IJSPT
ORIGINAL RESEARCH
ACCURACY OF SELF-REPORTED FOOT STRIKE PATTERN IN INTERCOLLEGIATE AND RECREATIONAL RUNNERS DURING SHOD RUNNING Michael B. Bade, PT, DPT, PhD1,3 Katie Aaron, PT, DPT2 Thomas G. McPoil, PT, PhD1
ABSTRACT Background: Clinicians are interested in the foot strike pattern (FSP) in runners because of the suggested relationship between the strike pattern and lower extremity injury. Purpose: The purpose of this study was to assess the ability of collegiate cross-country runners and recreational runners to self-report their foot strike pattern during running. Study Design: Cross-sectional Study Methods: Twenty-three collegiate cross-country and 23 recreational runners voluntarily consented to participate. Inclusion criteria included running at least 18 miles per week, experience running on a treadmill, no history of lower extremity congenital or traumatic deformity, or acute injury three months prior to the start of the study. All participants completed a pre-test survey to indicate their typical foot strike pattern during a training run (FSPSurvey). Prior to running, reflective markers were placed on the posterior midsole and the vamp of the running shoe. A high-speed camera was used to film each runner in standing and while running at his or her preferred speed on a treadmill. The angle between the vector formed by the two reflective markers and the superior surface of the treadmill was used to calculate the foot strike angle (FSA). To determine the foot strike pattern from the video data (FSPVideo), the static standing angle was subtracted from the FSA at initial contact of the shoe on the treadmill. In addition to descriptive statistics, percent agreement and Chi square analysis was used to determine distribution differences between the video analysis results and the survey. Results: The results of the chi-square analysis on the distribution of the FSPSurvey in comparison to the FSPVideo were significantly different for both the XCRunners (p < .01; Chi-square = 8.77) and the REC Runners (p < .0002; Chi-square = 16.70). The cross-country and recreational runners could correctly self-identified their foot strike pattern 56.5% and 43.5% of the time, respectively. Conclusion: The findings of this study suggest that the clinician cannot depend on an experienced runner to correctly self-identify their FSP. Clinicians interested in knowing the FSP of a runner should consider performing the two-dimensional video analysis described in this paper. Level of Evidence: 3 Keywords: Foot strike, kinematics, running
1
School of Physical Therapy, Regis University, Denver, CO, USA 2 Next Level Sports Performance, Golden, CO, USA 3 Physcial Therapy Program, University of Colorado, Aurora, CO, USA
CORRESPONDING AUTHOR Thomas G. McPoil, PT, PhD School of Physical Therapy Regis University 3333 Regis Blvd., G-4 Denver, CO, 80221 Phone: 303 964-5137 E-mail: [email protected]
The International Journal of Sports Physical Therapy | Volume 11, Number 3 | June 2016 | Page 350
INTRODUCTION In the last several years, there has been an increased interest in the foot strike pattern in runners as a result of the suggested relationship between the strike pattern and lower extremity injury. The three common classifications of the pattern of foot strike during running are rearfoot, midfoot, and forefoot.1 A rearfoot strike pattern occurs when the heel or posterior aspect of the foot or shoe initially contacts the ground. A midfoot strike pattern occurs when the posterior and anterior portions of the foot or shoe contact the ground at the same time. A forefoot strike pattern occurs when the anterior part of the foot or shoe strikes the ground first. A recent systematic review assessing the biomechanical variations in foot strike patterns during running reported that rearfoot strikers have significantly higher vertical loading rates when initially contacting the ground in comparison to forefoot strikers when running with or without shoes.2 Several authors have reported that higher vertical impact forces during running may be a factor in the development of running-related injuries.3,4,5 In addition, Williams et al demonstrated that when running with shoes, a forefoot strike pattern resulted in a reduction of power absorption at both the hip and knee in comparison to a rearfoot strike pattern.6 Thus, changing from a rearfoot to forefoot strike pattern could be an intervention strategy for a runner with a running-related hip or knee injury. Based on this information, it would appear to be important for the clinician to know the foot strike pattern as part of the physical examination of a runner with a running-related overuse injury. An important question is whether the clinician can depend on the runner to provide an accurate self-report of their foot strike pattern. The ability of the runner to be able to accurately self-report their foot strike pattern was first reported on by Goss and Gross.7 These researchers conducted a retrospective study to determine the relationships between shoe type, foot strike pattern, and injury incidence in runners who reported running at least six miles per week. They reported that of the 904 runners who returned the survey 31% were rearfoot strikers, 43% were midfoot strikers, and 20% were forefoot strikers. In their paper, these authors indicated that they had tested 87 runners using an instrumented treadmill and that 69% of the runners could
accurately self-report their own foot strike pattern. Unfortunately, no information regarding the methodology used to collect the foot strike data on these 87 runners was provided in the paper. In a more recent study, Goss et al assessed the accuracy of 60 healthy runners to self-report their foot strike pattern using an instrumented treadmill and 2-dimensional video assessment.8 The runners selected for this study had more than six months experience wearing traditional or minimalist footwear and ran a minimum of 12 miles per week. They found that only 41 runners (68.3%) accurately self-reported their foot strike patterns in comparison to the foot strike captured with the video recordings while running on the treadmill. While this study was the first to assess the ability of a runner to reliably self-report their foot strike pattern, a limitation of the research was the sampling speed of the video camera (60 Hz) as well as the method used to determine the runner’s foot strike pattern from the video recordings. Although the traditional method for quantifying the foot strike pattern has been the strike index which is determined using a force platform, Goss et al classified the foot strike pattern from the video recordings based on visual observations by two experienced physical therapists. Unfortunately, Goss et al provided no information regarding the reliability of the visual observations by the two raters.8 The strike index, first reported by Cavanagh and Lafortune, is a measure of the location on the center of pressure line along the long axis of the foot based on the point of the runners initial contact with the force platform and is expressed as a percentage of total foot length.1 While the strike index is a very accurate method to determine the foot strike pattern, few clinics have access to force platforms. In attempt to determine the runners strike pattern without using a force platform, Altman and Davis described a kinematic method to calculate a foot strike angle that they compared to the strike index derived from a force platform.9 They reported that the foot strike angle, determined from video recordings of 20 shod runners obtained while running on a treadmill, was highly correlated with the strike index (R2 = 0.85). These authors note that the foot strike angle can be easily done in the clinical setting on a treadmill using only a high-speed video camera and a simple angle extraction program.
