest runners were 10%

less massive as the others, running experience increased with age, BMI was consistent across

ages, and weekly distance was not associated with age (Table 1). Prior to testing, an informed

consent document, approved by the university’s institutional review board, was explained to and

signed by each participant.

Instruments

An AMTI Model OR6-5-1 (Advanced Mechanical Technology, Inc., Watertown, MA)

force platform was embedded in a 22.5 m raised walkway at the Wake Forest University

Runners’ Clinic and used to measure GRFs (480 Hz). The force platform was interfaced with an

AMTI Model SGA6-4 six channel amplifier, an IBM PC, and software to analyze the three-

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dimensional forces and moments applied to the instrument during running. Equipment used for

kinematic analysis included six Motion Analysis Corporation Eagle High Speed (200 Hz) digital

cameras and EVa (Motion Analysis Corporation, Santa Rosa, California) and Visual 3D (C-

Motion Inc., Germantown, Maryland) software. The video and force platform systems were

interfaced such that three dimensional motion of the lower extremity, including rearfoot motion,

and joint moments of the hip, knee, and ankle were calculated. A photoelectric timer (Lafayette

Model 63501) with photocells placed 7.3 m apart was used to monitor running speed as

participants ran along a 22 m runway with a capture volume of 7m (length) x 1.2m (width) x

2.3m (height). Acceptable trials were within ±3.5% of each subject’s training pace.

Testing Protocol

Anthropometric Measurements

Body mass and height were measured and recorded with the participants wearing running

shorts, a tight fitting T-shirt, and no shoes. Instruments were calibrated weekly.

Running Measurements

Participants wore running shorts, a tight fitting T-shirt, and their normal running shoes. A

set of 37 passive reflective markers arranged in the Cleveland Clinic full-body configuration

were attached to the runners. Additional markers were placed on the rearfoot and shank to

calculate 3D rearfoot motion (shank - lateral knee, anterior tibia, and lateral ankle markers;

rearfoot - heel, lower heel, and lateral calcaneal markers). A static calibration trial was first

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performed in which the participant stood in an anatomical position in the center of the capture

volume to determine the participants’ joint angular positions in a static position. Participants

practiced running on the runway to familiarize themselves with the markers and training pace.

Three acceptable trials for each side were obtained; an acceptable trial was defined as running

within the predetermined range of normal training pace and contacting the force platform with

the appropriate foot in normal stride, as determined by visual observation.

Data Reduction

All data processing was conducted with Eva and Visual 3D software. Raw coordinate

data from the 3D system were signal-processed using a second-order, low-pass Butterworth

digital filter with a cut-off frequency of 6 Hz to remove high frequency error. The processed

coordinate data were used to calculate stride length, stride rate and joint angular positions

through the gait cycle at hip, knee, and ankle joints. GRF data were filtered at 50 Hz.

The lower extremity was modeled as a rigid linked segment system. Magnitude and

location of the segmental masses, mass centers, and segmental moments of inertia were

estimated from position data using a mathematical model of relative segmental masses reported

by Dempster (7) and the participant’s anthropometric data. Inverse dynamics using linear and

angular Newtonian equations of motion were used to calculate the joint reaction forces and

moments at the hip, knee and ankle joints. Joint moments represented the internal moments

produced by the muscles and other tissues crossing the joints. Three-dimensional joint powers

were calculated as the product of the joint moments and joint angular velocities. Support moment

and total power curves in the sagittal plane were calculated as the sum of the individual, sagittal,

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joint moments and powers (9,49). Maximum sagittal joint moments and powers, the extensor

angular impulses, and positive and negative work were derived at each individual joint and from

the support and total power curves. All variables were then averaged over the six trials per

participant and these mean values were used in the data analysis as each participant’s best

representative value.

Data Analysis

Descriptive statistics including means and standard deviations were derived for each

biomechanical variable. Mean values were also calculated for the 20-29 year olds and 50-59 year

olds to describe the magnitude of change between these age groups. Pearson product moment

correlation coefficients and their p-values were calculated to examine the correlations of age

with velocity, stride length, stride rate, and the variables derived from the GRFs, joint angular

positions, moments, and powers using α = 0.05 to identify significant relationships. Simple linear

regressions were used to establish predictive models for providing representative values of

variables that were found significantly related to age at the ages of 20, 40, 60, and 80 years of

age.

