Foot Strike and Injury Rates in Endurance Runners
Foot Strike and Injury Rates in Endurance Runners
A retrospective cohort study was performed to investigate differences in injury types and rates among 52 athletes who were on the Harvard University Cross Country team between August 2006 and January 2011 (Table 1). All were experienced runners, sufficiently talented to compete at the Division I of the National Collegiate Athletic Association. All subjects were middle- and long-distance runners who competed in races between 800 m and 10 km, and who followed similar training plans developed by the same coach. In the fall cross country season (approximately 3 months), most runners ran four to six races on natural surfaces such as packed dirt and grass: female subjects ran 6- and 8-km races and male subjects 8- and 10-km races. In the winter and spring track season (approximately 6 months), middle-distance runners usually ran eight to twelve 800-m, 1500-m, 1600-m, and 3-km races and long-distance runners usually ran eight to twelve 3-, 5-, and 10-km races on track. The use of medical and training records and the collection of data on running biomechanics for all subjects were approved by the Harvard University Committee on the Use of Human Subjects. Prior written informed consent was obtained from all subjects.
Information on each subject's training during the study period was collected from an online running log Web site. Each athlete was required to record daily all running and cross-training information including distance run, times, and comments on performance throughout the 9-month athletic season. The total number of running days, total miles run, total minutes run, average miles per week, and average running pace were computed for each subject while on the team.
Foot strike patterns vary during workouts and races depending on several factors such as incline, fatigue, and speed. It is not possible to assess precisely the percentage of different foot strikes each runner uses throughout training, but we can measure the predominant foot strike used for the majority of miles run. To accomplish this, strike type was visually identified using a 500-Hz video camera (FastecInLine 500M; Fastec Imaging, San Diego, CA) from a lateral perspective. Some subjects (n = 31) were recorded while running at four speeds (females: 3.0, 3.5, 4.0, and 4.5 m·s; males: 3.5, 4.0, 4.5, and 5.0 m·s) on a treadmill with the camera placed 2 m lateral from the recording region 0.25 m above ground level; other subjects (n = 28) were recorded while running on a track at three self-selected speeds (recovery pace, intermediate pace, and 5000-m race pace) with the camera placed 4 m lateral from the recording region, 0.5 m above ground level. Seven subjects ran in both experimental setups to validate the reliability between methods. For these subjects, agreement was 100% in categorizing a runner's habitual strike type in overground and treadmill conditions (intraclass correlation (ICC) = 1.0).
The plantar foot angle at foot strike was determined as the angle between earth horizontal and the plantar surface of the foot. The plantar foot angle was examined to determine the foot strike type using methods reported in Lieberman et al.. Strikes in which the heel was the first part of the foot to contact the ground and the plantar angle was positive were categorized as RFS; strikes in which the ball of the foot contacts the ground first and the plantar angle was negative were classified as FFS; strikes in which the ball of the foot and heel landed simultaneously (within the 2-ms resolution available from the video) were classified as MFS. A minimum of three strikes was assessed for each runner. For the nine subjects who changed foot strike type with increased speed, the foot strike at which the subject ran the majority of their miles was used to classify that runner (four were classified as FFS runners and five as RFS runners).
All athletes on the team are required to report all injuries, which were diagnosed and recorded by the same athletic trainer/physical therapist (G.G.); follow-up consultations were performed by the same team of four physicians at the Harvard University Health Services. Injury diagnosis, physical activity restrictions, treatment plan, and administered treatment were documented approximately 5 d·wk during the 9-month athletic season. This system allowed for consistent injury diagnosis and treatment across subjects.
Each injury diagnosis was made by the medical staff after consultations with physicians, if necessary, and after incorporating any medical imaging data that were acquired (e.g., radiographs, magnetic resonance imaging, and computed tomographic scans). Injuries caused by accidents (e.g., falls and collisions) were excluded from this study. The remaining running injuries were grouped into the following categories: tendinopathies (by tendon); plantar fasciitis; stress reactions and stress fractures (by bone, including medial tibial stress syndrome); iliotibial band syndrome; knee pain including patellofemoral pain syndrome, plica syndrome, and bursitis; lower back pain (including sacroiliac joint pain); muscle strains; cartilage damage (by joint); sprains (by joint); and generalized pain (by region).