The International Journal of Sports Physical Therapy | Volume 11, Number 3 | June 2016 | Page 351
Based on current research suggesting an association between the foot strike pattern and the potential for increased running-related injuries, the foot strike pattern of the runner would appear to be important information for the clinician as part of the examination process. Although the study by Goss et al has some limitations, based on these authors’ findings, the ability of the runner to accurately self-report their foot strike pattern is questionable.8 In addition, since Goss et al only included recreational runners in their study, the ability of runners involved in an organized team-based running program that requires greater weekly running mileage and provides consistent coaching could enhance the accuracy of the runner to self-report their foot strike pattern. Thus, the purpose of this study was to assess the ability of collegiate cross-country runners and recreational runners to self-report their foot strike pattern during running. The authors hypothesized that the collegiate cross-country runners would have a higher level of accuracy in predicting their foot strike pattern in comparison to the recreational runners because of greater weekly mileage and regular coaching. METHODS Participant Characteristics Twenty-three Division II cross-country runners (XCRunners - 12 females and 11 males) and 23 experienced recreational runners (RECRunners - 9 females and 14 males) voluntarily consented to participate in this study. Participants were recruited from three local university Division II collegiate cross-country teams and the recreational runners were recruited from the Regis University population as well as the greater Denver, Colorado, metropolitan area through community advertisements and public information sessions. All runners selected for the study met the following inclusion criteria: (1) between the ages of 18 to 40 years; (2) ran at least 18 miles per week for oneyear prior to participation in the study; (3) had previous experience running on a treadmill; (4) always utilized shoes when running; (5) no previous history of lower extremity congenital or traumatic deformity or previous surgery that resulted in altered bony alignment; and (6) no acute injury 3 months prior to the start of the study that led to inability to run at least 3 consecutive days during that time. The Institutional Review Board of Regis University approved the
study protocol and all participants provided written informed consent prior to participation in the study. Procedures Upon arrival to the testing center, each participant’s height, weight, and blood pressure were recorded. The participants were then asked to complete a short running history survey which asked the average number of days they ran per week, the average number of miles they ran per week, and what they believed to be their typical foot strike pattern (FSPSurvey) would be during a training run. After completing the survey, each participant was asked to begin running on a treadmill for at least six minutes so that they could acclimate to the treadmill (Model Mercury S, Woodway USA Inc., Waukesha, WI 53186) used in the study as well as determine their preferred running speed for testing. Once the preferred running speed was selected and the participant indicated they were acclimated to the treadmill, the participant was asked to stand with equal weight on both lower extremities so that reflective markers could be placed on the right shoe at the most posterior aspect of the midsole and at the vamp of the shoe just below the laces (Figure 1). The participant was then instructed to maintain the right leg perpendicular to the floor and place equal weight on both feet while video data was recorded for static standing. Once static standing data was recorded, the participant was then asked to start running on the tread-
Figure 1. Pacement of the reflective markers on the most posterior aspect of the midsole and at the vamp of the shoe just below the laces.
The International Journal of Sports Physical Therapy | Volume 11, Number 3 | June 2016 | Page 352
mill at his or her pre-selected running speed. Once the subject indicated they were running comfortably and what they thought was his or her typical running style, they were asked to continue running for five minutes while video data was recorded. All standing and running images were recorded using a high speed camera (Model# EX FH25, Casio America Inc., Dover, NJ 07801) at a rate of 240 frames per second.
was classified as a rearfoot strike pattern if the foot strike angle was greater than 8 degrees. If the foot strike angle was between -1.6 and 8.0 degrees, the FSPVideo was classified as a midfoot strike pattern. If the foot strike angle was less than -1.6 degrees, the FSPVideo was classified as a forefoot strike pattern. For further statistical analysis, the mean of the FSPVideo for the five running trials was utilized.