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Results

Temporospatial variables in running

The mean running velocity was 3.00 ± 0.35 m/s. The mean stride length was 2.19 ± 0.28

m and stride rate was 82.6 ± 5.4 strides per minute. Stride length and running velocity were

significantly inversely correlated with age, r = -0.253 (p = 0.008) and r = -0.267, (p = 0.005,

Figure 1). On average, stride length and running velocity decreased from to 2.27±0.29 to 2.06

±0.23 m and 3.16±0.41 to 2.82±0.30 m/s respectively between runners aged in their 20s and 50s.

The correlation between stride rate and age was not statistically significant.

Ground Reaction Forces

The maximum braking and propelling anteroposterior GRFs averaged across all

participants were -2.86 ± 0.52 N/kg and 2.52 ± 0.48 N/kg and mean maximum vertical GRF was

22.5 ± 0.2 N/kg. Pearson-product moment correlations for maximum propelling (r = -0.383,

p<0.0001; and vertical (r = -0.230, p = 0.0156) GRFs showed significant inverse correlations

with age; as age increased anterior and vertical GRFs decreased (Figure 2). On average,

maximum propelling and vertical GRFs decreased from to 3.18±0.71 to 2.70±0.41 and

23.9±0.2.9 to 21.5±2.4 N/kg, respectively between runners in their 20s and 50s.

Joint Angular Positions

Mean hip angular position throughout stance was 20.7 ± 6.1° of flexion and maximum

knee flexion and ankle dorsiflexion near midstance were 39.6 ± 5.0° and 23.4 ± 3.1°. Only the

knee kinematics were significantly related to age (r=-0.203, p=0.033) showing younger runners

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flexed more in midstance (40.6±5.5° for 20-29 year olds) than older runners (36.8±4.5° for 50-59

year olds).

Joint Moments and Powers

The mean maximum support moment for all participants was 4.30 ± 0.79 Nm/kg and

mean maximum hip, knee, and ankle extension or plantar flexion moments were 2.26 ± 0.52,

1.97 ± 0.42, and 2.53 ± 0.37 Nm/kg, respectively. Maximum hip and knee moments were not

significantly correlated with age. Maximum support moment (r=-0.292, p=0.0019) and

maximum ankle plantarflexion moment (r=-0.319, p=0.0007) were however both significantly

and inversely correlated with age (Figure 3). Older runners reduced their ankle moments

(2.28±0.32 vs 2.74±0.43 Nm/kg in runners aged 20s and 50s) leading to a reduction in the

support moment (4.74±1.02 vs 3.72±0.63 Nm/kg over this age range).

The mean maximum negative total power for all participants was -9.96 ± 3.05 W/kg and

mean maximum negative hip, knee, and ankle powers were -1.60 ± 0.72, -7.10 ± 2.07, and -5.85

± 1.62 W/kg. Mean maximum positive total power was 9.81 ± 2.77 W/kg and mean maximum

positive hip, knee, and ankle powers were 2.33 ± 1.46, 3.67 ± 1.24, 8.90 ± 2.04 W/kg. Pearson

product-moment correlations for both negative and positive maximum powers were statistically

significant in the total power and the ankle joint power with all results showing reduced power

with age (Figure 4). Negative power correlation coefficients were respectively r=-0.204

(p=0.0324) and r=-0.336 (p=0.0003) for total and ankle powers and on average these values

changed from -10.82±3.41 to -8.39±2.60 and -6.87±2.15 to -4.73±1.27 W/kg respectively

between runners in their 20s and 50s. Positive power coefficients were respectively r=-0.371

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(p<0.0001) and r=-0.372 (p<0.0001) for total and ankle powers and on average these values

changed from 11.71±3.55 to 7.86±2.52 and 10.10±2.31 to 7.58±2.00 W/kg respectively between

runners in their 20s and 50s. No significant correlations were observed in the hip or knee power

variables.

Average Reductions with Age

The regression equations from the statistically significant relationships were used to

predict the per year percentage reductions in these variables and also representative values for

runners at the ages of 20, 40, 60, and 80 years of age to provide more concrete examples of the

absolute reductions that occur with age (Table 2). The largest reductions with age in both

absolute and relative terms were seen in the maximum negative and positive ankle power,

negative and positive total power and maximum anterior propelling GRF. These variables

decreased 31% on average between the ages of 20 and 60 yrs and were predicted to decrease

47% on average by age 80 yrs. These decreased mechanical outputs resulted in 13% reductions

in stride length and running velocity by age 60 years and predict 20% reductions by age 80 years.