The severity of each diagnosed injury was quantified in its effect on training using a numerical scoring system based on physical activity restrictions during the entire period that the injury persisted. The following categories of restriction were used: Full, athlete continues running without restrictions; >50%, athlete runs at a reduced intensity or distance, greater than half of normal training; <50%, athlete runs at a reduced intensity or distance, less than half of normal training; Cross-training, athlete is not running, but is cross-training; Off, athlete is neither running nor cross-training. A Running Injury Severity Score (RISS) was computed by summing the days at each grade of physical restriction multiplied by a coefficient relative to the extent of restriction:
Although the RISS is a continuous measure of injury severity, we binned all injuries into three major grades: mild (≤10), moderate (11–70), and severe (>70). The following are examples: a mild injury could cause a runner to take at most 2 d completely off or to train through the injury for 10 d; a moderate injury could cause a runner to take up to two complete weeks off or to train through the injury for up to 10 wk; and a severe injury, such as a stress fracture, could cause a runner to take 6 wk off, cross-train for 2 wk, and run at a reduced intensity than normal training for 2 wk. Mild injuries are probably underreported because subjects may have sometimes neglected to report injuries that did not prevent them from training.
Injuries were grouped by type into those predicted to be more common in FFS and RFS runners. On the basis of the general model presented above, predicted FFS injuries were Achilles tendinopathies, foot pain, and stress fractures of metatarsals; predicted RFS injuries were hip pain, knee pain, lower back pain, tibial stress injuries, plantar fasciitis, and stress fractures of lower limb bones excluding the metatarsals. Injuries were also grouped into those likely to be caused by repetitive stress (repetitive injuries) and trauma such as muscle soreness and strains from speed work (traumatic injuries).
To correct for the distance run by each subject, injury rates per 10,000 miles run were quantified for each subject.
t-Tests were used to compare mean injury rates of four continuous variables (repetitive injury rate, traumatic injury rate, predicted FFS injury rate, and predicted RFS injury rate) between RFS runners and FFS runners (pooled and by sex). These analyses were run separately for mild, moderate, and severe injuries and for combined moderate and severe injuries; for all comparisons, Welch's t-tests were used to account for potentially unequal variances. In addition, a generalized linear model (GLM) was used for the four injury groups with the following covariates: foot strike type, sex, BMI, race distance (middle or long distance), average miles per week, duration in study, and the quadratic terms of average miles per week and duration in study. The GLM assesses the association between independent variables and a dependent, response variable (in this case, rate of injury). Specifically, the response variable was assumed to have a Poisson distribution, and a log link function was used. This allows the magnitude of the variance of each measurement to be a function of its predicted value and is defined as follows:
where β0 is the intercept term, βi is the coefficient of the ith covariate, and Xi is the ith covariate.
Descriptive statistics and statistical tests were weighted by total miles run by each subject during the study period to account for the greater robustness of injury rates from subjects who had run more miles during the study period.
Methods
A retrospective cohort study was performed to investigate differences in injury types and rates among 52 athletes who were on the Harvard University Cross Country team between August 2006 and January 2011 (Table 1). All were experienced runners, sufficiently talented to compete at the Division I of the National Collegiate Athletic Association. All subjects were middle- and long-distance runners who competed in races between 800 m and 10 km, and who followed similar training plans developed by the same coach. In the fall cross country season (approximately 3 months), most runners ran four to six races on natural surfaces such as packed dirt and grass: female subjects ran 6- and 8-km races and male subjects 8- and 10-km races. In the winter and spring track season (approximately 6 months), middle-distance runners usually ran eight to twelve 800-m, 1500-m, 1600-m, and 3-km races and long-distance runners usually ran eight to twelve 3-, 5-, and 10-km races on track. The use of medical and training records and the collection of data on running biomechanics for all subjects were approved by the Harvard University Committee on the Use of Human Subjects. Prior written informed consent was obtained from all subjects.
Running Training Data
Information on each subject's training during the study period was collected from an online running log Web site. Each athlete was required to record daily all running and cross-training information including distance run, times, and comments on performance throughout the 9-month athletic season. The total number of running days, total miles run, total minutes run, average miles per week, and average running pace were computed for each subject while on the team.
Strike Type Characterization
Foot strike patterns vary during workouts and races depending on several factors such as incline, fatigue, and speed. It is not possible to assess precisely the percentage of different foot strikes each runner uses throughout training, but we can measure the predominant foot strike used for the majority of miles run. To accomplish this, strike type was visually identified using a 500-Hz video camera (FastecInLine 500M; Fastec Imaging, San Diego, CA) from a lateral perspective. Some subjects (n = 31) were recorded while running at four speeds (females: 3.0, 3.5, 4.0, and 4.5 m·s; males: 3.5, 4.0, 4.5, and 5.0 m·s) on a treadmill with the camera placed 2 m lateral from the recording region 0.25 m above ground level; other subjects (n = 28) were recorded while running on a track at three self-selected speeds (recovery pace, intermediate pace, and 5000-m race pace) with the camera placed 4 m lateral from the recording region, 0.5 m above ground level. Seven subjects ran in both experimental setups to validate the reliability between methods. For these subjects, agreement was 100% in categorizing a runner's habitual strike type in overground and treadmill conditions (intraclass correlation (ICC) = 1.0).