Data Analysis For each participant, one static image and five dynamic images for the right foot were captured from the video recordings for analysis. The five dynamic images captured at the point of initial shoe contact with the treadmill were selected after three minutes from the start of video recording. Based on the method described by Altman and Davis9 the angle formed by the two reflective markers and the superior surface of the treadmill was used to calculate the foot strike angle with the posterior shoe marker serving as the apex of the angle (Figure 2). A free-access video analysis software program (Kinovea, version 0.8.15, http://www.kinovea.org) was then used to calculate the FSA on all six images. To determine the foot strike pattern from the video data (FSPVideo), the static standing angle was subtracted from the each of the five dynamic foot strike angles recorded for the right foot at the point of initial contact of the shoe with the treadmill while running. Based on the criteria provided by Altman and Davis9, the FSPVideo
Statistical Analysis In addition to descriptive statistics, percent agreement was determined between the video analysis results and the survey responses for foot strike angle. Chi-square analysis was used to determine if differences existed in the distribution of self-reported foot strike pattern responses in comparison to the FSA between the XCRunners and the RECRunners. All statistical analyses were performed using JMP software, Version 8 (SAS Institute Inc., Cary NC 27513). An alpha level of .05 was established for all tests of significance. RESULTS Demographic data for all subjects are listed in Table 1. The distribution of the FSPSurvey and the FSPVideo for the XCRunners and the RECRunners are listed in Table 2 and 3, respectively. The majority of the XCRunners ran on average six to seven times each week, while the RECRunners ran on average four to five times each week. The average number of miles run for all participants was 39.6 miles with the XCRunners and RECRunners averaging 54.1 and 25.0 miles per week, respectively. The results of the chisquare analysis on the distribution of the FSPSurvey in comparison to the FSPVideo were significantly different for both the XCRunners (p < .01; X2 = 8.77) and the REC Runners (p < .0002; X2 = 16.70), indicating for both groups a lack of agreement between Table 1. Means (standard deviations) for participant demographics
Figure 2. The foot strike angle formed by the two reflective markers and the superior surface of the treadmill with the posterior shoe marker the apex of the angle.
All Runners (n = 46) Recreational Runners (n = 23) CrossCountry Runners (n = 23)
Age (years) 24.4 (5.6) 29.3 (3.4)
Height (cm) 142.5 (22.3) 147.3 (23.3)
Weight (kg) 78.6 (4.6) 78.4 (4.5)
BMI (kg/cm2) 21.7 (1.8) 22.2 (1.7)
Mileage per Week 39.6 (18.2) 25.0 (9.5)
19.5 (51.1)
137.7 (20.8)
77.7 (4.8)
21.2 (1.9)
54.1 (12.1)
The International Journal of Sports Physical Therapy | Volume 11, Number 3 | June 2016 | Page 353
Table 2. Distribution of foot strike patterns from the questionnaire (FSPSurvey) and video analysis data (FSPVideo) for the cross-country runners FSPSurvey
R e a r f o o t St r i k e Pattern 7
Midfoot Strike Pattern 9
Forefoot Strike Pattern 7
FSPVideo
17
3
3
Chi Square statistic = 8,77, p < .012
Table 3. Distribution of foot strike patterns from the questionnaire (FSPSurvey) and video analysis data (FSPVideo) for the recreational runners FSPSurvey
R e a r f o o t St r i k e Pattern 6
Midfoot Strike Pattern 15
Forefoot Strike Pattern 2
FSPVideo
19
2
2
Chi Square statistic = 16.70; p < .0002
their perceived and actual strike pattern. The chisquare analysis for the distribution of the FSPSurvey responses for the XCRunners in comparison to the RECRunners was not significantly different (p = .113; X2 = 4.35). The chi-square analysis for the FSPVideo classification distribution between the XCRunners and RECRunners was also not significantly different (p = .77; X2 = 0.51). The XCRunners correctly selfidentified their foot strike pattern 56.5% of the time, whereas the RECRunners could only correctly selfidentified their foot strike pattern 43.5% of the time. DISCUSSION The intent of this study was to assess the ability of collegiate cross-country runners in comparison to recreational runners to self-report their foot strike pattern during running. Previous authors have reported that recreational runners can self-identify their foot strike pattern in running approximately 68% of the time.8 In their study, the accuracy of the runners ability to self-report their foot strike pattern was assessed using visual observations by two experienced physical therapists to classify foot strike patterns rather than the traditional method of using a force platform to determine the strike index. In addition, this study only assessed recreational runners and did not include runners involved in an organized team-based running program.8 Even though none of the RECRunners who participated in this study ran less than 18 miles per week, as expected the XCRunners ran almost twice as many miles per week and ran more frequently each week
than the RECRunners. Even with this difference in training, the XCRunners in this study could correctly self-report their foot strike pattern 56.5% of the time in comparison to 43.5% for the RECRunners. As noted in Table 2, while the FSPVideo showed that 17 (74%) of the XCRunner group were rearfoot strikers, the FSPSurvey indicated that only 7 (30%) of the XCRunners self-reported they were a rearfoot striker. While the distribution of the XCRunners self-identified foot strike pattern was evenly distributed across all three strike patterns (7 rearfoot, 9 midfoot, 7 forefoot), the FSPSurvey indicated that 65% of the RECRunners selfreported that they were a midfoot striker. Similar to the XCRunners, the FSPVideo data showed that 83% of the RECRunners were rearfoot strikers. The authors hypothesized that the collegiate crosscountry runners would have a higher level of accuracy in predicting their foot strike pattern in comparison to the recreational runners because of greater