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APPLIED SCIENCES

The Relationships between Age and Running Biomechanics

DEVITA, PAUL1; FELLIN, REBECCA E.2; SEAY, JOSEPH F.2; IP, EDWARD3; STAVRO, NICOLE4; MESSIER, STEPHEN P.4

Author Information
Medicine & Science in Sports & Exercise: January 2016 - Volume 48 - Issue 1 - p 98-106
doi: 10.1249/MSS.0000000000000744
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Abstract

Running is a popular physical activity with an estimated 30 million people having run at least 50 d·yr−1 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 yr) reduces age-related attenuation of oxidative capacity, bone mineral density, functioning motor units, and cardiovascular health compared with 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 with those in 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. In addition, age is associated with increased risk of developing a running-related overuse injury, especially over the age of 45 yr (31,35) presumably because of reduced muscle strength, flexibility, and altered gait biomechanics (12). Older compared with 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 because of the natural history of aging may face increased health risks associated with running injuries and reduced physical activity.

It is well supported that older runners use a variety of biomechanical adaptations compared with younger runners. Older runners select a lower preferred training pace because of a shorter stride length despite a higher stride rate (3,6,11). Although running at their self-selected training pace, older runners use less knee range of motion and exert lower vertical and anteroposterior ground reaction forces (GRF) and impulses (3). While running at the identical pace of 2.7 m·s−1, older compared with 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 GRF 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 plantarflexors (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).

Because most age-related literature on biomechanical gait adaptations treats age as a dichotomous variable comparing distinct young and old populations (e.g., (11), mean ages of young and old adults were 31 and 69 yr), 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 yr ((21), n = 77) and 30–94 yr ((15), n = 162). However, these studies were limited to maximal running speed. Korhonen et al. (21) showed substantial declines in stride length (4.3 to 3.2 m) and maximal running speed (9.8 to 6.5 m·s−1) from youngest to oldest runners. Stride rate was also attenuated but to a lesser extent (2.2 to 2.1 Hz). These changes were associated with a linear decline in the braking posterior GRF, but a quadratic decline in propulsive anterior GRF that increased in rate of change with age. Hamilton et al. (15) showed similar results with declines in stride length (4.4 to 2.8 m), maximal running speed (8.9 to 4.9 m·s−1), and range of motion in the knee joints (122° to 95°) 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 represent 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 the adaptations that 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 18–60 yr. 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.

METHODS

Participants

Runners who were injury free for at least 6 months and with current training unaffected by any previous injury (males, n = 59; females, n = 51; mean age, 41.8 ± 8.8 yr; mean mass, 70.9 ± 12.8 kg; mean body mass index (BMI), 23.4 ± 2.8 kg·m−2) were recruited through advertisements on the Internet and in newspapers and by brochures posted at local running stores. Inclusion criteria were age 18–60 yr 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, anterior cruciate ligament injury, reconstructive joint surgery or replacement, acute musculoskeletal injury that affects running, overuse running injury during the past 6 months), and pregnancy. We note, however, that the youngest and oldest participants meeting all criteria and entered into the study were 23 and 59 yr old. Mean running experience for all participants was 11.3 ± 9.4 yr, and mean weekly running distance was 33.8 ± 22.0 miles. In addition, subject characteristics by 10-yr 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). Before testing, an informed consent document, approved by the university’s institutional review board, was explained to and signed by each participant.

TABLE 1
TABLE 1:
Selected participant characteristics grouped by age and decade.

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 GRF (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-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, CA) and Visual 3D (C-Motion Inc., Germantown, MD) software. The video and force platform systems were interfaced such that the 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 7 (length) × 1.2 (width) × 2.3 m (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 three-dimensional 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 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 three-dimensional system were signal-processed using a second-order, low-pass Butterworth digital filter with a cutoff 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 the 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 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 SD were derived for each biomechanical variable. Mean values were also calculated for the 20- to 29-yr-old and 50- to 59-yr-old participants 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 GRF, joint angular positions, moments, and powers using α = 0.05 to identify significant relations. 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 yr.

RESULTS

Temporospatial variables in running

The mean running velocity was 3.00 ± 0.35 m·s−1. The mean stride length was 2.19 ± 0.28 m, and the 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) (Fig. 1). On average, stride length and running velocity decreased from to 2.27 ± 0.29 to 2.06 ± 0.23 m and from 3.16 ± 0.41 to 2.82 ± 0.30 m·s−1, respectively, between runners in their 20s and 50s. The correlation between stride rate and age was not statistically significant.