The plantar foot angle at foot strike was determined as the angle between earth horizontal and the plantar surface of the foot. The plantar foot angle was examined to determine the foot strike type using methods reported in Lieberman et al.. Strikes in which the heel was the first part of the foot to contact the ground and the plantar angle was positive were categorized as RFS; strikes in which the ball of the foot contacts the ground first and the plantar angle was negative were classified as FFS; strikes in which the ball of the foot and heel landed simultaneously (within the 2-ms resolution available from the video) were classified as MFS. A minimum of three strikes was assessed for each runner. For the nine subjects who changed foot strike type with increased speed, the foot strike at which the subject ran the majority of their miles was used to classify that runner (four were classified as FFS runners and five as RFS runners).
Injury Data
All athletes on the team are required to report all injuries, which were diagnosed and recorded by the same athletic trainer/physical therapist (G.G.); follow-up consultations were performed by the same team of four physicians at the Harvard University Health Services. Injury diagnosis, physical activity restrictions, treatment plan, and administered treatment were documented approximately 5 d·wk during the 9-month athletic season. This system allowed for consistent injury diagnosis and treatment across subjects.
Each injury diagnosis was made by the medical staff after consultations with physicians, if necessary, and after incorporating any medical imaging data that were acquired (e.g., radiographs, magnetic resonance imaging, and computed tomographic scans). Injuries caused by accidents (e.g., falls and collisions) were excluded from this study. The remaining running injuries were grouped into the following categories: tendinopathies (by tendon); plantar fasciitis; stress reactions and stress fractures (by bone, including medial tibial stress syndrome); iliotibial band syndrome; knee pain including patellofemoral pain syndrome, plica syndrome, and bursitis; lower back pain (including sacroiliac joint pain); muscle strains; cartilage damage (by joint); sprains (by joint); and generalized pain (by region).
The severity of each diagnosed injury was quantified in its effect on training using a numerical scoring system based on physical activity restrictions during the entire period that the injury persisted. The following categories of restriction were used: Full, athlete continues running without restrictions; >50%, athlete runs at a reduced intensity or distance, greater than half of normal training; <50%, athlete runs at a reduced intensity or distance, less than half of normal training; Cross-training, athlete is not running, but is cross-training; Off, athlete is neither running nor cross-training. A Running Injury Severity Score (RISS) was computed by summing the days at each grade of physical restriction multiplied by a coefficient relative to the extent of restriction:
Although the RISS is a continuous measure of injury severity, we binned all injuries into three major grades: mild (≤10), moderate (11–70), and severe (>70). The following are examples: a mild injury could cause a runner to take at most 2 d completely off or to train through the injury for 10 d; a moderate injury could cause a runner to take up to two complete weeks off or to train through the injury for up to 10 wk; and a severe injury, such as a stress fracture, could cause a runner to take 6 wk off, cross-train for 2 wk, and run at a reduced intensity than normal training for 2 wk. Mild injuries are probably underreported because subjects may have sometimes neglected to report injuries that did not prevent them from training.
Injuries were grouped by type into those predicted to be more common in FFS and RFS runners. On the basis of the general model presented above, predicted FFS injuries were Achilles tendinopathies, foot pain, and stress fractures of metatarsals; predicted RFS injuries were hip pain, knee pain, lower back pain, tibial stress injuries, plantar fasciitis, and stress fractures of lower limb bones excluding the metatarsals. Injuries were also grouped into those likely to be caused by repetitive stress (repetitive injuries) and trauma such as muscle soreness and strains from speed work (traumatic injuries).
To correct for the distance run by each subject, injury rates per 10,000 miles run were quantified for each subject.
Statistical Analysis
t-Tests were used to compare mean injury rates of four continuous variables (repetitive injury rate, traumatic injury rate, predicted FFS injury rate, and predicted RFS injury rate) between RFS runners and FFS runners (pooled and by sex). These analyses were run separately for mild, moderate, and severe injuries and for combined moderate and severe injuries; for all comparisons, Welch's t-tests were used to account for potentially unequal variances. In addition, a generalized linear model (GLM) was used for the four injury groups with the following covariates: foot strike type, sex, BMI, race distance (middle or long distance), average miles per week, duration in study, and the quadratic terms of average miles per week and duration in study. The GLM assesses the association between independent variables and a dependent, response variable (in this case, rate of injury). Specifically, the response variable was assumed to have a Poisson distribution, and a log link function was used. This allows the magnitude of the variance of each measurement to be a function of its predicted value and is defined as follows:
where β0 is the intercept term, βi is the coefficient of the ith covariate, and Xi is the ith covariate.
Descriptive statistics and statistical tests were weighted by total miles run by each subject during the study period to account for the greater robustness of injury rates from subjects who had run more miles during the study period.
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