weekly mileage and regular coaching. Based on the findings of the current study, even though the collegiate runners did run more miles per week and ran more frequently each week, there was only a 13% difference in the ability of the cross-country runners to self-report their foot strike pattern during running in comparison to the recreational runners. It is interesting to note that there were no significant differences in the FSPSurvey foot strike classification distribution between the collegiate and recreational runner groups with the midfoot strike pattern the most self-identified for both groups. Irrespective of the self-identified foot strike patterns for either running group, the result of the FSPVideo indicated that was no difference in the foot strike pattern distribution between the collegiate and recreational runners with the rearfoot strike pattern most prevalent in both groups. In light of these findings the authors failed to accept the proposed hypothesis. More importantly, the results of the current study indicate that the clinician cannot depend on an experienced runner to correctly self-report their foot strike pattern. Clinicians interested in knowing the foot strike pattern of a runner they are examining would need to consider utilizing a method similar to the one described in this paper. The accuracy of the runners in the current study to self-report their foot strike pattern was lower than previously reported by Goss et al.8 One of the rea-
The International Journal of Sports Physical Therapy | Volume 11, Number 3 | June 2016 | Page 354
sons for this discrepancy could be that Goss et al used visual observation to determine the foot strike pattern as well as a video camera with a relatively slow sampling speed (60 Hz). As previously noted, the authors used the kinematic method described by Altman and Davis to determine the foot strike pattern that was shown to be highly correlated with the strike index which requires the use of a force platform.9 In addition, the authors also utilized a video camera with a higher sampling rate (240 Hz) as recommended by Altman and Davis. A limitation of this study was that the participant’s foot strike pattern was assessed while running on a treadmill instead of while running overground. To enhance the clinical applicability of the current study, the authors utilized a treadmill to perform the running analysis since a treadmill would be commonly used in the typical clinical setting. Several authors have reported that the use of a treadmill when performing a running analysis can cause alterations in the pattern of lower extremity movement as well as ground reaction forces.10,11,12 In one of the only studies to compare overground versus treadmill running kinematics and kinetics using a force-transducer instrumented treadmill, Riley et al reported that a treadmill-based analysis of running mechanics can be generalized to overground running mechanics, provided the running speed on the treadmill is similar to individuals overground running speed.13 The fact that the authors of the current study only assessed the foot strike pattern for the right foot for each participant could also be considered a limitation. Although only the right foot was analyzed, the strike pattern for both feet of each runner was visually assessed to ensure they were similar. CONCLUSION While the etiology of running-related overuse injuries have been shown to be multifactorial, knowledge of the foot strike pattern would appear to be important information for the clinician managing a runner with running-related overuse injury. The results of this study substantiate the findings of previous research demonstrating that the ability of collegiate or recreational runners to accurately selfreport their foot strike pattern is poor. If the clinician is interested in understanding the foot strike pattern of a runner as part of their physical examination, the
methodology described in this paper can be easily performed in the clinical setting using a treadmill, a low cost high-speed camera, and a simple computerbased angle extraction program. REFERENCES 1. Cavanagh PR, Lafortune MA. Ground reaction forces in distance running. J Biomech. 1980;13:397-406. 2. Almeida MO, Davis IS, Lopes AD. Biomechanical differences of foot-strike patterns during running: a systematic review with meta-analysis. J Orthop Sports Phys Ther. 2015;45:738-755. 3. Lieberman DE, Venkadesan M, Werbal WA, et al. Foot strike patterns and collision forces in habitually barefoot versus shod runners. Nature. 2010;463:531535. 4. Milner CE, Ferber R, Pollard CD, et al. Biomechancial factors associated with tibial stress facture in female runners. Clin J Sport Med. 2009;19:323-328. 5. Pohl MB, Hamill J, Davis IS. Biomechanical and anatomic factors associated with a history of plantar fasciitis in female runners. Clin J Sport Med. 2009;19:372-376. 6. Williams DS 3rd, Green DH, Wurzinger B. Changes in lower extremity movement and power absorption during forefoot striking and barefoot running. Int J Sports Phys Ther. 2012;7:525-532. 7. Goss DL, Gross MT. Relationships among selfreported shoe type, footstrike pattern, and injury incidence. US Army Med Dep J. 2012:Oct-Dec:25-30. 8. Goss DL, Lewek M, Yu B, et al. Lower extremity biomechanics and self-reported foot-strike patterns among runners in traditional and minimalist shoes. J Ath Train. 2015;603-611. 9. Altman AR, Davis IS. A kinematic method for footstrike pattern detection in barefoot and shod runners. Gait Posture. 2012;35:298-300. 10. Frishberg RA. An analysis of overground and treadmill sprinting. Med Sci Sports Exerc. 1983;15:478485. 11. Nigg BM, DeBoer RW, Fisher V. A kinematic comparison of overground and treadmill running. Med Sci Sports Exerc. 1995;27:98-105. 12. Schache AG, Blanch PD, Rath DA, et al. A comparison of overground and treadmill running for measuring the three-dimensional kinematics of lumbo-pelvic-hip complex. Clin Biomech. 2001;16:667680. 13. Riley PO, Dicharry J, Franz J, et al: A kinematics and kinetic comparison of overground and treadmill running. Med Sci Sports Exerc. 2008;40:1093-1100.