FIGURE 1
FIGURE 1:
Scattergrams between age and stride length (A) and velocity (B). Regression line and equation indicate statistically significant relations, minimumP < 0.05.

GRF

The maximum braking and propelling anteroposterior GRF averaged across all participants were −2.86 ± 0.52 and 2.52 ± 0.48 N·kg−1, and the mean maximum vertical GRF was 22.5 ± 0.2 N·kg−1. Pearson product–moment correlations for maximum propelling (r = −0.383, P < 0.0001) and vertical (r = −0.230, P = 0.0156) GRF showed significant inverse correlations with age; as age increased, anterior and vertical GRF decreased (Fig. 2). On average, maximum propelling and vertical GRF decreased from 3.18 ± 0.71 to 2.70 ± 0.41 and from 23.9 ± 0.2.9 to 21.5 ± 2.4 N·kg−1, respectively, between runners in their 20s and 50s.

FIGURE 2
FIGURE 2:
Mean vertical (A) and anterior–posterior (B) GRF curves (shaded area is ±2 SD) and scattergrams between age and maximum GRF in each direction (C and D). Arrows identify the variables on the curves. Regression lines and equations indicate statistically significant relations, 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-yr-old participants. The younger and older runners were near the upper and lower force values in the range, respectively.

Joint angular positions

Mean hip angular position throughout stance was 20.7° ± 6.1° of flexion, and the maximum knee flexion and ankle dorsiflexion near midstance were 39.6° ± 5.0° and 23.4° ± 3.1°, respectively. Only the knee kinematics was significantly related to age (r = −0.203, P = 0.033), showing that younger runners flexed more in midstance (40.6° ± 5.5° for 20- to 29-yr-old participants) than older runners (36.8° ± 4.5° for 50- to 59-yr-old participants).

Joint moments and powers

The mean maximum support moment for all participants was 4.30 ± 0.79 N·m·kg−1 and mean maximum hip, knee, and ankle extension or plantarflexion moments were 2.26 ± 0.52, 1.97 ± 0.42, and 2.53 ± 0.37 N·m·kg−1, 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 (Fig. 3). Older runners reduced their ankle moments (2.28 ± 0.32 vs 2.74 ± 0.43 N·m·kg−1 in runners in their 20s and 50s), leading to a reduction in the support moment (4.74 ± 1.02 vs 3.72 ± 0.63 N·m·kg−1 over this age range).

FIGURE 3
FIGURE 3:
Mean support and ankle joint moment curves (A and B) during the stance phase (shaded area is ±2 SD) and scattergrams between age and maximum moments on each curve (C and D). Arrows identify the variables on the curves. Positive values are extensor or plantarflexor moments. Regression lines and equations indicate statistically significant relations, minimum P < 0.05.

The mean maximum negative total power for all participants was −9.96 ± 3.05 W·kg−1, and the mean maximum negative hip, knee, and ankle powers were −1.60 ± 0.72, −7.10 ± 2.07, and −5.85 ± 1.62 W·kg−1. The mean maximum positive total power was 9.81 ± 2.77 W·kg−1, and the mean maximum positive hip, knee, and ankle powers were 2.33 ± 1.46, 3.67 ± 1.24, and 8.90 ± 2.04 W·kg−1. 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 (Fig. 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 from −6.87 ± 2.15 to −4.73 ± 1.27 W·kg−1, respectively, between runners in their 20s and 50s. Positive power coefficients were, respectively, r = −0.371 (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 from 10.10 ± 2.31 to 7.58 ± 2.00 W·kg−1, respectively, between runners in their 20s and 50s. No significant correlations were observed in the hip or knee power variables.

FIGURE 4
FIGURE 4:
Mean total and ankle joint power curves (A and B) during the stance phase (shaded area is ±2 SD) and scattergrams between age and maximum powers on each curve (C and D). Arrows identify the variables on the curves. Positive values indicate that 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 relations, minimum P < 0.05.

Average reductions with age

The regression equations from the statistically significant relations 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 yr old 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 yr and were predicted to decrease 47% on average by age 80 yr. These decreased mechanical outputs resulted in 13% reductions in stride length and running velocity by age 60 yr and predict 20% reductions by age 80 yr.

TABLE 2
TABLE 2:
Predicted reductions in the statistically significant variables.

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 ages, thereby enhancing cardiovascular, neuromuscular, and skeletal health, functional capacity, and overall quality of life.