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Discussion:

Understanding age-related adaptations and deficits in running will enhance the design

and implementation of training programs that focus on attenuating these changes (21) and

programs aimed at reducing injury and/or improving performance in older runners (31).

Reducing the rate of decline in running biomechanics with age and the incidence of injury and

improving performance will enable older runners to maintain their running programs longer into

older age thereby enhancing cardiovascular, neuromuscular, and skeletal health, functional

capacity, and overall quality of life.

We found inverse and linear relationships between age and basic running kinematics: as

age increased, stride length and running velocity decreased. Based on the regression equations,

we estimated that stride length and running velocity were reduced 0.08 m and 0.10 m/s over each

decade. During the aging process from 20 to 60 years of age, these equations predict average

stride length and running velocity will decrease from 2.37m to 2.05m and from 3.23 to 2.81 m/s

(both 20%). These decreases in stride length (0.47 m) and velocity (0.64 m/s) with age were

similar to those reported by Conoboy et al. (6) and Bus et al. (3) both of whom directly compared

younger vs older runners of similar ages. This agreement between studies suggests our novel

regression equations may be used to accurately estimate stride length and running velocity at

various adult ages and these predictions can be used to assess an individual’s current

performance level and as target goals by coaches, medical personnel, or runners themselves. We

suggest that our estimates for the decline in biomechanical characteristics for runners at 80 years

of age may be conservative because the rate of decrease may accelerate after 60 years of age as it

does for number of nerve and muscle fibers (1), both concentric and eccentric muscle strength

(28), and walking speed (17). Stride rate was not related to age as observed by Conoboy et al. (6)

whose participants had similar ages to our sample extremes. Stride rate was related to age

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however in older runners aged 67 to 73 years (11) Within our age range, the inverse relationship

of decreased velocity with age was then a direct function of reduced stride length; the correlation

between velocity and stride length was r=0.88. We also note the linear nature of all present

significant relationships agrees with linear declines in physiological properties including aerobic

capacity (23), muscle strength and power (32) and neurological properties including reduced

number of motor units and motor neuron conduction velocity (47) through the ages of 20 to 60

years.

The reduced kinematics with age were due to reduced kinetic and energetic variables

with age as conjectured by Conoboy et al. (6). The current decreases in the GRF variables were

similar to those reported by Bus (3), ~10% for vertical and ~25% for anterior maximum forces

for the same age range and also to other reports (12,20,21). The relative decrease in anterior GRF

maximum was also nearly 3-fold larger than the relative decrease in vertical GRF maximum

(0.70% vs. 0.24% per year). As runners age, they reduce their horizontal propulsive effort more

than their vertical propulsive effort. Indeed, we observed much stronger relationships between

both stride length and velocity in the anterior compared to vertical GRFs (r = 0.744 and 0.784 vs.

r = 0.521 and 0.582, respectively). Our data also agree with classic studies of GRFs and running

speed which showed that maximum anterior GRF changes more dramatically with speed than

does maximum vertical GRF (12,14,20,21,33).

Altered kinematics and GRFs during running are associated with altered muscle function

and joint mechanical output. Notably, we observed reduced ankle joint moment and power with

age but not reduced knee or hip joint moments and powers. These results agree well with

previous reports on running comparisons between ~20 and ~60 year old age groups. Both

Karamanidis et al. (19) and Kulmala et al. (22) reported reduced moment and power at the ankle

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but not at the hip or knee (note: Karamanidis et al. did not report hip joint data). Fukuchi et al.

(12) showed older runners maintain hip biomechanics but had the largest reductions in moment,

power and work at the ankle joint. The magnitude of the reductions in ankle moments and

powers from age ~20 to ~60 years were about 20-30% in our data (2.82 to 2.29 Nm/kg and 10.8

to 7.34 W/kg) and agree with these previous studies. Reduced ankle but not knee or hip joint

function in our data may be due to a generalized decline in muscle strength and power (19,38)

but also agree with previous reports showing the triceps surae have larger or earlier losses in

muscle strength (5), mitochondrial function (18), and number of motor neurons (16) compared to

other muscles. Reduced elasticity in the Achilles tendon in runners aged 35 to 65 years (39) may

also contribute to the attenuation of ankle power and interact with plantarflexor weakness

reducing energy storage and return from this tendon (42). Overall, reduced ankle power may be

related to the increased rate of Achilles and plantarflexor injuries in older vs younger runners in

that aged ankle muscles and tendons may be not be able to withstand the rigors of running

especially in individuals running more frequently (31). Reduced stride length, running velocity,

and maximum propelling GRF with age were directly associated with ankle muscle function.