The International Journal of Sports Physical Therapy | Volume 11, Number 3 | June 2016 | Page 355
ORIGINAL RESEARCH
ACCURACY OF SELF-REPORTED FOOT STRIKE PATTERN IN INTERCOLLEGIATE AND RECREATIONAL RUNNERS DURING SHOD RUNNING Michael B. Bade, PT, DPT, PhD1,3 Katie Aaron, PT, DPT2 Thomas G. McPoil, PT, PhD1
ABSTRACT Background: Clinicians are interested in the foot strike pattern (FSP) in runners because of the suggested relationship between the strike pattern and lower extremity injury. Purpose: The purpose of this study was to assess the ability of collegiate cross-country runners and recreational runners to self-report their foot strike pattern during running. Study Design: Cross-sectional Study Methods: Twenty-three collegiate cross-country and 23 recreational runners voluntarily consented to participate. Inclusion criteria included running at least 18 miles per week, experience running on a treadmill, no history of lower extremity congenital or traumatic deformity, or acute injury three months prior to the start of the study. All participants completed a pre-test survey to indicate their typical foot strike pattern during a training run (FSPSurvey). Prior to running, reflective markers were placed on the posterior midsole and the vamp of the running shoe. A high-speed camera was used to film each runner in standing and while running at his or her preferred speed on a treadmill. The angle between the vector formed by the two reflective markers and the superior surface of the treadmill was used to calculate the foot strike angle (FSA). To determine the foot strike pattern from the video data (FSPVideo), the static standing angle was subtracted from the FSA at initial contact of the shoe on the treadmill. In addition to descriptive statistics, percent agreement and Chi square analysis was used to determine distribution differences between the video analysis results and the survey. Results: The results of the chi-square analysis on the distribution of the FSPSurvey in comparison to the FSPVideo were significantly different for both the XCRunners (p < .01; Chi-square = 8.77) and the REC Runners (p < .0002; Chi-square = 16.70). The cross-country and recreational runners could correctly self-identified their foot strike pattern 56.5% and 43.5% of the time, respectively. Conclusion: The findings of this study suggest that the clinician cannot depend on an experienced runner to correctly self-identify their FSP. Clinicians interested in knowing the FSP of a runner should consider performing the two-dimensional video analysis described in this paper. Level of Evidence: 3 Keywords: Foot strike, kinematics, running
1
School of Physical Therapy, Regis University, Denver, CO, USA 2 Next Level Sports Performance, Golden, CO, USA 3 Physcial Therapy Program, University of Colorado, Aurora, CO, USA
CORRESPONDING AUTHOR Thomas G. McPoil, PT, PhD School of Physical Therapy Regis University 3333 Regis Blvd., G-4 Denver, CO, 80221 Phone: 303 964-5137 E-mail: [email protected]
The International Journal of Sports Physical Therapy | Volume 11, Number 3 | June 2016 | Page 350
INTRODUCTION In the last several years, there has been an increased interest in the foot strike pattern in runners as a result of the suggested relationship between the strike pattern and lower extremity injury. The three common classifications of the pattern of foot strike during running are rearfoot, midfoot, and forefoot.1 A rearfoot strike pattern occurs when the heel or posterior aspect of the foot or shoe initially contacts the ground. A midfoot strike pattern occurs when the posterior and anterior portions of the foot or shoe contact the ground at the same time. A forefoot strike pattern occurs when the anterior part of the foot or shoe strikes the ground first. A recent systematic review assessing the biomechanical variations in foot strike patterns during running reported that rearfoot strikers have significantly higher vertical loading rates when initially contacting the ground in comparison to forefoot strikers when running with or without shoes.2 Several authors have reported that higher vertical impact forces during running may be a factor in the development of running-related injuries.3,4,5 In addition, Williams et al demonstrated that when running with shoes, a forefoot strike pattern resulted in a reduction of power absorption at both the hip and knee in comparison to a rearfoot strike pattern.6 Thus, changing from a rearfoot to forefoot strike pattern could be an intervention strategy for a runner with a running-related hip or knee injury. Based on this information, it would appear to be important for the clinician to know the foot strike pattern as part of the physical examination of a runner with a running-related overuse injury. An important question is whether the clinician can depend on the runner to provide an accurate self-report of their foot strike pattern. The ability of the runner to be able to accurately self-report their foot strike pattern was first reported on by Goss and Gross.7 These researchers conducted a retrospective study to determine the relationships between shoe type, foot strike pattern, and injury incidence in runners who reported running at least six miles per week. They reported that of the 904 runners who returned the survey 31% were rearfoot strikers, 43% were midfoot strikers, and 20% were forefoot strikers. In their paper, these authors indicated that they had tested 87 runners using an instrumented treadmill and that 69% of the runners could
accurately self-report their own foot strike pattern. Unfortunately, no information regarding the methodology used to collect the foot strike data on these 87 runners was provided in the paper. In a more recent study, Goss et al assessed the accuracy of 60 healthy runners to self-report their foot strike pattern using an instrumented treadmill and 2-dimensional video assessment.8 The runners selected for this study had more than six months experience wearing traditional or minimalist footwear and ran a minimum of 12 miles per week. They found that only 41 runners (68.3%) accurately self-reported their foot strike patterns in comparison to the foot strike captured with the video recordings while running on the treadmill. While this study was the first to assess the ability of a runner to reliably self-report their foot strike pattern, a limitation of the research was the sampling speed of the video camera (60 Hz) as well as the method used to determine the runner’s foot strike pattern from the video recordings. Although the traditional method for quantifying the foot strike pattern has been the strike index which is determined using a force platform, Goss et al classified the foot strike pattern from the video recordings based on visual observations by two experienced physical therapists. Unfortunately, Goss et al provided no information regarding the reliability of the visual observations by the two raters.8 The strike index, first reported by Cavanagh and Lafortune, is a measure of the location on the center of pressure line along the long axis of the foot based on the point of the runners initial contact with the force platform and is expressed as a percentage of total foot length.1 While the strike index is a very accurate method to determine the foot strike pattern, few clinics have access to force platforms. In attempt to determine the runners strike pattern without using a force platform, Altman and Davis described a kinematic method to calculate a foot strike angle that they compared to the strike index derived from a force platform.9 They reported that the foot strike angle, determined from video recordings of 20 shod runners obtained while running on a treadmill, was highly correlated with the strike index (R2 = 0.85). These authors note that the foot strike angle can be easily done in the clinical setting on a treadmill using only a high-speed video camera and a simple angle extraction program.