We found inverse and linear relations between age and basic running kinematics: as age increased, stride length and running velocity decreased. On the basis of the regression equations, we estimated that stride length and running velocity were reduced 0.08 m and 0.10 m·s−1 over each decade. During the aging process from 20 to 60 yr of age, these equations predict that average stride length and running velocity will decrease from 2.37 to 2.05 m and from 3.23 to 2.81 m·s−1 (both 20%). These decreases in stride length (0.47 m) and velocity (0.64 m·s−1) with age were similar to those reported by Conoboy et al. (6) and Bus (3), both of whom directly compared younger with older runners of similar ages. This agreement between studies suggests that our novel regression equations may be used to accurately estimate stride length and running velocity at various adult ages and that 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 yr of age may be conservative because the rate of decrease may accelerate after 60 yr of age as it does for a 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, however, in older runners age 67–73 yr (11) Within our age range, the inverse relations 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 relations 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 20–60 yr.

The reduced kinematics with age was 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), approximately 10% for vertical and approximately 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 threefold 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 relations between both stride length and velocity in the anterior compared with vertical GRF (r = 0.744 and 0.784 vs r = 0.521 and 0.582, respectively). Our data also agree with classic studies of GRF and running speed, which showed that maximum anterior GRF changes more dramatically with speed than maximum vertical GRF does (12,14,20,21,33).

Altered kinematics and GRF 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 approximately 20-yr-old and approximately 60-yr-old age groups. Both Karamanidis and Arampatzis (19) as well as Kulmala et al. (22) reported reduced moment and power at the ankle but not at the hip or knee (note: Karamanidis and Arampatzis (19) did not report hip joint data). Fukuchi et al. (12) showed that 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 approximately 20 to approximately 60-yr was about 20%–30% in our data (from 2.82 to 2.29 N·m·kg−1 and from 10.8 to 7.34 W·kg−1) 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 agrees with previous reports showing that the triceps surae have larger or earlier losses in muscle strength (5), mitochondrial function (18), and number of motor neurons (16) compared with those in other muscles. Reduced elasticity in the Achilles tendon in runners age 35–65 yr (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 versus younger runners in that age 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-yr 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- to 85-yr-old participants) while walking (8) by suggesting the distal to proximal redistribution of joint moments and powers with age may originate as reduced ankle 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 that the present emergence of reduced ankle function from ages 23 to 59 yr 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 yr in lower-power activities such as walking. Our data also suggest that either strength or power training the ankle plantarflexors 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 yr, leading to shorter strides and lower running velocity. We suggest that such programs may be particularly beneficial to competitive Masters athletes. Furthermore, 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 the ankle but not at the 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-age adults (27,29,48). Present 30- to 59-yr-old participants had nearly identical mass and BMI values, and their values were only slightly higher than those in 20- to 29-yr-old participants. Hence, it seems that long-term running may be an effective nonpharmacologic weight maintenance intervention that may be effective in combating obesity-related comorbidities.

Limitations

This study was limited by age range from 23 to 59 yr old. Although declines were seen with age, this study was not able to observe these declines past the age of 60 yr. We expect but cannot confirm that reductions in gait biomechanics with age over 60 yr may shift towards curvilinear relations with the rate of biomechanical decline increasing with age as noted earlier. 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 laboratory 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 relations. 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 relations may not have been strong, they were reliable (statistically significant) and they were entirely reasonable in direction on the basis of the completely established relations among age and neuromuscular–skeletal biomechanics. We were also unable to investigate the particular physiological deficits that led to reduced biomechanics and particularly 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.

CONCLUSIONS

Because 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 yr 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 and maintaining cardiovascular health.

This study was sponsored by grant W81XWH-10-1-0455, USAMRAA (US Army). Research supported partly by an appointment to the Postgraduate Research Participation Program (REF) funded by the US 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.