Maximum positive ankle power throughout our 40 year span was strongly and directly associated

with stride length (r=0.684), velocity (r=0.661), and maximum propelling GRF (r=0.844).

Reduced ankle joint function with age was also strongly associated with the reduced

overall limb mechanical output. Maximum ankle moment was correlated with maximum support

moment, r=0.747 and maximum ankle positive power was correlated with total positive power,

r=0.925. We find these results interesting because they relate to the biomechanical plasticity with

age observed in older adults (i.e. 70-85 year olds) while walking (8) by suggesting the distal to

proximal redistribution of joint moments and powers with age may originate as reduced ankle

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joint mechanical output. This observation is novel in that no one has yet to report when the

fundamental biomechanical gait adaptations emerge or the sequence in which they do so. We

suggest the present emergence of reduced ankle function from ages 23 to 59 years is most likely

enhanced in this relatively high power activity of running and that this linear reduction with time

might not be evident by age 59 years in lower power activities such as walking. Our data also

suggest that either strength or power training the ankle plantar flexors may be a viable

mechanism for attenuating the reduction in running biomechanics with age. Specifically, training

and rehabilitation protocols for older runners should be based on the fact that ankle but not hip

and knee moment and power are reduced by age 59 years leading to shorter strides and lower

running velocity. We suggest that such programs may be particularly beneficial to competitive

Masters athletes. Further, joint and muscle power are critical for performing both high level

physical activities as presently observed but also for daily activities in older adults (25). Our

identification of power deficits at ankle but not knee or hip provides a joint-specific basis for

ankle power training programs that lead to improved performance in challenging daily activities

such as ascending and descending gaits. We should also consider, however, that the magnitude

of the observed reductions in ankle joint function with age may represent healthy aging because

our participants were presently injury free and relatively healthy.

We also note that our data support the emerging proposition that long term running

behavior ameliorates the increase in body weight and BMI evident in sedentary, middle aged

adults (27,29,48). Present 30 to 59 year old participants had nearly identical mass and BMI

values and their values were only slightly higher than those in the 20 to 29 year olds. Hence, it

appears that long-term running may be an effective non-pharmacologic weight maintenance

intervention that may be effective in combating obesity-related comorbidities.

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Limitations

This study was limited by age range from 23 to 59 years old. While declines were seen

with age, this study was not able to observe these declines past the age of 60 years. We expect

but cannot confirm that reductions in gait biomechanics with age over 60 years may shift

towards curvilinear relationships with the rate of biomechanical decline increasing with age as

noted above. We call for studies similar to ours but including much older adults to clarify the

continued declines at older ages. Although logistically difficult, we note that longitudinal studies

measuring the same individuals over years or decades of time would provide even stronger data

documenting aging related biomechanical adaptations in running. We note that all testing

occurred in a lab setting and it was possible that the gait observed in the laboratory might differ

somewhat from that performed outdoors. We acknowledge the age-related correlations

coefficients showed low to moderate but not strong relationships. There is extensive variability

in running biomechanics throughout the population and the present protocol included this

variability by having all participants run at their own training paces and not at a standard speed.

While the relationships may not have been strong, they were reliable (statistically significant)

and they were entirely reasonable in direction based on the completely established relationships

among age and neuromuscular-skeletal biomechanics. We were also unable to investigate the

particular physiological deficits that led to reduced biomechanics and in particular to reduced

ankle joint torque and power with age. Continued investigations with age as a continuous

variable and that include more comprehensive data sets would add depth to our current

understanding of aging.

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Conclusions

Since running continues to be a popular form of exercise, it is important for athletic

trainers, physical therapists, and physicians to understand the biomechanical adaptations that

occur with age. Overall, our data show that running biomechanics decline linearly and they

provide estimates of the magnitude of the reductions on a per-year basis. Reductions in the basic

running characteristics of stride length and velocity between the ages of 23 and 59 years are due

primarily to reduced ankle moment and power production during the stance phase but not

reduced knee or hip function. Whether these reductions were due to physiological limitations or

the conscious selection of lowering ankle mechanics remains to be clarified. We propose

however that attenuating the biomechanical deficits observed with aging may enable people to

continue running longer into older age, prolonging disability-free lives as well as maintaining

cardiovascular health.