The International Journal of Sports Physical Therapy | Volume 11, Number 3 | June 2016 | Page 351
Based on current research suggesting an association between the foot strike pattern and the potential for increased running-related injuries, the foot strike pattern of the runner would appear to be important information for the clinician as part of the examination process. Although the study by Goss et al has some limitations, based on these authors’ findings, the ability of the runner to accurately self-report their foot strike pattern is questionable.8 In addition, since Goss et al only included recreational runners in their study, the ability of runners involved in an organized team-based running program that requires greater weekly running mileage and provides consistent coaching could enhance the accuracy of the runner to self-report their foot strike pattern. Thus, the purpose of this study was to assess the ability of collegiate cross-country runners and recreational runners to self-report their foot strike pattern during running. The authors hypothesized that the collegiate cross-country runners would have a higher level of accuracy in predicting their foot strike pattern in comparison to the recreational runners because of greater weekly mileage and regular coaching. METHODS Participant Characteristics Twenty-three Division II cross-country runners (XCRunners - 12 females and 11 males) and 23 experienced recreational runners (RECRunners - 9 females and 14 males) voluntarily consented to participate in this study. Participants were recruited from three local university Division II collegiate cross-country teams and the recreational runners were recruited from the Regis University population as well as the greater Denver, Colorado, metropolitan area through community advertisements and public information sessions. All runners selected for the study met the following inclusion criteria: (1) between the ages of 18 to 40 years; (2) ran at least 18 miles per week for oneyear prior to participation in the study; (3) had previous experience running on a treadmill; (4) always utilized shoes when running; (5) no previous history of lower extremity congenital or traumatic deformity or previous surgery that resulted in altered bony alignment; and (6) no acute injury 3 months prior to the start of the study that led to inability to run at least 3 consecutive days during that time. The Institutional Review Board of Regis University approved the
study protocol and all participants provided written informed consent prior to participation in the study. Procedures Upon arrival to the testing center, each participant’s height, weight, and blood pressure were recorded. The participants were then asked to complete a short running history survey which asked the average number of days they ran per week, the average number of miles they ran per week, and what they believed to be their typical foot strike pattern (FSPSurvey) would be during a training run. After completing the survey, each participant was asked to begin running on a treadmill for at least six minutes so that they could acclimate to the treadmill (Model Mercury S, Woodway USA Inc., Waukesha, WI 53186) used in the study as well as determine their preferred running speed for testing. Once the preferred running speed was selected and the participant indicated they were acclimated to the treadmill, the participant was asked to stand with equal weight on both lower extremities so that reflective markers could be placed on the right shoe at the most posterior aspect of the midsole and at the vamp of the shoe just below the laces (Figure 1). The participant was then instructed to maintain the right leg perpendicular to the floor and place equal weight on both feet while video data was recorded for static standing. Once static standing data was recorded, the participant was then asked to start running on the tread-
Figure 1. Pacement of the reflective markers on the most posterior aspect of the midsole and at the vamp of the shoe just below the laces.
The International Journal of Sports Physical Therapy | Volume 11, Number 3 | June 2016 | Page 352
mill at his or her pre-selected running speed. Once the subject indicated they were running comfortably and what they thought was his or her typical running style, they were asked to continue running for five minutes while video data was recorded. All standing and running images were recorded using a high speed camera (Model# EX FH25, Casio America Inc., Dover, NJ 07801) at a rate of 240 frames per second.
was classified as a rearfoot strike pattern if the foot strike angle was greater than 8 degrees. If the foot strike angle was between -1.6 and 8.0 degrees, the FSPVideo was classified as a midfoot strike pattern. If the foot strike angle was less than -1.6 degrees, the FSPVideo was classified as a forefoot strike pattern. For further statistical analysis, the mean of the FSPVideo for the five running trials was utilized.