REFERENCES

1. Aagaard P, Suetta C, Caserotti P, Magnusson SP, Kjaer M. Role of the nervous system in sarcopenia and muscle atrophy with aging: strength training as a countermeasure. Scand J Med Sci Sports. 2 011; 20 (1): 49–64.
2. Bruce B, Fries JF, Lubeck DP. Aerobic exercise and its impact on musculoskeletal pain in older adults: a 14 year prospective, longitudinal study. Arthritis Res Ther. 2005; 7 (6): R1263–70.
3. Bus SA. Ground reaction forces and kinematics in distance running in older-aged men. Med Sci Sports Exerc. 2003; 35 (7): 1167–75.
4. Chakravarty EF, Hubert HB, Lingala VB, Fries JF. Reduced disability and mortality among aging runners: a 21-year longitudinal study. Arch Intern Med. 2008; 168 (15): 1638–46.
5. Christ CB, Boileau RA, Slaughter MH, Stillman RJ, Cameron JA, Massey BH. Maximal voluntary isometric force production characteristics of six muscle groups in women aged 25 to 74 years. Amer J Hum Biol. 1992; 4 (4): 537–45.
6. Conoboy P, Dyson R. Effect of aging on the stride pattern of veteran marathon runners. Br J Sports Med. 2006; 40 (7): 601–4; discussion 4.
7. Dempster W. Space requirements of the seated operator. Ohio: U.S. Air Force, Wright Patterson Air force Base; 1959. Report No.: 55–159.
8. DeVita P, Hortobagyi T. Age causes a redistribution of joint torques and powers during gait. J Appl Physiol. 2000; 88 (5): 1804–11.
9. DeVita P, Stibling J. Lower extremity joint kinetics and energetics during backward running. Med Sci Sports Exerc. 1991; 23 (5): 602–10.
10. Fries JF, Singh G, Morfeld D, Hubert HB, Lane NE, Brown BW Jr. Running and the development of disability with age. Ann Intern Med. 1994; 121 (7): 502–9.
11. Fukuchi RK, Duarte M. Comparison of three-dimensional lower extremity running kinematics of young adult and elderly runners. J Sports Sci. 2008; 26 (13): 1447–54.
12. Fukuchi RK, Stefanyshyn DJ, Stirling L, Duarte M, Ferber R. Flexibility, muscle strength and running biomechanical adaptations in older runners. Clin Biomech (Bristol, Avon). 2014; 29 (3): 304–10.
13. Goss DL, Gross MT. A review of mechanics and injury trends among various running styles. US Army Med Dep J. 2012; 62–71.
14. Hamill J, Bates BT, Knutzen KM, Sawhill JA. Variations in ground reaction force parameters at different running speeds. Hum Mov Sci. 1983; 2: 47–56.
15. Hamilton N. Changes in stride kinematics with age in master’s athletes. J Appl Biomech. 1993; 9 (1): 15–26.
16. Hashizume K, Kanda K. Differential effects of aging on motoneurons and peripheral nerves innervating the hindlimb and forelimb muscles of rats. Neurosci Res. 1995; 22 (2): 189–96.
17. Himann JE, Cunningham DA, Rechnitzer PA, Paterson DH. Age-related changes in speed of walking. Med Sci Sports Exerc. 1988; 20 (2): 161–6.
18. Houmard JA, Weidner ML, Gavigan KE, Tyndall GL, Hickey MS, Alshami A. Fiber type and citrate synthase activity in the human gastrocnemius and vastus lateralis with aging. J Appl Physiol (1985). 1998; 85 (4): 1337–41.
19. Karamanidis K, Arampatzis A. Mechanical and morphological properties of different muscle-tendon units in the lower extremity and running mechanics: effect of aging and physical activity. J Exp Biol. 2005; 208 (20): 3907–23.
20. Karamanidis K, Arampatzis A. Mechanical and morphological properties of human quadriceps femoris and triceps surae muscle-tendon unit in relation to aging and running. J Biomech. 2006; 39 (3): 406–17.
21. Korhonen MT, Mero AA, Alén M, et al. Biomechanical and skeletal muscle determinants of maximum running speed with aging. Med Sci Sports Exerc. 2009; 41 (4): 844–56.
22. Kulmala JP, Korhonen MT, Kuitunen S, et al. Which muscles compromise human locomotor performance with age? J R Soc Interface. 2014; 11 (100): 1754–68.
23. Kusy K, Zieliński J. Aerobic capacity in speed-power athletes aged 20–90 years vs endurance runners and untrained participants. Scand J Med Sci Sports. 2014; 24 (1): 68–79.
24. Lane NE, Bloch DA, Jones HH, Marshall WH, Wood PD, Fries JF. Long-distance running, bone density, and osteoarthritis. JAMA. 1986; 255 (9): 1147–51.
25. Larsen AH, Sørensen H, Puggaard L, Aagaard P. Biomechanical determinants of maximal stair climbing capacity in healthy elderly women. Scand J Med Sci Sports. 2009; 19 (5): 678–86.