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Acknowledgements:

This study was sponsored by grant W81XWH-10-1-0455, USAMRAA (U.S. Army). Research

supported in part by an appointment to the Postgraduate Research Participation Program (REF)

funded by U.S. Army Research Institute of Environmental Medicine and administered by Oak

Ridge Institute for Science and Engineering. The authors report no conflict of interest. Citations

of commercial organizations and trade names in this report do not constitute an official

Department of the Army endorsement or approval of the products or services of these

organizations. The results of the present study do not constitute endorsement by the American

College of Sports Medicine.

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Figure Captions:

Figure 1: Scattergrams between age and stride length (a) and velocity (b). Regression line and

equation indicate statistically significant relationship, minimum p<0.05.

Figure 2: Mean vertical (a) and anterior-posterior (b) GRF curves (shaded area is ±2 SDs) and

scattergrams between age and maximum GRF in each direction (c,d). Arrows identify the

variables on the curves. Regression lines and equations indicate statistically significant

relationships, minimum p<0.05. As is the case for the data in figures 3-4, we mention here that

the 2 SD range provides an approximate estimate of the values for the 20 and 60 year olds. The

younger and older runners were near the upper and lower force values in the range, respectively.

Figure 3. Mean support and ankle joint moment curves (a-b) during the stance phase (shaded

area is ±2 SDs) and scattergrams between age and maximum moments on each curve (c-d).

Arrows identify the variables on the curves. Positive values are extensor or plantarflexor

moments. Regression lines and equations indicate statistically significant relationships, minimum

p<0.05.

Figure 4. Mean total and ankle joint power curves (a-b) during the stance phase (shaded area is

±2 SDs) and scattergrams between age and maximum powers on each curve (c-d). Arrows

identify the variables on the curves. Positive values indicate joint moment and angular velocity

were in the same direction and positive work was done through concentric contractions.

Regression lines and equations indicate statistically significant relationships, minimum p<0.05.

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Figure 1

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Figure 2

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Figure 3

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Figure 4

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Table 1. Selected participant characteristics grouped by age and decade

Participant Characteristics (n=110) Age Groups By Decade

20 - 29 30 – 39 40 – 49 50 – 60

Body mass (kg) 64.7 ± 8.8 72.1 ± 13.8 71.8 ± 12.0 70.7 ± 15.2

BMI (kg/m²) 22.6 ± 1.7 23.5 ± 2.6 23.4 ± 2.5 23.7 ± 3.9

Running experience (years) 6.4 ± 3.6 9.5 ± 6.9 11.5 ± 8.3 15.6 ± 14.1

Average weekly distance (mi) 41.0 ± 31.0 29.4 ± 18.0 35.0 ± 22.4 31.8 ± 19.2

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Table 2. Predicted reductions in the statistically significant variables

Value At Age (yrs) Predicted Reduction

Variable % Reduction / Yr 20 40 60 80 at age 80 years (%)

Stride length (m) 0.33 2.37 2.21 2.05 1.89 20.0

Velocity (m/s) 0.33 3.23 3.02 2.81 2.60 19.7

Maximum Knee Flexion Angle (deg) 0.27 42.1 39.8 37.5 35.2 16.3

Maximum Ant-Post Propelling Force (N/kg) 0.70 2.98 2.56 2.14 1.72 42.1

Maximum Vertical Force (N/kg) 0.24 23.7 22.6 21.4 20.3 14.6

Maximum Ankle Moment (Nm/kg) 0.47 2.82 2.55 2.29 2.02 28.1

Maximum Total Negative Power (W/kg) 0.61 -11.5 -10.1 -8.7 -7.3 36.8

Maximum Total Positive Power (W/kg) 0.94 12.3 10.0 7.7 5.4 56.6

Maximum Ankle Negative Power (W/kg) 0.85 -7.20 -5.97 -4.74 -3.51 51.3

Maximum Ankle Positive Power (W/kg) 0.80 10.78 9.06 7.34 5.62 47.9

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Research Paper 2015

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