Data Analysis For each participant, one static image and five dynamic images for the right foot were captured from the video recordings for analysis. The five dynamic images captured at the point of initial shoe contact with the treadmill were selected after three minutes from the start of video recording. Based on the method described by Altman and Davis9 the angle formed by the two reflective markers and the superior surface of the treadmill was used to calculate the foot strike angle with the posterior shoe marker serving as the apex of the angle (Figure 2). A free-access video analysis software program (Kinovea, version 0.8.15, http://www.kinovea.org) was then used to calculate the FSA on all six images. To determine the foot strike pattern from the video data (FSPVideo), the static standing angle was subtracted from the each of the five dynamic foot strike angles recorded for the right foot at the point of initial contact of the shoe with the treadmill while running. Based on the criteria provided by Altman and Davis9, the FSPVideo
Statistical Analysis In addition to descriptive statistics, percent agreement was determined between the video analysis results and the survey responses for foot strike angle. Chi-square analysis was used to determine if differences existed in the distribution of self-reported foot strike pattern responses in comparison to the FSA between the XCRunners and the RECRunners. All statistical analyses were performed using JMP software, Version 8 (SAS Institute Inc., Cary NC 27513). An alpha level of .05 was established for all tests of significance. RESULTS Demographic data for all subjects are listed in Table 1. The distribution of the FSPSurvey and the FSPVideo for the XCRunners and the RECRunners are listed in Table 2 and 3, respectively. The majority of the XCRunners ran on average six to seven times each week, while the RECRunners ran on average four to five times each week. The average number of miles run for all participants was 39.6 miles with the XCRunners and RECRunners averaging 54.1 and 25.0 miles per week, respectively. The results of the chisquare analysis on the distribution of the FSPSurvey in comparison to the FSPVideo were significantly different for both the XCRunners (p < .01; X2 = 8.77) and the REC Runners (p < .0002; X2 = 16.70), indicating for both groups a lack of agreement between Table 1. Means (standard deviations) for participant demographics
Figure 2. The foot strike angle formed by the two reflective markers and the superior surface of the treadmill with the posterior shoe marker the apex of the angle.
All Runners (n = 46) Recreational Runners (n = 23) CrossCountry Runners (n = 23)
Age (years) 24.4 (5.6) 29.3 (3.4)
Height (cm) 142.5 (22.3) 147.3 (23.3)
Weight (kg) 78.6 (4.6) 78.4 (4.5)
BMI (kg/cm2) 21.7 (1.8) 22.2 (1.7)
Mileage per Week 39.6 (18.2) 25.0 (9.5)
19.5 (51.1)
137.7 (20.8)
77.7 (4.8)
21.2 (1.9)
54.1 (12.1)
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Table 2. Distribution of foot strike patterns from the questionnaire (FSPSurvey) and video analysis data (FSPVideo) for the cross-country runners FSPSurvey
R e a r f o o t St r i k e Pattern 7
Midfoot Strike Pattern 9
Forefoot Strike Pattern 7
FSPVideo
17
3
3
Chi Square statistic = 8,77, p < .012
Table 3. Distribution of foot strike patterns from the questionnaire (FSPSurvey) and video analysis data (FSPVideo) for the recreational runners FSPSurvey
R e a r f o o t St r i k e Pattern 6
Midfoot Strike Pattern 15
Forefoot Strike Pattern 2
FSPVideo
19
2
2
Chi Square statistic = 16.70; p < .0002
their perceived and actual strike pattern. The chisquare analysis for the distribution of the FSPSurvey responses for the XCRunners in comparison to the RECRunners was not significantly different (p = .113; X2 = 4.35). The chi-square analysis for the FSPVideo classification distribution between the XCRunners and RECRunners was also not significantly different (p = .77; X2 = 0.51). The XCRunners correctly selfidentified their foot strike pattern 56.5% of the time, whereas the RECRunners could only correctly selfidentified their foot strike pattern 43.5% of the time. DISCUSSION The intent of this study was to assess the ability of collegiate cross-country runners in comparison to recreational runners to self-report their foot strike pattern during running. Previous authors have reported that recreational runners can self-identify their foot strike pattern in running approximately 68% of the time.8 In their study, the accuracy of the runners ability to self-report their foot strike pattern was assessed using visual observations by two experienced physical therapists to classify foot strike patterns rather than the traditional method of using a force platform to determine the strike index. In addition, this study only assessed recreational runners and did not include runners involved in an organized team-based running program.8 Even though none of the RECRunners who participated in this study ran less than 18 miles per week, as expected the XCRunners ran almost twice as many miles per week and ran more frequently each week
than the RECRunners. Even with this difference in training, the XCRunners in this study could correctly self-report their foot strike pattern 56.5% of the time in comparison to 43.5% for the RECRunners. As noted in Table 2, while the FSPVideo showed that 17 (74%) of the XCRunner group were rearfoot strikers, the FSPSurvey indicated that only 7 (30%) of the XCRunners self-reported they were a rearfoot striker. While the distribution of the XCRunners self-identified foot strike pattern was evenly distributed across all three strike patterns (7 rearfoot, 9 midfoot, 7 forefoot), the FSPSurvey indicated that 65% of the RECRunners selfreported that they were a midfoot striker. Similar to the XCRunners, the FSPVideo data showed that 83% of the RECRunners were rearfoot strikers. The authors hypothesized that the collegiate crosscountry runners would have a higher level of accuracy in predicting their foot strike pattern in comparison to the recreational runners because of greater weekly mileage and regular