26. Larsen RG, Callahan DM, Foulis SA, Kent-Braun JA. Age-related changes in oxidative capacity differ between locomotory muscles and are associated with physical activity behavior. Appl Physiol Nutr Metab. 2012; 37 (1): 88–99.
27. Latorre-Román PÁ, Izquierdo-Sánchez JM, Salas-Sánchez J, García-Pinillos F. Comparative analysis between two models of active aging and its influence on body composition, strength levels and quality of life: long-distance runners versus bodybuilders practitioners. Nutr Hosp. 2015; 31 (4): 1717–25.
28. Lindle RS, Metter EJ, Lynch NA, et al. Age and gender comparisons of muscle strength in 654 women and men aged 20-93 yr. J Appl Physiol (1985). 1997; 83 (5): 1581–7.
29. Littman AJ, Kristal AR, White E. Effects of physical activity intensity, frequency, and activity type on 10-y weight change in middle-aged men and women. Int J Obes (Lond). 2005; 29 (5): 524–33.
30. Magnan B, Bondi M, Pierantoni S, Samaila E. The pathogenesis of achilles tendinopathy: a systematic review. Foot Ankle Surg. 2014; 20 (3): 154–9.
31. McKean KA, Manson NA, Stanish WD. Musculoskeletal injury in the masters runners. Clin J Sport Med. 2006; 16 (2): 149–54.
32. Metter EJ, Conwit R, Tobin J, Fozard JL. Age-associated loss of power and strength in the upper extremities in women and men. J Gerontol A Biol Sci Med Sci. 1997; 52 (5): B267–76.
33. Munro CF, Miller DI, Fuglevand AJ. Ground reaction forces in running: a reexamination. J Biomech. 1987; 20 (2): 147–55.
34. Näslund J, Näslund UB, Odenbring S, Lundeberg T. Comparison of symptoms and clinical findings in subgroups of individuals with patellofemoral pain. Physiother Theory Pract. 2006; 22 (3): 105–18.
35. Nielsen RO, Buist I, Parner ET, et al. Predictors of running-related injuries among 930 novice runners a 1–year prospective follow-up study. Orthopaedic Journal of Sports Medicine. 2013; 1 (1): 1–7.
36. Power GA, Dalton BH, Behm DG, Doherty TJ, Vandervoort AA, Rice CL. Motor unit survival in lifelong runners is muscle dependent. Med Sci Sports Exerc. 2012; 44 (7): 1235–42.
37. Power GA, Dalton BH, Behm DG, Vandervoort AA, Doherty TJ, Rice CL. Motor unit number estimates in masters runners: use it or lose it? Med Sci Sports Exerc. 2010; 42 (9): 1644–50.
38. Quinn TJ, Manley MJ, Aziz J, Padham JL, MacKenzie AM. Aging and factors related to running economy. J Strength Cond Res. 2011; 25 (11): 2971–9.
39. Ruan Z, Zhao B, Qi H, et al. Elasticity of healthy achilles tendon decreases with the increase of age as determined by acoustic radiation force impulse imaging. Int J Clin Exp Med. 2015; 8 (1): 1043–50.
40. Running U. Running USA 2014 state of the sport - part ii: running industry report http://wwwrunningusaorg/2014-running-industry-report?returnTo=annual-reports. 2014.
41. Schroeder TE, Hawkins SA, Hyslop D, Vallejo AF, Jensky NE, Wiswell RA. Longitudinal change in coronary heart disease risk factors in older runners. Age Ageing. 2007; 36 (1): 57–62.
42. Snyder KL, Kram R, Gottschall JS. The role of elastic energy storage and recovery in downhill and uphill running. J Exp Biol. 2012; 215 (Pt 13): 2283–7.
43. Taunton JE, Clement DB, Smart GW, McNicol KL. Non-surgical management of overuse knee injuries in runners. Can J Sport Sci. 1987; 12 (1): 11–8.
44. Trappe S. Marathon runners: how do they age? Sports Med. 2007; 374– : 302–5.
45. van Mechelen W. Running injuries. A review of the epidemiological literature. Sports Med. 1992; 14 (5): 320–35.
46. Wang BW, Ramey DR, Schettler JD, Hubert HB, Fries JF. Postponed development of disability in elderly runners: a 13-year longitudinal study. Arch Intern Med. 2002; 162 (20): 2285–94.
47. Wang FC, de Pasqua V, Delwaide PJ. Age-related changes in fastest and slowest conducting axons of thenar motor units. Muscle Nerve. 1999; 22 (8): 1002–29.
48. Williams PT, Satariano WA. Relationships of age and weekly running distance to BMI and circumferences in 41,582 physically active women. Obes Res. 2005; 13 (8): 1370–80.
49. Winter DA. Moments of force and mechanical power in jogging. J Biomech. 1983; 16 (1): 91–7.
Keywords:

OLDER RUNNERS; JOINT TORQUE; POWER; AGING; GAIT

© 2016 American College of Sports Medicine