coaching. Based on the findings of the current study, even though the collegiate runners did run more miles per week and ran more frequently each week, there was only a 13% difference in the ability of the cross-country runners to self-report their foot strike pattern during running in comparison to the recreational runners. It is interesting to note that there were no significant differences in the FSPSurvey foot strike classification distribution between the collegiate and recreational runner groups with the midfoot strike pattern the most self-identified for both groups. Irrespective of the self-identified foot strike patterns for either running group, the result of the FSPVideo indicated that was no difference in the foot strike pattern distribution between the collegiate and recreational runners with the rearfoot strike pattern most prevalent in both groups. In light of these findings the authors failed to accept the proposed hypothesis. More importantly, the results of the current study indicate that the clinician cannot depend on an experienced runner to correctly self-report their foot strike pattern. Clinicians interested in knowing the foot strike pattern of a runner they are examining would need to consider utilizing a method similar to the one described in this paper. The accuracy of the runners in the current study to self-report their foot strike pattern was lower than previously reported by Goss et al.8 One of the rea-
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sons for this discrepancy could be that Goss et al used visual observation to determine the foot strike pattern as well as a video camera with a relatively slow sampling speed (60 Hz). As previously noted, the authors used the kinematic method described by Altman and Davis to determine the foot strike pattern that was shown to be highly correlated with the strike index which requires the use of a force platform.9 In addition, the authors also utilized a video camera with a higher sampling rate (240 Hz) as recommended by Altman and Davis. A limitation of this study was that the participant’s foot strike pattern was assessed while running on a treadmill instead of while running overground. To enhance the clinical applicability of the current study, the authors utilized a treadmill to perform the running analysis since a treadmill would be commonly used in the typical clinical setting. Several authors have reported that the use of a treadmill when performing a running analysis can cause alterations in the pattern of lower extremity movement as well as ground reaction forces.10,11,12 In one of the only studies to compare overground versus treadmill running kinematics and kinetics using a force-transducer instrumented treadmill, Riley et al reported that a treadmill-based analysis of running mechanics can be generalized to overground running mechanics, provided the running speed on the treadmill is similar to individuals overground running speed.13 The fact that the authors of the current study only assessed the foot strike pattern for the right foot for each participant could also be considered a limitation. Although only the right foot was analyzed, the strike pattern for both feet of each runner was visually assessed to ensure they were similar. CONCLUSION While the etiology of running-related overuse injuries have been shown to be multifactorial, knowledge of the foot strike pattern would appear to be important information for the clinician managing a runner with running-related overuse injury. The results of this study substantiate the findings of previous research demonstrating that the ability of collegiate or recreational runners to accurately selfreport their foot strike pattern is poor. If the clinician is interested in understanding the foot strike pattern of a runner as part of their physical examination, the
methodology described in this paper can be easily performed in the clinical setting using a treadmill, a low cost high-speed camera, and a simple computerbased angle extraction program. REFERENCES 1. Cavanagh PR, Lafortune MA. Ground reaction forces in distance running. J Biomech. 1980;13:397-406. 2. Almeida MO, Davis IS, Lopes AD. Biomechanical differences of foot-strike patterns during running: a systematic review with meta-analysis. J Orthop Sports Phys Ther. 2015;45:738-755. 3. Lieberman DE, Venkadesan M, Werbal WA, et al. Foot strike patterns and collision forces in habitually barefoot versus shod runners. Nature. 2010;463:531535. 4. Milner CE, Ferber R, Pollard CD, et al. Biomechancial factors associated with tibial stress facture in female runners. Clin J Sport Med. 2009;19:323-328. 5. Pohl MB, Hamill J, Davis IS. Biomechanical and anatomic factors associated with a history of plantar fasciitis in female runners. Clin J Sport Med. 2009;19:372-376. 6. Williams DS 3rd, Green DH, Wurzinger B. Changes in lower extremity movement and power absorption during forefoot striking and barefoot running. Int J Sports Phys Ther. 2012;7:525-532. 7. Goss DL, Gross MT. Relationships among selfreported shoe type, footstrike pattern, and injury incidence. US Army Med Dep J. 2012:Oct-Dec:25-30. 8. Goss DL, Lewek M, Yu B, et al. Lower extremity biomechanics and self-reported foot-strike patterns among runners in traditional and minimalist shoes. J Ath Train. 2015;603-611. 9. Altman AR, Davis IS. A kinematic method for footstrike pattern detection in barefoot and shod runners. Gait Posture. 2012;35:298-300. 10. Frishberg RA. An analysis of overground and treadmill sprinting. Med Sci Sports Exerc. 1983;15:478485. 11. Nigg BM, DeBoer RW, Fisher V. A kinematic comparison of overground and treadmill running. Med Sci Sports Exerc. 1995;27:98-105. 12. Schache AG, Blanch PD, Rath DA, et al. A comparison of overground and treadmill running for measuring the three-dimensional kinematics of lumbo-pelvic-hip complex. Clin Biomech. 2001;16:667680. 13. Riley PO, Dicharry J, Franz J, et al: A kinematics and kinetic comparison of overground and treadmill running. Med Sci Sports Exerc. 2008;40:1093-1100.
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